Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12

Proteins: Structure, Function and Bioinformatics

Volumn 86, Issue , 2018, Pages 136-151

  Zhang, Chengxin ^a   Mortuza, S M ^a   He, Baoji ^a,b   Wang, Yanting ^b   Zhang, Yang ^a  

^a UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

^b INSTITUTE OF THEORETICAL PHYSICS   (China)

Author keywords

ab initio folding; CASP12; contact prediction; protein structure prediction; residue quality estimation; threading

Indexed keywords

PROTEIN;

AB INITIO CALCULATION; ANALYTICAL ERROR; ARTICLE; COMPUTER MODEL; COMPUTER SIMULATION; CONTROLLED STUDY; FREE MODEL; HUMAN; I TASSER SOFTWARE; INTERNET; PREDICTION; PRIORITY JOURNAL; PROTEIN FOLDING; PROTEIN PROTEIN INTERACTION; PROTEIN STRUCTURE; QUARK SOFTWARE; SOFTWARE; STRUCTURAL BIOINFORMATICS; TEMPLATE BASED MODEL; ALGORITHM; BIOLOGY; CHEMISTRY; MACHINE LEARNING; MOLECULAR MODEL; PROCEDURES; PROTEIN CONFORMATION; PROTEIN DATABASE; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS; X RAY CRYSTALLOGRAPHY;

ALGORITHMS; COMPUTATIONAL BIOLOGY; CRYSTALLOGRAPHY, X-RAY; DATABASES, PROTEIN; HUMANS; MACHINE LEARNING; MODELS, MOLECULAR; PROTEIN CONFORMATION; PROTEIN FOLDING; PROTEINS; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, PROTEIN;

EID: 85033794178     PISSN: 08873585     EISSN: 10970134     Source Type: Journal    
DOI: 10.1002/prot.25414     Document Type: Article

References (52)

1. Mariani V, Kiefer F, Schmidt T, Haas J, Schwede T. Assessment of template based protein structure predictions in CASP9. Proteins. 2011;79(S10):37–58.
2. Huang YJP, Mao BC, Aramini JM, Montelione GT. Assessment of template-based protein structure predictions in CASP10. Proteins. 2014;82:43–56.
3. Modi V, Xu QF, Adhikari S, Dunbrack RL. Assessment of template-based modeling of protein structure in CASP11. Proteins. 2016;84:200–220.
4. Zhang Y. Progress and challenges in protein structure prediction. Curr Opin Struct Biol. 2008;18(3):342–348.
5. Kinch LN, Li WL, Monastyrskyy B, Kryshtafovych A, Grishin NV. Evaluation of free modeling targets in CASP11 and ROLL. Proteins. 2016;84:51–66.
6. Monastyrskyy B, D'andrea D, Fidelis K, Tramontano A, Kryshtafovych A. New encouraging developments in contact prediction: assessment of the CASP11 results. Proteins. 2016;84:131–144.
7. Wu ST, Szilagyi A, Zhang Y. Improving protein structure prediction using multiple sequence-based contact predictions. Structure. 2011;19(8):1182–1191.
8. Ovchinnikov S, Kim DE, Wang RYR, Liu Y, DiMaio F, Baker D. Improved de novo structure prediction in CASP11 by incorporating coevolution information into Rosetta. Proteins. 2016;84:67–75.
9. Roy A, Kucukural A, Zhang Y. I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc. 2010;5(4):725–738.
10. Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y. The I-TASSER Suite: protein structure and function prediction. Nat Methods. 2015;12(1):7–8.
11. Xu D, Zhang Y. Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field. Proteins. 2012;80(7):1715–1735.
12. Xu D, Zhang Y. Toward optimal fragment generations for ab initio protein structure assembly. Proteins. 2013;81(2):229–239.
13. Zhang Y. Interplay of I-TASSER and QUARK for template-based and ab initio protein structure prediction in CASP10. Proteins. 2014;82:175–187.
14. Zhang WX, Yang JY, He BJ, et al. Integration of QUARK and I-TASSER for ab initio protein structure prediction in CASP11. Proteins. 2016;84:76–86.
15. Wu ST, Zhang Y. LOMETS: A local meta-threading-server for protein structure prediction. Nucleic Acids Res. 2007;35(10):3375–3382.
16. Yang JY, Zhang WX, He BJ, et al. Template-based protein structure prediction in CASP11 and retrospect of I-TASSER in the last decade. Proteins. 2016;84:233–246.
17. Kinch L, Shi SY, Cong Q, Cheng H, Liao YX, Grishin NV. CASP9 assessment of free modeling target predictions. Proteins. 2011;79(S10):59–73.
18. Tai CH, Bai HJ, Taylor TJ, Lee B. Assessment of template-free modeling in CASP10 and ROLL. Proteins. 2014;82:57–83.
19. Weigt M, White RA, Szurmant H, Hoch JA, Hwa T. Identification of direct residue contacts in protein-protein interaction by message passing. Proc Natl Acad Sci U S A. 2009;106(1):67–72.
20. Jones DT, Buchan DWA, Cozzetto D, Pontil M. PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. Bioinformatics. 2012;28(2):184–190.
21. Kamisetty H, Ovchinnikov S, Baker D. Assessing the utility of coevolution-based residue-residue contact predictions in a sequence- and structure-rich era. Proc Natl Acad Sci U S A. 2013;110(39):15674–15679.
22. Cheng JL, Baldi P. Three-stage prediction of protein beta-sheets by neural networks, alignments and graph algorithms. Bioinformatics. 2005;21(Suppl 1):I75–I84.
23. Wu S, Zhang Y. A comprehensive assessment of sequence-based and template-based methods for protein contact prediction. Bioinformatics. 2008;24(7):924–931.
24. Kosciolek T, Jones DT, Deane CM. De novo structure prediction of globular proteins aided by sequence variation-derived contacts. PLoS One. 2014;9(3):e92197
25. He B, Mortuza SM, Wang Y, Shen HB, Zhang Y. NeBcon: protein contact map prediction using neural network training coupled with naive Bayes classifiers. Bioinformatics. 2017;33(15):2296–2306.
26. Zhang Y. Protein structure prediction: when is it useful?. Curr Opin Struct Biol. 2009;19(2):145–155.
27. Yang J, Wang Y, Zhang Y. ResQ: an approach to unified estimation of B-factor and residue-specific error in protein structure prediction. J Mol Biol. 2016;428(4):693–701.
28. Xue Z, Xu D, Wang Y, Zhang Y. ThreaDom: extracting protein domain boundary information from multiple threading alignments. Bioinformatics. 2013;29(13):i247–i256.
29. Zhang Y, Skolnick J. SPICKER: a clustering approach to identify near-native protein folds. J Comput Chem. 2004;25(6):865–871.
30. Xu D, Zhang Y. Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophys J. 2011;101(10):2525–2534.
31. Zhang J, Liang Y, Zhang Y. Atomic-level protein structure refinement using fragment-guided molecular dynamics conformation sampling. Structure. 2011;19(12):1784–1795.
32. Zhang Y, Kihara D, Skolnick J. Local energy landscape flattening: parallel hyperbolic Monte Carlo sampling of protein folding. Proteins. 2002;48(2):192–201.
33. Zhang Y, Kolinski A, Skolnick J. TOUCHSTONE II: a new approach to ab initio protein structure prediction. Biophys J. 2003;85(2):1145–1164.
34. Wu ST, Skolnick J, Zhang Y. Ab initio modeling of small proteins by iterative TASSER simulations. BMC Biol. 2007;5(1):17
35. Zhang Y, Skolnick J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res. 2005;33(7):2302–2309.
36. Li YQ, Zhang Y. REMO: a new protocol to refine full atomic protein models from C-alpha traces by optimizing hydrogen-bonding networks. Proteins. 2009;76(3):665–674.
37. Zhang Y. I-TASSER server for protein 3D structure prediction. BMC Bioinformatics. 2008;9(1):1:40
38. Zhang J, Zhang Y, Fernandez-Fuentes N. A novel side-chain orientation dependent potential derived from random-walk reference state for protein fold selection and structure prediction. PLoS One. 2010;5(10):e15386
39. Zhou HY, Skolnick J. GOAP: a generalized orientation-dependent, all-atom statistical potential for protein structure prediction. Biophys J. 2011;101(8):2043–2052.
40. Shen MY, Sali A. Statistical potential for assessment and prediction of protein structures. Protein Sci. 2006;15(11):2507–2524.
41. Zhang Y, Skolnick J. Scoring function for automated assessment of protein structure template quality. Proteins. 2004;57(4):702–710.
42. Cheng JL, Baldi P. Improved residue contact prediction using support vector machines and a large feature set. BMC Bioinformatics. 2007;8(1):113
43. Seemayer S, Gruber M, Söding J. CCMpred—fast and precise prediction of protein residue-residue contacts from correlated mutations. Bioinformatics. 2014;30(21):3128–3130.
44. Kaján L, Hopf TA, Kalaš M, Marks DS, Rost B. FreeContact: fast and free software for protein contact prediction from residue co-evolution. BMC Bioinformatics. 2014;15(1):1:85
45. Yang J, Shen H-B. An ensemble predictor by fusing multiple base predictors composed by both coevolution-based and machine learning-based approaches. Abstract of CASP11 experiment. http://www.predictioncenter.org/casp11/doc/CASP11_Abstracts.pdf; 2014. p 209–210.
46. Jones DT, Singh T, Kosciolek T, Tetchner S. MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins. Bioinformatics. 2015;31(7):999–1006.
47. Yan RX, Xu D, Yang JY, Walker S, Zhang Y. A comparative assessment and analysis of 20 representative sequence alignment methods for protein structure prediction. Sci Rep. 2013;3(1):
48. Kryshtafovych A, Barbato A, Monastyrskyy B, Fidelis K, Schwede T, Tramontano A. Methods of model accuracy estimation can help selecting the best models from decoy sets: assessment of model accuracy estimations in CASP11. Proteins. 2016;84:349–369.
49. Zhang Y. I-TASSER: Fully automated protein structure prediction in CASP8. Proteins. 2009;77(S9):100–113.
50. Wang S, Peng J, Ma J, Xu J. Protein secondary structure prediction using deep convolutional neural fields. Sci Rep. 2016;6(1):
51. Magnan CN, Baldi P. SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity. Bioinformatics. 2014;30(18):2592–2597.
52. Towns J, Cockerill T, Dahan M, et al. XSEDE: accelerating scientific discovery. Comput Sci Eng. 2014;16(5):62–74.