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. 2003 Jul;4(4):397–401. doi: 10.1002/cfg.305

Ab Initio Protein Structure Prediction Using Pathway Models

Xin Yuan 1, Yu Shao 1, Christopher Bystroff 1,
PMCID: PMC2447365  PMID: 18629080

Abstract

Ab initio prediction is the challenging attempt to predict protein structures based only on sequence information and without using templates. It is often divided into two distinct sub-problems: (a) the scoring function that can distinguish native, or native-like structures, from non-native ones; and (b) the method of searching the conformational space. Currently, there is no reliable scoring function that can always drive a search to the native fold, and there is no general search method that can guarantee a significant sampling of near-natives. Pathway models combine the scoring function and the search. In this short review, we explore some of the ways pathway models are used in folding, in published works since 2001, and present a new pathway model, HMMSTR-CM, that uses a fragment library and a set of nucleation/propagation-based rules. The new method was used for ab initio predictions as part of CASP5. This work was presented at the Winter School in Bioinformatics, Bologna, Italy, 10–14 February 2003.

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Selected References

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  1. Bonneau Richard, Strauss Charlie E. M., Rohl Carol A., Chivian Dylan, Bradley Phillip, Malmström Lars, Robertson Tim, Baker David. De novo prediction of three-dimensional structures for major protein families. J Mol Biol. 2002 Sep 6;322(1):65–78. doi: 10.1016/s0022-2836(02)00698-8. [DOI] [PubMed] [Google Scholar]
  2. Brünger A. T., Clore G. M., Gronenborn A. M., Karplus M. Three-dimensional structure of proteins determined by molecular dynamics with interproton distance restraints: application to crambin. Proc Natl Acad Sci U S A. 1986 Jun;83(11):3801–3805. doi: 10.1073/pnas.83.11.3801. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bystroff C., Thorsson V., Baker D. HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins. J Mol Biol. 2000 Aug 4;301(1):173–190. doi: 10.1006/jmbi.2000.3837. [DOI] [PubMed] [Google Scholar]
  4. Bystroff Christopher, Shao Yu. Fully automated ab initio protein structure prediction using I-SITES, HMMSTR and ROSETTA. Bioinformatics. 2002;18 (Suppl 1):S54–S61. doi: 10.1093/bioinformatics/18.suppl_1.s54. [DOI] [PubMed] [Google Scholar]
  5. Crivelli Silvia, Eskow Elizabeth, Bader Brett, Lamberti Vincent, Byrd Richard, Schnabel Robert, Head-Gordon Teresa. A physical approach to protein structure prediction. Biophys J. 2002 Jan;82(1 Pt 1):36–49. doi: 10.1016/S0006-3495(02)75372-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Fariselli P., Olmea O., Valencia A., Casadio R. Progress in predicting inter-residue contacts of proteins with neural networks and correlated mutations. Proteins. 2001;Suppl 5:157–162. doi: 10.1002/prot.1173. [DOI] [PubMed] [Google Scholar]
  7. Gibbs N., Clarke A. R., Sessions R. B. Ab initio protein structure prediction using physicochemical potentials and a simplified off-lattice model. Proteins. 2001 May 1;43(2):186–202. doi: 10.1002/1097-0134(20010501)43:2<186::aid-prot1030>3.0.co;2-l. [DOI] [PubMed] [Google Scholar]
  8. Jones D. T. Predicting novel protein folds by using FRAGFOLD. Proteins. 2001;Suppl 5:127–132. doi: 10.1002/prot.1171. [DOI] [PubMed] [Google Scholar]
  9. Kolodny Rachel, Koehl Patrice, Guibas Leonidas, Levitt Michael. Small libraries of protein fragments model native protein structures accurately. J Mol Biol. 2002 Oct 18;323(2):297–307. doi: 10.1016/s0022-2836(02)00942-7. [DOI] [PubMed] [Google Scholar]
  10. Liu Yongxing, Beveridge D. L. Exploratory studies of ab initio protein structure prediction: multiple copy simulated annealing, AMBER energy functions, and a generalized born/solvent accessibility solvation model. Proteins. 2002 Jan 1;46(1):128–146. doi: 10.1002/prot.10020. [DOI] [PubMed] [Google Scholar]
  11. Olmea O., Rost B., Valencia A. Effective use of sequence correlation and conservation in fold recognition. J Mol Biol. 1999 Nov 12;293(5):1221–1239. doi: 10.1006/jmbi.1999.3208. [DOI] [PubMed] [Google Scholar]
  12. Pokarowski Piotr, Kolinski Andrzej, Skolnick Jeffrey. A minimal physically realistic protein-like lattice model: designing an energy landscape that ensures all-or-none folding to a unique native state. Biophys J. 2003 Mar;84(3):1518–1526. doi: 10.1016/S0006-3495(03)74964-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Pollastri G., Baldi P. Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners. Bioinformatics. 2002;18 (Suppl 1):S62–S70. doi: 10.1093/bioinformatics/18.suppl_1.s62. [DOI] [PubMed] [Google Scholar]
  14. Saunders J. A., Gibson K. D., Scheraga H. A. Ab initio folding of multiple-chain proteins. Pac Symp Biocomput. 2002:601–612. doi: 10.1142/9789812799623_0056. [DOI] [PubMed] [Google Scholar]
  15. Srinivasan Rajgopal, Rose George D. Ab initio prediction of protein structure using LINUS. Proteins. 2002 Jun 1;47(4):489–495. doi: 10.1002/prot.10103. [DOI] [PubMed] [Google Scholar]
  16. Vendruscolo M., Kussell E., Domany E. Recovery of protein structure from contact maps. Fold Des. 1997;2(5):295–306. doi: 10.1016/S1359-0278(97)00041-2. [DOI] [PubMed] [Google Scholar]
  17. Zhang C., Kim S. H. Environment-dependent residue contact energies for proteins. Proc Natl Acad Sci U S A. 2000 Mar 14;97(6):2550–2555. doi: 10.1073/pnas.040573597. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Zhang Yang, Kihara Daisuke, Skolnick Jeffrey. Local energy landscape flattening: parallel hyperbolic Monte Carlo sampling of protein folding. Proteins. 2002 Aug 1;48(2):192–201. doi: 10.1002/prot.10141. [DOI] [PubMed] [Google Scholar]

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