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| author | Karl Berry <karl@freefriends.org> | 2006-01-09 00:44:40 +0000 |
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| committer | Karl Berry <karl@freefriends.org> | 2006-01-09 00:44:40 +0000 |
| commit | b4fc5f639874db951177ec539299d20908adb654 (patch) | |
| tree | 52f08823ca58fffe3db6a9b075635038c567626c /Master/texmf-dist/doc/latex/texshade | |
| parent | dec3d98ebe442d7ea93efbaa8dd2e2be8149a467 (diff) | |
doc 4
git-svn-id: svn://tug.org/texlive/trunk@80 c570f23f-e606-0410-a88d-b1316a301751
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| -rw-r--r-- | Master/texmf-dist/doc/latex/texshade/AQP1.phd | 1907 | ||||
| -rw-r--r-- | Master/texmf-dist/doc/latex/texshade/AQP1.top | 14 | ||||
| -rw-r--r-- | Master/texmf-dist/doc/latex/texshade/AQP2spec.ALN | 19 | ||||
| -rw-r--r-- | Master/texmf-dist/doc/latex/texshade/AQPDNA.MSF | 127 | ||||
| -rw-r--r-- | Master/texmf-dist/doc/latex/texshade/AQP_HMM.ext | 148 | ||||
| -rw-r--r-- | Master/texmf-dist/doc/latex/texshade/AQP_HMM.sgl | 6 | ||||
| -rw-r--r-- | Master/texmf-dist/doc/latex/texshade/AQPpro.MSF | 50 | ||||
| -rw-r--r-- | Master/texmf-dist/doc/latex/texshade/README | 149 | ||||
| -rw-r--r-- | Master/texmf-dist/doc/latex/texshade/ciliate.cod | 19 | ||||
| -rw-r--r-- | Master/texmf-dist/doc/latex/texshade/standard.cod | 34 | ||||
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| -rw-r--r-- | Master/texmf-dist/doc/latex/texshade/tsfaq.tex | 432 |
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diff --git a/Master/texmf-dist/doc/latex/texshade/AQP1.phd b/Master/texmf-dist/doc/latex/texshade/AQP1.phd new file mode 100644 index 00000000000..4f6fa61c328 --- /dev/null +++ b/Master/texmf-dist/doc/latex/texshade/AQP1.phd @@ -0,0 +1,1907 @@ + + +From phd@EMBL-Heidelberg.de Wed Nov 25 10:24:25 1998 +Date: Tue, 24 Nov 1998 17:45:25 +0100 +From: Protein Prediction <phd@EMBL-Heidelberg.de> +To: eric.beitz@uni-tuebingen.de +Subject: PredictProtein + +The following information has been received by the server: +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +________________________________________________________________________________ + +reference predict_h25873 (Tue Nov 24 17:43:21 MET 1998) +from eric.beitz@uni-tuebingen.de +password(###) +resp MAIL +orig HTML +prediction of: -secondary structure (PHDsec)-solvent accessibility (PHDacc)- +return msf format +# no description +MASEIKKKLFWRAVVAEFLAMTLFVFISIGSALGFNYPLERNQTLVQDNVKVSLAFGLSIATLAQSVGHISGAHSNPAVT +LGLLLSCQISILRAVMYIIAQCVGAIVASAILSGITSSLLENSLGRNDLARGVNSGQGLGIEIIGTLQLVLCVLATTDRR +RRDLGGSAPLAIGLSVALGHLLAIDYTGCGINPARSFGSAVLTRNFSNHWIFWVGPFIGSALAVLIYDFILAPRSSDFTD +RMKVWTSGQVEEYDLDADDINSRVEMKPK + +________________________________________________________________________________ + +Result of PROSITE search (Amos Bairoch): +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +please quote: A Bairoch, P Bucher & K Hofmann: The PROSITE database, +its status in 1997. Nucl. Acids Res., 1997, 25, 217-221. + +________________________________________________________________________________ + +-------------------------------------------------------- + +-------------------------------------------------------- + +Pattern-ID: ASN_GLYCOSYLATION PS00001 PDOC00001 +Pattern-DE: N-glycosylation site +Pattern: N[^P][ST][^P] + 42 NQTL + 250 NFSN + +Pattern-ID: GLYCOSAMINOGLYCAN PS00002 PDOC00002 +Pattern-DE: Glycosaminoglycan attachment site +Pattern: SG.G + 135 SGQG + +Pattern-ID: PKC_PHOSPHO_SITE PS00005 PDOC00005 +Pattern-DE: Protein kinase C phosphorylation site +Pattern: [ST].[RK] + 157 TDR + 398 TDR + +Pattern-ID: CK2_PHOSPHO_SITE PS00006 PDOC00006 +Pattern-DE: Casein kinase II phosphorylation site +Pattern: [ST].{2}[DE] + 118 SLLE + 383 SRVE + +Pattern-ID: MYRISTYL PS00008 PDOC00008 +Pattern-DE: N-myristoylation site +Pattern: G[^EDRKHPFYW].{2}[STAGCN][^P] + 30 GSALGF + 92 GLSIAT + 179 GLLLSC + 288 GAIVAS + 407 GITSSL + 544 GVNSGQ + 722 GLSVAL + 917 GINPAR + 1141 GSALAV + +Pattern-ID: PROKAR_LIPOPROTEIN PS00013 PDOC00013 +Pattern-DE: Prokaryotic membrane lipoprotein lipid attachment site +Pattern: [^DERK]{6}[LIVMFWSTAG]{2}[LIVMFYSTAGCQ][AGS]C + 77 PAVTLGLLLSC + +Pattern-ID: MIP PS00221 PDOC00193 +Pattern-DE: MIP family signature +Pattern: [HNQA].NP[STA][LIVMF][ST][LIVMF][GSTAFY] + 74 HSNPAVTLG + +________________________________________________________________________________ + +Result of ProDom domain search (Corpet, Gouzy, Kahn): +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +- please quote: ELL Sonnhammer & D Kahn, Prot. Sci., 1994, 3, 482-492 + +________________________________________________________________________________ + +--- ------------------------------------------------------------ +--- Results from running BLAST against PRODOM domains +--- +--- PLEASE quote: +--- F Corpet, J Gouzy, D Kahn (1998). The ProDom database +--- of protein domain families. Nucleic Ac Res 26:323-326. +--- +--- BEGIN of BLASTP output +BLASTP 1.4.7 [16-Oct-94] [Build 17:06:52 Oct 31 1994] + +Reference: Altschul, Stephen F., Warren Gish, Webb Miller, Eugene W. Myers, +and David J. Lipman (1990). Basic local alignment search tool. J. Mol. Biol. +215:403-10. + +Query= prot (#) ppOld, no description /home/phd/server/work/predict_h25873 + (269 letters) + +Database: /home/phd/ut/prodom/prodom_34_2 + 53,597 sequences; 6,740,067 total letters. +Searching..................................................done + + Smallest + Sum + High Probability +Sequences producing High-scoring Segment Pairs: Score P(N) N + + 390 p34.2 (45) MIP(6) AQP1(4) GLPF(4) // PROTEIN INTRIN... 270 2.0e-32 1 + 45663 p34.2 (1) AQPZ_ECOLI // AQUAPORIN Z. 90 3.2e-13 2 + 45611 p34.2 (1) AQP2_HUMAN // AQUAPORIN-CD (AQP-CD) (WAT... 136 6.0e-13 1 + 304 p34.2 (61) AQP2(10) GLPF(6) MIP(5) // PROTEIN CHANN... 121 9.2e-11 1 + 45607 p34.2 (1) PMIP_NICAL // POLLEN-SPECIFIC MEMBRANE I... 80 1.2e-07 2 + 45606 p34.2 (1) BIB_DROME // NEUROGENIC PROTEIN BIG BRAIN. 80 1.2e-05 2 + 2027 p34.2 (15) GLPF(9) AQP3(2) // PROTEIN FACILITATOR ... 60 3.4e-05 2 + 45615 p34.2 (1) GLPF_STRPN // GLYCEROL UPTAKE FACILITATO... 63 0.024 1 + 45638 p34.2 (1) AQP5_HUMAN // AQUAPORIN 5. 61 0.044 1 + +>390 p34.2 (45) MIP(6) AQP1(4) GLPF(4) // PROTEIN INTRINSIC CHANNEL WATER + AQUAPORIN TONOPLAST MEMBRANE FOR PLASMA LENS + Length = 88 + + Score = 270 (125.3 bits), Expect = 2.0e-32, P = 2.0e-32 + Identities = 47/67 (70%), Positives = 56/67 (83%) + +Query: 156 TTDRRRRDLGGSAPLAIGLSVALGHLLAIDYTGCGINPARSFGSAVLTRNFSNHWIFWVG 215 + T D+RR +GGSAPL IG SVALGHL+ I YTGCG+NPARSFG AV+T NF+NHW++WVG +Sbjct: 22 TDDKRRGSVGGSAPLPIGFSVALGHLIGIPYTGCGMNPARSFGPAVVTGNFTNHWVYWVG 81 + +Query: 216 PFIGSAL 222 + P IG+ L +Sbjct: 82 PIIGAVL 88 + + Score = 95 (44.1 bits), Expect = 2.3e-06, P = 2.3e-06 + Identities = 20/33 (60%), Positives = 23/33 (69%) + +Query: 136 GQGLGIEIIGTLQLVLCVLATTDRRRRDLGGSA 168 + GQ L +EIIGT QLV CV ATTD +RR G + +Sbjct: 1 GQNLVVEIIGTFQLVYCVFATTDDKRRGSVGGS 33 + +>45663 p34.2 (1) AQPZ_ECOLI // AQUAPORIN Z. + Length = 96 + + Score = 90 (41.8 bits), Expect = 3.2e-13, Sum P(2) = 3.2e-13 + Identities = 18/36 (50%), Positives = 25/36 (69%) + +Query: 166 GSAPLAIGLSVALGHLLAIDYTGCGINPARSFGSAV 201 + G AP+AIGL++ L HL++I T +NPARS A+ +Sbjct: 25 GFAPIAIGLALTLIHLISIPVTNTSVNPARSTAVAI 60 + + Score = 63 (29.2 bits), Expect = 3.2e-13, Sum P(2) = 3.2e-13 + Identities = 11/25 (44%), Positives = 14/25 (56%) + +Query: 210 WIFWVGPFIGSALAVLIYDFILAPR 234 + W FWV P +G + LIY +L R +Sbjct: 71 WFFWVVPIVGGIIGGLIYRTLLEKR 95 + +>45611 p34.2 (1) AQP2_HUMAN // AQUAPORIN-CD (AQP-CD) (WATER CHANNEL PROTEIN FOR + RENAL COLLECTING DUCT) (ADH WATER CHANNEL) (AQUAPORIN 2) (COLLECTING DUCT + WATER CHANNEL PROTEIN) (WCH-CD). + Length = 49 + + Score = 136 (63.1 bits), Expect = 6.0e-13, P = 6.0e-13 + Identities = 23/42 (54%), Positives = 34/42 (80%) + +Query: 50 VKVSLAFGLSIATLAQSVGHISGAHSNPAVTLGLLLSCQISI 91 + +++++AFGL I TL Q++GHISGAH NPAVT+ L+ C +S+ +Sbjct: 8 LQIAMAFGLGIGTLVQALGHISGAHINPAVTVACLVGCHVSV 49 + +>304 p34.2 (61) AQP2(10) GLPF(6) MIP(5) // PROTEIN CHANNEL WATER AQUAPORIN + INTRINSIC DUCT COLLECTING FOR TONOPLAST WCH-CD + Length = 43 + + Score = 121 (56.1 bits), Expect = 9.2e-11, P = 9.2e-11 + Identities = 24/43 (55%), Positives = 31/43 (72%) + +Query: 70 ISGAHSNPAVTLGLLLSCQISILRAVMYIIAQCVGAIVASAIL 112 + ISG H NPAVT+GLL+ + LRAV YI AQ +GA+ +A+L +Sbjct: 1 ISGGHINPAVTIGLLIGGRFPFLRAVFYIAAQLLGAVAGAALL 43 + +>45607 p34.2 (1) PMIP_NICAL // POLLEN-SPECIFIC MEMBRANE INTEGRAL PROTEIN. + Length = 69 + + Score = 80 (37.1 bits), Expect = 1.2e-07, Sum P(2) = 1.2e-07 + Identities = 17/54 (31%), Positives = 32/54 (59%) + +Query: 149 LVLCVLATTDRRRRDLGGSAPLAIGLSVALGHLLAIDYTGCGINPARSFGSAVL 202 + L++ V++ R +G A +A+G+++ L +A +G +NPARS G A++ +Sbjct: 13 LLMFVISGVATDDRAIGQVAGIAVGMTITLNVFVAGPISGASMNPARSIGPAIV 66 + + Score = 34 (15.8 bits), Expect = 1.2e-07, Sum P(2) = 1.2e-07 + Identities = 8/18 (44%), Positives = 11/18 (61%) + +Query: 136 GQGLGIEIIGTLQLVLCV 153 + GQ L IEII + L+ + +Sbjct: 1 GQSLAIEIIISFLLMFVI 18 + +>45606 p34.2 (1) BIB_DROME // NEUROGENIC PROTEIN BIG BRAIN. + Length = 119 + + Score = 80 (37.1 bits), Expect = 1.2e-05, Sum P(2) = 1.2e-05 + Identities = 15/34 (44%), Positives = 24/34 (70%) + +Query: 1 MASEIKKKLFWRAVVAEFLAMTLFVFISIGSALG 34 + M +EI+ FWR++++E LA ++VFI G+A G +Sbjct: 55 MQAEIRTLEFWRSIISECLASFMYVFIVCGAAAG 88 + + Score = 39 (18.1 bits), Expect = 1.2e-05, Sum P(2) = 1.2e-05 + Identities = 9/17 (52%), Positives = 12/17 (70%) + +Query: 53 SLAFGLSIATLAQSVGH 69 + +LA GL++ATL Q H +Sbjct: 103 ALASGLAMATLTQCFLH 119 + +>2027 p34.2 (15) GLPF(9) AQP3(2) // PROTEIN FACILITATOR GLYCEROL UPTAKE + AQUAPORIN DIFFUSION UPTAKE/EFFLUX PEPX 5'REGION ORF1 + Length = 55 + + Score = 60 (27.8 bits), Expect = 3.4e-05, Sum P(2) = 3.4e-05 + Identities = 17/46 (36%), Positives = 20/46 (43%) + +Query: 156 TTDRRRRDLGGSAPLAIGLSVALGHLLAIDYTGCGINPARSFGSAV 201 + T D GG PL +G V + TG INPAR FG + +Sbjct: 10 TDDGNNVPSGGLHPLMVGFLVMGIGMSLGGTTGYAINPARDFGPRI 55 + + Score = 37 (17.2 bits), Expect = 3.4e-05, Sum P(2) = 3.4e-05 + Identities = 7/10 (70%), Positives = 8/10 (80%) + +Query: 149 LVLCVLATTD 158 + L+ CVLA TD +Sbjct: 2 LIACVLALTD 11 + +>45615 p34.2 (1) GLPF_STRPN // GLYCEROL UPTAKE FACILITATOR PROTEIN. + Length = 26 + + Score = 63 (29.2 bits), Expect = 0.025, P = 0.024 + Identities = 13/23 (56%), Positives = 18/23 (78%) + +Query: 205 NFSNHWIFWVGPFIGSALAVLIY 227 + ++S WI VGP IG+ALAVL++ +Sbjct: 1 DWSYAWIPVVGPVIGAALAVLVF 23 + +>45638 p34.2 (1) AQP5_HUMAN // AQUAPORIN 5. + Length = 27 + + Score = 61 (28.3 bits), Expect = 0.045, P = 0.044 + Identities = 11/19 (57%), Positives = 18/19 (94%) + +Query: 50 VKVSLAFGLSIATLAQSVG 68 + ++++LAFGL+I TLAQ++G +Sbjct: 8 LQIALAFGLAIGTLAQALG 26 + +Parameters: + E=0.1 + B=500 + + V=500 + -ctxfactor=1.00 + + Query ----- As Used ----- ----- Computed ---- + Frame MatID Matrix name Lambda K H Lambda K H + +0 0 BLOSUM62 0.322 0.138 0.394 same same same + + Query + Frame MatID Length Eff.Length E S W T X E2 S2 + +0 0 269 269 0.10 69 3 11 22 0.22 33 + +Statistics: + Query Expected Observed HSPs HSPs + Frame MatID High Score High Score Reportable Reported + +0 0 59 (27.4 bits) 270 (125.3 bits) 14 14 + + Query Neighborhd Word Excluded Failed Successful Overlaps + Frame MatID Words Hits Hits Extensions Extensions Excluded + +0 0 5349 3124825 609708 2510548 4569 2 + + Database: /home/phd/ut/prodom/prodom_34_2 + Release date: unknown + Posted date: 12:24 PM MET DST May 06, 1998 + # of letters in database: 6,740,067 + # of sequences in database: 53,597 + # of database sequences satisfying E: 9 + No. of states in DFA: 564 (111 KB) + Total size of DFA: 226 KB (256 KB) + Time to generate neighborhood: 0.03u 0.00s 0.03t Real: 00:00:00 + Time to search database: 9.80u 0.03s 9.83t Real: 00:00:10 + Total cpu time: 9.90u 0.06s 9.96t Real: 00:00:10 +--- END of BLASTP output +--- ------------------------------------------------------------ +--- +--- Again: these results were obtained based on the domain data- +--- base collected by Daniel Kahn and his coworkers in Toulouse. +--- +--- PLEASE quote: +--- F Corpet, J Gouzy, D Kahn (1998). The ProDom database +--- of protein domain families. Nucleic Ac Res 26:323-326. +--- +--- The general WWW page is on: +---- --------------------------------------- +--- http://www.toulouse.inra.fr/prodom.html +---- --------------------------------------- +--- +--- For WWW graphic interfaces to PRODOM, in particular for your +--- protein family, follow the following links (each line is ONE +--- single link for your protein!!): +--- +http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom1=390 ==> multiple alignment, consensus, PDB and PROSITE links of domain 390 +http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom2=390 ==> graphical output of all proteins having domain 390 +http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom1=45663 ==> multiple alignment, consensus, PDB and PROSITE links of domain 45663 +http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom2=45663 ==> graphical output of all proteins having domain 45663 +http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom1=45611 ==> multiple alignment, consensus, PDB and PROSITE links of domain 45611 +http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom2=45611 ==> graphical output of all proteins having domain 45611 +http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom1=304 ==> multiple alignment, consensus, PDB and PROSITE links of domain 304 +http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom2=304 ==> graphical output of all proteins having domain 304 +http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom1=45607 ==> multiple alignment, consensus, PDB and PROSITE links of domain 45607 +http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom2=45607 ==> graphical output of all proteins having domain 45607 +http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom1=45606 ==> multiple alignment, consensus, PDB and PROSITE links of domain 45606 +http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom2=45606 ==> graphical output of all proteins having domain 45606 +http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom1=2027 ==> multiple alignment, consensus, PDB and PROSITE links of domain 2027 +http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom2=2027 ==> graphical output of all proteins having domain 2027 +http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom1=45615 ==> multiple alignment, consensus, PDB and PROSITE links of domain 45615 +http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom2=45615 ==> graphical output of all proteins having domain 45615 +http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom1=45638 ==> multiple alignment, consensus, PDB and PROSITE links of domain 45638 +http://www.toulouse.inra.fr/prodom/cgi-bin/ReqProdomII.pl?id_dom2=45638 ==> graphical output of all proteins having domain 45638 +--- +--- NOTE: if you want to use the link, make sure the entire line +--- is pasted as URL into your browser! +--- +--- END of PRODOM +--- ------------------------------------------------------------ + +________________________________________________________________________________ + +--- Database used for sequence comparison: +--- SEQBASE RELEASE 34.0 OF EMBL/SWISS-PROT WITH 59021 SEQUENCES + +The alignment that has been used as input to the network is: +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +________________________________________________________________________________ + +--- ------------------------------------------------------------ +--- MAXHOM multiple sequence alignment +--- ------------------------------------------------------------ +--- +--- MAXHOM ALIGNMENT HEADER: ABBREVIATIONS FOR SUMMARY +--- ID : identifier of aligned (homologous) protein +--- STRID : PDB identifier (only for known structures) +--- PIDE : percentage of pairwise sequence identity +--- WSIM : percentage of weighted similarity +--- LALI : number of residues aligned +--- NGAP : number of insertions and deletions (indels) +--- LGAP : number of residues in all indels +--- LSEQ2 : length of aligned sequence +--- ACCNUM : SwissProt accession number +--- NAME : one-line description of aligned protein +--- +--- MAXHOM ALIGNMENT HEADER: SUMMARY +ID STRID IDE WSIM LALI NGAP LGAP LEN2 ACCNUM NAME +aqp1_rat 100 100 269 0 0 269 P29975 PROXIMAL TUBULE) (AQUAPOR +aqp1_mouse 98 99 269 0 0 269 Q02013 PROXIMAL TUBULE) (AQUAPOR +aqp1_human 93 97 269 0 0 269 P29972 PROXIMAL TUBULE) (AQUAPOR +aqp1_bovin 90 95 269 1 2 271 P47865 PROXIMAL TUBULE) (AQUAPOR +aqp1_sheep 90 94 269 2 3 272 P56401 PROXIMAL TUBULE) (AQUAPOR +aqpa_ranes 78 89 268 2 5 272 P50501 AQUAPORIN FA-CHIP. +aqp2_dasno 49 73 109 1 7 109 P79164 PROTEIN) (WCH-CD) (FRAGME +aqp2_bovin 49 73 109 1 7 109 P79099 PROTEIN) (WCH-CD) (FRAGME +aqp2_canfa 48 72 109 1 7 109 P79144 PROTEIN) (WCH-CD) (FRAGME +aqp2_rabit 48 73 109 1 7 109 P79213 PROTEIN) (WCH-CD) (FRAGME +aqp2_elema 47 72 109 1 7 109 P79168 PROTEIN) (WCH-CD) (FRAGME +aqp2_horse 47 72 109 1 7 109 P79165 PROTEIN) (WCH-CD) (FRAGME +aqp2_proha 47 73 109 1 7 109 P79229 PROTEIN) (WCH-CD) (FRAGME +mip_rat 46 73 259 1 7 261 P09011 LENS FIBER MAJOR INTRINSI +aqp2_oryaf 46 72 109 1 7 109 P79200 PROTEIN) (WCH-CD) (FRAGME +mip_mouse 46 73 261 1 7 263 P51180 LENS FIBER MAJOR INTRINSI +mip_ranpi 45 73 261 1 7 263 Q06019 LENS FIBER MAJOR INTRINSI +mip_bovin 45 73 261 1 7 263 P06624 LENS FIBER MAJOR INTRINSI +mip_human 45 73 261 1 7 263 P30301 LENS FIBER MAJOR INTRINSI +mip_chick 45 72 110 1 1 112 P28238 LENS FIBER MAJOR INTRINSI +aqp5_rat 44 71 262 2 8 265 P47864 AQUAPORIN 5. +aqp5_human 44 71 262 2 8 265 P55064 AQUAPORIN 5. +aqp2_human 44 72 261 2 8 271 P41181 PROTEIN) (WCH-CD). +aqp4_human 43 70 266 2 5 323 P55087 AQUAPORIN 4 (WCH4) (MERCU +aqp4_rat 43 70 266 2 5 323 P47863 AQUAPORIN 4 (WCH4) (MERCU +aqp4_mouse 43 69 265 3 6 322 P55088 AQUAPORIN 4 (WCH4) (MERCU +aqp2_rat 42 71 261 2 8 271 P34080 PROTEIN) (WCH-CD). +aqp2_mouse 42 71 261 2 8 271 P56402 PROTEIN) (WCH-CD). +wc2a_arath 42 67 248 4 12 287 P43286 PLASMA MEMBRANE INTRINSIC +aqp6_human 42 68 260 2 9 282 Q13520 AQUAPORIN 6 (AQUAPORIN-2 +wc2c_arath 41 66 248 4 12 285 P30302 INTRINSIC PROTEIN) (WSI-T +wc2b_arath 41 66 248 4 12 285 P43287 PLASMA MEMBRANE INTRINSIC +wc1c_arath 41 65 238 4 10 286 Q08733 (TMP-B). +wc1b_arath 41 65 238 4 10 286 Q06611 (TMP-A). +tipw_lyces 40 65 237 4 10 286 Q08451 (RIPENING-ASSOCIATED MEMB +wc1a_arath 40 64 238 4 10 286 P43285 PLASMA MEMBRANE INTRINSIC +tipw_pea 40 64 237 4 11 289 P25794 RESPONSIVE PROTEIN 7A). +tipa_arath 38 64 250 3 9 268 P26587 TONOPLAST INTRINSIC PROTE +aqua_atrca 38 64 246 4 10 282 P42767 AQUAPORIN. +dip_antma 38 65 242 2 4 250 P33560 PROBABLE TONOPLAST INTRIN +aqpz_ecoli 37 59 220 4 17 231 P48838 AQUAPORIN Z (BACTERIAL NO +tip2_tobac 37 64 242 2 4 250 P24422 TONOPLAST INTRINSIC PROTE +tip1_tobac 37 64 242 2 4 250 P21653 TONOPLAST INTRINSIC PROTE +tipg_arath 33 62 241 2 4 251 P25818 TONOPLAST INTRINSIC PROTE +bib_drome 33 60 260 4 10 700 P23645 NEUROGENIC PROTEIN BIG BR +tipr_arath 33 62 243 2 4 253 P21652 TONOPLAST INTRINSIC PROTE +tipa_phavu 33 62 246 2 4 256 P23958 TONOPLAST INTRINSIC PROTE +tipg_orysa 32 62 240 2 5 250 P50156 TONOPLAST INTRINSIC PROTE +--- +--- MAXHOM ALIGNMENT: IN MSF FORMAT +MSF of: /home/phd/server/work/predict_h25873-22040.hssp from: 1 to: 269 + /home/phd/server/work/predict_h25873-22040.msfRet MSF: 269 Type: P 24-Nov-98 17:44:5 Check: 3448 .. + + Name: predict_h258 Len: 269 Check: 8331 Weight: 1.00 + Name: aqp1_rat Len: 269 Check: 8331 Weight: 1.00 + Name: aqp1_mouse Len: 269 Check: 7552 Weight: 1.00 + Name: aqp1_human Len: 269 Check: 6501 Weight: 1.00 + Name: aqp1_bovin Len: 269 Check: 7067 Weight: 1.00 + Name: aqp1_sheep Len: 269 Check: 7582 Weight: 1.00 + Name: aqpa_ranes Len: 269 Check: 4844 Weight: 1.00 + Name: aqp2_dasno Len: 269 Check: 8933 Weight: 1.00 + Name: aqp2_bovin Len: 269 Check: 9649 Weight: 1.00 + Name: aqp2_canfa Len: 269 Check: 8990 Weight: 1.00 + Name: aqp2_rabit Len: 269 Check: 8787 Weight: 1.00 + Name: aqp2_elema Len: 269 Check: 9381 Weight: 1.00 + Name: aqp2_horse Len: 269 Check: 8993 Weight: 1.00 + Name: aqp2_proha Len: 269 Check: 8855 Weight: 1.00 + Name: mip_rat Len: 269 Check: 9773 Weight: 1.00 + Name: aqp2_oryaf Len: 269 Check: 8554 Weight: 1.00 + Name: mip_mouse Len: 269 Check: 9723 Weight: 1.00 + Name: mip_ranpi Len: 269 Check: 5937 Weight: 1.00 + Name: mip_bovin Len: 269 Check: 1430 Weight: 1.00 + Name: mip_human Len: 269 Check: 372 Weight: 1.00 + Name: mip_chick Len: 269 Check: 4658 Weight: 1.00 + Name: aqp5_rat Len: 269 Check: 9033 Weight: 1.00 + Name: aqp5_human Len: 269 Check: 6547 Weight: 1.00 + Name: aqp2_human Len: 269 Check: 6209 Weight: 1.00 + Name: aqp4_human Len: 269 Check: 2589 Weight: 1.00 + Name: aqp4_rat Len: 269 Check: 4412 Weight: 1.00 + Name: aqp4_mouse Len: 269 Check: 2845 Weight: 1.00 + Name: aqp2_rat Len: 269 Check: 5748 Weight: 1.00 + Name: aqp2_mouse Len: 269 Check: 6526 Weight: 1.00 + Name: wc2a_arath Len: 269 Check: 4866 Weight: 1.00 + Name: aqp6_human Len: 269 Check: 9404 Weight: 1.00 + Name: wc2c_arath Len: 269 Check: 6187 Weight: 1.00 + Name: wc2b_arath Len: 269 Check: 7328 Weight: 1.00 + Name: wc1c_arath Len: 269 Check: 8575 Weight: 1.00 + Name: wc1b_arath Len: 269 Check: 9544 Weight: 1.00 + Name: tipw_lyces Len: 269 Check: 9283 Weight: 1.00 + Name: wc1a_arath Len: 269 Check: 598 Weight: 1.00 + Name: tipw_pea Len: 269 Check: 9253 Weight: 1.00 + Name: tipa_arath Len: 269 Check: 6544 Weight: 1.00 + Name: aqua_atrca Len: 269 Check: 2848 Weight: 1.00 + Name: dip_antma Len: 269 Check: 9619 Weight: 1.00 + Name: aqpz_ecoli Len: 269 Check: 5641 Weight: 1.00 + Name: tip2_tobac Len: 269 Check: 490 Weight: 1.00 + Name: tip1_tobac Len: 269 Check: 622 Weight: 1.00 + Name: tipg_arath Len: 269 Check: 3231 Weight: 1.00 + Name: bib_drome Len: 269 Check: 7687 Weight: 1.00 + Name: tipr_arath Len: 269 Check: 4476 Weight: 1.00 + Name: tipa_phavu Len: 269 Check: 5563 Weight: 1.00 + Name: tipg_orysa Len: 269 Check: 3537 Weight: 1.00 + +// + + 1 50 +predict_h258 MASEIKKKLF WRAVVAEFLA MTLFVFISIG SALGFNYPLE RNQTLVQDNV +aqp1_rat MASEIKKKLF WRAVVAEFLA MTLFVFISIG SALGFNYPLE RNQTLVQDNV +aqp1_mouse MASEIKKKLF WRAVVAEFLA MTLFVFISIG SALGFNYPLE RNQTLVQDNV +aqp1_human MASEFKKKLF WRAVVAEFLA TTLFVFISIG SALGFKYPVG NNQTAVQDNV +aqp1_bovin MASEFKKKLF WRAVVAEFLA MILFIFISIG SALGFHYPIK SNQTtvQDNV +aqp1_sheep MASEFKKKLF WRAVVAEFLA MILFIFISIG SALGFHYPIK SNQTtvQDNV +aqpa_ranes MASEFKKKAF WRAVIAEFLA MILFVFISIG AALGFNFPIE EKANQtqDIV +aqp2_dasno ......SVAF SRAVLAEFLA TLIFVFFGLG SALSWPQALP S.......VL +aqp2_bovin ......SIAF SRAVLAEFLA TLLFVFFGLG SALNWPQALP S.......VL +aqp2_canfa ......SVAF SRAVFAEFLA TLLFVFFGLG SALNWPQALP S.......VL +aqp2_rabit ......SIAF SRAVFAEFLA TLLFVFFGLG SALNWPSALP S.......TL +aqp2_elema ......SIAF SRAVFSEFLA TLLFVFFGLG SALNWPQALP S.......VL +aqp2_horse ......SIAF SRAVLAEFLA TLLFVFFGLG SALNWPQAMP S.......VL +aqp2_proha ......SIAF SRAVLSEFLA TLLFVFFGLG SALNWPQALP S.......VL +mip_rat ...ELRSASF WRAIFAEFFA TLFYVFFGLG SSLRWA.... ...PGPLHVL +aqp2_oryaf ......SIAF SKAVFSEFLA TLLFVFFGLG SALNWPQALP S.......GL +mip_mouse .MWELRSASF WRAIFAEFFA TLFYVFFGLG ASLRWA.... ...PGPLHVL +mip_ranpi .MWEFRSFSF WRAVFAEFFG TMFYVFFGLG ASLKWAAGPA .......NVL +mip_bovin .MWELRSASF WRAICAEFFA SLFYVFFGLG ASLRWA.... ...PGPLHVL +mip_human .MWELRSASF WRAIFAEFFA TLFYVFFGLG SSLRWA.... ...PGPLHVL +mip_chick .......... .......... .......... .......... .......... +aqp5_rat MKKEVCSLAF FKAVFAEFLA TLIFVFFGLG SALKWPSALP T.......IL +aqp5_human MKKEVCSVAF LKAVFAEFLA TLIFVFFGLG SALKWPSALP T.......IL +aqp2_human .MWELRSIAF SRAVFAEFLA TLLFVFFGLG SALNWPQALP S.......VL +aqp4_human AFKGVWTQAF WKAVTAEFLA MLIFVLLSLG STINWG...G TEKPLPVDMV +aqp4_rat AFKGVWTQAF WKAVTAEFLA MLIFVLLSVG STINWG...G SENPLPVDMV +aqp4_mouse AFKGVWTQAF WKAVSAEFLA TLIFVL.GVG STINWG...G SENPLPVDMV +aqp2_rat .MWELRSIAF SRAVLAEFLA TLLFVFFGLG SALQWASSPP S.......VL +aqp2_mouse .MWELRSIAY CRAVLAEFLA TLLFVFFGLG SALQWASSPP S.......VL +wc2a_arath DGAELKKWSF YRAVIAEFVA TLLFLYITVL TVIGYKIQSD TDAGGVdgIL +aqp6_human MLACRLWKAI SRALFAEFLA TGLYVFFGVG SVMRWPTALP S.......VL +wc2c_arath DAEELTKWSL YRAVIAEFVA TLLFLYVTVL TVIGYKIQSD TKAGGVdgIL +wc2b_arath DADELTKWSL YRAVIAEFVA TLLFLYITVL TVIGYKIQSD TKAGGVdgIL +wc1c_arath EPGELSSWSF YRAGIAEFIA TFLFLYITVL TVMGVKRA.. PNMCASVGIQ +wc1b_arath EPGELASWSF WRAGIAEFIA TFLFLYITVL TVMGVKR..S PNMCASVGIQ +tipw_lyces EPGELSSWSF YRAGIAEFMA TFLFLYITIL TVMGLKRSDS LCSSV..GIQ +wc1a_arath EPGELSSWSF WRAGIAEFIA TFLFLYITVL TVMGVKR..S PNMCASVGIQ +tipw_pea EPSELTSWSF YRAGIAEFIA TFLFLYITVL TVMGVVRESS KCKTV..GIQ +tipa_arath RADEATHPDS IRATLAEFLS TFVFVFAAEG SILSLDKLYW EHAAHAGTni +aqua_atrca DMGELKLWSF WRAAIAEFIA TLLFLYITVA TVIGYKKETD PCASVGL..L +dip_antma SIGDSFSVAS IKAYVAEFIA TLLFVFAGVG SAIAYNKLTS DAALDPAGLV +aqpz_ecoli .........M FRKLAAECFG TFWLVFGGCG SAVLAAGFPE ....LGIGFA +tip2_tobac SIGDSFSVGS LKAYVAEFIA TLLFVFAGVG SAIAYNKLTA DAALDPAGLV +tip1_tobac SIGDSFSVGS LKAYVAEFIA TLLFVFAGVG SAIAYNKLTA DAALDPAGLV +tipg_arath RPDEATRPDA LKAALAEFIS TLIFVVAGSG SGMAFNKLTE NGATTPSGLV +bib_drome MQAEIRTLEF WRSIISECLA SFMYVFIVCG AAAGVGVGAS VSSVL....L +tipr_arath RPDEATRPDA LKAALAEFIS TLIFVVAGSG SGMAFNKLTE NGATTPSGLV +tipa_phavu RTDEATHPDS MRASLAEFAS TFIFVFAGEG SGLALVKIYQ DSAFSAGELL +tipg_orysa SHQEVYHPGA LKAALAEFIS TLIFVFAGQG SGMAFSKLTG GGATTPAGLI + + 51 100 +predict_h258 KVSLAFGLSI ATLAQSVGHI SGAHSNPAVT LGLLLSCQIS ILRAVMYIIA +aqp1_rat KVSLAFGLSI ATLAQSVGHI SGAHSNPAVT LGLLLSCQIS ILRAVMYIIA +aqp1_mouse KVSLAFGLSI ATLAQSVGHI SGAHLNPAVT LGLLLSCQIS ILRAVMYIIA +aqp1_human KVSLAFGLSI ATLAQSVGHI SGAHLNPAVT LGLLLSCQIS IFRALMYIIA +aqp1_bovin KVSLAFGLSI ATLAQSVGHI SGAHLNPAVT LGLLLSCQIS VLRAIMYIIA +aqp1_sheep KVSLAFGLSI ATLAQSVGHI SGAHLNPAVT LGLLLSCQIS ILRAIMYIIA +aqpa_ranes KVSLAFGISI ATMAQSVGHV SGAHLNPAVT LGCLLSCQIS ILKAVMYIIA +aqp2_dasno QIALAFGLAI GTLVQALGHV SGAHINPAVT VACLVGCHVS FLRAAFYVAA +aqp2_bovin QIAMAFGLAI GTLVQALGHV SGAHINPAVT VACLVGCHVS FLRAVFYVAA +aqp2_canfa QIAMAFGLGI GTLVQALGHV SGAHINPAVT VACLVGCHVS FLRAAFYVAA +aqp2_rabit QIAMAFGLGI GTLVQALGHV SGAHINPAVT VACLVGCHVS FLRAAFYVAA +aqp2_elema QIAMAFGLAI GTLVQTLGHI SGAHINPAVT VACLVGCHVS FLRATFYLAA +aqp2_horse QIAMAFGLAI GTLVQALGHV SGAHINPAVT VACLVGCHVS FLRAAFYVAA +aqp2_proha QIAMAFGLAI GTLVQTLGHI SGAHINPAVT IACLVGCHVS FLRALFYLAA +mip_rat QVALAFGLAL ATLVQTVGHI SGAHVNPAVT FAFLVGSQMS LLRAFCYIAA +aqp2_oryaf QIAMAFGLAI GTLVQTLGHI SGAHINPAVT VACLVGCHVS FLRAIFYVAA +mip_mouse QVALAFGLAL ATLVQTVGHI SGAHVNPAVT FAFLVGSQMS LLRAFCYIAA +mip_ranpi VIALAFGLVL ATMVQSIGHV SGAHINPAVT FAFLIGSQMS LFRAIFYIAA +mip_bovin QVALAFGLAL ATLVQAVGHI SGAHVNPAVT FAFLVGSQMS LLRAICYMVA +mip_human QVAMAFGLAL ATLVQSVGHI SGAHVNPAVT FAFLVGSQMS LLRAFCYMAA +mip_chick .......... .......... .......... .......... .......... +aqp5_rat QISIAFGLAI GTLAQALGPV SGGHINPAIT LALLIGNQIS LLRAVFYVAA +aqp5_human QIALAFGLAI GTLAQALGPV SGGHINPAIT LALLVGNQIS LLRAFFYVAA +aqp2_human QIAMAFGLGI GTLVQALGHI SGAHINPAVT VACLVGCHVS VLRAAFYVAA +aqp4_human LISLCFGLSI ATMVQCFGHI SGGHINPAVT VAMVCTRKIS IAKSVFYIAA +aqp4_rat LISLCFGLSI ATMVQCFGHI SGGHINPAVT VAMVCTRKIS IAKSVFYITA +aqp4_mouse LISLCFGLSI ATMVQCLGHI SGGHINPAVT VAMVCTRKIS IAKSVFYIIA +aqp2_rat QIAVAFGLGI GILVQALGHV SGAHINPAVT VACLVGCHVS FLRAAFYVAA +aqp2_mouse QIAVAFGLGI GTLVQALGHV SGAHINPAVT VACLVGCHVS FLRAAFYVAA +wc2a_arath GIAWAFGGMI FILVYCTAGI SGGHINPAVT FGLFLARKVS LPRALLYIIA +aqp6_human QIAITFNLVT AMAVQVTWKT SGAHANPAVT LAFLVGSHIS LPRAVAYVAA +wc2c_arath GIAWAFGGMI FILVYCTAGI SGGHINPAVT FGLFLARKVS LIRAVLYMVA +wc2b_arath GIAWAFGGMI FILVYCTAGI SGGHINPAVT FGLFLARKVS LIRAVLYMVA +wc1c_arath GIAWAFGGMI FALVYCTAGI SGGHINPAVT FGLFLARKLS LTRAVFYIVM +wc1b_arath GIAWAFGGMI FALVYCTAGI SGGHINPAVT FGLFLARKLS LTRAVYYIVM +tipw_lyces GVAWAFGGMI FALVYCTAGI SGGHINPAVT FGLFLARKLS LTRAVFYMVM +wc1a_arath GIAWAFGGMI FALVYCTAGI SGGHINPAVT FGLFLARKLS LTRALYYIVM +tipw_pea GIAWAFGGMI FALVYCTAGI SGGHINPAVT FGLFLARKLS LTRAIFYMVM +tipa_arath LVALAHAFAL FAAVSAAINV SGGHVNPAVT FGALVGGRVT AIRAIYYWIA +aqua_atrca GIAWSFGGMI FVLVYCTAGI SGGHINPAVT FGLFLARKVS LLRALVYMIA +dip_antma AVAVAHAFAL FVGVSMAANV SGGHLNPAVT LGLAVGGNIT ILTGLFYWIA +aqpz_ecoli GVALAFGLTV LTMAFAVGHI SGGHFNPAVT IGLWAGGRFP AKEVVGYVIA +tip2_tobac AVAVAHAFAL FVGVSIAANI SGGHLNPAVT LGLAVGGNIT ILTGFFYWIA +tip1_tobac AVAVAHAFAL FVGVSIAANI SGGHLNPAVT LGLAVGGNIT ILTGFFYWIA +tipg_arath AAAVAHAFGL FVAVSVGANI SGGHVNPAVT FGAFIGGNIT LLRGILYWIA +bib_drome ATALASGLAM ATLTQCFLHI SGAHINPAVT LALCVVRSIS PIRAAMYITA +tipr_arath AAAVAHAFGL FVAVSVGANI SGGHVNPAVT FGAFIGGNIT LLRGILYWIA +tipa_phavu ALALAHAFAL FAAVSASMHV SGGHVNPAVS FGALIGGRIS VIRAVYYWIA +tipg_orysa AAAVAHAFAL FVAVSVGANI SGGHVNPAVT FGAFVGGNIT LFRGLLYWIA + + 101 150 +predict_h258 QCVGAIVASA ILSGITSSLL ENSLGRNDLA RGVNSGQGLG IEIIGTLQLV +aqp1_rat QCVGAIVASA ILSGITSSLL ENSLGRNDLA RGVNSGQGLG IEIIGTLQLV +aqp1_mouse QCVGAIVATA ILSGITSSLV DNSLGRNDLA HGVNSGQGLG IEIIGTLQLV +aqp1_human QCVGAIVATA ILSGITSSLT GNSLGRNDLA DGVNSGQGLG IEIIGTLQLV +aqp1_bovin QCVGAIVATA ILSGITSSLP DNSLGLNALA PGVNSGQGLG IEIIGTLQLV +aqp1_sheep QCVGAIVATV ILSGITSSLP DNSLGLNALA PGVNSGQGLG IEIIGTLQLV +aqpa_ranes QCLGAVVATA ILSGITSGLE NNSLGLNGLS PGVSAGQGLG VEILVTFQLV +aqp2_dasno QLLGAVAGAA ILHEITPPDV RG........ .......... .......... +aqp2_bovin QLLGAVAGAA LLHEITPPAI RG........ .......... .......... +aqp2_canfa QLLGAVAGAA LLHEITPPHV RG........ .......... .......... +aqp2_rabit QLLGAVAGAA LLHEITPAEV RG........ .......... .......... +aqp2_elema QLLGAVAGAA LLHELTPPDI RG........ .......... .......... +aqp2_horse QLLGAVAGAA LLHEITPPDI RR........ .......... .......... +aqp2_proha QLLGAVAGAA LLHELTPPDI RG........ .......... .......... +mip_rat QLLGAVAGAA VLYSVTPPAV RGNLALNTLH AGVSVGQATT VEIFLTLQFV +aqp2_oryaf QLLGAVAGAA LLHELTPPDI RG........ .......... .......... +mip_mouse QLLGAVAGAA VLYSVTPPAV RGNLALNTLH TGVSVGQATT VEIFLTLQFV +mip_ranpi QLLGAVAGAA VLYGVTPAAI RGNLALNTLH PGVSLGQATT VEIFLTLQFV +mip_bovin QLLGAVAGAA VLYSVTPPAV RGNLALNTLH PGVSVGQATI VEIFLTLQFV +mip_human QLLGAVAGAA VLYSVTPPAV RGNLALNTLH PAVSVGQATT VEIFLTLQFV +mip_chick .......... .......... .......... .......... .......... +aqp5_rat QLVGAIAGAG ILYWLAPLNA RGNLAVNALN NNTTPGKAMV VELILTFQLA +aqp5_human QLVGAIAGAG ILYGVAPLNA RGNLAVNALN NNTTQGQAMV VELILTFQLA +aqp2_human QLLGAVAGAA LLHEITPADI RGDLAVNALS NSTTAGQAVT VELFLTLQLV +aqp4_human QCLGAIIGAG ILYLVTPPSV VGGLGVTMVH GNLTAGHGLL VELIITFQLV +aqp4_rat QCLGAIIGAG ILYLVTPPSV VGGLGVTTVH GNLTAGHGLL VELIITFQLV +aqp4_mouse QCLGAIIGAG ILYLVTPPSV VGGLGVTTVH GNLTAGHGLL VELIITFQLV +aqp2_rat QLLGAVAGAA ILHEITPVEI RGDLAVNALH NNATAGQAVT VELFLTMQLV +aqp2_mouse QLLGAVAGAA ILHEITPVEI RGDLAVNALH NNATAGQAVT VELFLTMQLV +wc2a_arath QCLGAICGVG FVKAFQSSYY TRYGGgnSLA DGYSTGTGLA AEIIGTFVLV +aqp6_human QLVGATVGAA LLYGVMPGDI RETLGINVVR NSVSTGQAVA VELLLTLQLV +wc2c_arath QCLGAICGVG FVKAFQSSHY VNYGGgnFLA DGYNTGTGLA AEIIGTFVLV +wc2b_arath QCLGAICGVG FRQSFQSSYY DRYGGgnSLA DGYNTGTGLA AEIIGTFVLV +wc1c_arath QCLGAICGAG VVKGFQPNPY QtgGGANTVA HGYTKGSGLG AEIIGTFVLV +wc1b_arath QCLGAICGAG VVKGFQPKQY QagGGANTIA HGYTKGSGLG AEIIGTFVLV +tipw_lyces QCLGAICGAG VVKGFMVGPY QrgGGANVVN PGYTKGDGLG AEIIGTFVLV +wc1a_arath QCLGAICGAG VVKGFQPKQY QagGGANTVA HGYTKGSGLG AEIIGTFVLV +tipw_pea QVLGAICGAG VVKGFEGKQR FGDLNgnFVA PGYTKGDGLG AEIVGTFILV +tipa_arath QLLGAILACL LLRLTTNGMR PVGFR...LA SGVGAVNGLV LEIILTFGLV +aqua_atrca QCAGAICGVG LVKAFMKGPY NqgGGANSVA LGYNKGTAFG AELIGTFVLV +dip_antma QCLGSTVACL LLKFVTNGL. ..SVPTHGVA AGMDAIQGVV MEIIITFALV +aqpz_ecoli QVVGGIVAAA LLYLIASGKT GFDAAASGFA sgYSMLSALV VELVLSAGFL +tip2_tobac QLLGSTVACL LLKYVTNGL. ..AVPTHGVA AGLNGFQGVV MEIIITFALV +tip1_tobac QLLGSTVACL LLKYVTNGL. ..AVPTHGVA AGLNGLQGVV MEIIITFALV +tipg_arath QLLGSVVACL ILKFATGGLA VPAFG...LS AGVGVLNAFV FEIVMTFGLV +bib_drome QCGGGIAGAA LLYGVTVPGY QGNLQAasHS AALAAWERFG VEFILTSLVV +tipr_arath QLLGSVVACL ILKFATGGLA VPPFG...LS AGVGVLNAFV FEIVMTFGLV +tipa_phavu QLLGSIVAAL VLRLVTNNMR PSGF...HVS PGVGVGHMFI LEVVMTFGLM +tipg_orysa QLLGSTVACF LLRFSTGGLA TGTFGL.... TGVSVWEALV LEIVMTFGLV + + 151 200 +predict_h258 LCVLATTDRR RRDLGGSAPL AIGLSVALGH LLAIDYTGCG INPARSFGSA +aqp1_rat LCVLATTDRR RRDLGGSAPL AIGLSVALGH LLAIDYTGCG INPARSFGSA +aqp1_mouse LCVLATTDRR RRDLGGSAPL AIGLSVALGH LLAIDYTGCG INPARSFGSA +aqp1_human LCVLATTDRR RRDLGGSAPL AIGLSVALGH LLAIDYTGCG INPARSFGSA +aqp1_bovin LCVLATTDRR RRDLGGSGPL AIGFSVALGH LLAIDYTGCG INPARSFGSS +aqp1_sheep LCVLATTDRR RrdLGDSGPL AIGFSVALGH LLAIDYTGCG INPARSFGSS +aqpa_ranes LCVVAVTDRR RHDVSGSVPL AIGLSVALGH LIAIDYTGCG MNPARSFGSA +aqp2_dasno .......... .......... .......... .......... .......... +aqp2_bovin .......... .......... .......... .......... .......... +aqp2_canfa .......... .......... .......... .......... .......... +aqp2_rabit .......... .......... .......... .......... .......... +aqp2_elema .......... .......... .......... .......... .......... +aqp2_horse .......... .......... .......... .......... .......... +aqp2_proha .......... .......... .......... .......... .......... +mip_rat LCIFATYDER RNGRMGSVAL AVGFSLTLGH LFGMYYTGAG MNPARSFAPA +aqp2_oryaf .......... .......... .......... .......... .......... +mip_mouse LCIFATYDER RNGRMGSVAL AVGFSLTLGH LFGMYYTGAG MNPARSFAPA +mip_ranpi LCIFATYDER RNGRLGSVSL AIGFSLTLGH LFGLYYTGAS MNPARSFAPA +mip_bovin LCIFATYDER RNGRLGSVAL AVGFSLTLGH LFGMYYTGAG MNPARSFAPA +mip_human LCIFATYDER RNGQLGSVAL AVGFSLALGH LFGMYYTGAG MNPARSFAPA +mip_chick ........DR HDGRPGSAAL PVGFSLALGH LFGIPFTGAG MNPARSFAPA +aqp5_rat LCIFSSTDSR RTSPVGSPAL SIGLSVTLGH LVGIYFTGCS MNPARSFGPA +aqp5_human LCIFASTDSR RTSPVGSPAL SIGLSVTLGH LVGIYFTGCS MNPARSFGPA +aqp2_human LCIFASTDER RGENPGTPAL SIGFSVALGH LLGIHYTGCS MNPARSLAPA +aqp4_human FTIFASCDSK RTDVTGSIAL AIGFSVAIGH LFAINYTGAS MNPARSFGPA +aqp4_rat FTIFASCDSK RTDVTGSVAL AIGFSVAIGH LFAINYTGAS MNPARSFGPA +aqp4_mouse FTVFASCDSK RTDVTGSIAL AIGFSVAIGH LFAINYTGAS MNPARSFGPA +aqp2_rat LCIFASTDER RGDNLGSPAL SIGFSVTLGH LLGIYFTGCS MNPARSLAPA +aqp2_mouse LCIFASTDER RSDNLGSPAL SIGFSVTLGH LLGIYFTGCS MNPARSLAPA +wc2a_arath YTVFSATDPK RSavPVLAPL PIGFAVFMVH LATIPITGTG INPARSFGAA +aqp6_human LCVFASTDSR QTS..GSPAT MIGISWALGH LIGILFTGCS MNPARSFGPA +wc2c_arath YTVFSATDPK RNavPVLAPL PIGFAVFMVH LATIPITGTG INPARSFGAA +wc2b_arath YTVFSATDPK RNavPVLAPL PIGFAVFMVH LATIPITGTG INPARSFGAS +wc1c_arath YTVFSATDAK RSavPILAPL PIGFAVFLVH LATIPITGTG INPARSLGAA +wc1b_arath YTVFSATDAK RNavPILAPL PIGFAVFLVH LATIPITGTG INPARSLGAA +tipw_lyces YTVFSATDAK RNavPILAPL PIGFAVFLVH LATIPITGTG INPARSLGAA +wc1a_arath YTVFSATDAK RNavPILAPL PIGFAVFLVH LATIPITATG INPARSLGAA +tipw_pea YTVFSATDAK RSavPILAPL PIGFAVFLVH LATIPITGTG INPARSLGAA +tipa_arath YVVYStiDPK RGSLGIIAPL AIGLIVGANI LVGGPFSGAS MNPARAFGPA +aqua_atrca YTVFSATDPK RSavPILAPL PIGFAVFMVH LATIPITGTG INPARSFGAA +dip_antma YTVYAtaDPK KGSLGVIAPI AIGFIVGANI LAAGPFSGGS MNPARSFGPA +aqpz_ecoli LVIHGATDKF APA..GFAPI AIGLALTLIH LISIPVTNTS VNPARSTAVA +tip2_tobac YTVYAtaDPK KGSLGTIAPI AIGFIVGANI LAAGPFSGGS MNPARSFGPA +tip1_tobac YTVYAtaDPK KGSLGTIAPI AIGFIVGANI LAAGPFSGGS MNPARSFGPA +tipg_arath YTVYAtiDPK NGSLGTIAPI AIGFIVGANI LAGGAFSGAS MNPAVAFGPA +bib_drome LCYFVSTDPM KKFMGNS.AA SIGCAYSACC FVSMPYLN.. ..PARSLGPS +tipr_arath YTVYAtiDPK NGSLGTIAPI AIGFIVGANI LAGGAFSGAS MNPAVAFGPA +tipa_phavu YTVYGtiDPK RGAVSYIAPL AIGLIVGANI LVGGPFDGAC MNPALAFGPS +tipg_orysa YTVYAtvDPK KGSLGTIAPI AIGFIVGANI LVGGAFDGAS MNPAVSFGPA + + 201 250 +predict_h258 VLTRNFSNHW IFWVGPFIGS ALAVLIYDFI LAPRSSDFTD RMKVWTSGQV +aqp1_rat VLTRNFSNHW IFWVGPFIGS ALAVLIYDFI LAPRSSDFTD RMKVWTSGQV +aqp1_mouse VLTRNFSNHW IFWVGPFIGG ALAVLIYDFI LAPRSSDFTD RMKVWTSGQV +aqp1_human VITHNFSNHW IFWVGPFIGG ALAVLIYDFI LAPRSSDLTD RVKVWTSGQV +aqp1_bovin VITHNFQDHW IFWVGPFIGA ALAVLIYDFI LAPRSSDLTD RVKVWTSGQV +aqp1_sheep VITHNFQDHW IFWVGPFIGA ALAVLIYDFI LAPRSSDLTD RVKVWTSGQV +aqpa_ranes VLTKNFTYHW IFWVGPMIGG AAAAIIYDFI LAPRTSDLTD RMKVWTNGQV +aqp2_dasno .......... .......... .......... .......... .......... +aqp2_bovin .......... .......... .......... .......... .......... +aqp2_canfa .......... .......... .......... .......... .......... +aqp2_rabit .......... .......... .......... .......... .......... +aqp2_elema .......... .......... .......... .......... .......... +aqp2_horse .......... .......... .......... .......... .......... +aqp2_proha .......... .......... .......... .......... .......... +mip_rat ILTRNFSNHW VYWVGPIIGG GLGSLLYDFL LFPRLKSVSE RLSILKGARP +aqp2_oryaf .......... .......... .......... .......... .......... +mip_mouse ILTRNFSNHW VYWVGPIIGG GLGSLLYDFL LFPRLKSVSE RLSILKGARP +mip_ranpi VLTRNFTNHW VYWVGPIIGG ALGGLVYDFI LFPRMRGLSE RLSILKGARP +mip_bovin ILTRNFTNHW VYWVGPVIGA GLGSLLYDFL LFPRLKSVSE RLSILKGSRP +mip_human ILTGNFTNHW VYWVGPIIGG GLGSLLYDFL LFPRLKSISE RLSVLKGAKP +mip_chick VITRNFTNHW VFWAGPLLGA ALAALLYELA LCPRARSMAE RLAV.LRGEP +aqp5_rat VVMNRFssHW VFWVGPIVGA MLAAILYFYL LFPSSLSLHD RVAVVKGTYE +aqp5_human VVMNRFsaHW VFWVGPIVGA VLAAILYFYL LFPNSLSLSE RVAIIKGTYE +aqp2_human VVTGKFDDHW VFWIGPLVGA ILGSLLYNYV LFPPAKSLSE RLAVLKGLEp +aqp4_human VIMGNWENHW IYWVGPIIGA VLAGGLYEYV FCPDVEFKRR FKEAFSKaqT +aqp4_rat VIMGNWENHW IYWVGPIIGA VLAGALYEYV FCPDVELKRR LKEAFSKaqT +aqp4_mouse VIMGNWANHW IYWVGPIMGA VLAGALYEYV FCPDVELKRR LKEAFSKaqT +aqp2_rat VVTGKFDDHW VFWIGPLVGA IIGSLLYNYL LFPSAKSLQE RLAVLKGLEp +aqp2_mouse VVTGKFDDHW VFWIGPLVGA IIGSLLYNYL LFPSTKSLQE RLAVLKGLEp +wc2a_arath VIYnpWDDHW IFWVGPFIGA AIAAFYHQFV LRASGSKSLG SFRSAANV.. +aqp6_human IIIGKFTVHW VFWVGPLMGA LLASLIYNFV LFPDTKTLAQ RLAILTGTVE +wc2c_arath VIFnpWDDHW IFWVGPFIGA TIAAFYHQFV LRASGSKSLG SFRSAANV.. +wc2b_arath VIYnpWDDHW IFWVGPFIGA AIAAFYHQFV LRASGSKSLG SFRSAANV.. +wc1c_arath IIYnaWDDHW IFWVGPFIGA ALAALYHQLV IRAIPFKSRS .......... +wc1b_arath IIFnaWDDHW VFWVGPFIGA ALAALYHVIV IRAIPFKSRS .......... +tipw_lyces IIYnaWNDHW IFWVGPMIGA ALAAIYHQII IRAMPFHRS. .......... +wc1a_arath IIYnsWDDHW VFWVGPFIGA ALAALYHVVV IRAIPFKSRS .......... +tipw_pea IVFngWNDHW IFWVGPFIGA ALAALYHQVV IRAIPFKSK. .......... +tipa_arath LVGWRWHDHW IYWVGPFIGS ALAALIYEYM VIPTEPPTHH AHGVHQPLAP +aqua_atrca VIyrVWDDHW IFWVGPFVGA LAAAAYHQYV LRAAAIKALG SFRSNPTN.. +dip_antma VASGDFSQNW IYWAGPLIGG ALAGFIYGDV FITAHAPLPT SEDYA..... +aqpz_ecoli IFQgaLEQLW FFWVVPIVGG IIGGLIYRTL LEKRD..... .......... +tip2_tobac VVAGDFSQNW IYWAGPLIGG GLAGFIYGDV FIGCHTPLPT SEDYA..... +tip1_tobac VVAGDFSQNW IYWAGPLIGG GLAGFIYGDV FIGCHTPLPT SEDYA..... +tipg_arath VVSWTWTNHW VYWAGPLVGG GIAGLIYEVF FINTTHEQLP TTDY...... +bib_drome FVLNKWDSHW VYWFGPLVGG MASGLVYEYI FNSRNRNLRH NKGSIDNDSS +tipr_arath VVSWTWTNHW VYWAGPLVGG GIAGLIYEVF FINTTHTSSS NHRLLN.... +tipa_phavu LVGWQWHQHW IFWVGPLLGA ALAALVYEYA VIPIEPPPHH HQPLATEDY. +tipg_orysa LVSWSWESQW VYWVGPLIGG GLAGVIYEVL FISHTHEQLP TTDY...... + + 251 269 +predict_h258 EEYDLDADDI NSRVEMKPK +aqp1_rat EEYDLDADDI NSRVEMKPK +aqp1_mouse EEYDLDADDI NSRVEMKPK +aqp1_human EEYDLDADDI NSRVEMKPK +aqp1_bovin EEYDLDADDI NSRVEMKPK +aqp1_sheep EEYDLDADDI NSRVEMKPK +aqpa_ranes EEYELDGDD. NTRVEMKPK +aqp2_dasno .......... ......... +aqp2_bovin .......... ......... +aqp2_canfa .......... ......... +aqp2_rabit .......... ......... +aqp2_elema .......... ......... +aqp2_horse .......... ......... +aqp2_proha .......... ......... +mip_rat SDSNGQPEGT GEPVELKTQ +aqp2_oryaf .......... ......... +mip_mouse SDSNGQPEGT GEPVELKTQ +mip_ranpi AEPEGQQEAT GEPIELKTQ +mip_bovin SESNGQPEVT GEPVELKTQ +mip_human DVSNGQPEVT GEPVELNTQ +mip_chick PAAAPPPEPP AEPLELKTQ +aqp5_rat PEEDWEDHRE ERKKTIELT +aqp5_human PDEDWEEQRE ERKKTMELT +aqp2_human tDWEEREVRR RQSVELHSP +aqp4_human KGSYMEVEDN RSQVETDDL +aqp4_rat KGSYMEVEDN RSQVETEDL +aqp4_mouse KGSYMEVEDN RSQVETEDL +aqp2_rat tDWEEREVRR RQSVELHSP +aqp2_mouse tDWEEREVRR RQSVELHSP +wc2a_arath .......... ......... +aqp6_human VGTGARAGAE PLKKESQPG +wc2c_arath .......... ......... +wc2b_arath .......... ......... +wc1c_arath .......... ......... +wc1b_arath .......... ......... +tipw_lyces .......... ......... +wc1a_arath .......... ......... +tipw_pea .......... ......... +tipa_arath EDY....... ......... +aqua_atrca .......... ......... +dip_antma .......... ......... +aqpz_ecoli .......... ......... +tip2_tobac .......... ......... +tip1_tobac .......... ......... +tipg_arath .......... ......... +bib_drome SIHSEDELNY DMDMEKPNK +tipr_arath .......... ......... +tipa_phavu .......... ......... +tipg_orysa .......... ......... + +________________________________________________________________________________ + + Prediction of: + + - secondary structure, by PHDsec + - solvent accessibility, by PHDacc + - and helical transmembrane regions, by PHDhtm + + PHD: Profile fed neural network systems from HeiDelberg + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + Author: Burkhard Rost + EMBL, Heidelberg, FRG + Meyerhofstrasse 1, 69 117 Heidelberg + Internet: Predict-Help@EMBL-Heidelberg.DE + + All rights reserved. + + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + Secondary structure prediction by PHDsec: + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + Author: Burkhard Rost + EMBL, Heidelberg, FRG + Meyerhofstrasse 1, 69 117 Heidelberg + Internet: Rost@EMBL-Heidelberg.DE + + All rights reserved. + +About the network method +~~~~~~~~~~~~~~~~~~~~~~~ + +The network procedure is described in detail in: +1) Rost, Burkhard; Sander, Chris: + Prediction of protein structure at better than 70% accuracy. + J. Mol. Biol., 1993, 232, 584-599. + +A brief description is given in: + Rost, Burkhard; Sander, Chris: + Improved prediction of protein secondary structure by use of se- + quence profiles and neural networks. + Proc. Natl. Acad. Sci. U.S.A., 1993, 90, 7558-7562. + +The PHD mail server is described in: +2) Rost, Burkhard; Sander, Chris; Schneider, Reinhard: + PHD - an automatic mail server for protein secondary structure + prediction. + CABIOS, 1994, 10, 53-60. + +The latest improvement steps (up to 72%) are explained in: +3) Rost, Burkhard; Sander, Chris: + Combining evolutionary information and neural networks to predict + protein secondary structure. + Proteins, 1994, 19, 55-72. + +To be quoted for publications of PHD output: + Papers 1-3 for the prediction of secondary structure and the pre- + diction server. + +About the input to the network +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The prediction is performed by a system of neural networks. +The input is a multiple sequence alignment. It is taken from an HSSP +file (produced by the program MaxHom: + Sander, Chris & Schneider, Reinhard: Database of Homology-Derived + Structures and the Structural Meaning of Sequence Alignment. + Proteins, 1991, 9, 56-68. + +For optimal results the alignment should contain sequences with varying +degrees of sequence similarity relative to the input protein. +The following is an ideal situation: + ++-----------------+----------------------+ +| sequence: | sequence identity | ++-----------------+----------------------+ +| target sequence | 100 % | +| aligned seq. 1 | 90 % | +| aligned seq. 2 | 80 % | +| ... | ... | +| aligned seq. 7 | 30 % | ++-----------------+----------------------+ + +Estimated Accuracy of Prediction +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +A careful cross validation test on some 250 protein chains (in total +about 55,000 residues) with less than 25% pairwise sequence identity +gave the following results: + +++================++-----------------------------------------+ +|| Qtotal = 72.1% || ("overall three state accuracy") | +++================++-----------------------------------------+ + ++----------------------------+-----------------------------+ +| Qhelix (% of observed)=70% | Qhelix (% of predicted)=77% | +| Qstrand(% of observed)=62% | Qstrand(% of predicted)=64% | +| Qloop (% of observed)=79% | Qloop (% of predicted)=72% | ++----------------------------+-----------------------------+ +.......................................................................... + +These percentages are defined by: +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +| number of correctly predicted residues +|Qtotal = --------------------------------------- (*100) +| number of all residues +| +| no of res correctly predicted to be in helix +|Qhelix (% of obs) = -------------------------------------------- (*100) +| no of all res observed to be in helix +| +| +| no of res correctly predicted to be in helix +|Qhelix (% of pred)= -------------------------------------------- (*100) +| no of all residues predicted to be in helix + +.......................................................................... + +Averaging over single chains +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The most reasonable way to compute the overall accuracies is the above +quoted percentage of correctly predicted residues. However, since the +user is mainly interested in the expected performance of the prediction +for a particular protein, the mean value when averaging over protein +chains might be of help as well. Computing first the three state +accuracy for each protein chain, and then averaging over 250 chains +yields the following average: + ++-------------------------------====--+ +| Qtotal/averaged over chains = 72.2% | ++-------------------------------====--+ +| standard deviation = 9.3% | ++-------------------------------------+ + +.......................................................................... + +Further measures of performance +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Matthews correlation coefficient: + ++---------------------------------------------+ +| Chelix = 0.63, Cstrand = 0.53, Cloop = 0.52 | ++---------------------------------------------+ +.......................................................................... + +Average length of predicted secondary structure segments: + +. +------------+----------+ +. | predicted | observed | ++-----------+------------+----------+ +| Lhelix = | 10.3 | 9.3 | +| Lstrand = | 5.0 | 5.3 | +| Lloop = | 7.2 | 5.9 | ++-----------+------------+----------+ +.......................................................................... + +The accuracy matrix in detail: + ++---------------------------------------+ +| number of residues with H, E, L | ++---------+------+------+------+--------+ +| |net H |net E |net L |sum obs | ++---------+------+------+------+--------+ +| obs H |12447 | 1255 | 3990 | 17692 | +| obs E | 949 | 7493 | 3750 | 12192 | +| obs L | 2604 | 2875 |19962 | 25441 | ++---------+------+------+------+--------+ +| sum Net |16000 |11623 |27702 | 55325 | ++---------+------+------+------+--------+ + +Note: This table is to be read in the following manner: + 12447 of all residues predicted to be in helix, were observed to + be in helix, 949 however belong to observed strands, 2604 to + observed loop regions. The term "observed" refers to the DSSP + assignment of secondary structure calculated from 3D coordinates + of experimentally determined structures (Dictionary of Secondary + Structure of Proteins: Kabsch & Sander (1983) Biopolymers, 22, + 2577-2637). + +Position-specific reliability index +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The network predicts the three secondary structure types using real +numbers from the output units. The prediction is assigned by choosing +the maximal unit ("winner takes all"). However, the real numbers +contain additional information. +E.g. the difference between the maximal and the second largest output +unit can be used to derive a "reliability index". This index is given +for each residue along with the prediction. The index is scaled to +have values between 0 (lowest reliability), and 9 (highest). +The accuracies (Qtot) to be expected for residues with values above a +particular value of the index are given below as well as the fraction +of such residues (%res).: + ++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+ +| index| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | +| %res |100.0| 99.2| 90.4| 80.9| 71.6| 62.5| 52.8| 42.3| 29.8| 14.1| ++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+ +| | | | | | | | | | | | +| Qtot | 72.1| 72.3| 74.8| 77.7| 80.3| 82.9| 85.7| 88.5| 91.1| 94.2| +| | | | | | | | | | | | ++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+ +| H%obs| 70.4| 70.6| 73.7| 77.1| 80.1| 83.1| 86.0| 89.3| 92.5| 96.4| +| E%obs| 61.5| 61.7| 63.7| 66.6| 69.1| 71.7| 74.6| 77.0| 77.8| 68.1| +| | | | | | | | | | | | +| H%prd| 77.8| 78.0| 80.0| 82.6| 84.7| 86.9| 89.2| 91.3| 93.1| 95.4| +| E%prd| 64.5| 64.7| 67.8| 71.0| 74.2| 77.6| 81.4| 85.1| 89.8| 93.5| ++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+ + +The above table gives the cumulative results, e.g. 62.5% of all +residues have a reliability of at least 5. The overall three-state +accuracy for this subset of almost two thirds of all residues is 82.9%. +For this subset, e.g., 83.1% of the observed helices are correctly +predicted, and 86.9% of all residues predicted to be in helix are +correct. + +.......................................................................... + +The following table gives the non-cumulative quantities, i.e. the +values per reliability index range. These numbers answer the question: +how reliable is the prediction for all residues labeled with the +particular index i. + ++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+ +| index| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | +| %res | 8.8| 9.5| 9.3| 9.1| 9.7| 10.5| 12.5| 15.7| 14.1| ++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+ +| | | | | | | | | | | +| Qtot | 46.6| 50.6| 57.7| 62.6| 67.9| 74.2| 82.2| 88.3| 94.2| +| | | | | | | | | | | ++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+ +| H%obs| 36.8| 42.3| 49.5| 55.2| 61.7| 69.9| 78.8| 87.4| 96.4| +| E%obs| 44.7| 44.5| 52.1| 55.4| 60.9| 68.0| 75.9| 81.0| 68.1| +| | | | | | | | | | | +| H%prd| 49.9| 52.5| 60.3| 64.2| 69.2| 77.5| 85.4| 89.9| 95.4| +| E%prd| 41.7| 47.1| 53.6| 57.0| 64.0| 71.6| 78.8| 88.8| 93.5| ++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+ + +For example, for residues with Relindex = 5 64% of all predicted betha- +strand residues are correctly identified. + + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + Solvent accessibility prediction by PHDacc: + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + Author: Burkhard Rost + EMBL, Heidelberg, FRG + Meyerhofstrasse 1, 69 117 Heidelberg + Internet: Rost@EMBL-Heidelberg.DE + + All rights reserved. + +About the network method +~~~~~~~~~~~~~~~~~~~~~~~ + +The network for prediction of secondary structure is described in +detail in: + Rost, Burkhard; Sander, Chris: + Prediction of protein structure at better than 70% accuracy. + J. Mol. Biol., 1993, 232, 584-599. + +The analysis of the prediction of solvent exposure is given in: + Rost, Burkhard; Sander, Chris: + Conservation and prediction of solvent accessibility in protein + families. Proteins, 1994, 20, 216-226. + +To be quoted for publications of PHD exposure prediction: + Both papers quoted above. + +Definition of accessibility +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +For training the residue solvent accessibility the DSSP (Dictionary of +Secondary Structure of Proteins; Kabsch & Sander (1983) Biopolymers, 22, +2577-2637) values of accessible surface area have been used. The +prediction provides values for the relative solvent accessibility. The +normalisation is the following: + +| ACCESSIBILITY (from DSSP in Angstrom) +|RELATIVE_ACCESSIBILITY = ------------------------------------- * 100 +| MAXIMAL_ACC (amino acid type i) + +where MAXIMAL_ACC (i) is the maximal accessibility of amino acid type i. +The maximal values are: + ++----+----+----+----+----+----+----+----+----+----+----+----+ +| A | B | C | D | E | F | G | H | I | K | L | M | +| 106| 160| 135| 163| 194| 197| 84| 184| 169| 205| 164| 188| ++----+----+----+----+----+----+----+----+----+----+----+----+ +| N | P | Q | R | S | T | V | W | X | Y | Z | +| 157| 136| 198| 248| 130| 142| 142| 227| 180| 222| 196| ++----+----+----+----+----+----+----+----+----+----+----+ + +Notation: one letter code for amino acid, B stands for D or N; Z stands + for E or Q; and X stands for undetermined. + +The relative solvent accessibility can be used to estimate the number +of water molecules (W) in contact with the residue: + +W = ACCESSIBILITY /10 + +The prediction is given in 10 states for relative accessibility, with + +RELATIVE_ACCESSIBILITY = (PREDICTED_ACC * PREDICTED_ACC) + +where PREDICTED_ACC = 0 - 9. + +Estimated Accuracy of Prediction +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +A careful cross validation test on some 238 protein chains (in total +about 62,000 residues) with less than 25% pairwise sequence identity +gave the following results: + +Correlation +........... + +The correlation between observed and predicted solvent accessibility +is: + +----------- +corr = 0.53 +----------- + +This value ought to be compared to the worst and best case prediction +scenario: random prediction (corr = 0.0) and homology modelling +(corr = 0.66). (Note: homology modelling yields a relative accurate +prediction in 3D if, and only if, a significantly identical sequence +has a known 3D structure.) + +3-state accuracy +................ + +Often the relative accessibility is projected onto, e.g., 3 states: + b = buried (here defined as < 9% relative accessibility), + i = intermediate ( 9% <= rel. acc. < 36% ), + e = exposed ( rel. acc. >= 36% ). + +A projection onto 3 states or 2 states (buried/exposed) enables the +compilation of a 3- and 2-state prediction accuracy. PHD reaches an +overall 3-state accuracy of: + Q3 = 57.5% +(compared to 35% for random prediction and 70% for homology modelling). + +In detail: + ++-----------------------------------+-------------------------+ +| Qburied (% of observed)=77% | Qb (% of predicted)=60% | +| Qintermediate (% of observed)= 9% | Qi (% of predicted)=44% | +| Qexposed (% of observed)=78% | Qe (% of predicted)=56% | ++-----------------------------------+-------------------------+ + +10-state accuracy +................. + +The network predicts relative solvent accessibility in 10 states, with +state i (i = 0-9) corresponding to a relative solvent accessibility of +i*i %. The 10-state accuracy of the network is: + + Q10 = 24.5% + +.......................................................................... + +These percentages are defined by: +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +| number of correctly predicted residues +|Q3 = --------------------------------------- (*100) +| number of all residues +| +| no of res. correctly predicted to be buried +|Qburied (% of obs) = ------------------------------------------- (*100) +| no of all res. observed to be buried +| +| +| no of res. correctly predicted to be buried +|Qburied (% of pred)= ------------------------------------------- (*100) +| no of all residues predicted to be buried + +.......................................................................... + +Averaging over single chains +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The most reasonable way to compute the overall accuracies is the above +quoted percentage of correctly predicted residues. However, since the +user is mainly interested in the expected performance of the prediction +for a particular protein, the mean value when averaging over protein +chains might be of help as well. Computing first the correlation +between observed and predicted accessibility for each protein chan, and +then averaging over all 238 chains yields the following average: + ++-------------------------------====--+ +| corr/averaged over chains = 0.53 | ++-------------------------------====--+ +| standard deviation = 0.11 | ++-------------------------------------+ + +.......................................................................... + +Further details of performance accuracy +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The accuracy matrix in detail: +.............................. + +-------+----------------------------------------------------+----------- +\ PHD | 0 1 2 3 4 5 6 7 8 9 | SUM %obs +-------+----------------------------------------------------+----------- +OBS 0 | 8611 140 8 44 82 169 772 334 27 0 | 10187 16.6 +OBS 1 | 4367 164 0 50 106 231 738 346 44 3 | 6049 9.8 +OBS 2 | 3194 168 1 68 125 303 951 513 42 7 | 5372 8.7 +OBS 3 | 2760 159 8 80 136 327 1246 746 58 19 | 5539 9.0 +OBS 4 | 2312 144 2 72 166 396 1615 1245 124 19 | 6095 9.9 +OBS 5 | 1873 96 3 84 138 425 1979 1834 187 27 | 6646 10.8 +OBS 6 | 1387 67 1 60 80 278 2237 2627 231 51 | 7019 11.4 +OBS 7 | 1082 35 0 32 56 225 1871 3107 302 60 | 6770 11.0 +OBS 8 | 660 25 0 27 43 136 1206 2374 325 87 | 4883 7.9 +OBS 9 | 325 20 2 27 29 74 648 1159 366 214 | 2864 4.7 +-------+----------------------------------------------------+----------- +SUM |26571 1018 25 544 961 2564 13263 14285 1706 487 | +-------+----------------------------------------------------+----------- + +Note: This table is to be read in the following manner: + 8611 of all residues predicted to be in exposed by 0%, were + observed with 0% relative accessibility. However, 325 of all + residues predicted to have 0% are observed as completely exposed + (obs = 9 -> rel. acc. >= 81%). The term "observed" refers to the + DSSP compilation of area of solvent accessibility calculated from + 3D coordinates of experimentally determined structures (Diction- + ary of Secondary Structure of Proteins: Kabsch & Sander (1983) + Biopolymers, 22, 2577-2637). + +Accuracy for each amino acid: +............................. + ++---+------------------------------+-----+-------+------+ +|AA | Q3 b%o b%p i%o i%p e%o e%p | Q10 | corr | N | ++---+------------------------------+-----+-------+------+ +| A | 59.0 87 60 2 38 66 57 | 31 | 0.530 | 5054 | +| C | 62.0 91 67 5 39 25 21 | 34 | 0.244 | 893 | +| D | 56.5 21 45 6 49 94 57 | 20 | 0.321 | 3536 | +| E | 60.8 9 40 3 41 98 61 | 21 | 0.347 | 3743 | +| F | 63.3 94 67 9 46 29 37 | 27 | 0.366 | 2436 | +| G | 52.1 75 51 1 31 67 53 | 22 | 0.405 | 4787 | +| H | 50.9 63 53 23 45 71 50 | 18 | 0.442 | 1366 | +| I | 64.9 95 68 6 41 30 38 | 34 | 0.360 | 3437 | +| K | 66.6 2 11 2 37 98 67 | 23 | 0.267 | 3652 | +| L | 61.6 93 65 8 44 31 40 | 31 | 0.368 | 5016 | +| M | 60.1 92 64 5 39 45 44 | 29 | 0.452 | 1371 | +| N | 55.5 45 45 8 38 87 59 | 17 | 0.410 | 2923 | +| P | 53.0 48 48 9 39 83 56 | 18 | 0.364 | 2920 | +| Q | 54.3 27 44 7 44 92 56 | 20 | 0.344 | 2225 | +| R | 49.9 15 47 36 47 76 51 | 18 | 0.372 | 2765 | +| S | 55.6 69 53 3 51 81 56 | 22 | 0.464 | 3981 | +| T | 51.8 61 51 8 38 78 53 | 21 | 0.432 | 3740 | +| V | 61.1 93 65 5 40 39 42 | 34 | 0.418 | 4156 | +| W | 56.2 85 62 20 49 29 27 | 21 | 0.318 | 891 | +| Y | 49.7 73 52 33 49 36 38 | 19 | 0.359 | 2301 | ++---+------------------------------+-----+-------+------+ + +Abbreviations: + +AA: amino acid in one-letter code +b%o, i%o, e%o: = Qburied, Qintermediate, Qexposed (% of observed), + i.e. percentage of correct prediction in each state, see above +b%p, i%p, e%p: = Qburied, Qintermediate, Qexposed (% of predicted), + i.e. probability of correct prediction in each state, see above +b%o: = Qburied (% of observed), see above +Q10: percentage of correctly predicted residues in each of the 10 + states of predicted relative accessibility. +corr: correlation between predicted and observed rel. acc. +N: number of residues in data set + +Accuracy for different secondary structure: +........................................... + ++--------+------------------------------+----+-------+-------+ +| type | Q3 b%o b%p i%o i%p e%o e%p |Q10 | corr | N | ++--------+------------------------------+----+-------+-------+ +| helix | 59.5 79 64 8 44 80 56 | 27 | 0.574 | 20100 | +| strand | 61.3 84 73 9 46 69 37 | 35 | 0.524 | 13356 | +| loop | 54.4 64 43 11 44 78 61 | 18 | 0.442 | 27968 | ++--------+------------------------------+----+-------+-------+ + +Abbreviations as before. + +Position-specific reliability index +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The network predicts the 10 states for relative accessibility using real +numbers from the output units. The prediction is assigned by choosing +the maximal unit ("winner takes all"). However, the real numbers +contain additional information. +E.g. the difference between the maximal and the second largest output +unit (with the constraint that the second largest output is compiled +among all units at least 2 positions off the maximal unit) can be used +to derive a "reliability index". This index is given for each residue +along with the prediction. The index is scaled to have values between +0 (lowest reliability), and 9 (highest). +The accuracies (Q3, corr, asf.) to be expected for residues with values +above a particular value of the index are given below as well as the +fraction of such residues (%res).: + ++---+------------------------------+----+-------+-------+ +|RI | Q3 b%o b%p i%o i%p e%o e%p |Q10 | corr | %res | ++---+------------------------------+----+-------+-------+ +| 0 | 57.5 77 60 9 44 78 56 | 24 | 0.535 | 100.0 | +| 1 | 59.1 76 63 9 45 82 57 | 25 | 0.560 | 91.2 | +| 2 | 61.7 79 66 4 47 87 58 | 27 | 0.594 | 77.1 | +| 3 | 66.6 87 70 1 51 89 63 | 30 | 0.650 | 57.1 | +| 4 | 70.0 89 72 0 83 91 67 | 32 | 0.686 | 45.8 | +| 5 | 72.9 92 75 0 0 93 70 | 34 | 0.722 | 35.6 | +| 6 | 76.3 95 77 0 0 93 75 | 36 | 0.769 | 24.7 | +| 7 | 79.0 97 79 0 0 93 78 | 39 | 0.803 | 16.0 | +| 8 | 80.9 98 80 0 0 91 81 | 43 | 0.824 | 9.6 | +| 9 | 81.2 99 80 0 0 88 83 | 45 | 0.828 | 5.9 | ++---+------------------------------+----+-------+-------+ + +Abbreviations as before. + +The above table gives the cumulative results, e.g. 45.8% of all +residues have a reliability of at least 4. The correlation for this +most reliably predicted half of the residues is 0.686, i.e. a value +comparable to what could be expected if homology modelling were +possible. For this subset of 45.8% of all residues, 89% of the buried +residues are correctly predicted, and 72% of all residues predicted to +be buried are correct. + +.......................................................................... + +The following table gives the non-cumulative quantities, i.e. the +values per reliability index range. These numbers answer the question: +how reliable is the prediction for all residues labeled with the +particular index i. + ++---+------------------------------+----+-------+-------+ +|RI | Q3 b%o b%p i%o i%p e%o e%p |Q10 | corr | %res | ++---+------------------------------+----+-------+-------+ +| 0 | 40.9 79 40 16 41 21 40 | 14 | 0.175 | 8.8 | +| 1 | 45.4 61 46 28 44 48 44 | 17 | 0.278 | 14.1 | +| 2 | 47.4 53 52 10 46 80 44 | 19 | 0.343 | 19.9 | +| 3 | 52.9 75 59 4 50 77 47 | 23 | 0.439 | 11.4 | +| 4 | 60.0 81 63 0 83 84 56 | 25 | 0.547 | 10.1 | +| 5 | 65.2 82 70 0 0 93 62 | 28 | 0.607 | 10.9 | +| 6 | 71.3 90 72 0 0 94 70 | 31 | 0.692 | 8.8 | +| 7 | 76.0 94 76 0 0 95 75 | 34 | 0.762 | 6.3 | +| 8 | 80.5 97 81 0 0 94 79 | 39 | 0.808 | 3.8 | +| 9 | 81.2 99 80 0 0 88 83 | 45 | 0.828 | 5.9 | ++---+------------------------------+----+-------+-------+ + +For example, for residues with RI = 4 83% of all predicted intermediate +residues are correctly predicted as such. + + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + Prediction of helical transmembrane segments by PHDhtm: + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + Author: Burkhard Rost + EMBL, Heidelberg, FRG + Meyerhofstrasse 1, 69 117 Heidelberg + Internet: Rost@EMBL-Heidelberg.DE + + All rights reserved. + +About the network method +~~~~~~~~~~~~~~~~~~~~~~~ + +The PHD mail server is described in: + Rost, Burkhard; Sander, Chris; Schneider, Reinhard: + PHD - an automatic mail server for protein secondary structure + prediction. + CABIOS, 1994, 10, 53-60. + +To be quoted for publications of PHDhtm output: + Rost, Burkhard; Casadio, Rita; Fariselli, Piero; Sander, Chris: + Prediction of helical transmembrane segments at 95% accuracy. + Protein Science, 1995, 4, 521-533. + +Estimated Accuracy of Prediction +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +A cross validation test on 69 helical trans-membrane proteins (in total +about 30,000 residues) with less than 25% pairwise sequence identity +gave the following results: + +++================++-----------------------------------------+ +|| Qtotal = 94.7% || ("overall two state accuracy") | +++================++-----------------------------------------+ + ++----------------------------+-----------------------------+ +| Qhelix (% of observed)=92% | Qhelix (% of predicted)=83% | +| Qloop (% of observed)=96% | Qloop (% of predicted)=97% | ++----------------------------+-----------------------------+ + +.......................................................................... + +These percentages are defined by: +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +| number of correctly predicted residues +|Qtotal = --------------------------------------- (*100) +| number of all residues +| +| no of res correctly predicted to be in helix +|Qhelix (% of obs) = -------------------------------------------- (*100) +| no of all res observed to be in helix +| +| +| no of res correctly predicted to be in helix +|Qhelix (% of pred)= -------------------------------------------- (*100) +| no of all residues predicted to be in helix + +.......................................................................... + +Further measures of performance +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Matthews correlation coefficient: + ++---------------------------------------------+ +| Chelix = 0.84, Cloop = 0.84 | ++---------------------------------------------+ +.......................................................................... + +Average length of predicted secondary structure segments: + +| +------------+----------+ +| | predicted | observed | ++-----------+------------+----------+ +| Lhelix = | 24.6 | 22.2 | ++-----------+------------+----------+ +.......................................................................... + +The accuracy matrix in detail: + ++---------------------------------+ +| number of residues with H, L | ++---------+------+-------+--------+ +| |net H | net L |sum obs | ++---------+------+-------+--------+ +| obs H | 5214 | 492 | 5706 | +| obs L | 1050 | 22423 | 23473 | ++---------+------+-------+--------+ +| sum Net | 6264 | 22915 | 29179 | ++---------+------+-------+--------+ + +Note: This table is to be read in the following manner: + 5214 of all residues predicted to be in a helical trans-membrane + region, were observed to be in the lipid bilayer, 1050 however + were observed either inside or outside of the protein, i.e. in + loop (or non-membrane) regions. The term "observed" refers to DSSP + assignment of secondary structure calculated from 3D coordinates + of experimentally determined structures (Dictionary of Secondary + Structure of Proteins: Kabsch & Sander (1983) Biopolymers, 22, + 2577-2637) where these were available. For all other proteins, + the assignment of trans-membrane segments has been taken from the + Swissprot data bank (Bairoch, A.; Boeckmann, B.: The SWISS-PROT + protein sequence data bank. Nucl. Acids Res. 20: 2019-2022, 1992). + +.......................................................................... + +Overlap between predicted and observed segments: + ++-----------------+---------------+----------------+ +| segment overlap | % of observed | % of predicted | +| Sov helix | 95.6% | 95.5% | +| Sov loop | 83.6% | 97.2% | ++-----------------+---------------+----------------+ +| Sov total | 86.0% | 96.8% | ++-----------------+---------------+----------------+ + + Definition of Sov in: Rost et al., JMB, 1994, 235, 13-26. + + As helical trans-membrane segments are longer than globular heli- + ces, correctly predicted segments can easily be made out. PHDhtm + misses 5 out of 258 observed segments, predicts 6 where non is + observed and 3 times the predicted helical segment overlaps two + observed regions. Thus, in total more than 95% of all segments + are correctly predicted. + +.......................................................................... + +Entropy of prediction (information measure): + ++-----------------+ +| I = 0.64 | ++-----------------+ + + (For comparison: homology modelling of globular proteins in three + states: I=0.62.) + Definition of Sov in: Rost et al., JMB, 1994, 235, 13-26. + +Position-specific reliability index +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The network predicts two states: helical trans-membrane region and rest +using two output units. The prediction is assigned by choosing the ma- +ximal unit ("winner takes all"). However, the real numbers of the out- +put units contain additional information. +E.g. the difference between the two output units can be used to derive +a "reliability index". This index is given for each residue along with +the prediction. The index is scaled to have values between 0 (lowest +reliability), and 9 (highest). +The accuracies (Qtot) to be expected for residues with values above a +particular value of the index are given below as well as the fraction +of such residues (%res).: + ++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+ +| index| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | +| %res |100.0| 98.8| 97.3| 95.9| 94.1| 92.3| 89.9| 86.2| 75.0| 66.8| ++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+ +| | | | | | | | | | | | +| Qtot | 94.7| 95.2| 95.6| 96.2| 96.7| 97.2| 97.7| 98.4| 99.4| 99.8| +| | | | | | | | | | | | ++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+ +| H%obs| 91.8| 92.9| 93.8| 94.4| 95.0| 95.7| 96.2| 96.8| 95.5| 78.7| +| L%obs| 95.3| 95.7| 96.1| 96.6| 97.0| 97.5| 98.1| 98.8| 99.7|100.0| +| | | | | | | | | | | | +| H%prd| 82.7| 83.8| 85.0| 86.7| 88.1| 89.7| 91.4| 93.8| 96.3| 97.1| +| L%prd| 97.9| 98.3| 98.5| 98.7| 98.8| 99.0| 99.2| 99.4| 99.7| 99.9| ++------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+ + +The above table gives the cumulative results, e.g. 92.3% of all +residues have a reliability of at least 5. The overall two-state +accuracy for this subset is 97.2%. For this subset, e.g., 95.7% of +the observed helical trans-membrane residues are correctly predicted, +and 89.7% of all residues predicted to be in helical trans-membrane +segment are correct. + +The resulting network (PHD) prediction is: +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +________________________________________________________________________________ + + PHD: Profile fed neural network systems from HeiDelberg + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + Prediction of: + secondary structure, by PHDsec + solvent accessibility, by PHDacc + and helical transmembrane regions, by PHDhtm + + Author: + Burkhard Rost + EMBL, 69012 Heidelberg, Germany + Internet: Rost@EMBL-Heidelberg.DE + + All rights reserved. + + The network systems are described in: + + PHDsec: B Rost & C Sander: JMB, 1993, 232, 584-599. + B Rost & C Sander: Proteins, 1994, 19, 55-72. + PHDacc: B Rost & C Sander: Proteins, 1994, 20, 216-226. + PHDhtm: B Rost et al.: Prot. Science, 1995, 4, 521-533. + + Some statistics + ~~~~~~~~~~~~~~~ + + Percentage of amino acids: + +--------------+--------+--------+--------+--------+--------+ + | AA: | L | A | S | G | I | + | % of AA: | 13.0 | 10.0 | 9.7 | 8.9 | 8.6 | + +--------------+--------+--------+--------+--------+--------+ + | AA: | V | R | T | F | D | + | % of AA: | 7.8 | 5.2 | 4.5 | 4.5 | 4.5 | + +--------------+--------+--------+--------+--------+--------+ + | AA: | N | Q | E | P | K | + | % of AA: | 4.1 | 3.0 | 3.0 | 2.6 | 2.6 | + +--------------+--------+--------+--------+--------+--------+ + | AA: | Y | M | W | H | C | + | % of AA: | 1.9 | 1.9 | 1.5 | 1.5 | 1.5 | + +--------------+--------+--------+--------+--------+--------+ + + Percentage of secondary structure predicted: + +--------------+--------+--------+--------+ + | SecStr: | H | E | L | + | % Predicted: | 43.9 | 16.7 | 39.4 | + +--------------+--------+--------+--------+ + + According to the following classes: + all-alpha: %H>45 and %E< 5; all-beta : %H<5 and %E>45 + alpha-beta : %H>30 and %E>20; mixed: rest, + this means that the predicted class is: mixed class + + PHD output for your protein + ~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + Tue Nov 24 17:44:57 1998 + Jury on: 10 different architectures (version 5.94_317 ). + Note: differently trained architectures, i.e., different versions can + result in different predictions. + + About the protein + ~~~~~~~~~~~~~~~~~ + + HEADER /home/phd/server/work/predict_h25873-220 + COMPND + SOURCE + AUTHOR + SEQLENGTH 269 + NCHAIN 1 chain(s) in predict_h25873-22040 data set + NALIGN 48 + (=number of aligned sequences in HSSP file) + + Abbreviations: PHDsec + ~~~~~~~~~~~~~~~~~~~~~ + + sequence: + AA : amino acid sequence + secondary structure: + HEL: H=helix, E=extended (sheet), blank=other (loop) + PHD: Profile network prediction HeiDelberg + Rel: Reliability index of prediction (0-9) + detail: + prH: 'probability' for assigning helix + prE: 'probability' for assigning strand + prL: 'probability' for assigning loop + note: the 'probabilites' are scaled to the interval 0-9, e.g., + prH=5 means, that the first output node is 0.5-0.6 + subset: + SUB: a subset of the prediction, for all residues with an expected + average accuracy > 82% (tables in header) + note: for this subset the following symbols are used: + L: is loop (for which above " " is used) + ".": means that no prediction is made for this residue, as the + reliability is: Rel < 5 + + Abbreviations: PHDacc + ~~~~~~~~~~~~~~~~~~~~~ + + SS : secondary structure + HEL: H=helix, E=extended (sheet), blank=other (loop) + solvent accessibility: + 3st: relative solvent accessibility (acc) in 3 states: + b = 0-9%, i = 9-36%, e = 36-100%. + PHD: Profile network prediction HeiDelberg + Rel: Reliability index of prediction (0-9) + O_3: observed relative acc. in 3 states: B, I, E + note: for convenience a blank is used intermediate (i). + P_3: predicted relative accessibility in 3 states + 10st:relative accessibility in 10 states: + = n corresponds to a relative acc. of n*n % + subset: + SUB: a subset of the prediction, for all residues with an expected + average correlation > 0.69 (tables in header) + note: for this subset the following symbols are used: + "I": is intermediate (for which above " " is used) + ".": means that no prediction is made for this residue, as the + reliability is: Rel < 4 + + Abbreviations: PHDhtm + ~~~~~~~~~~~~~~~~~~~~~ + + secondary structure: + HL: T=helical transmembrane region, blank=other (loop) + PHD: Profile network prediction HeiDelberg + PHDF:filtered prediction, i.e., too long transmembrane segments + are split, too short ones are deleted + Rel: Reliability index of prediction (0-9) + detail: + prH: 'probability' for assigning helical transmembrane region + prL: 'probability' for assigning loop + note: the 'probabilites' are scaled to the interval 0-9, e.g., + prH=5 means, that the first output node is 0.5-0.6 + subset: + SUB: a subset of the prediction, for all residues with an expected + average accuracy > 82% (tables in header) + note: for this subset the following symbols are used: + L: is loop (for which above " " is used) + ".": means that no prediction is made for this residue, as the + reliability is: Rel < 5 + + protein: predict length 269 + + ....,....1....,....2....,....3....,....4....,....5....,....6 + AA |MASEIKKKLFWRAVVAEFLAMTLFVFISIGSALGFNYPLERNQTLVQDNVKVSLAFGLSI| + PHD sec | HHHHHHHHHHHHHHHHHHHHHHHHHHEE HHHHHHHHHHHHH| + Rel sec |998443148899999999999998997676530312469989998623353579999999| + detail: + prH sec |001223468899999999999998888777653112210000000145566788999999| + prE sec |000011000000000000000001001111233542100000000000323211000000| + prL sec |998665420100000000000000000011112244578988998753100000000000| + subset: SUB sec |LLL.....HHHHHHHHHHHHHHHHHHHHHHH......LLLLLLLLL...H.HHHHHHHHH| + + ACCESSIBILITY + 3st: P_3 acc |eeeebee bbb bbbbbbbbbbbbbbbbbbbbbebeee eeeeeeeeebbbbbbbbbbbb| + 10st: PHD acc |997706650005000000000000000000000607775779776677000000000000| + Rel acc |735421110541467608662789996343122133420454330023453975664547| + subset: SUB acc |e.ee.....bb.bbbb.bbb.bbbbbb.b.......e..eee......bb.bbbbbbbbb| + ....,....7....,....8....,....9....,....10...,....11...,....12 + AA |ATLAQSVGHISGAHSNPAVTLGLLLSCQISILRAVMYIIAQCVGAIVASAILSGITSSLL| + PHD sec |HHHHHHHHHE HHHHEHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH | + Rel sec |999996412122653167703135552356779999999999999999999998467213| + detail: + prH sec |998986544334223477843456665567779999999999999999999998611343| + prE sec |001001123420010000145432101221110000000000000000000000000000| + prL sec |000001232245765521000000123210000000000000000000000000278555| + subset: SUB sec |HHHHHH......LL..HHH....HHH..HHHHHHHHHHHHHHHHHHHHHHHHHH.LL...| + + ACCESSIBILITY + 3st: P_3 acc |bbbbebbbebbbbbb bbbbbbbbbbbebbbbbbbbbbbbbbbbbbbbbbbbeebbeeeb| + 10st: PHD acc |000060006000000500000000000600000000000000000000000067006760| + Rel acc |456515321655013144869663400154551757478936465465467713401400| + subset: SUB acc |bbbb.b...bbb....bbbbbbb.b...bbbb.bbbbbbb.bbbbbbbbbbb..b..e..| + ....,....13...,....14...,....15...,....16...,....17...,....18 + AA |ENSLGRNDLARGVNSGQGLGIEIIGTLQLVLCVLATTDRRRRDLGGSAPLAIGLSVALGH| + PHD sec | HHH EEEEEEEEEEEEEEEEEEE E E HHHHHH| + Rel sec |359985212134223651899898866789799875436658889963211351457756| + detail: + prH sec |320002345432332111000000000000100000221120000000001113567767| + prE sec |100000000000011014899888877789789886100000000013544222221111| + prL sec |568986543466545763100000011100000112567768889975454564210111| + subset: SUB sec |.LLLLL.........LL.EEEEEEEEEEEEEEEEEE..LLLLLLLLL.....L..HHHHH| + + ACCESSIBILITY + 3st: P_3 acc |eeebbbebbbeebeebeebbbbbbbbbbbbbbbbbbbeeeeeeeebbbbbbbbbbbbbbb| + 10st: PHD acc |677000600077076077000000000000000000077767767000000000000000| + Rel acc |133100124043040233247198656399879530035414413123255869586654| + subset: SUB acc |........b.e..e.....bb.bbbbb.bbbbbb....ee.ee......bbbbbbbbbbb| + ....,....19...,....20...,....21...,....22...,....23...,....24 + AA |LLAIDYTGCGINPARSFGSAVLTRNFSNHWIFWVGPFIGSALAVLIYDFILAPRSSDFTD| + PHD sec |HEEEE E HHHEEEE EEEEEE HHHHHHHHHHHHHEEEEE | + Rel sec |321341126989622145152653534229996251699999999973147525556642| + detail: + prH sec |521100000000145432463121122000000114789999999875421111121124| + prE sec |244564431000000000015765121358997510000000000013467642110000| + prL sec |233234457889754567411012655530002364200000000010010136667765| + subset: SUB sec |........LLLLL....H.H.EE.L....EEEE.L.HHHHHHHHHHH...EE.LLLLL..| + + ACCESSIBILITY + 3st: P_3 acc |bbbbebbbbbbebb bbbbbbbbeebeebbbbbbbbbbbbbbbbbbbbbbbbeeeee ee| + 10st: PHD acc |000060000006005000000007606600000000000000000000000076777577| + Rel acc |754424240102242141047612131118967874356346635751777031345044| + subset: SUB acc |bbbb.b.b.....b..b..bbb.......bbbbbbb.bb.bbb.bbb.bbb....ee.ee| + ....,....25...,....26...,....27...,....28...,....29...,....30 + AA |RMKVWTSGQVEEYDLDADDINSRVEMKPK| + PHD sec |HHHHHH | + Rel sec |66775259975467555457776422699| + detail: + prH sec |77887520012221222221111100000| + prE sec |00000000000000000000001233200| + prL sec |11112379987678777678887655799| + subset: SUB sec |HHHHH.LLLLL.LLLLL.LLLLL...LLL| + + ACCESSIBILITY + 3st: P_3 acc |ebebbeeeeeeeeeeeeeeeeeebeeeee| + 10st: PHD acc |60700787677777677777767067789| + Rel acc |10411563134335144444514212559| + subset: SUB acc |..e..ee...e..e.eeeeee.e...eee| + + PHDhtm Helical transmembrane prediction + note: PHDacc and PHDsec are reliable for water- + soluble globular proteins, only. Thus, + please take the predictions above with + particular caution wherever transmembrane + helices are predicted by PHDhtm! + + PHDhtm +--- +--- PhdTopology REFINEMENT AND TOPOLOGY PREDICTION: SYMBOLS +--- AA : amino acid in one-letter code +--- PHD htm : HTM's predicted by the PHD neural network +--- system (T=HTM, ' '=not HTM) +--- Rel htm : Reliability index of prediction (0-9, 0 is low) +--- detail : Neural network output in detail +--- prH htm : 'Probability' for assigning a helical trans- +--- membrane region (HTM) +--- prL htm : 'Probability' for assigning a non-HTM region +--- note: 'Probabilites' are scaled to the interval +--- 0-9, e.g., prH=5 means, that the first +--- output node is 0.5-0.6 +--- subset : Subset of more reliable predictions +--- SUB htm : All residues for which the expected average +--- accuracy is > 82% (tables in header). +--- note: for this subset the following symbols are used: +--- L: is loop (for which above ' ' is used) +--- '.': means that no prediction is made for this, +--- residue as the reliability is: Rel < 5 +--- other : predictions derived based on PHDhtm +--- PHDFhtm : filtered prediction, i.e., too long HTM's are +--- split, too short ones are deleted +--- PHDRhtm : refinement of neural network output +--- PHDThtm : topology prediction based on refined model +--- symbols used: +--- i: intra-cytoplasmic +--- T: transmembrane region +--- o: extra-cytoplasmic +--- +--- PhdTopology REFINEMENT AND TOPOLOGY PREDICTION + ....,....1....,....2....,....3....,....4....,....5....,....6 + AA |MASEIKKKLFWRAVVAEFLAMTLFVFISIGSALGFNYPLERNQTLVQDNVKVSLAFGLSI| + PHD htm | TTTTTTTTTTTTTTTTTTT TTTTTTTTTTTT| + detail: | | + prH htm |000000000001136788999999999988875321110000000123678889999988| + prL htm |999999999998863211000000000011124678889999999876321110000011| + other: | | + PHDFhtm | TTTTTTTTTTTTTTTTTTT TTTTTTTTTTT| + PHDRhtm | TTTTTTTTTTTTTTTTTT TTTTTTTTTTT| + PHDThtm |iiiiiiiiiiiiiiTTTTTTTTTTTTTTTTTToooooooooooooooooTTTTTTTTTTT| + subset: | | + SUB htm |............................................................| + ....,....7....,....8....,....9....,....10...,....11...,....12 + AA |ATLAQSVGHISGAHSNPAVTLGLLLSCQISILRAVMYIIAQCVGAIVASAILSGITSSLL| + PHD htm |TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT | + detail: | | + prH htm |888888877777666677788888888888888888888888888888888876543211| + prL htm |111111122222333322211111111111111111111111111111111123456788| + other: | | + PHDFhtm |TTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT | + PHDRhtm |TTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTT | + PHDThtm |TTTTTTTTiiiiiiiiiiiiiTTTTTTTTTTTTTTTTTTTTTTTTToooooooooooooo| + subset: | | + SUB htm |............................................................| + ....,....13...,....14...,....15...,....16...,....17...,....18 + AA |ENSLGRNDLARGVNSGQGLGIEIIGTLQLVLCVLATTDRRRRDLGGSAPLAIGLSVALGH| + PHD htm | TTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTT| + detail: | | + prH htm |000000000001234567788888999988887643211111111235788899998888| + prL htm |999999999998765432211111000011112356788888888764211100001111| + other: | | + PHDFhtm | TTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTT| + PHDRhtm | TTTTTTTTTTTTTTTTTT TTTTTTTTTTTT| + PHDThtm |ooooooooooooooooTTTTTTTTTTTTTTTTTTiiiiiiiiiiiiiiTTTTTTTTTTTT| + subset: | | + SUB htm |............................................................| + ....,....19...,....20...,....21...,....22...,....23...,....24 + AA |LLAIDYTGCGINPARSFGSAVLTRNFSNHWIFWVGPFIGSALAVLIYDFILAPRSSDFTD| + PHD htm |TTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT | + detail: | | + prH htm |888887765443432233334566777777788888888888888888887542100000| + prL htm |111112234556567766665433222222211111111111111111112457899999| + other: | | + PHDFhtm |TTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT | + PHDRhtm |TTTTTT TTTTTTTTTTTTTTTTTTT | + PHDThtm |TTTTTToooooooooooooooooooooooooTTTTTTTTTTTTTTTTTTTiiiiiiiiii| + subset: | | + SUB htm |............................................................| + ....,....25...,....26...,....27...,....28...,....29...,....30 + AA |RMKVWTSGQVEEYDLDADDINSRVEMKPK| + PHD htm | | + detail: | | + prH htm |00000000000000000000000000000| + prL htm |99999999999999999999999999999| + other: | | + PHDFhtm | | + PHDRhtm | | + PHDThtm |iiiiiiiiiiiiiiiiiiiiiiiiiiiii| + subset: | | + SUB htm |.............................| +--- +--- PhdTopology REFINEMENT AND TOPOLOGY PREDICTION END +--- + +________________________________________________________________________________ + +________________________________________________________________________________ + +----------------------------------------------------------------------------- +--- PredictProtein: NEWS from January, 1997 --- +--- --- +--- Dear user, --- +--- --- +--- as of January 1, 1997, EMBL has effectively decided to not --- +--- support the PredictProtein service by personal resources. I do --- +--- maintain the program, so to speak, in my private time. However, --- +--- my contract obliges me to do science, instead. Unfortunately, --- +--- the computer environment at EMBL is at the same time starting --- +--- to become increasingly unstable. Consequence of these two re- --- +--- cent developments is that the PredictProtein service is not as --- +--- stable as it was. --- +--- --- +--- I apologise for the problems this may cause. In particular, --- +--- I apologise for my inability to reply to the 20-30 daily, per- --- +--- sonal mails, and suggest to re-submit requests after 24 hours! --- +--- --- +--- Hoping that I shall find a more convenient solution for the --- +--- future of the PredictProtein I remain with my best regards, --- +--- --- +--- Burkhard Rost --- +----------------------------------------------------------------------------- +--- PredictProtein: NEWS from April, 1998 --- +--- --- +-------------------------------- --- +--- MOVING PredictProtein --- +--- There appears to be light on the horizon! PP will may be having --- +--- many hickups over the next months (as I shall leave EMBL). How- --- +--- ever, the server seems to have a fair chance of survival thanks --- +--- to a major support that is being raised by Columbia University, --- +--- New York, U.S.A.). I hope that this will settle the issue for --- +--- the years to come ... --- +-------------------------------- --- +--- WARNING --- +--- After a major rewriting of most of the PP code over the last, --- +--- I am afraid that not all errors have been traced by me, yet. --- +--- Thus, please have mercy and report any bug you'll encounter! --- +--- THANKS, Burkhard Rost --- +-------------------------------- --- +--- NEW PREDICTION DEFAULTS --- +--- * Coiled-coil regions: now by default the program COILS written by --- +--- Andrei Lupas is run on your sequence. An output is returned if a --- +--- coiled-coil region has been detected. --- +--- * Functional sequence motifs: now by default the PROSITE database --- +--- written by Amos Bairoch, Philip Bucher and Kay Hofmann is scanned --- +--- for sequence motifs. An output is returned if any motif has been --- +--- detected. --- +-------------------------------- --- +--- see http://www.embl-heidelberg.de/predictprotein/ppNews.html --- +--- for a description of the following new options. --- +--- NEW INPUT OPTION --- +--- * Your input sequence(s) in FASTA-list format ("# FASTA list ") --- +--- NEW OUTPUT OPTIONS --- +--- * Return also BLASTP output ("return blast") --- +--- * Return prediction additionally in RDB format ("return phd rdb") --- +--- * Return topits hssp ("return topits hssp") --- +--- * Return topits strip ("return topits strip") --- +--- * Return topits own ("return topits own") --- +--- * Return no coils ("return no coils") --- +--- * Return no prosite ("return no prosite") --- +----------------------------------------------------------------------------- diff --git a/Master/texmf-dist/doc/latex/texshade/AQP1.top b/Master/texmf-dist/doc/latex/texshade/AQP1.top new file mode 100644 index 00000000000..55c62f73681 --- /dev/null +++ b/Master/texmf-dist/doc/latex/texshade/AQP1.top @@ -0,0 +1,14 @@ + +\feature{bottom}{1}{1..14}{'-'}{int.\ A} +\feature{top}{1}{15..32}{box[LightGray]:TM1}{} +\feature{top}{1}{33..49}{,-,}{ext.\ B} +\feature{top}{1}{50..68}{box[LightGray]:TM2}{} +\feature{bottom}{1}{69..81}{'-'}{int.\ C} +\feature{top}{1}{82..106}{box[LightGray]:TM3}{} +\feature{top}{1}{107..136}{,-,}{ext.\ D} +\feature{top}{1}{137..154}{box[LightGray]:TM4}{} +\feature{bottom}{1}{155..168}{'-'}{int.\ E} +\feature{top}{1}{169..186}{box[LightGray]:TM5}{} +\feature{top}{1}{187..211}{,-,}{ext.\ F} +\feature{top}{1}{212..230}{box[LightGray]:TM6}{} +\feature{bottom}{1}{231..269}{'-'}{int.\ G} diff --git a/Master/texmf-dist/doc/latex/texshade/AQP2spec.ALN b/Master/texmf-dist/doc/latex/texshade/AQP2spec.ALN new file mode 100644 index 00000000000..2c36822f0f8 --- /dev/null +++ b/Master/texmf-dist/doc/latex/texshade/AQP2spec.ALN @@ -0,0 +1,19 @@ + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%% %%%%% +%%%%% This is a minimal .ALN file--many sequence aligners can produce them %%%%% +%%%%% %%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +AQP2bt SIAFSRAVLAEFLATLLFVFFGLGSALNWPQALPSVLQIAMAFGLAIGTLVQALGHVSGA +AQP2cf SVAFSRAVFAEFLATLLFVFFGLGSALNWPQALPSVLQIAMAFGLGIGTLVQALGHVSGA +AQP2dd SIAFSRAVFSEFLATLLFVFFGLGSALNWPQALPSVLQIAMAFGLAIGTLVQALGHISGA +AQP2ec SIAFSRAVLAEFLATLLFVFFGLGSALNWPQAMPSVLQIAMAFGLAIGTLVQALGHVSGA +AQP2em SIAFSRAVFSEFLATLLFVFFGLGSALNWPQALPSVLQIAMAFGLAIGTLVQTLGHISGA + +AQP2bt HINPAVTVACLVGCHVSFLRAVFYVAAQLLGAVAGAALLHEITPPAIRG +AQP2cf HINPAVTVACLVGCHVSFLRAAFYVAAQLLGAVAGAALLHEITPPHVRG +AQP2dd HINPAVTVACLVGCHVSFLRATFYLAAQLLGAVAGAAILHEITPPDIRG +AQP2ec HINPAVTVACLVGCHVSFLRAAFYVAAQLLGAVAGAALLHEITPPDIRR +AQP2em HINPAVTVACLVGCHVSFLRATFYLAAQLLGAVAGAALLHELTPPDIRG + diff --git a/Master/texmf-dist/doc/latex/texshade/AQPDNA.MSF b/Master/texmf-dist/doc/latex/texshade/AQPDNA.MSF new file mode 100644 index 00000000000..07707367d8a --- /dev/null +++ b/Master/texmf-dist/doc/latex/texshade/AQPDNA.MSF @@ -0,0 +1,127 @@ + +AQPDNA.MSF MSF: 979 Type: N Freitag, 12. Februar 1999 Check: 2594 .. +Name: AQP1nuc.SEQ Len: 807 Check: 8330 Weight: 1.00 +Name: AQP2nuc.SEQ Len: 813 Check: 7220 Weight: 1.00 +Name: AQP3nuc.SEQ Len: 855 Check: 7590 Weight: 1.00 +Name: AQP4nuc.SEQ Len: 960 Check: 8696 Weight: 1.00 +Name: AQP5nuc.SEQ Len: 795 Check: 758 Weight: 1.00 +// + 1 60 +AQP1nuc.SEQ ATGGCCAGCGAAATCAAGAAGAAGC.................TCTTCT........GGAG +AQP2nuc.SEQ ATGTG....GGAACTCAG.........................ATCCAT........... +AQP3nuc.SEQ ATG........AACC........GTTGCGGGG.AGATG.....CTCC............. +AQP4nuc.SEQ ATGAGTGACGGAGCTGCAGCGAGGCGGTGGGGTAAGTGTGGACCTCCCTGCAGCAGAGAG +AQP5nuc.SEQ ATGAAAAA.GGAGGTGTG.........................CTCCCT........... + + 61 120 +AQP1nuc.SEQ GGC..TGTGGTGGCT.....GAGTTCCTGGCCATGA.CCCTCTTCG.............. +AQP2nuc.SEQ ...................................AGCCTTCTCCCGAGCAGTGCTGGCT +AQP3nuc.SEQ .ACATCC.....GCTACCGG......CTG.........CTTCGCCA....GGCTCTGGCG +AQP4nuc.SEQ AGCATCATGGTGGCTTTCAAAGGCGTCTGGACTCAAGCCTTCTGGAAGGCGGTCACAGCA +AQP5nuc.SEQ ...................................TGCCTTCTTCAAGGCGGTGTTCGCA + + 121 180 +AQP1nuc.SEQ ....TCTTCATCAGCATCGGTTCTGCCCTA...GGCTT.....CAATTACCCACTGGAGA +AQP2nuc.SEQ GAGTTCTTGGCCACGCTCCTTTTTGTCTTCTTTGGCCTTGGCTCAGCCCTCCA.....GT +AQP3nuc.SEQ GAGTGCCTGGGGACCCTCATCCTTGTGATGTTCGGCTGTGGTTCCGTGGCTCAA.GTGGT +AQP4nuc.SEQ GAGTTCCTGGCCATGCTCATCTTTGTTCTGCTCAGCGTGGGATCCACCATTAACTGGGGT +AQP5nuc.SEQ GAGTTCCTGGCCACCCTCATCTTCGTCTTCTTTGGCCTGGGCTCAGCACTCAA.....GT + + 181 240 +AQP1nuc.SEQ GA...AACCAGACGCTGGTCCA.GGACAATGTGAAGGTGTCACTGGCCTTTGGTCTGAGC +AQP2nuc.SEQ GGGCCAGCT....CCCCACCCTC...TGTGCTCCAGATCGCCGTGGCCTTTGGTCTGGGC +AQP3nuc.SEQ GCTCAGCCGAGGGACCCATG.GTGG.CTTCCTCACCATCAACTTGGCTTTTGGCTTCGCT +AQP4nuc.SEQ GGCTCAGAGAACCCCCTACCTGTGGACATGGTCCTCATCTCCCTCTGCTTTGGACTCAGC +AQP5nuc.SEQ GGCCCTCGG....CTCTGCCCAC...CATTCTGCAAATCTCAATTGCCTTTGGCCTGGCC + + 241 300 +AQP1nuc.SEQ ATCGCTACTCTGGCCCAAAGTGTGGGTCACATCAGTGGTGCTCACTCCAACCCAGCGGTC +AQP2nuc.SEQ ATCGGCATCCTGGTTCAGGCTCTGGGCCATGTCAGCGGGGCACACATCAACCCCGCCGTG +AQP3nuc.SEQ GTCACCCTTGCCATCTTGGTGGCTGGCCAAGTGTCTGGAGCCCACTTGAACCCTGCTGTG +AQP4nuc.SEQ ATTGCCACCATGGTTCAGTGCTTCGGCCACATCAGCGGTGGCCACATCAACCCAGCGGTG +AQP5nuc.SEQ ATAGGTACCTTAGCCCAAGCTCTGGGACCTGTGAGTGGTGGCCACATCAATCCAGCCATT + + 301 360 +AQP1nuc.SEQ ACACTGGGGCTTCTGCTCAGCTGTCAGATCAGCATCCTCCGGGCTGTCA.TGTATATCAT +AQP2nuc.SEQ ACTGTGGCATGCCTGGTGGGTTGCCATGTCTCCTTCCTTCGAGCTGCCT.TCTATGTGGC +AQP3nuc.SEQ ACCTTTGCAATG.TGCTTCCTGGCACGAGAGCCCTGGATCAAGCTGCCCATCTACACACT +AQP4nuc.SEQ ACAGTGGCCATGGTGTGCACACGAAAGATCAGCATCGCCAAGTCTGTCT.TCTACATCAC +AQP5nuc.SEQ ACTCTGGCCCTCTTAATAGGAAACCAGATCTCGCTGCTCCGAGCTGTCT.TCTACGTGGC + + 361 420 +AQP1nuc.SEQ CGCCCAGTGTGTGGGAGCCATCGTTGCCTCCGCCATCCTCTCCGGCATCACCTCCTCCCT +AQP2nuc.SEQ TGCCCAGCTGCTGGGCGCCGTGGCTGGGGCTGCCATCCTCCATGAGATTAC.TCCAGTAG +AQP3nuc.SEQ GGCACAGACCCTCGGGGCCTTCTTGGGTGCTGGGATTGTTTTTGGGCT..CTACTA..TG +AQP4nuc.SEQ TGCGCAGTGCCTGGGGGCCATCATCGGAGCTGGGATCCTCTACCTGGTCAC.ACCCCCCA +AQP5nuc.SEQ AGCCCAGCTGGTGGGCGCCATTGCTGGGGCAGGCATCCTGTACTGGCTGGC.GCCACTCA + + 421 480 +AQP1nuc.SEQ GCTCGAGAACTCACTTGGCCGA.AATGACCTGGCTCGAGGTGTGAACTCCGGCCAGGGCC +AQP2nuc.SEQ AAATCCGTGGGGACCTGGCTGTCAATGCTCTCCACAACAACGCCACAGCTGGCCAGGCTG +AQP3nuc.SEQ ATGCAATCTGGGCCTTTGCTGGCAATGAGCT.........TGTTGTCTCCGGCC.....C +AQP4nuc.SEQ GCGTGGTGGGAGGATTGGGAGTCACCACGGTTCATGGAAACCTCACTGCTGGCCATGGGC +AQP5nuc.SEQ ATGCCCGGGGTAACCTGGCCGTCAATGCGCTGAACAACAACACAACGCCTGGCAAGGCCA + + 481 540 +AQP1nuc.SEQ TGGGCATTGAGATCATTGGCACCCTGCAGCTGGTGCTGTGCGT.TCTGGCTACCACTGAC +AQP2nuc.SEQ TGACTGTAGAGCTCTTCCTGACCATGCAGCTGGTGCTGTGCAT.CTTTGCCTCCACCGAC +AQP3nuc.SEQ CAATGGCACAGCTGGTATC..TTTGCCACCTATCCCTCTGGACACTTGGATATGGTCAAT +AQP4nuc.SEQ TCCTGGTGGAGCTAATAATCACTTTCCAGCTGGTATTCACCAT.TTTTGCCAGCTGTGAT +AQP5nuc.SEQ TGGTGGTGGAGTTAATCTTGACTTTCCAGCTAGCCCTCTGCAT.CTTCTCCTCCACCGAC + + 541 600 +AQP1nuc.SEQ CGGAGGCGCCGAGACTTAGGTGGCTCAGCCCCACTTGCCATTGGCTTGTCTGTGGCTCTT +AQP2nuc.SEQ GAGCGCCGCGGTGACAACCTGGGTAGCCCTGCCCTCTCCATTGGTTTCTCTGTTACCCTG +AQP3nuc.SEQ GGCTTCTTTGATCAGTTCATAGGCACAGCAGCCCTTATTGTGTGTGTGCTGGCCATTGTT +AQP4nuc.SEQ TCCAAACGGACTGATGTTACTGGTTCCGTTGCTTTAGCAATTGGGTTTTCCGTTGCAATT +AQP5nuc.SEQ TCTCGCCGAACCAGCCCTGTGGGCTCCCCAGCCTTATCCATTGGCTTGTCTGTCACACTG + + 601 660 +AQP1nuc.SEQ GGACACCTGCTGGCCATTGACTACACTGGCTGTGGGATCAACCCTGCCCGGTCATT.TGG +AQP2nuc.SEQ GGCCACCTCCTTGGGATCTATTTCACCGGTTGCTCCATGAATCCAGCCCGCTCCCT.GGC +AQP3nuc.SEQ GACC..CTTATAACAACCCTGTGCCCCGGGGCCTGGAGGCCTTCACTGTGGGCCTTGTGG +AQP4nuc.SEQ GGACATTTGTTTGCAATCAATTATACCGGAGCCAGCATGAATCCAGCTCGATCCTT.TGG +AQP5nuc.SEQ GGCCATCTTGTGGGGATCTACTTCACCGGCTGTTCCATGAACCCAGCCCGATCTTT.CGG + + 661 720 +AQP1nuc.SEQ CTCTGCTGTGCTCACCCGCAACTTCTCAAAC...CACTGGATTTTCTGGGTGGGACCATT +AQP2nuc.SEQ TCCAGCAGTTGTCACTGGCAAGTTTGATGA...TCACTGGGTCTTCTGGATCGGACCCCT +AQP3nuc.SEQ TCCTG.....GTCATTGGGACCTCCATGGGCTTCAATTCTGGCTATGCCGTCAACCCAGC +AQP4nuc.SEQ CCCTGCAGTTATCATGGGAAACTGGGAAAAC...CACTGGATATATTGGGTTGGACCAAT +AQP5nuc.SEQ CCCTGCGGTGGTCATGAACCGGTTCAGCCCCTCTCACTGGGTCTTCTGGGTAGGGCCTAT + + 721 780 +AQP1nuc.SEQ CATTGGGAGTGCCCTGGCAGTGCTGATCTATGACTTCATC..CTGGCCCCACGC..AGC. +AQP2nuc.SEQ GGTGGGCGCCATCATCGGCTCCCTCCTCTACAACTAC..CTGCTGTTC..........CC +AQP3nuc.SEQ T.....CGTGACTTTGG..ACCTCGCCTTTTCACTGCCCTGGCTGGC......TGGGGTT +AQP4nuc.SEQ CATAGGCGCTGTGCTGGCAGGTGCACTTTACGAGTATGTCTTCTGTCCTGACGTGGAGCT +AQP5nuc.SEQ TGTGGGGGCCATGCTGGCGGCCATCCTCTATTTCTAC..CTGCTCTTC..........CC + + 781 840 +AQP1nuc.SEQ ..AGCG.........................ACTTTACAG.............ACCGCAT +AQP2nuc.SEQ C.....TCGGCAAAG...AGCCTGCAGGAGCGCTTGGCAGTGCTCAAGGG.......CCT +AQP3nuc.SEQ CAGAAGTC.TTTACGACTGGCC...AGAACTGGTGGTGGGTACCCATCGTCTCTCCACTC +AQP4nuc.SEQ CAAACGTCGCCTAAAGGAAGCCTTCAGCAAAGCTGCACAGCAGACGAAAGGGAGCTACAT +AQP5nuc.SEQ C.....TCCTCTCTG...AGCCTCCATGATCGCGTGGCTGTCGTCAAAGG.......CAC + + 841 900 +AQP1nuc.SEQ GAAGGTGTGGACCAGT...GGCCAAGTGGA.....GGAGTATGACCTGGATGC....... +AQP2nuc.SEQ GGAGCCCGACACCGACTGGGA.......GGAACGTGAAGTGCGG..CGGCGGCAGTCGGT +AQP3nuc.SEQ CTGGGTTC.CATTGGTGGTGTCTTCGTGT.ACCAGCT..CATGAT.TGGCTGCCACC..T +AQP4nuc.SEQ GGAGGTGGAGGACAACCGGAGCCAAGTGGAGACAGAAGACTTGATCCTGAAGCCCGGGGT +AQP5nuc.SEQ ATA...TGA.GCCGG..AGGA.......GGACTGGGAAGATCAT..CGAGAGGAGAGGAA + + 901 960 +AQP1nuc.SEQ ........TGAT.GATATCAACTCCAGGGTGGAGATGAAG.................... +AQP2nuc.SEQ GGAGC......TC..CACTCTCCTCAGAG...................CCTGCCTCGCG. +AQP3nuc.SEQ GGAGCA.GCCCCCGCCTTCCACT..GAGGCAGAGAATGTGAAGCTGG.CCCACATGAAGC +AQP4nuc.SEQ GGTGCATGTGATCGACATTGACCGTGGAGACGAGAAGAAGGGGAAGGACTCGTCTGGAGA +AQP5nuc.SEQ GAAG............ACCATC....GAG........................CTGACG. + + 961 979 +AQP1nuc.SEQ ..........CCCAAATAG +AQP2nuc.SEQ .GCAGCAAGGCCTG....A +AQP3nuc.SEQ ACAAGGA..GCAGATCTGA +AQP4nuc.SEQ GGTATTATCTTCTGTATGA +AQP5nuc.SEQ .GCA.CA....CTG....A + diff --git a/Master/texmf-dist/doc/latex/texshade/AQP_HMM.ext b/Master/texmf-dist/doc/latex/texshade/AQP_HMM.ext new file mode 100644 index 00000000000..d95988ad1e3 --- /dev/null +++ b/Master/texmf-dist/doc/latex/texshade/AQP_HMM.ext @@ -0,0 +1,148 @@ + +Protein: AQP1 +Length: 269 +N-terminus: IN +Number of transmembrane helices: 6 +Transmembrane helices: 14-33 54-73 94-112 139-156 165-184 211-230 + +Total entropy of the model: 17.0025 +Entropy of the best path: 17.0049 + +The best path: + + seq MASEIKKKLF WRAVVAEFLA MTLFVFISIG SALGFNYPLE RNQTLVQDNV 50 + pred IIIIiiiiii iiiHHHHHHH HHHHHHHHHH HHHooooooo oooooooooo + + seq KVSLAFGLSI ATLAQSVGHI SGAHSNPAVT LGLLLSCQIS ILRAVMYIIA 100 + pred oooHHHHHHH HHHHHHHHHH HHHiiiiiii iiiiiiiiii iiiHHHHHHH + + seq QCVGAIVASA ILSGITSSLL ENSLGRNDLA RGVNSGQGLG IEIIGTLQLV 150 + pred HHHHHHHHHH HHoooooooo oooooooooo ooooooooHH HHHHHHHHHH + + seq LCVLATTDRR RRDLGGSAPL AIGLSVALGH LLAIDYTGCG INPARSFGSA 200 + pred HHHHHHiiii iiiiHHHHHH HHHHHHHHHH HHHHoooooo oooooooooo + + seq VLTRNFSNHW IFWVGPFIGS ALAVLIYDFI LAPRSSDFTD RMKVWTSGQV 250 + pred oooooooooo HHHHHHHHHH HHHHHHHHHH iiiiiiiiii iiiiiIIIII + + seq EEYDLDADDI NSRVEMKPK 269 + pred IIIIIIIIII IIIIIIIII + +Protein: AQP2 +Length: 271 +N-terminus: IN +Number of transmembrane helices: 6 +Transmembrane helices: 17-35 44-65 86-104 131-148 157-176 203-224 + +Total entropy of the model: 17.0017 +Entropy of the best path: 17.0046 + +The best path: + + seq MWELRSIAFS RAVLAEFLAT LLFVFFGLGS ALQWASSPPS VLQIAVAFGL 50 + pred IIIIIIiiii iiiiiiHHHH HHHHHHHHHH HHHHHooooo oooHHHHHHH + + seq GIGILVQALG HVSGAHINPA VTVACLVGCH VSFLRAAFYV AAQLLGAVAG 100 + pred HHHHHHHHHH HHHHHiiiii iiiiiiiiii iiiiiHHHHH HHHHHHHHHH + + seq AAILHEITPV EIRGDLAVNA LHNNATAGQA VTVELFLTMQ LVLCIFASTD 150 + pred HHHHoooooo oooooooooo oooooooooo HHHHHHHHHH HHHHHHHHii + + seq ERRGDNLGSP ALSIGFSVTL GHLLGIYFTG CSMNPARSLA PAVVTGKFDD 200 + pred iiiiiiHHHH HHHHHHHHHH HHHHHHoooo oooooooooo oooooooooo + + seq HWVFWIGPLV GAIIGSLLYN YLLFPSAKSL QERLAVLKGL EPDTDWEERE 250 + pred ooHHHHHHHH HHHHHHHHHH HHHHiiiiii iiiiiiiiiI IIIIIIIIII + + seq VRRRQSVELH SPQSLPRGSK A 271 + pred IIIIIIIIII IIIIIIIIII I + +Protein: AQP3 +Length: 285 +N-terminus: IN +Number of transmembrane helices: 6 +Transmembrane helices: 22-41 50-72 103-122 153-172 185-207 238-260 + +Total entropy of the model: 17.0059 +Entropy of the best path: 17.0075 + +The best path: + + seq MNRCGEMLHI RYRLLRQALA ECLGTLILVM FGCGSVAQVV LSRGTHGGFL 50 + pred IIIIIIiiii iiiiiiiiii iHHHHHHHHH HHHHHHHHHH HooooooooH + + seq TINLAFGFAV TLAILVAGQV SGAHLNPAVT FAMCFLAREP WIKLPIYTLA 100 + pred HHHHHHHHHH HHHHHHHHHH HHiiiiiiii iiiiiiiiii iiiiiiiiii + + seq QTLGAFLGAG IVFGLYYDAI WAFAGNELVV SGPNGTAGIF ATYPSGHLDM 150 + pred iiHHHHHHHH HHHHHHHHHH HHoooooooo oooooooooo oooooooooo + + seq VNGFFDQFIG TAALIVCVLA IVDPYNNPVP RGLEAFTVGL VVLVIGTSMG 200 + pred ooHHHHHHHH HHHHHHHHHH HHiiiiiiii iiiiHHHHHH HHHHHHHHHH + + seq FNSGYAVNPA RDFGPRLFTA LAGWGSEVFT TGQNWWWVPI VSPLLGSIGG 250 + pred HHHHHHHooo oooooooooo oooooooooo oooooooHHH HHHHHHHHHH + + seq VFVYQLMIGC HLEQPPPSTE AENVKLAHMK HKEQI 285 + pred HHHHHHHHHH iiiiiiiiii iiiiiIIIII IIIII + +Protein: AQP4 +Length: 323 +N-terminus: IN +Number of transmembrane helices: 6 +Transmembrane helices: 37-57 70-92 123-147 160-177 186-205 232-254 + +Total entropy of the model: 17.0058 +Entropy of the best path: 17.0091 + +The best path: + + seq MSDGAAARRW GKCGPPCSRE SIMVAFKGVW TQAFWKAVTA EFLAMLIFVL 50 + pred IIIIIIIIII IIIIIIIIII Iiiiiiiiii iiiiiiHHHH HHHHHHHHHH + + seq LSVGSTINWG GSENPLPVDM VLISLCFGLS IATMVQCFGH ISGGHINPAV 100 + pred HHHHHHHooo oooooooooH HHHHHHHHHH HHHHHHHHHH HHiiiiiiii + + seq TVAMVCTRKI SIAKSVFYIT AQCLGAIIGA GILYLVTPPS VVGGLGVTTV 150 + pred iiiiiiiiii iiiiiiiiii iiHHHHHHHH HHHHHHHHHH HHHHHHHooo + + seq HGNLTAGHGL LVELIITFQL VFTIFASCDS KRTDVTGSVA LAIGFSVAIG 200 + pred oooooooooH HHHHHHHHHH HHHHHHHiii iiiiiHHHHH HHHHHHHHHH + + seq HLFAINYTGA SMNPARSFGP AVIMGNWENH WIYWVGPIIG AVLAGALYEY 250 + pred HHHHHooooo oooooooooo oooooooooo oHHHHHHHHH HHHHHHHHHH + + seq VFCPDVELKR RLKEAFSKAA QQTKGSYMEV EDNRSQVETE DLILKPGVVH 300 + pred HHHHiiiiii iiiiiiiiiI IIIIIIIIII IIIIIIIIII IIIIIIIIII + + seq VIDIDRGDEK KGKDSSGEVL SSV 323 + pred IIIIIIIIII IIIIIIIIII III + +Protein: AQP5 +Length: 265 +N-terminus: IN +Number of transmembrane helices: 6 +Transmembrane helices: 13-32 59-78 87-110 131-149 158-177 204-228 + +Total entropy of the model: 17.0020 +Entropy of the best path: 17.0052 + +The best path: + + seq MKKEVCSLAF FKAVFAEFLA TLIFVFFGLG SALKWPSALP TILQISIAFG 50 + pred IIIIIIIIii iiHHHHHHHH HHHHHHHHHH HHoooooooo oooooooooo + + seq LAIGTLAQAL GPVSGGHINP AITLALLIGN QISLLRAVFY VAAQLVGAIA 100 + pred ooooooooHH HHHHHHHHHH HHHHHHHHii iiiiiiHHHH HHHHHHHHHH + + seq GAGILYWLAP LNARGNLAVN ALNNNTTPGK AMVVELILTF QLALCIFSST 150 + pred HHHHHHHHHH oooooooooo oooooooooo HHHHHHHHHH HHHHHHHHHi + + seq DSRRTSPVGS PALSIGLSVT LGHLVGIYFT GCSMNPARSF GPAVVMNRFS 200 + pred iiiiiiiHHH HHHHHHHHHH HHHHHHHooo oooooooooo oooooooooo + + seq PSHWVFWVGP IVGAMLAAIL YFYLLFPSSL SLHDRVAVVK GTYEPEEDWE 250 + pred oooHHHHHHH HHHHHHHHHH HHHHHHHHii iiiiiiiiii iiiIIIIIII + + seq DHREERKKTI ELTAH 265 + pred IIIIIIIIII IIIII diff --git a/Master/texmf-dist/doc/latex/texshade/AQP_HMM.sgl b/Master/texmf-dist/doc/latex/texshade/AQP_HMM.sgl new file mode 100644 index 00000000000..f3a0a659cd9 --- /dev/null +++ b/Master/texmf-dist/doc/latex/texshade/AQP_HMM.sgl @@ -0,0 +1,6 @@ + +>HP: 269 AQP1 IN 6 14 33 54 73 94 112 139 156 165 184 211 230 +>HP: 271 AQP2 IN 6 17 35 44 65 86 104 131 148 157 176 203 224 +>HP: 285 AQP3 IN 6 22 41 50 72 103 122 153 172 185 207 238 260 +>HP: 323 AQP4 IN 6 37 57 70 92 123 147 160 177 186 205 232 254 +>HP: 265 AQP5 IN 6 13 32 59 78 87 110 131 149 158 177 204 228 diff --git a/Master/texmf-dist/doc/latex/texshade/AQPpro.MSF b/Master/texmf-dist/doc/latex/texshade/AQPpro.MSF new file mode 100644 index 00000000000..815bb157d66 --- /dev/null +++ b/Master/texmf-dist/doc/latex/texshade/AQPpro.MSF @@ -0,0 +1,50 @@ + +AQPpro.MSF MSF: 356 Type: P Freitag, 12. Februar 1999 Check: 2586 .. +Name: AQP1.PRO Len: 269 Check: 5367 Weight: 1.00 +Name: AQP2.PRO Len: 271 Check: 6176 Weight: 1.00 +Name: AQP3.PRO Len: 285 Check: 2893 Weight: 1.00 +Name: AQP4.PRO Len: 323 Check: 9737 Weight: 1.00 +Name: AQP5.PRO Len: 265 Check: 8413 Weight: 1.00 +// + 1 60 +AQP1.PRO MAS........................EIKKKLFWRAVVAEFLAMTLFVFISIGSALGFN +AQP2.PRO MW.........................ELRSIAFSRAVLAEFLATLLFVFFGLGSALQWA +AQP3.PRO M.........NRCG.....EMLHIRYR......LLRQALAECLGTLILVMFGCGSVAQVV +AQP4.PRO MSDGAAARRWGKCGPPCSRESIMVAFKGVWTQAFWKAVTAEFLAMLIFVLLSVGSTINWG +AQP5.PRO MK........................KEVCSLAFFKAVFAEFLATLIFVFFGLGSALKWP + + 61 120 +AQP1.PRO YPLERNQTLVQDNVKVSLAFGLSIATLAQSVGHISGAHSNPAVTLGLLLSCQISILRAVM +AQP2.PRO ...SS....PPSVLQIAVAFGLGIGILVQALGHVSGAHINPAVTVACLVGCHVSFLRAAF +AQP3.PRO LSRGTHGGF....LTINLAFGFAVTLAILVAGQVSGAHLNPAVTFAMCFLAREPWIKLPI +AQP4.PRO ...GSENPLPVDMVLISLCFGLSIATMVQCFGHISGGHINPAVTVAMVCTRKISIAKSVF +AQP5.PRO ...SA....LPTILQISIAFGLAIGTLAQALGPVSGGHINPAITLALLIGNQISLLRAVF + + 121 180 +AQP1.PRO YIIAQCVGAIVASAILSGI..........TSSLLENSLGRNDLARGVNSGQ.....GLGI +AQP2.PRO YVAAQLLGAVAGAAILHEI..........TPVEIRGDLAVNALHNNATAGQ.....AVTV +AQP3.PRO YTLAQTLGAFLGAGIVFGLYYDAIWAFAGNELVVSGPNGTAGIFATYPSGHLDMVNGFFD +AQP4.PRO YITAQCLGAIIGAGILYLV..........TPPSVVGGLGVTTVHGNLTAGH.....GLLV +AQP5.PRO YVAAQLVGAIAGAGILYWL..........APLNARGNLAVNALNNNTTPGK.....AMVV + + 181 240 +AQP1.PRO EIIGTLQLVLCVLATTDR.RRRDLGGSAPLAIGLSV.ALGHLLAIDYTGCGINPARSFGS +AQP2.PRO ELFLTMQLVLCIFASTDE.RRGDNLGSPALSIGFSV.TLGHLLGIYFTGCSMNPARSLAP +AQP3.PRO QFIGTAALIVCVLAIVDPYNNPVPRGLEAFTVGLVVLVIGTSMGFN.SGYAVNPARDFGP +AQP4.PRO ELIITFQLVFTIFASCDS.KRTDVTGSVALAIGFSV.AIGHLFAINYTGASMNPARSFGP +AQP5.PRO ELILTFQLALCIFSSTDS.RRTSPVGSPALSIGLSV.TLGHLVGIYFTGCSMNPARSFGP + + 241 300 +AQP1.PRO AVLTR..NFS.N......HWIFWVGPFIGSALAVL..IYDFILAPRSSDFTDRMK..... +AQP2.PRO AVVTG..KFD.D......HWVFWIGPLVGAIIGSL..LYNYLLFPSAKSLQERL..AVLK +AQP3.PRO RLFTALAGWGSEVFTTGQNW..WWVPIVSPLLGSIGGVFVYQL................. +AQP4.PRO AVIMG..NWE.N......HWIYWVGPIIGAVLAGA..LYEYV.FCPDVELKRRLKEAFSK +AQP5.PRO AVVMN..RFSPS......HWVFWVGPIVGAMLAAI..LYFYLLFPSSLSLHDRV..AVVK + + 301 356 +AQP1.PRO .......VWTS.....GQVEEYDLDAD.......DINSRVEMKPK........... +AQP2.PRO G.LEPDTDWEEREVRRRQ..SVELHSPQSLPRG...................SKA. +AQP3.PRO ..................MIGCHLEQPPPSTEAENV.KLAHMKHKE.......QI. +AQP4.PRO AAQQTKGSYMEVEDNRSQVETEDLILKPGVVHVIDIDRGDEKKGKDSSGEVLSSV. +AQP5.PRO GTYEPEEDWEDHREERKK..TIELTAH............................. + diff --git a/Master/texmf-dist/doc/latex/texshade/README b/Master/texmf-dist/doc/latex/texshade/README new file mode 100644 index 00000000000..752a63a8538 --- /dev/null +++ b/Master/texmf-dist/doc/latex/texshade/README @@ -0,0 +1,149 @@ + TeXshade v1.12 + >> + >> A LaTeX package for setting nucleotide and peptide alignments. + >> + >> Setting alignments of nucleotides and peptides for publication + >> or presentation purposes is usually a time consuming two-step + >> process. First, a scientific software is used for the calcula- + >> tion of the alignment. This is done in a few minutes. Then, in + >> order to highlight special sequence relationships and to label + >> positions and regions of interest a second software with high + >> output capability is needed. + >> + >> Manipulating sequence alignments with standard word processing + >> or graphics programs takes its time--often several hours--and + >> simple layout changes such as re-breaking lines, say from 50 + >> to 40 residues per line, elongate the working time considerab- + >> ly. + >> + >> TeXshade is an alignment shading software completely written + >> in TeX/LaTeX which can process multiple sequence alignments in + >> the MSF, ALN and FASTA file format. It provides in addition to + >> common shading algorithms special shading modes featuring + >> functional aspects, e.g. charge or hydropathy, and a plenitude + >> of commands for handling shading colors, text styles, labels, + >> legends and even allows the user to define completely new sha- + >> ding modes. TeXshade combines highest flexibility and the + >> habitual TeX output quality--with reasonable time expenditure. + >> + Copyright (C) 1999 - 2005 Eric Beitz + + + + FOR THE HASTY READER + + Be sure to use a docstrip version 2.4 or later! + Otherwise you will not be able to tex the documentation! + + + +1 - FILES DISTRIBUTED WITH THIS PACKAGE + + texshade.ins Batch file, run through LaTeX + texshade.dtx Docstrip archive, run twice through LaTeX + tsfaq.tex Frequently asked questions about TeXshade + texshade.txt This file + + + (a) FILES THAT WILL BE GENERATED FROM TEXSHADE.INS + + texshade.sty LaTeX package + texshade.def Standard definitions + AQPDNA.MSF Example nucleotide alignment file (MSF-format) + AQPpro.MSF Example protein alignment file (MSF-format) + AQP2spec.ALN Example protein alignment file (ALN-format) + AQP1.top Example topology data file generated from PHD + AQP1.phd Example PHD secondary structure file + Standard.cod Standard genetic code definitions + Ciliate.cod Ciliate macronuclear genetic code definitions + + + (b) FILE THAT WILL BE GENERATED FROM TEXSHADE.DTX + + texshade.dvi Package documentation + + + +2 - INSTALLATION + + (a) EXTRACTING FILES FROM THE DOCSTRIP ARCHIVE + + All files provided by TeXshade are compacted to one single file, + namely "texshade.dtx". To extract the archive run "texshade.ins" + - which contains the corresponding instructions - through LaTeX. + A list of the generated files is given above, see 1(a). + + AGAIN: Be sure to use a docstrip version 2.4 or later! Otherwise + you will not be able to tex the documentation! + + + (b) THE DOCUMENTATION + + The file "texshade.dtx" further contains the package documentation. + Therefore, run this file through LaTeX now. As you will recognize + two runs are needed to make proper references within the document. + + TeXshade needs lots of TeX's memory, so adjust your parameter set- + tings to make TeXshade feel comfortable. The documentation is a + good test for this. (If you encounter problems texing the doc, you + should tex and read section A of the FAQ-list (see d below) or + download an on-line version [PDF-, DVI-, or PostScript format] at + http://homepages.uni-tuebingen.de/beitz/) + + The resulting file "texshade.dvi" can be viewed and printed using a + DVI-viewer which is able to display embedded PostScript. Another + possibility is to run "texshade.dvi" through DVIPS, a DVI to Post- + Script converter, and finally view and print the converted file + which will be most likely "texshade.ps" with GhostView from the GNU + free software foundation. + + TeXshade makes use of "color.sty" by David Carlisle. This style is + part of the Standard LaTeX Graphics Bundle. Usually, the bundle is + present in a comprehensive LaTeX installation. If this is not the + case for your system you have to download the package from a CTAN- + server, e.g. ftp.dante.de. + + + (c) MAKING TEXSHADE.STY AVAILABLE FOR YOUR LATEX SYSTEM + + In the final step, copy at least the files "texshade.sty" and + "ciliate.cod" to a directory searched by TeX in order to make these + files available for all documents you'll produce in the future. The + remaining files are example files which are not necessary for run- + ning TeXshade. Nevertheless, it would be a good idea to keep all + the files together. + + + (d) THE FAQ LIST + + The FAQ list contains frequently asked questions about the package. + Use it as a helpful source for solving problems with TeXshade. You + get the list by simply running "tsfaq.tex" through LaTeX once. + + + +3 - CONTACT + + E-Mail: eric.beitz@uni-tuebingen.de + WWW: http://homepages.uni-tuebingen.de/beitz/ + (On-line documentation and updates) + Address: Eric Beitz, Universit"at T"ubingen, Pharmazeutische Chemie, + Auf der Morgenstelle 8, D-72076 T"ubingen (Germany) + + + +4 - AGREEMENT + + This program is free software; you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation; either version 2 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + In order to receive a copy of the GNU General Public License write to + the Free Software Foundation, Inc., 59 Temple Place - Suite 330, + Boston, MA 02111-1307, USA. diff --git a/Master/texmf-dist/doc/latex/texshade/ciliate.cod b/Master/texmf-dist/doc/latex/texshade/ciliate.cod new file mode 100644 index 00000000000..36748d765ed --- /dev/null +++ b/Master/texmf-dist/doc/latex/texshade/ciliate.cod @@ -0,0 +1,19 @@ + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%% %%%%% +%%%%% Ciliate macronuclear genetic code definitions %%%%% +%%%%% %%%%% +%%%%% Only exchanges compared to the standard code must be defined. %%%%% +%%%%% %%%%% +%%%%% (The last codon of the list is used for backtranslations %%%%% +%%%%% from protein to DNA sequences---therefore the wobbles) %%%%% +%%%%% %%%%% +%%%%% %%%%% +%%%%% Activate these definitions for your alignment by the following %%%%% +%%%%% command in the texshade environment: %%%%% +%%%%% %%%%% +%%%%% \geneticcode{ciliate} %%%%% +%%%%% %%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +\codon{Q}{TAA,TAG,UAA,UAG,YAR} diff --git a/Master/texmf-dist/doc/latex/texshade/standard.cod b/Master/texmf-dist/doc/latex/texshade/standard.cod new file mode 100644 index 00000000000..6eca648651f --- /dev/null +++ b/Master/texmf-dist/doc/latex/texshade/standard.cod @@ -0,0 +1,34 @@ + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%% %%%%% +%%%%% Standard genetic code definitions %%%%% +%%%%% %%%%% +%%%%% (The last codon of each list is used for backtranslations %%%%% +%%%%% from protein to DNA sequences---therefore the wobbles) %%%%% +%%%%% %%%%% +%%%%% These definitions are default in TeXshade. %%%%% +%%%%% There is no need to load them. This is an example file only. %%%%% +%%%%% %%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +\codon{A}{GCA,GCG,GCC,GCT,GCU,GCN} +\codon{C}{TGC,TGT,UGC,UGU,TGY} +\codon{D}{GAC,GAT,GAU,GAY} +\codon{E}{GAA,GAG,GAR} +\codon{F}{TTC,TTT,UUC,UUU,TTY} +\codon{G}{GGA,GGG,GGC,GGT,GGU,GGN} +\codon{H}{CAC,CAT,CAY} +\codon{I}{ATA,ATC,ATT,AUA,AUC,AUU,ATH} +\codon{K}{AAA,AAG,AAG,AAR} +\codon{L}{CTA,CTG,CTC,CTT,TTA,TTG,CUG,CUG,CUC,CUU,UUA,UUG,YTN} +\codon{M}{ATG,AUG,ATG} +\codon{N}{AAC,AAT,AAU,AAY} +\codon{P}{CCA,CCG,CCC,CCT,CCU,CCN} +\codon{Q}{CAA,CAG,CAR} +\codon{R}{AGA,AGG,CGA,CGG,CGC,CGT,CGU,MGN} +\codon{S}{TCT,TCC,TCG,TCA,AGT,AGC,UCU,UCC,UCG,UCA,AGU,WSN} +\codon{T}{ACT,ACC,ACG,ACA,ACU,ACN} +\codon{V}{GTA,GTG,GTC,GTT,GUA,GUG,GUC,GUU,GTN} +\codon{W}{TGG,UGG,TGG} +\codon{Y}{TAC,TAT,UAC,UAU,TAY} +\codon{.}{TAA,TAG,TGA,UAA,UAG,UGA,TRR} diff --git a/Master/texmf-dist/doc/latex/texshade/texshade.pdf b/Master/texmf-dist/doc/latex/texshade/texshade.pdf Binary files differnew file mode 100644 index 00000000000..d4a4bcd4d81 --- /dev/null +++ b/Master/texmf-dist/doc/latex/texshade/texshade.pdf diff --git a/Master/texmf-dist/doc/latex/texshade/tsfaq.pdf b/Master/texmf-dist/doc/latex/texshade/tsfaq.pdf Binary files differnew file mode 100644 index 00000000000..53b9b895288 --- /dev/null +++ b/Master/texmf-dist/doc/latex/texshade/tsfaq.pdf diff --git a/Master/texmf-dist/doc/latex/texshade/tsfaq.tex b/Master/texmf-dist/doc/latex/texshade/tsfaq.tex new file mode 100644 index 00000000000..0ab7a0ec378 --- /dev/null +++ b/Master/texmf-dist/doc/latex/texshade/tsfaq.tex @@ -0,0 +1,432 @@ +\documentclass[12pt]{article} + + +\begin{document} + + +\noindent +Eric Beitz \hfill March 2005 + +\section*{\TeX{}shade: frequently asked questions } +\bigskip + +This is the sixth update of the FAQ list for \TeX{}shade. Feel free to +contact me if you have problems, questions or suggestions about the +package. I will post them and provide hopefully helpful hints in +future issues of this list. +\bigskip + +\noindent +\qquad email: \texttt{eric.beitz@uni-tuebingen.de} +\smallskip + +\noindent +\TeX{}shade: +\texttt{http://homepages.uni-tuebingen.de/beitz/tse.html} + + +\subsection*{A. Increasing \TeX{}'s memory settings} +\medskip + + If you are using \TeX{}shade to align several large sequences (about 1000 + residues/sequence), LaTeX will probably stop compiling and quit with one + of the following messages: + \texttt{!\ TeX capacity exceeded, sorry [main memory size=384000]} or + \texttt{!\ TeX capacity exceeded, sorry [stack size=300]}. + +Due to several requests I want to start a list of protocols how +to increase the standard \TeX{} memory settings for bigger +alignments. Please contribute to this list by sending me the +procedure for your particular system. + +\begin{enumerate} + + \item + + \textbf{Oz\TeX{} 4.0 for the Macintosh:} + + Find the file `OzTeX:TeX:Configs:Default'. This file contains + all memory settings. Look for the section + `\% TeX parameters' and increase the values that \TeX{} complains + about during the run. You will have to restart Oz\TeX{} before the + changes are active. + + For older versions of Oz\TeX{} the configuration file has the + same name but the path is somewhat different. + + + \item + + \textbf{te\TeX{} for *NIX:} (contributed by Joerg Daehn) + + Find the file: `/usr/share/texmf/web2c/texmf.cnf' or + + use \verb|locate texmf.cnf| at the command prompt to find it. + + Login as super user. Backup `texmf.cnf' in case you destroy something and + then open the `texmf.cnf' file in your favorite text editor and use its + search function to locate \verb|main_memory|. This variable is set to 384000. + Change this to some higher value, i.e. 4000000 (works fine for me!). The + total amount of memory should not exceed 8000000, so check the other + values in that section. + + Next, you want to change the stack size. Search for \verb|stack_size|. This + will be set to 300. I changed it to 4000 and it works fine. + + There might be complains by \TeX{} about further specific parameters such + as \verb|stack_size|. You find all those in the same file. + + After this you have to run `texconfig init'. + + Logout as root. + + After this all should be set for large alignments. Happy \TeX{}ing! + + The information on how to achieve this was derived from a mail in the + te\TeX{} mail archive. The original question was posted by Pascal Francq and + answered by Rolf Nieprasch. + + + \item + + \textbf{MiK\TeX{} for Windows:} + + The MiK\TeX{} documentation describes very detailed how the memory + settings can be changed. In brief, you must locate the + configuration file `miktex/config/miktex.ini'. In the [MiKTeX] + section of this file you find all the parameters you need, e.\,g.\ + \verb|mem_min|, \verb|mem_max|, \verb|buf_size|, \verb|stack_size| etc. + + It appears, that the standard settings of MiK\TeX{} are bigger + than that of other \TeX{} installations, so it may not always be necessary + to increase the values. + + +\end{enumerate} + + +\subsection*{B. Problems using \TeX{}shade} +\medskip + +\begin{enumerate} + + \item + + \textbf{I cannot \TeX{} the manual because I get the error + message `\texttt{!\ TeX capacity exceeded, sorry \ldots}'.} + + \TeX{}shade needs a lot of memory for setting and shading + alignments. The manual is a good test for your memory settings + because it uses many alignments and fingerprints, which are + in particular memory consuming. If you do not know how to increase + \TeX's memory settings, and you do not know a \TeX{} wizard either, then + visit the \TeX{}shade homepage at + \texttt{http://homepages.uni-tuebingen.de/beitz/tse.html} for + downloading the manual in either of three formats: DVI, PDF or + PostScript. + + + \item + + \textbf{I can set my alignment only when I reduce the number of + base-pairs by about 11,000. Otherwise I get the `\texttt{!\ TeX + capacity exceeded, sorry \ldots}' error.} + + There are several parameters defining \TeX's + usable space. If you are a \TeX{} wizard (or you know one) + increase the values that + \TeX{}shade complains about during the run in order to set + bigger alignments. But do not be disappointed when your \TeX{} + system will not set an alignment containing thousands of residues. + There is definitely an upper limit (probably the new \LaTeX3 will + allow you to use even more memory). Setting alignments is a big job for a + typesetting system! + + + \item + + \textbf{I want to align 80 sequences but I get the + `\texttt{!\ No room for a new count}' message.} + + For each sequence two counter variables are used by \TeX{}shade, + further 14 counters for other purposes are needed (and \TeX{} + can handle only 255 counters). This limits the amount of sequences + to about 100 in theory. But \LaTeX{} itself and each of + the loaded packages allocates more counters further reducing the maximum + number of sequences. + + + \item + + \textbf{I receive error messages `\texttt{!\ Missing \$ inserted}' + when \TeX{}ing my alignment. What is wrong?} + + At least one of the sequence names in the alignment file contains an + underscore `\_' symbol. This makes \TeX{} to believe you missed to + enter math mode because subscript initiated by an underscore is + only allowed in math. You need to change the sequence name(s) either in the + alignment file using the `find \& replace' option of your editor or + by using the \verb|\nameseq| command in the \TeX{}shade environment. + Nevertheless, subscript and superscript are permitted in sequence names, + e.\,g. \verb|\nameseq{1}{Name$_{sub}^{super}$}| will result in + Name$_{sub}^{super}$. + + Since v1.3b \TeX{}shade{} is much more tolerant concering special + characters. Get it and read the section about sequence names. + + + \item + + \textbf{My sequence names start out with a number in the + alignment file. Why are they ignored by \TeX{}shade?} + + \TeX{}shade analyzes the first character of each line in the + alignment file in order to decide whether it is a comment, a + ruler or a sequence line etc. All lines starting out with a + non-letter character are interpreted as non-sequence lines. Hence, + you have to change those names in the alignment file. If you + want to have sequence names starting with a number you can + use the \verb|\nameseq| command in the \TeX{}shade environment to + introduce the number, e.\,g. \verb|\nameseq{1}{57th sequence}|. + + + \item + + \textbf{Only a fraction of the residues which are supposed to be + shaded actually are. Why?} + + Make sure that \TeX{}shade knows when protein sequences are to be + set. Align\-ments in the ALN-format do not contain information about the + sequence type (DNA or protein). In such cases DNA sequences are + assumed by \TeX{}shade leading to a shading of only A's, C's, G's, + R's, T's and Y's. A simple solution is to say \verb|\seqtype{P}| in the + \verb|texshade| environment. + + + \item + + \textbf{Functional shading does not work and I get an error message. Why?} + + Same problem as discussed in the point before this one. Functional + shading is permitted only on protein sequences. So, tell \TeX{}shade + that you are using a protein alignment. + + + \item + + \textbf{There is an incompatiblity between \TeX{}shade (v1.2) + and the multi-language package `\texttt{babel}'!} + + You are right! The command \verb|\language| is defined in both + packages which leads to error messages. This bug is fixed since + the release of \TeX{}shade version 1.3 from March 2000. In this + version \verb|\language| is replaced by two commands: + \verb|\germanlanguage| and \verb|\englishlanguage|. + + \item + + \textbf{\TeX{}shade crashes when dashes ``-'' are used as gap + symbols in alignment input files.} + + Yes. Be careful with all kinds of characters that are ``active'' + in \TeX{}, such as \verb|$ _ ^ & % " \|. The dash is not really active + but two or three consecutive dashes are amalgamated to one longer + dash in \TeX. Having those characters in an input file might result + in unforeseen errors or even crashes. + + \item + + \textbf{I have problems using PHD predictions in \TeX{}shade. An + empty \texttt{.top} or \texttt{.sec} file is created.} + + When you do the PHD run do not restrict the calculation to either + secondary structure or topology prediction. Turn on everything. + Otherwise the output will have some ambiguous lines which can not + be interpreted by \TeX{}shade. Result is an empty + \texttt{.top} or \texttt{.sec} file. + +\end{enumerate} + + + +\subsection*{C. Changing the output} +\medskip + +\begin{enumerate} + + \item + + \textbf{How can I force \TeX{}shade to print more residues per line?} + + Use the \verb|\residuesperline*| command with the `\verb|*|' extension. + This will allow you to set any number of residues per line that is + desired, e.\,g. \verb|\residuesperline*{97}|. But then expect numerous + `\texttt{!\ Overfull hbox}' errors due to printing lines that + are broader than the preset \verb|\textwidth|. The same command + without the `\verb|*|' will calculate the highest number of residues + fitting in one line and round it to be divisible by five. + + + \item + + \textbf{Is it possible to add a caption to the \TeX{}shade output?} + + Yes, it is. Since \TeX{}shade v1.5 the \verb|\showcaption| + command is + available to add captions on the top or the bottom of the + alignment. The caption behaves exactly as a figure caption + including the style, numbering and appearance in the list of + figures. + \medskip + + Example: \verb|\showcaption{Nice alignment!}|. + + + \item + + \textbf{I want a short version of the caption for the `List of + Figures'. Is this possible?} + + Yes, with \TeX{}shade v1.9 short captions have been introduced. + In addition to \verb|showcaption| use the command + \verb|shortcaption{|\emph{text}\verb|}|. + \medskip + + Example: \verb|\showcaption{Nice alignment!}|\ + \verb|\shortcaption{Nice}|. + + + \item + + \textbf{My alignment file contains the letters `B' and `Z' for + Asx and Glx, respectively. How can I apply a special shading for + these?} + + Use \verb|\funcgroup| to define `B' and `Z' as functional groups + and assign the colors and the printing style, e.\,g. + \medskip + + \verb|\funcgroup{B}{White}{Blue}{upper}{up}| + \smallskip + + \verb|\funcgroup{Z}{White}{Red}{upper}{up}| + \medskip + + or add the new residues to an existing group, e.\,g. + \medskip + + \verb|\funcgroup{acidic/amide}{DENQBZ}{Black}{Green}{upper}{up}|. + + + \item + + \textbf{How can I build a legend using the `\texttt{shadebox}' + command?} + + The \verb|\shadebox| command simply prints a color-filled box at + the very location it occurs in the text. This means you have to + use \verb|\shadebox| in the normal text after the \TeX{}shade environment + or inside the caption. You find a minimal example below: + \medskip + + \qquad\vbox{% + \verb|\begin{texshade}{alignmentfile.MSF}| + \medskip + + \qquad \verb|\showcpation{Red box: \shadebox{Red}}| + \medskip + + \qquad \emph{further commands, if needed} + \medskip + + \verb|\end{texshade}| + } + + \medskip + + Legend: + + \qquad\verb|\shadebox{conserved}|: conserved residues + + \qquad\verb|\shadebox{White}|: boring residues + + \qquad\verb|\shadebox{Red}|: exciting residues + + + + \item + + \textbf{I do not like the spacing between the feature lines. How + can I change it?} + + Employ the respective space controlling command from the + following list \verb|\ttopspace|, + \verb|\topspace|, \verb|\bottomspace|, \verb|\bbottomspace|. + Those are available since \TeX{}shade v1.5 (see manual). + + + + \item + + \textbf{How can I change gap and match symbols in diverse mode?} + + Since \TeX{}shade version 1.7, standard definitions for \verb|diverse| + mode are: + + \begin{verbatim} + \nomatchresidues{Black}{White}{lower}{up} + \similarresidues{Black}{White}{lower}{up} + \conservedresidues{Black}{White}{{.}}{up} + \allmatchresidues{Black}{White}{{.}}{up} + \gapchar{-} + \end{verbatim} + + After calling \verb|\shadingmode{diverse}| these commands can be + used to redefine the \verb|diverse| mode settings (mind the double + curly braces around the dot-symbol!). + + + + \item + + \textbf{I want to rotate the alignment on the page. Is this possible?} + + Yes. Stefan Vogt has found this solution: use pdflscape.sty and + activate it in the preamble with \verb|\usepackage{pdflscape}|. Then + put your \TeX{}shade environment inside a \verb|landscape|-environment. + You also need to adjust the number of residues per line with + \verb|\residuesperline*{number}| to make them fill the rotated page. + \medskip + + \qquad\vbox{% + \verb|\begin{landscape}| + + \verb|\centering| + + \qquad \verb|\begin{texshade}{alignmentfile.MSF}| + + \qquad \verb|\residuesperline*{|\emph{number}\verb|}| + \medskip + + \qquad \qquad \emph{further commands, if needed} + \medskip + + \qquad \verb|\end{texshade}| + + \verb|\end{landscape}| + } + + + + \item + + \textbf{I want use the \TeX{}shade and \TeX{}topo logos in my text. How?} + + Use the commands: \verb|\TeXshade| and \verb|\TeXtopo|. + + +\end{enumerate} + + + +\end{document}
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