Sigma-RF: Prediction of the variability of spatial restraints in template-based modeling by random forest

BMC Bioinformatics

Volumn 16, Issue 1, 2015, Pages

  Lee, Juyong ^a,b   Lee, Kiho ^b   Joung, InSuk ^b   Joo, Keehyoung ^b   Brooks, Bernard R ^a   Lee, Jooyoung ^b  

^a NATIONAL INSTITUTES OF HEALTH   (United States)

^b Korea Institute for Advanced Study   (South Korea)

Author keywords

Bioinformatics; Homology modeling; Machine learning; Protein sequence; Protein structure; Protein structure prediction; Random forest; Statistics; Template based modeling

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOINFORMATICS; DECISION TREES; LEARNING SYSTEMS; PROBABILITY DENSITY FUNCTION; PROTEINS; STATISTICS;

HOMOLOGY MODELING; PROTEIN SEQUENCES; PROTEIN STRUCTURE PREDICTION; PROTEIN STRUCTURES; RANDOM FORESTS; TEMPLATE-BASED MODELING;

FORECASTING;

ALGORITHM; ARTIFICIAL INTELLIGENCE; CHEMICAL STRUCTURE; SEQUENCE ALIGNMENT; STATISTICAL MODEL; STRUCTURAL HOMOLOGY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; MODELS, MOLECULAR; MODELS, STATISTICAL; SEQUENCE ALIGNMENT; STRUCTURAL HOMOLOGY, PROTEIN;

EID: 84925402614     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-015-0526-z     Document Type: Article

References (41)

1. Söding J. Protein homology detection by HMM-HMM comparison. Bioinformatics. 2005; 21(7):951-60.
2. Hildebrand A, Remmert M, Biegert A, Söding J. Fast and accurate automatic structure prediction with hhpred. Proteins: Struct, Funct, Bioinf. 2009; 77(S9):128-32.
3. Peng J, Xu J. Boosting protein threading accuracy. In: Research in Computational Molecular Biology. Heidelberg: Springer Berlin: 2009. p. 31-45. http://link.springer.com/chapter/10.1007%2F978-3-642-02008-7_3##.
4. Peng J, Xu J. RaptorX: Exploiting structure information for protein alignment by statistical inference. Proteins: Struct Funct Bioinf. 2011; 79(S10):161-71.
5. Wu S, Zhang Y. MUSTER: improving protein sequence profile-profile alignments by using multiple sources of structure information. Proteins: Struct Funct Bioinf. 2008; 72(2):547-56.
6. Yang Y, Faraggi E, Zhao H, Zhou Y. Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates. Bioinformatics. 2011; 27(15):2076-82.
7. Joo K, Lee J, Kim I, Lee SJ, Lee J. Multiple sequence alignment by conformational space annealing. Bioph J. 2008; 95(10):4813-9.
8. Pei J, Grishin NV. PROMALS: towards accurate multiple sequence alignments of distantly related proteins. Bioinformatics. 2007; 23(7):802-8.
9. Armougom F, Moretti S, Poirot O, Audic S, Dumas P, Schaeli B, et al.Expresso: automatic incorporation of structural information in multiple sequence alignments using 3D-Coffee. Nucleic Acids Res. 2006; 34(suppl 2):604-8.
10. Cozzetto D, Kryshtafovych A, Fidelis K, Moult J, Rost B, Tramontano A. Evaluation of template-based models in CASP8 with standard measures. Proteins: Struct Funct Bioinf. 2009; 77(S9):18-28.
11. Mariani V, Kiefer F, Schmidt T, Haas J, Schwede T. Assessment of template based protein structure predictions in CASP9. Proteins: Struct Funct Bioinf. 2011; 79(S10):37-58.
12. Kryshtafovych A, Fidelis K, Moult J. CASP9 results compared to those of previous casp experiments. Proteins: Struct Funct Bioinf. 2011; 79(S10):196-207.
13. Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A. Critical assessment of methods of protein structure prediction (CASP) - round X. Proteins: Struct Funct Bioinf. 2014; 82:1-6. doi:10.1002/prot.24452.
14. Kryshtafovych A, Moult J, Bales P, Bazan JF, Biasini M, Burgin A, et al.Challenging the state of the art in protein structure prediction: Highlights of experimental target structures for the 10th critical assessment of techniques for protein structure prediction experiment CASP10. Proteins: Struct Funct Bioinf. 2014; 82:26-42. doi:10.1002/prot.24489.
15. Joo K, Lee J, Lee S, Seo JH, Lee SJ, Lee J. High accuracy template based modeling by global optimization. Proteins: Struct Funct Bioinf. 2007; 69(S8):83-9.
16. Sali A, Blundell T. Comparative protein modelling by satisfaction of spatial restraints. Protein Struct Distance Anal. 1994; 64:86.
17. Fiser A, Šali A. Modeller: generation and refinement of homology-based protein structure models. Methods Enzymol. 2003; 374:461-91.
18. Krieger E, Joo K, Lee J, Lee J, Raman S, Thompson J, et al.Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: Four approaches that performed well in CASP8. Proteins: Struct Funct Bioinf. 2009; 77(S9):114-22.
19. Xu J, Peng J, Zhao F. Template-based and free modeling by RAPTOR++ in CASP8. Proteins: Struct Funct Bioinf. 2009; 77(S9):133-7.
20. Joo K, Lee J, Seo JH, Lee K, Kim BG, Lee J. All-atom chain-building by optimizing modeller energy function using conformational space annealing. Proteins: Struct Funct Bioinf. 2009; 75(4):1010-23.
21. Joo K, Lee J, Sim S, Lee SY, Lee K, Heo S, et al.Protein structure modeling for CASP10 by multiple layers of global optimization. Proteins: Struct Funct Bioinf. 2014; 82(Suppl 2(April)):188-95.
22. Thompson J, Baker D. Incorporation of evolutionary information into rosetta comparative modeling. Proteins: Struct Funct Bioinf. 2011; 79(8):2380-8.
23. Breiman L. Random forests. Mach Learn. 2001; 45(1):5-32.
24. Lee J, Lee J. Hidden information revealed by optimal community structure from a protein-complex bipartite network improves protein function prediction. PLoS ONE. 2013; 8(4):60372.
25. Lee J, Gross SP, Lee J. Improved network community structure improves function prediction. Sci Rep. 2013; 3:2197.
26. Ziegler A, König IR. Mining data with random forests: current options for real-world applications. Wiley Interdiscip Rev: Data Min Knowl Discov. 2014; 4(1):55-63.
27. Manavalan B, Lee J, Lee J. Random forest-based protein model quality assessment (RFMQA) using structural features and potential energy terms. PLoS ONE. 2014; 9(9):106542.
28. Caruana R, Karampatziakis N, Yessenalina A. An empirical evaluation of supervised learning in high dimensions. In: Proceedings of the 25th International Conference on Machine Learning. ICML '08. New York, NY, USA: ACM: 2008. p. 96-103.
29. Zhang Y, Skolnick J. Scoring function for automated assessment of protein structure template quality. Proteins. 2004; 57(4):702-10.
30. Mariani V, Biasini M, Barbato A, Schwede T. lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests. Bioinformatics (Oxford, England). 2013; 29(21):2722-8.
31. Wang G, Dunbrack RL. PISCES: a protein sequence culling server. Bioinformatics. 2003; 19(12):1589-91.
32. Kopp J, Bordoli L, Battey JND, Kiefer F, Schwede T. Assessment of CASP7 predictions for template-based modeling targets. Proteins: Struct Funct Bioinf. 2007; 69(S8):38-56.
33. Petersen TN, Lundegaard C, Nielsen M, Bohr H, Bohr J, Brunak S, et al.Prediction of protein secondary structure at 80% accuracy. Proteins: Struct Funct Bioinf. 2000; 41(1):17-20.
34. Joo K, Lee SJ, Lee J. SANN: solvent accessibility prediction of proteins by nearest neighbor method. Proteins: Struct Funct Bioinf. 2012; 80(7):1791-7.
35. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees. Statistics/Probability Series. Belmont, California, USA: Wadsworth Publishing Company; 1984.
36. Quinlan JR. Induction of decision trees. Mach Learn. 1986; 1(1):81-106.
37. Fiser A, Do RKG, Sali A. Modeling of loops in protein structures. Protein Sci. 2000; 9(9):1753-73. doi:10.1110/ps.9.9.1753.
38. Pastore A, Atkinson RA, Saudek V, Williams RJ. Topological mirror images in protein structure computation: an underestimated problem. Proteins. 1991; 10(1):22-32.
39. Liwo A, Lee J, Ripoll DR, Pillardy J, Scheraga HA. Protein structure prediction by global optimization of a potential energy function. Proc Nat Acad Sci USA. 1999; 96(10):5482-5.
40. Kihara D, Lu H, Kolinski A, Skolnick J. TOUCHSTONE: an ab initio protein structure prediction method that uses threading-based tertiary restraints. Proc Nat Acad Sci USA. 2001; 98(18):10125-30.
41. Zhang Y. I-TASSER: fully automated protein structure prediction in CASP8. Proteins. 2009; 77(Suppl 9(August)):100-13. doi:10.1002/prot.22588.