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Protein Structure Prediction pp 43–53Cite as

Modeling of Protein Side-Chain Conformations with RASP

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1137))

Abstract

Modeling of side-chain conformations on a fixed protein backbone, also called side-chain packing, plays an important role in protein structure prediction, protein design, molecular docking, and functional analysis. RASP, or RApid Side-chain Predictor, is a recently developed program that can model protein side-chain conformations with both high accuracy and high speed. Moreover, it can generate structures with few atomic clashes. This chapter first provides a brief introduction to the principle and performances of the RASP package. Then details on how to use RASP programs to predict protein side-chain conformations are elaborated. Finally, it describes case studies for structure refinement in homology modeling and residue substitution.

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References

  1. Canzar S, Toussaint NC, Klau GW (2011) An exact algorithm for side-chain placement in protein design. Optimization Letters 5(3):393–406

    Article  Google Scholar 

  2. Canutescu A, Shelenkov A, Dunbrack R (2003) A graph-theory algorithm for rapid protein side-chain prediction. Protein Sci 12(9):2001–2014

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  3. Cao Y et al (2011) Improved side-chain modeling by coupling clash-detection guided iterative search with rotamer relaxation. Bioinformatics 27(6):785–790

    Article  CAS  PubMed  Google Scholar 

  4. Bower M (1997) Prediction of protein side-chain rotamers from a backbone-dependent rotamer library: a new homology modeling tool. J Mol Biol 267(5):1268–1282

    Article  CAS  PubMed  Google Scholar 

  5. Fromer M et al (2010) SPRINT: side-chain prediction inference toolbox for multistate protein design. Bioinformatics (Oxford, England) 26(19):2466–2467

    Article  CAS  Google Scholar 

  6. Hartmann C, Antes I, Lengauer T (2007) IRECS: a new algorithm for the selection of most probable ensembles of side-chain conformations in protein models. Protein Sci 16(7):1294–1307

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  7. Wang C, Schueler-Furman O, Baker D (2005) Improved side-chain modeling for protein-protein docking. Protein Sci 14(5):1328–1339

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  8. Petrella R, Karplus M (2001) The energetics of off-rotamer protein side-chain conformations. J Mol Biol 312(5):1161–1175

    Article  CAS  PubMed  Google Scholar 

  9. Krivov G, Shapovalov M, Dunbrack R (2009) Improved prediction of protein side-chain conformations with SCWRL4. Proteins 77(4):778–795

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  10. Liang S, Grishin NV (2002) Side-chain modeling with an optimized scoring function. Protein Sci 11(2):322–331

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  11. Liang S et al (2011) Protein side chain modeling with orientation-dependent atomic force fields derived by series expansions. J Comput Chem 32(8):1680–1686

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  12. Lu M, Dousis A, Ma J (2008) OPUS-PSP: an orientation-dependent statistical all-atom potential derived from side-chain packing. J Mol Biol 376(1):288–301

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  13. McGregor M, Islam S, Sternberg M (1987) Analysis of the relationship between side-chain conformation and secondary structure in globular proteins. J Mol Biol 198(2):295–310

    Article  CAS  PubMed  Google Scholar 

  14. Peterson RW, Dutton PL, Wand AJ (2004) Improved side-chain prediction accuracy using an ab initio potential energy function and a very large rotamer library. Protein Sci 13(3):735–751

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  15. Tuffery P et al (1991) A new approach to the rapid determination of protein side chain conformations. J Biomol Struct Dyn 8(6):1267–1289

    Article  CAS  PubMed  Google Scholar 

  16. Xiang Z, Honig B (2001) Extending the accuracy limits of prediction for side-chain conformations1. J Mol Biol 311(2):421–430

    Article  CAS  PubMed  Google Scholar 

  17. Xu J (2005) Rapid protein side-chain packing via tree decomposition. In: Miyano S et al (eds) Research in computational molecular biology. Springer, Berlin, Heidelberg, pp 423–439

    Chapter  Google Scholar 

  18. Dunbrack RL Jr, Cohen FE (1997) Bayesian statistical analysis of protein side-chain rotamer preferences. Protein Sci 6(8):1661–1681

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  19. Lovell SC et al (2000) The penultimate rotamer library. Proteins 40(3):389–408

    Article  CAS  PubMed  Google Scholar 

  20. Desmet J et al (1992) The dead-end elimination theorem and its use in protein side-chain positioning. Nature 356(6369):539–542

    Article  CAS  PubMed  Google Scholar 

  21. Lee C, Subbiah S (1991) Prediction of protein side-chain conformation by packing optimization. J Mol Biol 217(2):373–388

    Article  CAS  PubMed  Google Scholar 

  22. Leach AR, Lemon AP (1998) Exploring the conformational space of protein side chains using dead-end elimination and the A* algorithm. Proteins 33(2):227–239

    Article  CAS  PubMed  Google Scholar 

  23. Kingsford L, Bernard C, Mona S (2005) Solving and analyzing side-chain positioning problems using linear and integer programming. Bioinformatics 21(7):1028–1039

    Article  CAS  PubMed  Google Scholar 

  24. Lee C (1994) Predicting protein mutant energetics by self-consistent ensemble optimization. J Mol Biol 236(3):918–939

    Article  CAS  PubMed  Google Scholar 

  25. Samudrala R, Moult J (1998) An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction. J Mol Biol 275(5):895–916

    Article  CAS  PubMed  Google Scholar 

  26. Vásquez M (1995) An evaluation of discrete and continuum search techniques for conformational analysis of side chains in proteins. Biopolymers 36(1):53–70

    Article  Google Scholar 

  27. Mendes J et al (2001) Implicit solvation in the self-consistent mean field theory method: side chain modelling and prediction of folding free energies of protein mutants. J Comput Aided Mol Des 15(8):721–740

    Article  CAS  PubMed  Google Scholar 

  28. Miao Z, Cao Y, Jiang T (2011) RASP: rapid modeling of protein side chain conformations. Bioinformatics 27(22):3117–3122

    Article  CAS  PubMed  Google Scholar 

  29. Lu M, Dousis A, Ma J (2008) OPUS-Rota: a fast and accurate method for side-chain modeling. Protein Sci 17(9):1576–1585

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  30. Berman HM et al (2000) The protein data bank. Nucleic Acids Res 28(1):235–242

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  31. Chao J, Williamson J (2004) Joint X-Ray and NMR refinement of the yeast L30e-mRNA complex. Structure 12(7):1165–1176

    Article  CAS  PubMed  Google Scholar 

  32. Vidovic I et al (2000) Crystal structure of the spliceosomal 15.5 kDa protein bound to a U4 snRNA fragment. Molecular cell 6(6):1331–1342

    Article  CAS  PubMed  Google Scholar 

  33. Eswar N et al (2006) Comparative protein structure modeling using modeller, in current protocols in bioinformatics. John Wiley & Sons, Inc. http://onlinelibrary.wiley.com/doi/10.1002/0471250953.bi0506s15/abstract

  34. Chothia C, Lesk AM (1986) The relation between the divergence of sequence and structure in proteins. EMBO J 5(4):823–826

    CAS  PubMed Central  PubMed  Google Scholar 

  35. Le QM et al (2005) Avian flu: isolation of drug-resistant H5N1 virus. Nature 437(7062):1108

    Article  CAS  PubMed  Google Scholar 

  36. Wang MZ, Tai CY, Mendel DB (2002) Mechanism by which mutations at his274 alter sensitivity of influenza a virus n1 neuraminidase to oseltamivir carboxylate and zanamivir. Antimicrob Agents Chemother 46(12):3809–3816

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  37. Ilyushina NA et al (2010) Effect of neuraminidase inhibitor-resistant mutations on pathogenicity of clade 2.2 A/Turkey/15/06 (H5N1) influenza virus in ferrets. PLoS Pathog 6(5):e1000933

    Article  PubMed Central  PubMed  Google Scholar 

  38. Abed Y, Goyette N, Boivin G (2004) A reverse genetics study of resistance to neuraminidase inhibitors in an influenza A/H1N1 virus. Antivir Ther 9(4):577–581

    CAS  PubMed  Google Scholar 

  39. Deng YM et al (2011) A comparison of pyrosequencing and neuraminidase inhibition assays for the detection of oseltamivir-resistant pandemic influenza A(H1N1) 2009 viruses. Antiviral Res 90(1):87–91

    Article  CAS  PubMed  Google Scholar 

  40. Pizzorno A et al (2011) Generation and characterization of recombinant pandemic influenza A(H1N1) viruses resistant to neuraminidase inhibitors. J Infect Dis 203(1):25–31

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  41. van der Vries E, Stelma FF, Boucher CA (2010) Emergence of a multidrug-resistant pandemic influenza A (H1N1) virus. N Engl J Med 363(14):1381–1382

    Article  PubMed  Google Scholar 

  42. Boltz DA et al (2010) Emergence of H5N1 avian influenza viruses with reduced sensitivity to neuraminidase inhibitors and novel reassortants in Lao People’s Democratic Republic. J Gen Virol 91(Pt 4):949–959

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  43. Nguyen HT et al (2010) Recovery of a multidrug-resistant strain of pandemic influenza A 2009 (H1N1) virus carrying a dual H275Y/I223R mutation from a child after prolonged treatment with oseltamivir. Clin Infect Dis 51(8):983–984

    Article  PubMed  Google Scholar 

  44. Okomo-Adhiambo M et al (2010) Host cell selection of influenza neuraminidase variants: implications for drug resistance monitoring in A(H1N1) viruses. Antiviral Res 85(2):381–388

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

We gratefully thank Pascal Auffinger from Institut de Biologie Moléculaire et Cellulaire, Centre national de la recherche scientifique, for his help on critical editing of the manuscript.

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Authors and Affiliations

  1. Key Lab of Protein and Peptide Pharmaceuticals, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China

    Zhichao Miao, Yang Cao & Taijiao Jiang

Authors
  1. Zhichao Miao
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  2. Yang Cao
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  3. Taijiao Jiang
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Editor information

Editors and Affiliations

  1. Purdue University Dept. Biological Science, West Lafayette, Indiana, USA

    Daisuke Kihara

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Miao, Z., Cao, Y., Jiang, T. (2014). Modeling of Protein Side-Chain Conformations with RASP. In: Kihara, D. (eds) Protein Structure Prediction. Methods in Molecular Biology, vol 1137. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-0366-5_4

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  • DOI: https://doi.org/10.1007/978-1-4939-0366-5_4

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-0365-8

  • Online ISBN: 978-1-4939-0366-5

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