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ITScorePro: An Efficient Scoring Program for Evaluating the Energy Scores of Protein Structures for Structure Prediction

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Protein Structure Prediction

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

Abstract

One important component in protein structure prediction is to evaluate the free energy of a given conformation. Given the enormous number of possible conformations for a sequence, it is extremely challenging to quickly and accurately score the energies of these conformations and predict a reasonable structure within a practical computational time. Here, we describe an efficient program for energy evaluation, referred to as ITScorePro (Copyright © 2012). The energy scoring function in the ITScorePro program is based on the distance-dependent, pairwise atomic potentials for protein structure prediction that we recently derived by using statistical mechanics principles (Huang and Zou, Proteins 79:2648–2661, 2011). ITScorePro is a stand-alone program and can also be easily implemented in other software suites for protein structure prediction.

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References

  1. Lazaridis T, Karplus M (2000) Effective energy functions for protein structure prediction. Curr Opin Struct Biol 10: 139–145

    Article  CAS  PubMed  Google Scholar 

  2. Buchete NV, Straub JE, Thirumalai D (2004) Development of novel statistical potentials for protein fold recognition. Curr Opin Struct Biol 14:225–232

    Article  CAS  PubMed  Google Scholar 

  3. Skolnick J (2006) In quest of an empirical potential for protein structure prediction. Curr Opin Struct Biol 16:166–171

    Article  CAS  PubMed  Google Scholar 

  4. Zhang Y (2008) Progress and challenges in protein structure prediction. Curr Opin Struct Biol 18:342–348

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  5. Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M (1983) CHARMM: a program for macromolecular energy minimization and dynamic calculations. J Comput Chem 4:187–217

    Article  CAS  Google Scholar 

  6. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general Amber force field. J Comput Chem 25:1157–1174

    Article  CAS  PubMed  Google Scholar 

  7. Case DA, Cheatham TE 3rd, Darden T, Gohlke H, Luo R, Merz KM Jr, Onufriev A, Simmerling C, Wang B, Woods RJ (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  8. Wroblewska L, Skolnick J (2007) Can a physics-based, all-atom potential find a protein’s native structure among misfolded structures? I. Large scale AMBER benchmarking. J Comput Chem 28:2059–2066

    Article  CAS  PubMed  Google Scholar 

  9. Jagielska A, Wroblewska L, Skolnick J (2008) Protein model refinement using an optimized physics-based all-atom force field. Proc Natl Acad Sci USA 105:8268–8273

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  10. Tanaka S, Scheraga HA (1976) Medium-and long-range interaction parameters between amino acids for predicting three-dimensional structures of proteins. Macromolecules 9: 945–950

    Article  CAS  PubMed  Google Scholar 

  11. Miyazawa S, Jernigan RL (1985) Estimation of effective interresidue contact energies from protein crystal structures: quasi-chemical approximation. Macromolecules 18:534–552

    Article  CAS  Google Scholar 

  12. Sippl MJ (1990) Calculation of conformational ensembles from potentials of mean force. An approach to the knowledge-based prediction of local structures in globular proteins. J Mol Biol 213:859–883

    Article  CAS  PubMed  Google Scholar 

  13. Maiorov VN, Crippen GM (1992) Contact potential that recognizes the correct folding of globular proteins. J Mol Biol 227:876–888

    Article  CAS  PubMed  Google Scholar 

  14. Tobi D, Elber R (2000) Distance-dependent, pair potential for protein folding: results from linear optimization. Proteins 41:40–46

    Article  CAS  PubMed  Google Scholar 

  15. Qiu J, Elber R (2005) Atomically detailed potentials to recognize native and approximate protein structures. Proteins 61:44–55

    Article  CAS  PubMed  Google Scholar 

  16. Rajgaria R, McAllister SR, Floudas CA (2008) Distance dependent centroid to centroid force.elds using high resolution decoys. Proteins 70: 950–970

    Article  CAS  PubMed  Google Scholar 

  17. Hao MH, Scheraga HA (1996) How optimization of potential function affects protein folding. Proc Natl Acad Sci USA 93:4984–4989

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  18. Bastolla U, Vendruscolo M, Knapp EW (2000) A statistical mechanical method to optimize energy functions for protein folding. Proc Natl Acad Sci USA 97:3977–3981

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  19. Mimy LA, Shakhnovich EI (1996) How to derive a protein folding potential? A new approach to an old problem. J Mol Biol 264:1164–1179

    Article  Google Scholar 

  20. Huber T, Torda AE (1998) Protein fold recognition without Boltzmann statistics or explicit physical basis. Protein Sci 7:142–149

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  21. Koretke KK, Luthey-Schulten Z, Wolynes PG (1998) Self-consistently optimized energy functions for protein structure prediction by molecular dynamics. Proc Natl Acad Sci USA 95:2932–2937

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  22. Thomas PD, Dill KA (1996) Statistical potentials extracted from protein structures: how accurate are they? J Mol Biol 257:457–469

    Article  CAS  PubMed  Google Scholar 

  23. Koppensteiner WA, Sippl MJ (1998) Knowledge-based potentials—back to the roots. Biochemistry (Mosc) 63:247–252

    CAS  Google Scholar 

  24. Thomas PD, Dill KA (1996) An iterative method for extracting energy-like quantities from protein structures. Proc Natl Acad Sci USA 93:11628–11633

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  25. McQuarrie DA (2000) Statistical mechanics, University Science Books, Sausalito, California

    Google Scholar 

  26. Huang S-Y, Zou X (2010) Mean-force scoring functions for protein-ligand binding. Annu Rep Comput Chem 6:281–296

    CAS  Google Scholar 

  27. Zhou H, Zhou Y (2002) Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Sci 11:2714–2726

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  28. Shen M-Y, Sali A (2006) Statistical potential for assessment and prediction of protein structures. Protein Sci 15:2507–2524

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  29. Huang S-Y, Zou X (2011) Statistical mechanics-based method to extract atomic distance-dependent potentials from protein structures. Proteins 79:2648–2661

    Article  CAS  PubMed  Google Scholar 

  30. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The Protein Data Bank. Nucleic Acids Res 28:235–242

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  31. Huang S-Y, Zou X (2008) An iterative knowledge-based scoring function for protein-protein recognition. Proteins 72:557–579

    Article  CAS  PubMed  Google Scholar 

  32. Yang Y, Zhou Y (2008) Ab initio folding of terminal segments with secondary structures reveals the.ne difference between two closely related all-atom statistical energy functions. Protein Sci 17:1212–1219

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  33. Yang Y, Zhou Y (2008) Specific interactions for ab initio folding of protein terminal regions with secondary structures. Proteins 72: 793–803

    Article  CAS  PubMed  Google Scholar 

  34. Rajgaria R, McAllister SR, Floudas CA (2006) Development of a novel high resolution Ca-Ca distance dependent force field using a high quality decoy set. Proteins 65:726–741

    Article  CAS  PubMed  Google Scholar 

  35. Hinds DA, Levitt M (1994) Exploring conformational space with a simple lattice model for protein structure. J Mol Biol 243: 668–682

    Article  CAS  PubMed  Google Scholar 

  36. Loose C, Klepeis JL, Floudas CA (2004) A new pairwise folding potential based on improved decoy generation and side-chain packing. Proteins 54:303–314

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

X.Z. is supported by NIH grant R21GM088517, NSF CAREER Award DBI-0953839, the Research Board Award RB-07-32 and the Research Council Grant URC 09-004 of the University of Missouri. The computations were performed on the HPC resources at the University of Missouri Bioinformatics Consortium (UMBC).

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Authors and Affiliations

  1. Department of Physics and Astronomy, Dalton Cardiovascular Research Center, Informatics Institute, University of Missouri, Columbia, MO, USA

    Sheng-You Huang & Xiaoqin Zou

  2. Department of Biochemistry, Dalton Cardiovascular Research Center, Informatics Institute, University of Missouri, Columbia, MO, USA

    Sheng-You Huang & Xiaoqin Zou

Authors
  1. Sheng-You Huang
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  2. Xiaoqin Zou
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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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Huang, SY., Zou, X. (2014). ITScorePro: An Efficient Scoring Program for Evaluating the Energy Scores of Protein Structures for Structure Prediction. 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_6

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  • DOI: https://doi.org/10.1007/978-1-4939-0366-5_6

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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

  • eBook Packages: Springer Protocols

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