High-accuracy prediction of transmembrane inter-helix contacts and application to GPCR 3D structure modeling

Bioinformatics

Volumn 29, Issue 20, 2013, Pages 2579-2587

  Yang, Jing ^a,b   Jang, Richard ^c   Zhang, Yang ^c   Shen, Hong Bin ^a,b,c  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^c UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

G PROTEIN COUPLED RECEPTOR;

AMINO ACID SEQUENCE; ARTICLE; CHEMICAL STRUCTURE; CHEMISTRY; COMPUTER PROGRAM; HUMAN; INTERNET; PROTEIN DATABASE; PROTEIN SECONDARY STRUCTURE; PROTEIN TERTIARY STRUCTURE;

AMINO ACID SEQUENCE; DATABASES, PROTEIN; HUMANS; INTERNET; MODELS, MOLECULAR; PROTEIN STRUCTURE, SECONDARY; PROTEIN STRUCTURE, TERTIARY; RECEPTORS, G-PROTEIN-COUPLED; SOFTWARE;

EID: 84885667789     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btt440     Document Type: Article

References (41)

1. Adamian, L. and Liang, J. (2006) Prediction of transmembrane helix orientation in polytopic membrane proteins. BMC Struct. Biol., 6, 13
2. Altschul, S.F. et al. (1997) Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res., 25, 3389-3402
3. Burger, L. and van Nimwegen, E. (2010) Disentangling direct from indirect coevolution of residues in protein alignments. PLoS Comput. Biol., 6, e1000633
4. Cheng, J. and Baldi, P. (2007) Improved residue contact prediction using support vector machines and a large feature set. BMC Bioinformatics, 8, 113
5. Di Lena, P. et al. (2012) Deep architectures for protein contact map prediction. Bioinformatics, 28, 2449-2457
6. Ding, X.R. and Cai, M.K. (1995) Matrix Theory in Engineering. Tianjin University Press, Tianjin
7. Dunn, S.D. et al. (2008) Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction. Bioinformatics, 24, 333-340
8. Elofsson, A. and von Heijne, G. (2007) Membrane protein structure: Prediction versus reality. Annu. Rev. Biochem., 76, 125-140
9. Fuchs, A. et al. (2007) Co-evolving residues in membrane proteins. Bioinformatics, 23, 3312-3319
10. Fuchs, A. et al. (2009) Prediction of helix-helix contacts and interacting helices in polytopic membrane proteins using neural networks. Proteins, 74, 857-871
11. Fukunaga, K. (1990) Introduction to Statistical Pattern Recognition. Academic Press, San Diego
12. Gobel, U. et al. (1994) Correlated mutations and residue contacts in proteins. Proteins, 18, 309-317
13. He, H.B. and Garcia, E.A. (2009) Learning from imbalanced data. IEEE Trans. Knowl. Data Eng., 21, 1263-1284
14. Hohl, M. et al. (2012) Crystal structure of a heterodimeric ABC transporter in its inward-facing conformation. Nat. Struct. Biol., 19, 395-402
15. Hopkins, A.L. and Groom, C.R. (2002) The druggable genome. Nat. Rev. Drug Discov., 1, 727-730
16. Jones, D.T. et al. (1994) A model recognition approach to the prediction of all-helical membrane protein structure and topology. Biochemistry, 33, 3038-3049
17. Jones, D.T. et al. (2012) PSICOV: Precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. Bioinformatics, 28, 184-190
18. Kato, H.E. et al. (2012) Crystal structure of the channelrhodopsin light-gated cation channel. Nature, 482, 369-374
19. Krogh, A. et al. (2001) Predicting transmembrane protein topology with a hidden Markov model: Application to complete genomes. J. Mol. Biol., 305, 567-580
20. Lo, A. et al. (2009) Predicting helix-helix interactions from residue contacts in membrane proteins. Bioinformatics, 25, 996-1003
21. Lomize, M.A. et al. (2006) OPM: Orientations of proteins in membranes database. Bioinformatics, 22, 623-625
22. Meinshausen, N. and Buhlmann, P. (2006) High-dimensional graphs and variable selection with the lasso. Ann. Stat., 34, 1436-1462
23. Nugent, T. and Jones, D.T. (2009) Transmembrane protein topology prediction using support vector machines. BMC Bioinformatics, 10, 159
24. Nugent, T. and Jones, D.T. (2010) Predicting transmembrane helix packing arrangements using residue contacts and a force-directed algorithm. PLoS Comput. Biol., 6, e1000714
25. Ortiz, A.R. et al. (1999) Ab initio folding of proteins using restraints derived from evolutionary information. Proteins, 37, 177-185
26. Overington, J.P. et al. (2006) How many drug targets are there? Nat. Rev. Drug Discov., 5, 993-996
27. Pollock, D.D. and Taylor, W.R. (1997) Effectiveness of correlation analysis in identifying protein residues undergoing correlated evolution. Protein Eng., 10, 647-657
28. Roy, A. et al. (2010) I-TASSER: A unified platform for automated protein structure and function prediction. Nat. Protoc., 5, 725-738
29. Shen, H.B. and Chou, J.J. (2008) MemBrain: Improving the accuracy of predicting transmembrane helices. PLoS One, 3, e2399
30. Shen, H.B. and Chou, K.C. (2006) Ensemble classifier for protein fold pattern recognition. Bioinformatics, 22, 1717-1722
31. Tusnady, G.E. et al. (2005) PDB-TM: Selection and membrane localization of transmembrane proteins in the protein data bank. Nucleic Acids Res., 33, D275-D278
32. Tusnady, G.E. et al. (2008) TOPDB: Topology data bank of transmembrane proteins. Nucleic Acids Res., 36, D234-D239
33. Wang, X.F. et al. (2011) Predicting residue-residue contacts and helix-helix interactions in transmembrane proteins using an integrative feature-based random forest approach. PLoS One, 6, e26767
34. White, S.H. (2003) Translocons, thermodynamics, and the folding of membrane proteins. FEBS Lett., 555, 116-121
35. Wu, S. et al. (2011) Improving protein structure prediction using multiple sequencebased contact predictions. Structure, 19, 1182-1191
36. Wu, S. and Zhang, Y. (2008) A comprehensive assessment of sequence-based and template-based methods for protein contact prediction. Bioinformatics, 24, 924-931
37. Xu, D. et al. (2011) Automated protein structure modeling in CASP9 by I-TASSER pipeline combined with QUARK-based ab initio folding and FG-MD-based structure refinement. Proteins, 79, 147-160
38. Yang, J. et al. (2003) Feature fusion: Parallel strategy vs. serial strategy. Pattern Recognit., 36, 1369-1381
39. Yarov-Yarovoy, V. et al. (2006) Multipass membrane protein structure prediction using Rosetta. Proteins, 62, 1010-1025
40. Zhou, Z.H. and Yu, Y. (2005) Ensembling local learners through multimodal perturbation. IEEE Trans. Syst. Man Cybern., 35, 725-735
41. Zouhal, L.M. and Denoeux, T. (1998) An evidence-Theoretic k-NN rule with parameter optimization. IEEE Trans. Syst. Man Cybern., 28, 263-271