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Schweizerisches Institut für Bioinformatik Institut Suisse de Bioinformatique Istituto Svizzero di Bioinformatica Swiss Institute Schweizerisches Institut für Bioinformatik Institut Suisse de Bioinformatique Istituto Svizzero di Bioinformatica Swiss Institute of Bioinformatics SWISS-MODEL: Giving the proteome a third dimension. Torsten Schwede Fortaleza, Brasil 1. August 2006 Torsten. [email protected] ch

Introduction Introduction

gggtctctcttgttagaccagatctgagcctgggagctctctggctaactagggaacccactgcttaagcctcaataaagcttgccttgagtgcttcaagtagtgtgtgcccgtctgttgtgtgactctgatagctagagatcccttc agaccaaatttagtcagtgtgaaaaatctctagcagtggcgcctgaacagggacttgaaagcgaaagagaaaccagagaagctctctcgacgcaggactcggcttgctgaagcgcgcacggcaagaggcgaggggacggcgactggtg agtacgccaaaattttgactagcggaggctagaaggagatgggtgcgagagcgtcgatattaagcgggggaggattagatgggaaaaaattcggttaaggccagggggaaaaaatatagattaaaacatttagtat gggcaagcagggagctagaacgattcgcagtcaatcctggcctattagaaacatcagaaggttgtagacaaatactgggacaactacaaccagcccttcagacaggatcagaagaacttagatcattatataatacagtagcaaccct ctattgtgtgcatcaaaagatgtaaaagacaccaaggaagctttagatagaggaagagcaaaagtaagaaaaaagcacagcagcagctgacacaggaaatagcagccaggtcagccaaaattaccccata gtgcagaacatccaggggcaaatggtacatcaggccatatcacctagaactttaaatgcatgggtaaaagtagtagaaggctttcagcccagaagtaatacccatgttttcagcattatcagaaggagccaccccacaagatt taaacaccatgctaaacacagtggggggacatcaagcagccatgcaaatgttaaaagagaccatcaatgaggaagctgcagaatgggatagattgcatccagtgcagggcctcatccaccaggccagatgagagaaccaagggg aagtgacatagcaggaactactagtacccttcaggaacaaatagcatggatgacaaataatccacctatcccagtaggagaaatctataagagatggataatcctgggattaaaatagtaaggatgtatagccctaccagcatt ctggacataaaacaaggaccaaaggaaccctttagagactatgtagaccggttctataagactctaagagccgagcaagcttcacaggaggtaaaaaattggatgacagaaaccttgttggtccaaaatgcgaacccagattgtaaga ctattttaaaagcattgggaccagcagctacactagaagaaatgatgacagcatgtcagggagtgggaggacccggccataaagcaagagttttggcagaagcaatgagccaagtaacaaattcagctaccataatgatgcagaaagg caattttaggaaccaaagaaaaattgttaagtgtttcaattgtggcaaagaagggcacatagccaaaaattgcagggcccctaggaaaaggggctgttggaaatgtggaaaggagggacaccaaatgaaagattgtactgagagacag gctaattttttagggaaaatctggccttcccacaggggaaggccagggaattttcctcagaacagactagagccaacagccccaccagaagagagcttcaggtttggggaagagacaacaactccctctcagaagcagg agctgatagacaaggaactgtatccttcagcttccctcaaatcactctttggcaacgaccccttgtcacaataaagataggggggcaactaaaggaagctctattagatacaggagcagatgatacagtattagaagaaatttg ccaggaagatggaaaccaaaaatgatagggggaattggaggttttatcaaagtaagacagtatgatcaaatactcgtagaaatctgtggacataaagctataggtacagtattagtaggacctacacctgtcaacataattggaagaa gggtctctcttgttagaccagatctgagcctgggagctctctggctaactagggaacccactgcttaagcctcaataaagcttgccttgagtgcttcaagtagtgtgtgcccgtctgttgtgtgactctgatagctagagatcccttc agaccaaatttagtcagtgtgaaaaatctctagcagtggcgcctgaacagggacttgaaagcgaaagagaaaccagagaagctctctcgacgcaggactcggcttgctgaagcgcgcacggcaagaggcgaggggacggcgactggtg agtacgccaaaattttgactagcggaggctagaaggagatgggtgcgagagcgtcgatattaagcgggggaggattagatgggaaaaaattcggttaaggccagggggaaaaaatatagattaaaacatttagtat gggcaagcagggagctagaacgattcgcagtcaatcctggcctattagaaacatcagaaggttgtagacaaatactgggacaactacaaccagcccttcagacaggatcagaagaacttagatcattatataatacagtagcaaccct ctattgtgtgcatcaaaagatgtaaaagacaccaaggaagctttagatagaggaagagcaaaagtaagaaaaaagcacagcagcagctgacacaggaaatagcagccaggtcagccaaaattaccccata gtgcagaacatccaggggcaaatggtacatcaggccatatcacctagaactttaaatgcatgggtaaaagtagtagaaggctttcagcccagaagtaatacccatgttttcagcattatcagaaggagccaccccacaagatt taaacaccatgctaaacacagtggggggacatcaagcagccatgcaaatgttaaaagagaccatcaatgaggaagctgcagaatgggatagattgcatccagtgcagggcctcatccaccaggccagatgagagaaccaagggg aagtgacatagcaggaactactagtacccttcaggaacaaatagcatggatgacaaataatccacctatcccagtaggagaaatctataagagatggataatcctgggattaaaatagtaaggatgtatagccctaccagcatt ctggacataaaacaaggaccaaaggaaccctttagagactatgtagaccggttctataagactctaagagccgagcaagcttcacaggaggtaaaaaattggatgacagaaaccttgttggtccaaaatgcgaacccagattgtaaga ctattttaaaagcattgggaccagcagctacactagaagaaatgatgacagcatgtcagggagtgggaggacccggccataaagcaagagttttggcagaagcaatgagccaagtaacaaattcagctaccataatgatgcagaaagg caattttaggaaccaaagaaaaattgttaagtgtttcaattgtggcaaagaagggcacatagccaaaaattgcagggcccctaggaaaaggggctgttggaaatgtggaaaggagggacaccaaatgaaagattgtactgagagacag gctaattttttagggaaaatctggccttcccacaggggaaggccagggaattttcctcagaacagactagagccaacagccccaccagaagagagcttcaggtttggggaagagacaacaactccctctcagaagcagg agctgatagacaaggaactgtatccttcagcttccctcaaatcactctttggcaacgaccccttgtcacaataaagataggggggcaactaaaggaagctctattagatacaggagcagatgatacagtattagaagaaatttg ccaggaagatggaaaccaaaaatgatagggggaattggaggttttatcaaagtaagacagtatgatcaaatactcgtagaaatctgtggacataaagctataggtacagtattagtaggacctacacctgtcaacataattggaagaa atctgttgactcagattggttgcactttaaattttcccattagtcctattgaaactgtaccagtaaaattaaagccaggaatggcccaaaagttaaacaatggccattgacagaagaaaaaataaaagcattagtagaaatctg tacagaaatggaaaaggaaaaatttcaaaaatcgggcctgaaaatccatataatactccagtatttgccataaagaaaaaagacagtactaaatggagaaaattagtagatttcagagaacttaataagaaaactcaagacttc tgggaagttcaattaggaataccacatcccgcagggttaaaaaagaaaaaatcagtaacagtactggatgtgggtgatgcatatttttcagttcccttagataaagaattcaggaagtacactgcatttaccatacctagtataaaca atgagacaccagggattagatatcagtacaatgtgcttccacagggatggaaaggatcaccagcaatattccaaagcagcatgacaaaaatcttagagccttttagaaaacaaaatccagacatagttatcaatacatggacga tttgtaggatctgacttagaaatagggcagcatagaacaaaaatagaggaactgagacaacatctgttgaagtggggatttaccacaccagacaaaaaacatcagaacctccattcctttggatgggttatgaactccat cctgataaatggacagtacagcctatagtgctgccagaaaaggacagctggactgtcaatgacatacagaagttagtgggaaaattgggcaagtcagatttacccagggattaaagcaattatgtagactccttaggg gaaccaaggcactaacagaagtaataccactaacaaaagaagcagagctagaactggcagaaaacagggaaattctaaaagaaccagtacatggagtgtattatgacccatcaaaagacttaatagcggaaatacagaagcaggggca aggtcaatggacatatcaaatttatcaagagccatttaaaaatctgaaaacaggaaaatatgcaagaatgaggggtgcccacactaatgatgtaaaacaattaacagaggcagtgcaaaaaataaccacagaaagcatagtaatatgg ggaaagactcctaaatttaaactacccatacaaaaagaaacatggtggacagagtattggcaagccacctggattcctgagtgggagtttgtcaataccccttagtaaaattatggtaccagttagagaac ccataataggagcagaaactttctatgtagatggggcagctaacagggagactaaattaggaaaagcaggatatgttactaacaaagggagacaaaaagttgtctccataactgacacaacaaatcagaagactgagttacaagcaat tcttctagcattacaggattctggattagaagtaaacatagtaacagactcacaatatgcattaggaatcattcaagcacaaccagataaaagtgaatcagagatagtcaaataatagagcagttaataaaaaaaggtc tacctgacatgggtaccagcgcacaaaggaattggaggaaatgaacaagtagataaattagtcagtactggaatcaggaaagtactctttttagatggaatagataaagcccaagaagaacatgaaaaatatcacagtaattggaggg caatggctagtgattttaacctgccacctgtggtagcaaaagagatagtagccagctgtgataaatgtcagctaaaaggagaagccatggacaagtagactgtagtccaggaatatggcaactagattgtacacatttagaagg aaaaattatcctggtagcagttcatgtagccagtggatatatagaagcagaagttattccagcagaaacagggcaggaaacagcatactttctcttaaaattagcaggaagatggccagtaaaaacagtacagacaatggcagc aatttcaccagtactacagttaaggccgcctgttggtgggcaggaatcaagcaggaatttggcattccctacaatccccaaagtcaaggagtagtagaatctataaagaattaaagttataggacagataagagatcagg ctgaacatcttaagacagcagtacaaatggcagtattcatccacaattttaaaaggggggattggggggtacagtgcaggggaaagaatagtagacataatagcaacagacatacaaactaaagaactacaaaaacaaattac aaaaattcaaaattttcgggtttattacagggacagcagagatccactttggaaaggaccagcaaagcttctctggaaaggtgaaggggcagtagtaatacaagataatagtgacataaaagtagtgccaagaagaaaagcaaagatc attagggattatggaaaacagatggcaggtgatgattgtgtggcaagtagacaggatgaggattagaacatggaaaagtttagtaaaacaccatatgtttcaaggaaagctaagggatggttttatagacatcactatgaaagt actcatccgagaataagttcagaagtacacatcccactagggaatgcaaaattggtaataacaacatattggggtctacaggagaaagagactggcatttgggtcaaggagtctccatagaattgaggaaaaggagatatagca cacaattagaccctaacctagcagaccaactaattcatctgcattactttgattgtttttcagaatctgctataagaaatgccatattaggacatatagttagccctaggtgtgaatatcaagcaggacataacaaggtaggatctct acagtacttggcactaacagcattagtaagaccaagaaaaaagataaagccacctttgcctagtgttacaaaactgacagaggatagatggaacaagccccagaagaccaagggccacaaagggaaccatacaatggacactag aacttttagaggagctcaagaatgaagctgttagacattttcctaggatatggctccatagcttagggcaacatatctatgaaacttatggagatacttgggcaggagtggaagccataataagaattctgcaacaactgctgtttat tcatttcagaattgggtgtcaacatagcagaatagacattcttcgacgaaggagagcaagaaatggagccagtagatcctagagccctggaagcatccaggaagtcagcctaggactgcttgtaccaattgctattgtaaaaa gtgttgctttcattgccaagtttcataacaaaaggcttaggcatctcctatggcaggaagaagcggagacagcgacgaagagctcctcaagacagtcagactcatcaagtttctctatcaaagcagtagtacatgtaatg caatctttacaaatattagcagtagtagcattagtagtagcagcaataatagcaatagttgtgtggtccatagtattcatagaatataggaaaataagaagacaaaatagaaaggttgatagaataatagaaagagcag aagacagtggcaatgagagtgacggagatcaggaagaattatcagcacttgtggaaatggggcacgatgctccttgggatgttaatgatctgtaaagctgcagaaaatttgtgggtcacagtttattatggggtacctgtgtggaaag aagcaaccaccactctattttgtgcctcagatgctaaagcgtatgatacagaggtacataatgtttgggccacacatgcctgtgtacccacagaccccaacccacaagaagtagaactgaagaatgtgacagaaaattttaacatgtg gaaaaataacatggtagaccaaatgcatgaggatataattagtttatgggatcaaagcctaaagccatgtgtaaaattaaccccactctgtgttactttaaattgcactgattatgggaatgatactaacaccaataatagtagtgct actaaccccactagtagtagcgggggaatggaggggagaaataaaaaattgctctttcaatatcaccagaagcataagagataaagtgaagaatatgcacttttttatagtcttgatgtaataccaataaaagatgata atactagctataggttgagaagttgtaacacctcagtcattacacaggcctgtccaaaggtatcctttgaaccaattcccatacattattgtgccccggctggttttgcgattctaaagtgtaatgataaaaagttcaatggaaaagg accatgtacaaatgtcagcacagtacaatgtacacatggaattaggccagtagtatcaactgctgttaaatggcagtctagcagaagaagaggtagtaattagatcagacaatttctcggacaatgctaaagtcataatagta catctgaatctgtagaaattgtacaagactcaacaacattacaaggagaagtatacatgtaggaccaggcagagcaatttatacaacaggaataataggaaaaataagacaagcacattgtaacattagta gagcaaaatggaataacactttaaaacagatagttacaaaattaagagaacaatttaagaataaaacaatagtctttaatcctcaggaggggacccagaaattgtaatgcacagttttaattgtggaggggaatttttctactg taattcaacacaactgtttaacagtacttggaatggtactgcatggtcaaataacactgaaggaaatgacacaatcacactcccatgcagaataaaacaaattataaacatgtggcaggaagtaggaaaagcaatgtatgca cctcccatcagaggacaaattagatgttcatcaaatattacagggctgatattaacaagagatggtggtattaaccagaccaacaccaccgagattttcaggcctggaggaggagatatgaaggacaattggagaagtgaattatata aatataaagtagtaaaaattgaaccattaggagtagcaccaaggcaaagagtggtgcaaagagaaaaaagagcagtgggaataataggagctatgctccttgggttcttgggagcagcaggaagcactatgggcgcagc gtcaatgacgctgacggtacaggccagacaattattgtctggtatagtgcaacagcagaacaatttgctgagggctattgaggcgcaacagcatctgttgcacctcacagtctggggcatcaagcagctccaagagtcctggct gtggaaagatacctaagggatcaacagctcctggggttttggggttgctctggaaaactcatttgcaccactgctgtgccttggaatactagttggagtaataaatctctgagtcagatttgggataacatgacctggatgcagtggg aaagggaaattgataattacacaagcttaatatacaacttaattgaagaatcgcaaaaccaacaagaatgaacaagagttattggaattagataactgggcaagtttgtggaattggtttagcataacaaattggctgtggta tataaaaatattcataatgatagtaggaggcttggtaggtttaagaatagtttttactgtactttctatagtaaatagagttaggcagggatactcaccattgtcgtttcagacgcgcctcccaggaggggacccgacaggccc gaaggaatcgaagaagaaggtggagagacagatccggtcaattagtggattcttagcaattatctgggtcgacctgcggagcctgtgcctcttcagctaccaccgcttgagagacttactcttgattgtaacga ggattgtggaacttctgggacgcagggggtgggaagccctcaaatattggtggaatctcctacaatattggattcaggaactaaagaatagtgctgttagcttgctcaacgccacagccatagcagtagctgagggaactgatagggt

gggtctctcttgttagaccagatctgagcctgggagctctctggctaactagggaacccactgcttaagcctcaataaagcttgccttgagtgcttcaagtagtgtgtgcccgtctgttgtgtgactctgatagctagagatcccttc agaccaaatttagtcagtgtgaaaaatctctagcagtggcgcctgaacagggacttgaaagcgaaagagaaaccagagaagctctctcgacgcaggactcggcttgctgaagcgcgcacggcaagaggcgaggggacggcgactggtg agtacgccaaaattttgactagcggaggctagaaggagatgggtgcgagagcgtcgatattaagcgggggaggattagatgggaaaaaattcggttaaggccagggggaaaaaatatagattaaaacatttagtat gggcaagcagggagctagaacgattcgcagtcaatcctggcctattagaaacatcagaaggttgtagacaaatactgggacaactacaaccagcccttcagacaggatcagaagaacttagatcattatataatacagtagcaaccct ctattgtgtgcatcaaaagatgtaaaagacaccaaggaagctttagatagaggaagagcaaaagtaagaaaaaagcacagcagcagctgacacaggaaatagcagccaggtcagccaaaattaccccata gtgcagaacatccaggggcaaatggtacatcaggccatatcacctagaactttaaatgcatgggtaaaagtagtagaaggctttcagcccagaagtaatacccatgttttcagcattatcagaaggagccaccccacaagatt taaacaccatgctaaacacagtggggggacatcaagcagccatgcaaatgttaaaagagaccatcaatgaggaagctgcagaatgggatagattgcatccagtgcagggcctcatccaccaggccagatgagagaaccaagggg aagtgacatagcaggaactactagtacccttcaggaacaaatagcatggatgacaaataatccacctatcccagtaggagaaatctataagagatggataatcctgggattaaaatagtaaggatgtatagccctaccagcatt ctggacataaaacaaggaccaaaggaaccctttagagactatgtagaccggttctataagactctaagagccgagcaagcttcacaggaggtaaaaaattggatgacagaaaccttgttggtccaaaatgcgaacccagattgtaaga ctattttaaaagcattgggaccagcagctacactagaagaaatgatgacagcatgtcagggagtgggaggacccggccataaagcaagagttttggcagaagcaatgagccaagtaacaaattcagctaccataatgatgcagaaagg caattttaggaaccaaagaaaaattgttaagtgtttcaattgtggcaaagaagggcacatagccaaaaattgcagggcccctaggaaaaggggctgttggaaatgtggaaaggagggacaccaaatgaaagattgtactgagagacag gctaattttttagggaaaatctggccttcccacaggggaaggccagggaattttcctcagaacagactagagccaacagccccaccagaagagagcttcaggtttggggaagagacaacaactccctctcagaagcagg agctgatagacaaggaactgtatccttcagcttccctcaaatcactctttggcaacgaccccttgtcacaataaagataggggggcaactaaaggaagctctattagatacaggagcagatgatacagtattagaagaaatttg ccaggaagatggaaaccaaaaatgatagggggaattggaggttttatcaaagtaagacagtatgatcaaatactcgtagaaatctgtggacataaagctataggtacagtattagtaggacctacacctgtcaacataattggaagaa gggtctctcttgttagaccagatctgagcctgggagctctctggctaactagggaacccactgcttaagcctcaataaagcttgccttgagtgcttcaagtagtgtgtgcccgtctgttgtgtgactctgatagctagagatcccttc agaccaaatttagtcagtgtgaaaaatctctagcagtggcgcctgaacagggacttgaaagcgaaagagaaaccagagaagctctctcgacgcaggactcggcttgctgaagcgcgcacggcaagaggcgaggggacggcgactggtg agtacgccaaaattttgactagcggaggctagaaggagatgggtgcgagagcgtcgatattaagcgggggaggattagatgggaaaaaattcggttaaggccagggggaaaaaatatagattaaaacatttagtat gggcaagcagggagctagaacgattcgcagtcaatcctggcctattagaaacatcagaaggttgtagacaaatactgggacaactacaaccagcccttcagacaggatcagaagaacttagatcattatataatacagtagcaaccct ctattgtgtgcatcaaaagatgtaaaagacaccaaggaagctttagatagaggaagagcaaaagtaagaaaaaagcacagcagcagctgacacaggaaatagcagccaggtcagccaaaattaccccata gtgcagaacatccaggggcaaatggtacatcaggccatatcacctagaactttaaatgcatgggtaaaagtagtagaaggctttcagcccagaagtaatacccatgttttcagcattatcagaaggagccaccccacaagatt taaacaccatgctaaacacagtggggggacatcaagcagccatgcaaatgttaaaagagaccatcaatgaggaagctgcagaatgggatagattgcatccagtgcagggcctcatccaccaggccagatgagagaaccaagggg aagtgacatagcaggaactactagtacccttcaggaacaaatagcatggatgacaaataatccacctatcccagtaggagaaatctataagagatggataatcctgggattaaaatagtaaggatgtatagccctaccagcatt ctggacataaaacaaggaccaaaggaaccctttagagactatgtagaccggttctataagactctaagagccgagcaagcttcacaggaggtaaaaaattggatgacagaaaccttgttggtccaaaatgcgaacccagattgtaaga ctattttaaaagcattgggaccagcagctacactagaagaaatgatgacagcatgtcagggagtgggaggacccggccataaagcaagagttttggcagaagcaatgagccaagtaacaaattcagctaccataatgatgcagaaagg caattttaggaaccaaagaaaaattgttaagtgtttcaattgtggcaaagaagggcacatagccaaaaattgcagggcccctaggaaaaggggctgttggaaatgtggaaaggagggacaccaaatgaaagattgtactgagagacag gctaattttttagggaaaatctggccttcccacaggggaaggccagggaattttcctcagaacagactagagccaacagccccaccagaagagagcttcaggtttggggaagagacaacaactccctctcagaagcagg agctgatagacaaggaactgtatccttcagcttccctcaaatcactctttggcaacgaccccttgtcacaataaagataggggggcaactaaaggaagctctattagatacaggagcagatgatacagtattagaagaaatttg ccaggaagatggaaaccaaaaatgatagggggaattggaggttttatcaaagtaagacagtatgatcaaatactcgtagaaatctgtggacataaagctataggtacagtattagtaggacctacacctgtcaacataattggaagaa atctgttgactcagattggttgcactttaaattttcccattagtcctattgaaactgtaccagtaaaattaaagccaggaatggcccaaaagttaaacaatggccattgacagaagaaaaaataaaagcattagtagaaatctg tacagaaatggaaaaggaaaaatttcaaaaatcgggcctgaaaatccatataatactccagtatttgccataaagaaaaaagacagtactaaatggagaaaattagtagatttcagagaacttaataagaaaactcaagacttc tgggaagttcaattaggaataccacatcccgcagggttaaaaaagaaaaaatcagtaacagtactggatgtgggtgatgcatatttttcagttcccttagataaagaattcaggaagtacactgcatttaccatacctagtataaaca atgagacaccagggattagatatcagtacaatgtgcttccacagggatggaaaggatcaccagcaatattccaaagcagcatgacaaaaatcttagagccttttagaaaacaaaatccagacatagttatcaatacatggacga tttgtaggatctgacttagaaatagggcagcatagaacaaaaatagaggaactgagacaacatctgttgaagtggggatttaccacaccagacaaaaaacatcagaacctccattcctttggatgggttatgaactccat cctgataaatggacagtacagcctatagtgctgccagaaaaggacagctggactgtcaatgacatacagaagttagtgggaaaattgggcaagtcagatttacccagggattaaagcaattatgtagactccttaggg gaaccaaggcactaacagaagtaataccactaacaaaagaagcagagctagaactggcagaaaacagggaaattctaaaagaaccagtacatggagtgtattatgacccatcaaaagacttaatagcggaaatacagaagcaggggca aggtcaatggacatatcaaatttatcaagagccatttaaaaatctgaaaacaggaaaatatgcaagaatgaggggtgcccacactaatgatgtaaaacaattaacagaggcagtgcaaaaaataaccacagaaagcatagtaatatgg ggaaagactcctaaatttaaactacccatacaaaaagaaacatggtggacagagtattggcaagccacctggattcctgagtgggagtttgtcaataccccttagtaaaattatggtaccagttagagaac ccataataggagcagaaactttctatgtagatggggcagctaacagggagactaaattaggaaaagcaggatatgttactaacaaagggagacaaaaagttgtctccataactgacacaacaaatcagaagactgagttacaagcaat tcttctagcattacaggattctggattagaagtaaacatagtaacagactcacaatatgcattaggaatcattcaagcacaaccagataaaagtgaatcagagatagtcaaataatagagcagttaataaaaaaaggtc tacctgacatgggtaccagcgcacaaaggaattggaggaaatgaacaagtagataaattagtcagtactggaatcaggaaagtactctttttagatggaatagataaagcccaagaagaacatgaaaaatatcacagtaattggaggg caatggctagtgattttaacctgccacctgtggtagcaaaagagatagtagccagctgtgataaatgtcagctaaaaggagaagccatggacaagtagactgtagtccaggaatatggcaactagattgtacacatttagaagg aaaaattatcctggtagcagttcatgtagccagtggatatatagaagcagaagttattccagcagaaacagggcaggaaacagcatactttctcttaaaattagcaggaagatggccagtaaaaacagtacagacaatggcagc aatttcaccagtactacagttaaggccgcctgttggtgggcaggaatcaagcaggaatttggcattccctacaatccccaaagtcaaggagtagtagaatctataaagaattaaagttataggacagataagagatcagg 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gaaaaataacatggtagaccaaatgcatgaggatataattagtttatgggatcaaagcctaaagccatgtgtaaaattaaccccactctgtgttactttaaattgcactgattatgggaatgatactaacaccaataatagtagtgct actaaccccactagtagtagcgggggaatggaggggagaaataaaaaattgctctttcaatatcaccagaagcataagagataaagtgaagaatatgcacttttttatagtcttgatgtaataccaataaaagatgata atactagctataggttgagaagttgtaacacctcagtcattacacaggcctgtccaaaggtatcctttgaaccaattcccatacattattgtgccccggctggttttgcgattctaaagtgtaatgataaaaagttcaatggaaaagg accatgtacaaatgtcagcacagtacaatgtacacatggaattaggccagtagtatcaactgctgttaaatggcagtctagcagaagaagaggtagtaattagatcagacaatttctcggacaatgctaaagtcataatagta catctgaatctgtagaaattgtacaagactcaacaacattacaaggagaagtatacatgtaggaccaggcagagcaatttatacaacaggaataataggaaaaataagacaagcacattgtaacattagta gagcaaaatggaataacactttaaaacagatagttacaaaattaagagaacaatttaagaataaaacaatagtctttaatcctcaggaggggacccagaaattgtaatgcacagttttaattgtggaggggaatttttctactg taattcaacacaactgtttaacagtacttggaatggtactgcatggtcaaataacactgaaggaaatgacacaatcacactcccatgcagaataaaacaaattataaacatgtggcaggaagtaggaaaagcaatgtatgca cctcccatcagaggacaaattagatgttcatcaaatattacagggctgatattaacaagagatggtggtattaaccagaccaacaccaccgagattttcaggcctggaggaggagatatgaaggacaattggagaagtgaattatata aatataaagtagtaaaaattgaaccattaggagtagcaccaaggcaaagagtggtgcaaagagaaaaaagagcagtgggaataataggagctatgctccttgggttcttgggagcagcaggaagcactatgggcgcagc gtcaatgacgctgacggtacaggccagacaattattgtctggtatagtgcaacagcagaacaatttgctgagggctattgaggcgcaacagcatctgttgcacctcacagtctggggcatcaagcagctccaagagtcctggct gtggaaagatacctaagggatcaacagctcctggggttttggggttgctctggaaaactcatttgcaccactgctgtgccttggaatactagttggagtaataaatctctgagtcagatttgggataacatgacctggatgcagtggg aaagggaaattgataattacacaagcttaatatacaacttaattgaagaatcgcaaaaccaacaagaatgaacaagagttattggaattagataactgggcaagtttgtggaattggtttagcataacaaattggctgtggta tataaaaatattcataatgatagtaggaggcttggtaggtttaagaatagtttttactgtactttctatagtaaatagagttaggcagggatactcaccattgtcgtttcagacgcgcctcccaggaggggacccgacaggccc gaaggaatcgaagaagaaggtggagagacagatccggtcaattagtggattcttagcaattatctgggtcgacctgcggagcctgtgcctcttcagctaccaccgcttgagagacttactcttgattgtaacga ggattgtggaacttctgggacgcagggggtgggaagccctcaaatattggtggaatctcctacaatattggattcaggaactaaagaatagtgctgttagcttgctcaacgccacagccatagcagtagctgagggaactgatagggt Same "Sequence" - still different. . . We need to understand structure and function of proteins.

Public Database content n Experimentally determined protein structures (PDB) 37'874 1588 214 (Source: PDB) Public Database content n Experimentally determined protein structures (PDB) 37'874 1588 214 (Source: PDB)

Public Database content n Experimentally known protein structures (PDB) Public Database content n Experimentally known protein structures (PDB)

Public Database content n Annotated Protein Sequences: Swiss-Prot Public Database content n Annotated Protein Sequences: Swiss-Prot

Public Database content n Protein Sequences translated from DNA: tr. EMBL è No experimental Public Database content n Protein Sequences translated from DNA: tr. EMBL è No experimental structure for most protein sequences (Sources: PDB, EBI, SIB)

Protein Folding MNIFEMLRID HLLTKSPSLN DEAEKLFNQD LDAVRRCALI LQQKRWDEAA TTFRTGTWDA n n n EGLRLKIYKD AAKSELDKAI VDAAVRGILR Protein Folding MNIFEMLRID HLLTKSPSLN DEAEKLFNQD LDAVRRCALI LQQKRWDEAA TTFRTGTWDA n n n EGLRLKIYKD AAKSELDKAI VDAAVRGILR NMVFQMGETG VNLAKSRWYN YKNL TEGYYTIGIG GRNCNGVITK NAKLKPVYDS VAGFTNSLRM QTPNRAKRVI Many proteins fold spontaneously to their native structure Protein folding is relatively fast (nsec – sec) Chaperones speed up folding, but do not alter the structure The protein sequence contains all information needed to create a correctly folded protein (Anfinsen principle). Can we model protein structures from their protein sequences?

Comparative protein structure modelling Comparative protein structure modelling

René Magritte. The Human Condition. 1935. Oil on canvas. René Magritte. The Human Condition. 1935. Oil on canvas.

Comparative Protein Structure Modelling Known Structures (Templates) Target Sequence Template Selection Alignment Template - Comparative Protein Structure Modelling Known Structures (Templates) Target Sequence Template Selection Alignment Template - Target Structure Evaluation & Assessment Structure modeling Homology Model(s)

Comparative Protein Structure Modelling n Why do we need automated expert systems for comparative Comparative Protein Structure Modelling n Why do we need automated expert systems for comparative protein modelling? n Too many new sequences, too many new structures n Objectivity and Reproducibility: remove individual human bias; predictable reliability of results n Ø Improve methods and algorithms Easy access for “end users”

User emails. . . To: <Torsten. Schwede@isb-sib. ch> From: Karin Schwarz <xxxxxxx@gmx. de> Dear User emails. . . To: From: Karin Schwarz Dear Mr. Schwede, I am looking for firms, which needs also models with a high from 168 cm. Do you need someone? Best regards K. Schwarz

SWISS-MODEL Server How to automate protein modeling? PDB Database -> SMTL Known Structures (Templates) SWISS-MODEL Server How to automate protein modeling? PDB Database -> SMTL Known Structures (Templates) Ex. PASY Target Sequence Swiss. Prot/Tr. EMBL EBI NR / Blast NCBI Template Selection Alignment Template - Target Structure Evaluation & Assessment 3 D - Model requests: Structure modeling 2005: >148’ 000 requests Biozentrum, SIB (Basel) & NCI (ABBC) ~ 400 models per work day ~ one model every 4 minutes Homology Model(s) 1. 2. 3. 4. RCSB M. C. Peitsch and C. V. Jongeneel (1993) Int. Immunol. 5, 233 -238 Peitsch MC (1995) Bio/Technology 13: 658 -660. Guex N and Peitsch MC (1997) Electrophoresis 18: 2714 -2723. Schwede T, Kopp J, Guex N, Peitsch MC (2003) Nucleic Acids Research 31, 3381 -3385.

SWISS-MODEL Workspace Arnold K, Bordoli L, Kopp J, Schwede T (2006) Bioinformatics 22, 195 SWISS-MODEL Workspace Arnold K, Bordoli L, Kopp J, Schwede T (2006) Bioinformatics 22, 195 -201.

SWISS-MODEL Template Library Arnold K, Bordoli L, Kopp J, Schwede T (2006) Bioinformatics 22, SWISS-MODEL Template Library Arnold K, Bordoli L, Kopp J, Schwede T (2006) Bioinformatics 22, 195 -201.

Deep. View - Swiss-Pdb. Viewer Collaboration with Nicolas Guex (GSK) [ http: //www. expasy. Deep. View - Swiss-Pdb. Viewer Collaboration with Nicolas Guex (GSK) [ http: //www. expasy. org/spdbv/ ] Guex N and Peitsch MC (1997) Electrophoresis 18: 2714 -2723.

SWISS-MODEL Repository PDB/SMTL Template updates Update Model? Reverse Proxy Services Swiss. Prot / Sequence SWISS-MODEL Repository PDB/SMTL Template updates Update Model? Reverse Proxy Services Swiss. Prot / Sequence updates Tr. EMBL Image Rendering Server SWISS-MODEL Server Coordinate Repository Server SWISS-MODEL Repository Oracle Control DB Quality ok? SWISS-MODEL Repository my. SQL DB / WEB CRC 64 Kopp J, and Schwede T (2004). Nucleic Acids Research 32 , D 230 -D 234. Computational Services e. g. Anolea quality control

SWISS-MODEL Repository Kopp J, and Schwede T (2006). Nucleic Acids Research 34 , D SWISS-MODEL Repository Kopp J, and Schwede T (2006). Nucleic Acids Research 34 , D 315 -D 318.

SWISS-MODEL Repository Kopp J, and Schwede T (2006). Nucleic Acids Research 34 , D SWISS-MODEL Repository Kopp J, and Schwede T (2006). Nucleic Acids Research 34 , D 315 -D 318.

SWISS-MODEL Repository Kopp J, and Schwede T (2006). Nucleic Acids Research 34 , D SWISS-MODEL Repository Kopp J, and Schwede T (2006). Nucleic Acids Research 34 , D 315 -D 318.

Evaluation of Model Accuracy Evaluation of Model Accuracy

Evaluation of model accuracy “. . . a model must be wrong, in some Evaluation of model accuracy “. . . a model must be wrong, in some respects -- else it would be thing itself. The trick is to see. . . where it is right. ” Henry A. Bent "Uses (and Abuses) of Models in Teaching Chemistry, " J. Chem. Ed. 1984 61, 774. SIV Model based on: Experimental structure: Seq. Identity: 61 % 1 BL 3 (C) HIV-1 Integrase core domain 1 C 6 V (C) SIV Integrase core domain

Evaluation of Model Accuracy 3 D - Crunch Experiment Very Large Scale Protein Modelling Evaluation of Model Accuracy 3 D - Crunch Experiment Very Large Scale Protein Modelling Project (1998) http: //www. expasy. org/swissmod/SM_Likely. Precision. html CASP Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction http: //Prediction. Center. org EVA Evaluation of Automatic protein structure prediction http: //eva. compbio. ucsf. edu/~eva/ (Koh et al. (2003) Nucleic Acids Res. 31, 3311 -5 )

Evaluation of automated protein structure prediction EVA Server Weekly PDB released target sequences 1 Evaluation of automated protein structure prediction EVA Server Weekly PDB released target sequences 1 MNIFEMLRID EGLRLKIYKD TEGYYTIGIG HLLTKSPSLN AAKSELDKAI GRNCNGVITK Prediction Servers MNIFEMLRID EGLRLKIYKD TEGYYTIGIG HLLTKSPSLN AAKSELDKAI GRNCNGVITK 2 n Predicted structure e. g. SWISS-MODEL Does the model have the correct fold? n n What would have been the best alignment? n 3 What would have been the best template? How difficult was the modeling? n Alignment accuracy n Overall accuracy (RMSD) n n n loop regions n Evaluation of prediction accuracy core regions side chain rotamer Stereo-chemical quality of the model (Koh et al. (2003) Nucleic Acids Res. 31, 3311 -5 )

Evaluation of Automated protein structure prediction n Evaluation of 246 weekly PDB releases (20'108 Evaluation of Automated protein structure prediction n Evaluation of 246 weekly PDB releases (20'108 models) The analysis of ~1 year of models for all PDB released structures is necessary for a statistically relevant comparison of modelling methods. [ Data source: EVA-CM http: //cubic. bioc. columbia. edu/eva/ ]

Evaluation of Automated protein structure prediction n Evaluation of 246 weekly PDB releases (20'108 Evaluation of Automated protein structure prediction n Evaluation of 246 weekly PDB releases (20'108 models) SWISS-MODEL has the lowest cumulative average global RMSD of the servers in the comparison. [ Data source: EVA-CM http: //cubic. bioc. columbia. edu/eva/ ]

Evaluation of Automated protein structure prediction n Evaluation of 246 weekly PDB releases (20'108 Evaluation of Automated protein structure prediction n Evaluation of 246 weekly PDB releases (20'108 models) The lower RMSD of SWISS-MODEL is related to a lower coverage in sequence space. [ Data source: EVA-CM http: //cubic. bioc. columbia. edu/eva/ ]

Evaluation of Model Accuracy Midnight Zone Twilight Zone Save Zone [ Data source: EVA-CM Evaluation of Model Accuracy Midnight Zone Twilight Zone Save Zone [ Data source: EVA-CM http: //cubic. bioc. columbia. edu/eva/ ]

Applications of comparative modelling Possible applications of comparative models depend on the expected accuracy. Applications of comparative modelling Possible applications of comparative models depend on the expected accuracy. (Kopp & Schwede, Pharmacogenomics, 2004 )

Application Examples Application Examples

Applications: Structural Assessment of Sequence Variations Comparative modeling allows for detailed structure-based assessment of Applications: Structural Assessment of Sequence Variations Comparative modeling allows for detailed structure-based assessment of sequence variations. Mutations, ns-c. SNPs, alternative splicing, isoforms and other sequence variations may be responsible for functional variations and involved in rare inherited diseases, individual differences in drug response, and susceptibility for common diseases. (Wattenhofer, et. al. J. Mol. Med. 2002 ) Rational design of Functional Mutations Comparative modeling allows for designing specific site directed mutations to study functional role of individual residues or structural features. Example: Design of partial and complete deletion mutations of the plug domain of Sec 61 p to study its functional role. Conclusion: The plug domain of Sec 61 p is non-essential, but influences topogenesis and Sec 61 assembly. Junne, Schwede, Goder & Spiess (2006) Molecular Cell, in press

Application: Models in structure based drug discovery n Can homology models be used in Application: Models in structure based drug discovery n Can homology models be used in structure based drug discovery when no experimental protein structures are available? n How do errors and inaccuracies of the homology models affect the subsequent molecular modeling of protein-ligand interaction? Estimating relative ligand binding free energies using MD-GBSA n 500 ps MD Simulation for conformational sampling (MM-GBSA) ∆Gbind = < ∆Evacuo > + < ∆Gdesolv > - < T∆S > n Collaboration with Markus Meuwly (Computational Chemistry, Uni Basel) & Vincent Zoete (SIB Lausanne) Thorsteinsdottir HB, Schwede T, Zoete V, and Meuwly M (2006). Proteins (in press).

Application: Models in structure based drug discovery n Example: 16 HIV-I protease inhibitor complexes Application: Models in structure based drug discovery n Example: 16 HIV-I protease inhibitor complexes n Validation: Correlation between experimental and calculated relative binding free energies n Application: Systematic analysis of the influence of typical modeling errors. Reference 1 HVI relative Gcalculated Reference Model: All ligands computed in 1 HVI as reference structure HM based on EIAV Rotamer model relative Gcalculated Twilight Zone Model: based on Equine infectious anemia virus protease (1 FMB) sharing 32% seq. id. RMSD (5 Å of ligand) 1. 30 Å SCWRL Rotamer Model: Rotamers of reference structure remodelled using SCWRL 3. 0. 1 RMSD (5 Å of ligand) 1. 26 Å 1 SCWRL 3. 0 (A. A. Canutescu, A. A. Shelenkov, and R. L. Dunbrack, Jr. Protein Science 12, 2001 -2014 (2003). Thorsteinsdottir HB, Schwede T, Zoete V, and Meuwly M (2006). Proteins (in press).

Summary and Outlook Summary and Outlook

Summary and outlook n Today's Bioinformatics view is still largely (DNA)- sequence centric. Understanding Summary and outlook n Today's Bioinformatics view is still largely (DNA)- sequence centric. Understanding macromolecular function in detail requires knowledge of its 3 D structure. n 10 years ago, for the vast the majority of proteins no information about its 3 -dimensional structure was available. n Today, structural genomics and comparative modelling are complementing each other in exploring the structural space. Within the next decade, 3 D information will become available for the majority of all globular protein domains. n > 90 % of this structural information will be based on comparative models. n SWISS-MODEL aims at providing a fully integrated protein-centric view of all available structural information on a given protein.

SWISS-MODEL is accessible freely at Expasy: n SWISS-MODEL Server: n n Deep. View (Swiss-Pdb. SWISS-MODEL is accessible freely at Expasy: n SWISS-MODEL Server: n n Deep. View (Swiss-Pdb. Viewer) n n http: //www. expasy. org/spdbv/ SWISS-MODEL Workspace: n n http: //swissmodel. expasy. org/workspace/ SWISS-MODEL Repository: n http: //swissmodel. expasy. org/repository/

Acknowledgements Biozentrum & SIB Basel SIB - Swiss Institute of EBI - European Bioinformatics Acknowledgements Biozentrum & SIB Basel SIB - Swiss Institute of EBI - European Bioinformatics Hólmfríður B. Þorsteinsdóttir Bioinformatics (LS & GE) Institute Lorenza Bordoli Amos Bairoch Kim Henrick Jürgen Kopp Elisabeth Gasteiger Rolf Apweiler Michael Podvinec Ernest Feytmans Nicola Moulder James Battey Olivier Michielin Ujjwal Das Konstantin Arnold Vincent Zoete Rainer Pöhlmann Victor Jongeneel Pontificia Universidad Católica Roger Jenni Jacques Rougemont de Chile Robert Gaisbauer Francisco Melo Mihaela Zavolan NCI Frederick Martin Spiess Jack Collins Karol Miaskiweicz University of Basel Robert W. Lebherz Markus Meuwly GSK R&D Novartis Basel Nicolas Guex Manuel Peitsch Alexander Diemand Financial support:

Acknowledgements Biozentrum & SIB Basel SIB - Swiss Institute of EBI - European Bioinformatics Acknowledgements Biozentrum & SIB Basel SIB - Swiss Institute of EBI - European Bioinformatics Hólmfríður B. Þorsteinsdóttir Bioinformatics (LS & GE) Institute Lorenza Bordoli Amos Bairoch Kim Henrick Jürgen Kopp Elisabeth Gasteiger Rolf Apweiler Michael Podvinec Ernest Feytmans Nicola Moulder James Battey Olivier Michielin Ujjwal Das Konstantin Arnold Vincent Zoete Rainer Pöhlmann Victor Jongeneel Pontificia Universidad Católica Roger Jenni Jacques Rougemont de Chile Robert Gaisbauer Francisco Melo Mihaela Zavolan NCI Frederick Martin Spiess Jack Collins Karol Miaskiweicz University of Basel Robert W. Lebherz Markus Meuwly GSK R&D Novartis Basel Nicolas Guex Manuel Peitsch Alexander Diemand Financial support: