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Biomolecular Modeling (practical)

Biomolecular Modeling (practical). Dr. Kong Lesheng Temasek Life Sciences Laboratory (TLL) (lesheng@tll.org.sg). Objective. To predict 3D structure with good quality when given a protein sequence. Protein structure prediction. Comparative (homology) modeling typically >20-30% identity

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Biomolecular Modeling (practical)

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  1. Biomolecular Modeling (practical) Dr. Kong Lesheng Temasek Life Sciences Laboratory (TLL) (lesheng@tll.org.sg)

  2. Objective • To predict 3D structure with good quality when given a protein sequence

  3. Protein structure prediction • Comparative (homology) modeling • typically >20-30% identity • Threading/fold recognition • cannot detect similarity in sequence level • Ab initio (de novo) modeling • cannot detect fold

  4. Adapted from Sali & Baker, Science (2001)

  5. Adapted from Sali & Baker, Science (2001)

  6. Comparative modeling • More reliable and accurate • Computationally less intensive • With the development of structural genomics project, most modeling problem will be reduced to comparative modeling!

  7. Before modeling • Check Protein Data Bank (PDB) • e.g. NCBI BLAST against PDB • Check Model Databases • SWISS-MODEL Repository • http://swissmodel.expasy.org/repository/ • Modbase • http://modbase.compbio.ucsf.edu/ • Caution! Those models generated by automated comparative modeling

  8. Few Terms • Target • Protein sequence without structure • Template • Known protein structure sharing detectable sequence similarity with target sequence • Model • Predicted 3D structure

  9. Comparative Modeling procedure • Template selection • Target-template alignment • Model building • Model evaluation

  10. Comparative modeling • Automated server • SDPMOD • SWISS-MODEL • 3D-JIGSAW • Standalone program • Modeller • WHATIF

  11. Modeling using automated server • If target is small disulfide-rich proteins (SDPs), try SDPMOD • Length < 100 a.a. & Cys > 4 • http://proline.bic.nus.edu.sg/sdpmod/ • Else, try SWISS-MODEL • http://swissmodel.expasy.org/

  12. A sample output by SDPMOD http://proline.bic.nus.edu.sg/sdpmod/cgi-bin/view.py?ukey=IPC03gMxvN

  13. Model Evaluation • Stereochemistry quality checking • Procheck • Whatcheck • Wrong fold vs. correct fold • ProSA II • Verify_3D

  14. SAVS:Structure Analysis and Validation Server http://nihserver.mbi.ucla.edu/SAVS/

  15. Want to know more? • Sali Lab • http://salilab.org/modeller/modeller.html • Baker lab • http://robetta.bakerlab.org/ • CASP (Critical Assessment of techniques for protein Structure Prediction) • http://predictioncenter.gc.ucdavis.edu/casp7/

  16. Any question?

  17. Modeling by Modeller • Template selection • Target-template alignment • Model building • Model evaluation

  18. An example • Target: influenza hemagglutinin (Swissprot ID: Q67087) • >Target • METISLITILLVVTVSNADKICIGYQSTNSTETVDTLTENNVPVTHAKELLHTEHNGMLCATNLGHPLIL • DTCTIEGLIYGNPSCNLLLGGREWSYIVERPSAVNGLCYPGNVENLEELRSLFSSASSFQRIQIFPDTIW • NVSYSGTSKACSDSFYRSMRWLTQKNNTYPIQDAQYTNNRGKSILFMWGINHPPTDTVQTNLYTRTDTTT • SVATEDINRTFRPLIGPRPLVNGQQGRIDYYWSVLKPGQTLRVRSNGNLIAPWYGHILSGESHGRILKTD • LNSGNCVVQCQTERGGLNTTLPFHNVSKYAFGNCPKYVGVKSLKLAVGLRNVPARSSRGLFGAIAGFIEG • GWSGLVAGWYGFQHSNDQGVGMAADRDSTQRAIDKITSKVNNIVDKMNKQYEIIDHEFSEVETRLNMINN • KIDDQIQDIWAYNAELLVLLENQKTLDEHDANVNNLYNKVKRALGSNAVEDGKGCFELYHKCDDQCMETI • RNGTYNKRKYKEESRLERQKIEGVKLESEGTYKILTIYSTVASSLVIAMGFAAFLFWAMSNGSCRCNICI

  19. NCBI BLAST against PDB

  20. Template selection

  21. Download template structure

  22. Target-template alignment • Needle at EMBOSS server • http://sf01.bic.nus.edu.sg/EMBOSS/ • Jalview • http://www.jalview.org/ • >Target • DKICIGYQSTNSTETVDTLTENNVPVTHAKELLHTEHNGMLCATNLGHPLIL • DTCTIEGLIYGNPSCNLLLGGREWSYIVERPSAVNGLCYPGNVENLEELRSLFSSASSFQRIQIFPDTIW • NVSYSGTSKACSDSFYRSMRWLTQKNNTYPIQDAQYTNNRGKSILFMWGINHPPTDTVQTNLYTRTDTTT • SVATEDINRTFRPLIGPRPLVNGQQGRIDYYWSVLKPGQTLRVRSNGNLIAPWYGHILSGESHGRILKTD • LNSGNCVVQCQTERGGLNTTLPFHNVSKYAFGNCPKYVGVKSLKLAVGLRNVPARSSRGLFGAIAGFIEG • GWSGLVAGWYGFQHSNDQGVGMAADRDSTQRAIDKITSKVNNIVDKMNKQYEIIDHEFSEVETRLNMINN • KIDDQIQDIWAYNAELLVLLENQKTLDEHDANVNNLYNKVKRALGSNAVEDGKGCFELYHKCDDQCMETI • RNGTYNKRKYKEESRLERQKIEGVKLESEGTYKILTIYSTVASSLVIAMGFAAFLFWAMSNGSCRCNICI • >1JSD • DKICIGYQSTNSTETVDTLTETNVPVTHAKELLHTSHNGMLCATNLGHPLILDTCTIEGLIYGNPSCDLL • LGGREWSYIVERPSAVNGMCYPGNVENLEELRSLFSSASSYQRIQIFPDTIWNVSYSGTSSACSDSFYRS • MRWLTQKNNAYPIQDAQYTNNRGKSILFMWGINHPPTDTVQTNLYTRTDTTTSVTTEDINRTFKPVIGPR • PLVNGLHGRIDYYWSVLKPGQTLRVRSNGNLIAPWYGHILSGESHGRILKTDLNSGNCVVQCQTERGGLN • TTLPFHNVSKYAFGNCPKYVGVKSLKLAVGLRNVPARSSGLFGAIAGFIEGGWPGLVAGWYGFQHSNDQG • VGMAADSDSTQKAIDKITSKVNNIVDKMNKQYGIIDHEFSEIETRLNMINNKIDDQIQDIWTYNAELLVL • LENQKTLDEHDANVNNLYNKVKRALGSNAMEDGKGCFELYHKCDDQCMETIRNGTYNRRKYKEESKLERQ • KIEGI

  23. Format the alignment in PIR format

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