Protein structure prediction and model quality assessment

Drug Discovery Today

Volumn 14, Issue 7-8, 2009, Pages 386-393

  Kryshtafovych, Andriy ^a   Fidelis, Krzysztof ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CASPASE 5; CASPASE 6; CASPASE 7;

AMINO ACID SEQUENCE; COMPUTER MODEL; COMPUTER PREDICTION; MEDICAL RESEARCH; PROTEIN STRUCTURE; QUALITY CONTROL; REVIEW; THEORETICAL MODEL;

ALGORITHMS; AMINO ACID SEQUENCE; ANIMALS; COMPUTATIONAL BIOLOGY; MODELS, MOLECULAR; MOLECULAR SEQUENCE DATA; PROTEIN CONFORMATION; PROTEINS; SEQUENCE ALIGNMENT;

EID: 62849107262     PISSN: 13596446     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.drudis.2008.11.010     Document Type: Review

References (65)

1. Sela M., et al. Reductive cleavage of disulfide bridges in ribonuclease. Science 125 (1957) 691-692
2. Ginalski K., et al. Practical lessons from protein structure prediction. Nucleic Acids Res. 33 (2005) 1874-1891
3. Ginalski K. Comparative modeling for protein structure prediction. Curr. Opin. Struct. Biol. 16 (2006) 172-177
4. Schueler-Furman O., et al. Progress in modeling of protein structures and interactions. Science 310 (2005) 638-642
5. Bujnicki J.M. Protein-structure prediction by recombination of fragments. Chembiochem 7 (2006) 19-27
6. Dunbrack Jr. R.L. Sequence comparison and protein structure prediction. Curr. Opin. Struct. Biol. 16 (2006) 374-384
7. Nayeem A., et al. A comparative study of available software for high-accuracy homology modeling: from sequence alignments to structural models. Protein Sci. 15 (2006) 808-824
8. Floudas C.A. Computational methods in protein structure prediction. Biotechnol. Bioeng. 97 (2007) 207-213
9. Zhang Y. Progress and challenges in protein structure prediction. Curr. Opin. Struct. Biol. 18 (2008) 342-348
10. Moult J. Rigorous performance evaluation in protein structure modelling and implications for computational biology. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 361 (2006) 453-458
11. Moult J. Comparative modeling in structural genomics. Structure 16 (2008) 14-16
12. Das R., and Baker D. Macromolecular modeling with rosetta. Annu. Rev. Biochem. 77 (2008) 363-382
13. Cozzetto D., et al. The evaluation of protein structure prediction results. Mol. Biotechnol. 39 (2008) 1-8
14. Tramontano A., et al. The assessment of methods for protein structure prediction. Methods Mol. Biol. 413 (2008) 43-57
15. Moult J., et al. Critical assessment of methods of protein structure prediction-Round VII. Proteins 69 Suppl. 8 (2007) 3-9
16. Kryshtafovych A., et al. New tools and expanded data analysis capabilities at the Protein Structure Prediction Center. Proteins 69 Suppl. 8 (2007) 19-26
17. Chothia C., and Lesk A.M. The relation between the divergence of sequence and structure in proteins. EMBO J. 5 (1986) 823-826
18. Yooseph S., et al. The Sorcerer II Global Ocean Sampling expedition: expanding the universe of protein families. PLoS Biol. 5 (2007) e16
19. Wu S., and Zhang Y. MUSTER: improving protein sequence profile-profile alignments by using multiple sources of structure information. Proteins 72 (2008) 547-556
20. Kryshtafovych A., et al. Progress over the first decade of CASP experiments. Proteins 61 Suppl. 7 (2005) 225-236
21. Kryshtafovych A., et al. Progress from CASP6 to CASP7. Proteins 69 Suppl. 8 (2007) 194-207
22. Kopp J., et al. Assessment of CASP7 predictions for template-based modeling targets. Proteins 69 Suppl. 8 (2007) 38-56
23. Zhang Y. Template-based modeling and free modeling by I-TASSER in CASP7. Proteins 69 Suppl. 8 (2007) 108-117
24. Das R., et al. Structure prediction for CASP7 targets using extensive all-atom refinement with Rosetta@home. Proteins 69 Suppl. 8 (2007) 118-128
25. Battey J.N., et al. Automated server predictions in CASP7. Proteins 69 Suppl. 8 (2007) 68-82
26. Read R.J., and Chavali G. Assessment of CASP7 predictions in the high accuracy template-based modeling category. Proteins 69 Suppl. 8 (2007) 27-37
27. Cheng J. A multi-template combination algorithm for protein comparative modeling. BMC Struct. Biol. 8 (2008) 18
28. Liu T., et al. Improving the accuracy of template-based predictions by mixing and matching between initial models. BMC Struct. Biol. 8 (2008) 24
29. Larsson P., et al. Using multiple templates to improve quality of homology models in automated homology modeling. Protein Sci. 17 (2008) 990-1002
30. Chakravarty S., et al. Systematic analysis of the effect of multiple templates on the accuracy of comparative models of protein structure. BMC Struct. Biol. 8 (2008) 31
31. Fernandez-Fuentes N., et al. Comparative protein structure modeling by combining multiple templates and optimizing sequence-to-structure alignments. Bioinformatics 23 (2007) 2558-2565
32. Baker D., and Sali A. Protein structure prediction and structural genomics. Science 294 (2001) 93-96
33. Raimondo D., et al. Automatic procedure for using models of proteins in molecular replacement. Proteins 66 (2007) 689-696
34. Kopp J., and Schwede T. Automated protein structure homology modeling: a progress report. Pharmacogenomics 5 (2004) 405-416
35. Jauch R., et al. Assessment of CASP7 structure predictions for template free targets. Proteins 69 Suppl. 8 (2007) 57-67
36. Bradley P., et al. Toward high-resolution de novo structure prediction for small proteins. Science 309 (2005) 1868-1871
37. Qian B., et al. High-resolution structure prediction and the crystallographic phase problem. Nature 450 (2007) 259-264
38. Jiang L., et al. De novo computational design of retro-aldol enzymes. Science 319 (2008) 1387-1391
39. Marti-Renom M.A., et al. Comparative protein structure modeling of genes and genomes. Annu. Rev. Biophys. Biomol. Struct. 29 (2000) 291-325
40. Melo F., and Sali A. Fold assessment for comparative protein structure modeling. Protein Sci. 16 (2007) 2412-2426
41. Fasnacht M., et al. Local quality assessment in homology models using statistical potentials and support vector machines. Protein Sci. 16 (2007) 1557-1568
42. Cozzetto D., et al. Assessment of predictions in the model quality assessment category. Proteins 69 Suppl. 8 (2007) 175-183
43. Wallner B., and Elofsson A. Prediction of global and local model quality in CASP7 using Pcons and ProQ. Proteins 69 Suppl. 8 (2007) 184-193
44. Chen H., and Kihara D. Estimating quality of template-based protein models by alignment stability. Proteins 71 (2008) 1255-1274
45. Gao X., et al. FragQA: predicting local fragment quality of a sequence-structure alignment. Genome Inform. 19 (2007) 27-39
46. Chodanowski P., et al. Local alignment refinement using structural assessment. PLoS ONE 3 (2008) e2645
47. Eramian D., et al. A composite score for predicting errors in protein structure models. Protein Sci. 15 (2006) 1653-1666
48. Shen M.Y., and Sali A. Statistical potential for assessment and prediction of protein structures. Protein Sci. 15 (2006) 2507-2524
49. Benkert P., et al. QMEAN: a comprehensive scoring function for model quality assessment. Proteins 71 (2008) 261-277
50. Mereghetti P., et al. Validation of protein models by a neural network approach. BMC Bioinformatics 9 (2008) 66
51. Qiu J., et al. Ranking predicted protein structures with support vector regression. Proteins 71 (2008) 1175-1182
52. Zhou H., and Skolnick J. Ab initio protein structure prediction using chunk-TASSER. Biophys. J. 93 (2007) 1510-1518
53. Zhou H., and Skolnick J. Protein model quality assessment prediction by combining fragment comparisons and a consensus C(alpha) contact potential. Proteins 71 (2008) 1211-1218
54. Sadowski M.I., and Jones D.T. Benchmarking template selection and model quality assessment for high-resolution comparative modeling. Proteins 69 (2007) 476-485
55. McGuffin L.J. The ModFOLD server for the quality assessment of protein structural models. Bioinformatics 24 (2008) 586-587
56. McGuffin L.J. Benchmarking consensus model quality assessment for protein fold recognition. BMC Bioinformatics 8 (2007) 345
57. Pettitt C.S., et al. Improving sequence-based fold recognition by using 3D model quality assessment. Bioinformatics 21 (2005) 3509-3515
58. Eswar N., et al. Protein structure modeling with MODELLER. Methods Mol. Biol. 426 (2008) 145-159
59. Bhattacharya A., et al. Assessing model accuracy using the homology modeling automatically software. Proteins 70 (2008) 105-118
60. Wiederstein M., and Sippl M.J. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 35 Web Server issue (2007) W407-410
61. Randall A., and Baldi P. SELECTpro: effective protein model selection using a structure-based energy function resistant to BLUNDERs. BMC Struct. Biol. 8 1 (2008) 52
62. Pawlowski M., et al. MetaMQAP: a meta-server for the quality assessment of protein models. BMC Bioinformatics 9 (2008) 403
63. Archie, J. and Karplus, K. (2008) Applying undertaker cost functions to model quality assessment. Proteins [Epub ahead of print]
64. Paluszewski, M. and Karplus, K. (2008) Model quality assessment using distance constraints from alignments. Proteins [Epub ahead of print]
65. Wang, Z. et al. (2008) Evaluating the absolute quality of a single protein model using structural features and support vector machines. Proteins [Epub ahead of print]