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Rapid and Accurate Protein Side Chain Prediction with Local Backbone Information

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Research in Computational Molecular Biology (RECOMB 2008)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 4955))

Abstract

High-accuracy protein structure modeling demands accurate and very fast side chain prediction since such a procedure must be repeatedly called at each step of structure refinement. Many known side chain prediction programs, such as SCWRL and TreePack, depend on the philosophy that global information and pairwise energy function must be used to achieve high accuracy. These programs are too slow to be used in the case when side chain packing has to be used thousands of times, such as protein structure refinement and protein design.

We present an unexpected study that local backbone information can determine side chain conformations accurately. LocalPack, our side chain packing program which is based on only local information, achieves equal accuracy as SCWRL and TreePack, yet runs 4-14 times faster, hence providing a key missing piece in our efforts to high-accuracy protein structure modeling.

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References

  1. Janin, J., Wodak, S., Levitt, M., Maigret, B.: The conformation of amino acid side chains in proteins. J. Mol. Biol. 125, 357–386 (1978)

    Article  Google Scholar 

  2. Bhat, T.N., Sasisekharan, V., Vijayan, M.: An analysis of side-chain conformation in proteins. Int. J. Pept. Protein Res. 14, 170–184 (1979)

    Google Scholar 

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

    Article  Google Scholar 

  4. Summers, N.L., Karplus, M.: Construction of side-chains in homology modeling: Application to the c-terminal lobe of rhizopuspepsin. J. Mol. Biol. 210, 785–810 (1989)

    Article  Google Scholar 

  5. Desjarlais, J., Handel, T.: De novo design of the hydrophobic cores of proteins. Protein Science 4, 2006–2018 (1995)

    Article  Google Scholar 

  6. Dahiyat, B., Mayo, S.: Protein design automation. Protein Science 5, 895–903 (1996)

    Article  Google Scholar 

  7. Dunbrack, R.: Rotamer libraries in the 21st century. Curr. Opin. Struct. Biol. 12, 431–440 (2002)

    Article  Google Scholar 

  8. Xu, J.: Rapid Protein Side-Chain Packing via Tree Decomposition. In: Miyano, S., Mesirov, J., Kasif, S., Istrail, S., Pevzner, P.A., Waterman, M. (eds.) RECOMB 2005. LNCS (LNBI), vol. 3500, pp. 423–439. Springer, Heidelberg (2005)

    Google Scholar 

  9. Xu, J., Berger, B.: Fast and accurate algorithms for protein side-chain packing. Journal of ACM 53, 533–557 (2006)

    Article  MathSciNet  Google Scholar 

  10. Dunbrack, R., Cohen, F.: Bayesian statistical analysis of protein side-chain rotamer preferences. Protein Science 6, 1661–1681 (1997)

    Google Scholar 

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

    Article  Google Scholar 

  12. Chandrasekaran, R., Ramachandran, G.: Studies on the conformation of amino acids. XI. Analysis of the observed side group conformations in proteins. Int. J. Protein Research 2, 223–233 (1994)

    Google Scholar 

  13. Benedetti, E., Morelli, G., Nemethy, G., Scheraga, H.: Statistical and energetic analysis of sidechain conformations in oligopeptides. Int. J. Peptide Protein Res. 22, 1–15 (1983)

    Google Scholar 

  14. Ponder, J., Richards, F.: Tertiary templates for proteins. use of packing criteria in the enumeration of allowed sequences for different structural classes. J. Mol. Biol. 193, 775–791 (1987)

    Article  Google Scholar 

  15. Kono, H., Doi, J.: A new method for side-chain conformation prediction using a hopfield network and reproduced rotamers. J. Comp. Chem. 17, 1667–1683 (1996)

    Google Scholar 

  16. Maeyer, M., Desmet, J., Lasters, I.: All in one: a highly detailed rotamer library improves both accuracy and speed in the modelling of sidechains by dead-end elimination. Fold Des. 2, 53–66 (1997)

    Article  Google Scholar 

  17. Dunbrack, R., Karplus, M.: Backbone-dependent rotamer library for proteins: Application to side-chain prediction. J. Mol. Biol. 230, 543–574 (1993)

    Article  Google Scholar 

  18. Schrauber, H., Eisenhaber, F., Argos, P.: Rotamers: To be or not to be? An analysis of amino acid sidechain conformations in globular proteins. J. Mol. Biol. 230, 592–612 (1993)

    Article  Google Scholar 

  19. Dunbrack, R., Karplus, M.: Conformational analysis of the backbone-dependent rotamer preferences of protein sidechains. Nature Struct. Biol. 1, 334–340 (1994)

    Article  Google Scholar 

  20. Liang, S., Grishin, N.: Side-chain modeling with an optimized scoring function. Protein Science 11, 322–331 (2002)

    Article  Google Scholar 

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

    Article  Google Scholar 

  22. Peterson, R., Dutton, P., Wand, A.: Improved side-chain prediction accuracy using an ab initio potential energy function and a very large rotamer library. Protein Science 13, 735–751 (2004)

    Article  Google Scholar 

  23. Chazelle, B., Kingsford, C., Singh, M.: A semidefinite programming approach to side chain positioning with new rounding strategies. Informs Journal on Computing 16, 380–392 (2004)

    Article  MathSciNet  Google Scholar 

  24. Kingsford, C., Chazelle, B., Singh, M.: Solving and analyzing side-chain positioning problems using linear and integer programming. Bioinformatics 21, 1028–1036 (2005)

    Article  Google Scholar 

  25. Jain, T., Cerutti, D., McCammon, J.: Configurational-bias sampling techinique for predicting side-chain conformations in proteins. Protein Science 15, 2029–2039 (2007)

    Article  Google Scholar 

  26. Yanover, C., Schueler-Furman, O., Weiss, Y.: Minimizing and learning energy functions for side-chain prediction. In: Speed, T., Huang, H. (eds.) RECOMB 2007. LNCS (LNBI), vol. 4453, pp. 381–395. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  27. Roitberg, A., Elber, R.: Modeling side chains in peptides and proteins: Application of the locally enhanced sampling and the simulated annealing methods to find minimum energy functions. Chem. Phys. 95, 9277–9287 (1991)

    Article  Google Scholar 

  28. Street, A., Mayo, S.: Intrinsic beta-sheet propensities result from van der waals interactions between side chains and the local backbone. PNAS 96, 9074–9076 (1999)

    Article  Google Scholar 

  29. Mendes, J., Nagarajaram, H., Soares, C., Blundell, T., Carrondo, M.: Incorporating knowledge-based biases into an energy-based side-chain modeling method: Application to comparative modeling of protein structure. Biopolymers 59, 72–86 (2001)

    Article  Google Scholar 

  30. Rohl, C., Strauss, C., Chivian, D., Baker, D.: Modeling structurally variable regions in homologous proteins with rosetta. Proteins: Structure, Function, and Bioinformatics 55, 656–677 (2004)

    Article  Google Scholar 

  31. Holm, L., Sander, C.: Fast and simple monte carlo algorithm for side chain optimization in proteins: Application to model building by homology. Proteins: Structure, Function and Genetics 14, 213–223 (1992)

    Article  Google Scholar 

  32. Vasquez, M.: An evaluation of discrete and continuum search techniques for conformational analysis of side-chains in proteins. Biopolymers 36, 53–70 (1995)

    Article  Google Scholar 

  33. Tuffery, P., Etchebest, C., Hazout, S., Lavery, R.: A new approach to the rapid determination of protein side chain conformations. J. Biomol. Struct. Dyn. 8, 1267–1289 (1991)

    Google Scholar 

  34. Desmet, J., Maeyer, M., Hazes, B., Laster, I.: The dead-end elimination theorem and its use in protein side-chain positioning. Nature 356, 539–542 (1992)

    Article  Google Scholar 

  35. Hwang, J., Liao, W.: Side-chain prediction by neural networks and simulated annealing optimization. Protein Eng. 8, 363–370 (1995)

    Article  Google Scholar 

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

    Article  Google Scholar 

  37. Eriksson, O., Zhou, Y., Elofsson, A.: Side chain-positioning as an integer programming problem. In: Gascuel, O., Moret, B.M.E. (eds.) WABI 2001. LNCS, vol. 2149, pp. 128–141. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  38. Akutsu, T.: NP-hardness results for protein side-chain packing. Genome Informatics 8, 180–186 (1997)

    Google Scholar 

  39. Pierce, N., Winfree, E.: Protein design is NP-hard. Protein Eng. 15, 779–782 (2002)

    Article  Google Scholar 

  40. Crammer, K., Singer, Y.: On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research 2, 265–292 (2001)

    Article  Google Scholar 

  41. Tsochantaridis, I., Hofmann, T., Joachims, T., Altun, Y.: Support vector machine learning for interdependent and structured output spaces. In: The 21st International Conference on Machine Learning, vol. 69, pp. 104–111 (2004)

    Google Scholar 

  42. Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y.: Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research 6, 1453–1484 (2005)

    MathSciNet  Google Scholar 

  43. Taskar, B., Guestrin, C., Koller, D.: Max-margin markov networks. NIPS 16 (2004)

    Google Scholar 

  44. Eyal, E., Najmanovich, R., Mcconkey, R.J., Enelman, M., Sobolev, V.: Importance of solvent accessibility and contact surfaces in modeling side-chain conformations in proteins. J. Comput. Chem. 25, 712–724 (2004)

    Article  Google Scholar 

  45. Labesse, G., Colloc’h, N., Pothier, J., Mornon, J.P.: P-SEA, a new efficient assignment of secondary structure from C α trace of proteins. CABIOS 13, 291–295 (1997)

    Google Scholar 

  46. Hubbard, S.J., Thornton, J.M.: ‘NACCESS’, Computer Program, Department of Biochemistry and Molecular Biology, University College London (1993)

    Google Scholar 

  47. Dasarathy, B.V.: Nearest neighbor (NN) norms: NN pattern classification techniques. IEEE Computer Society Press, Los Alamitos (1990)

    Google Scholar 

  48. Shakhnarovich, G., Darrell, T., Indyk, P.: Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing). The MIT Press, Cambridge (2006)

    Google Scholar 

  49. http://svmlight.joachims.org/svm_multiclass.html

  50. Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification. Technical report, Taipei (2003)

    Google Scholar 

  51. http://ca.expasy.org/sprot/relnotes/relstat.html

  52. Sali, A., Blundell, T.L.: Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993)

    Article  Google Scholar 

  53. Xu, J., Li, M., Kim, D., Xu, Y.: RAPTOR: optimal protein threading by linear programming. Journal of Bioinformatics and Computational Biology 1, 95–117 (2003)

    Article  Google Scholar 

  54. http://predictioncenter.org/casp6/Casp6.html

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Authors and Affiliations

  1. David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada, N2L 6P7

    Jing Zhang, Xin Gao & Ming Li

  2. The Institute for Theoretical Computer Science, Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

    Jing Zhang

  3. Toyota Technological Institute at Chicago, Chicago, IL, USA, 60637

    Jinbo Xu

Authors
  1. Jing Zhang
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  2. Xin Gao
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  3. Jinbo Xu
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  4. Ming Li
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Martin Vingron Limsoon Wong

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Zhang, J., Gao, X., Xu, J., Li, M. (2008). Rapid and Accurate Protein Side Chain Prediction with Local Backbone Information. In: Vingron, M., Wong, L. (eds) Research in Computational Molecular Biology. RECOMB 2008. Lecture Notes in Computer Science(), vol 4955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78839-3_25

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  • DOI: https://doi.org/10.1007/978-3-540-78839-3_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78838-6

  • Online ISBN: 978-3-540-78839-3

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