fRMSDPred: Predicting local RMSD between structural fragments using sequence information

Proteins: Structure, Function and Genetics

Volumn 72, Issue 3, 2008, Pages 1005-1018

  Rangwala, Huzefa ^a,b   Karypis, George ^a  

^a UNIVERSITY OF MINNESOTA   (United States)

^b NONE   (United States)

Author keywords

Classification; Comparative modeling; Machine learning; Regression; Structure prediction

Indexed keywords

PROTEIN;

ALGORITHM; ARTICLE; COMPARATIVE STUDY; PREDICTION; PRIORITY JOURNAL; PROTEIN STRUCTURE; RELIABILITY; SCORING SYSTEM; SEQUENCE ALIGNMENT;

ALGORITHMS; DATABASES, PROTEIN; PROTEINS; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, PROTEIN;

EID: 47349085903     PISSN: 08873585     EISSN: 10970134     Source Type: Journal    
DOI: 10.1002/prot.21998     Document Type: Article

References (52)

1. Sanchez R, Sali A. Advances in comparative protein-structure modelling. Curr Opin Struc Biol 1997;7:206-214.
2. Jones DT. Genthreader: an efficient and reliable protein fold recognition method for genomic sequences. J Mol Biol 1999;287:797-815.
3. Skolnick J, Kihara D. Defrosting the frozen approximation: Prospector - a new approach to threading. Proteins 2001;42:319-331.
4. Pillardy J, Czaplewski C, Liwo A, Lee J, Ripoll DR, Kazmierkiewicz R, Oldziej S, Wedemeyer WJ, Gibson KD, Arnautova YA, Saunders J, Ye YJ, Scheraga HA. Recent improvements in prediction of protein structure by global optimization of a potential energy function. Proc Natl Acad Sci USA 2001;98:2329-2333.
5. Simons KT, Strauss C, Baker D. Prospects for ab initio protein structural genomics. J Mol Biol 2001;306:1191-1199.
6. Schwede T, Kopp J, Guex N, Peitsch MC. Swiss-model: an automated protein homology-modeling server. Nucleic Acids Res 2003; 31:3381-3385.
7. Helen M. Berman, Bhat TN, Philip E. Bourne, Zukang Feng, Gary Gilliland Helge Weissig, John Westbrook. The Protein Data Bank and the challenge of structural genomics. Nat Struct Biol 2000;7: 957-959.
8. Zhang Y, Skolnick J. The protein structure prediction problem could be solved using the current pdb library. Proc Natl Acad Sci USA 2005;1024:1029-1034.
9. Altschul SF, Gish W, Miller EW, Lipman DJ. Basic local alignment search tool. J Mol Biol 1990;215:403-410.
10. William R. Pearson, David J. Lipman. Improved tools for biological sequence comparison. Proc Natl Acad Sci 1988;85:2444-2448.
11. Rangwala H, Karypis G. Incremental window-based protein sequence alignment algorithms. Bioinformatics 2007;23:e17-23.
12. Gribskov M, Robinson N. Use of receiver operating characteristic (roc) analysis to evaluate sequence matching. Comput Chem 1996; 20:25-33.
13. Altschul SF Madden LT, Schffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped blast and psi-blast: a new generation of protein database search programs. Nucleic Acids Res 1997;25:3389-402.
14. Edgar R, Sjolander K. A comparison of scoring functions for protein sequence profile alignment. Bioinformatics 2004;20:1301-1308.
15. Marti-Renom M, Madhusudhan M, Sali A. Alignment of protein sequences by their profiles. Protein Sci 2004;13:1071-1087.
16. Wang G, Dunbrack RL JR. Scoring profile-to-profile sequence alignments. Protein Sci 2004;13:1612-1626.
17. Heger A, Holm L. Picasso: generating a covering set of protein family profiles. Bioinformatics 2001;17:272-279.
18. Jones DT, Taylor WR, Thorton JM. A new approach to protein fold recognition. Nature 1992;358:86-89.
19. Qiu J, Elber R. Ssaln: an alignment algorithm using structure-dependent substitution matrices and gap penalties learned from structurally aligned protein pairs. Proteins 2006;62:881-891.
20. Venclovas C. Comparative modeling in casp5: progress is evident, but alignment errors remain a significant hindrance. Proteins 2003; 53:380-388.
21. Venclovas C, Margelevicius M. Comparative modeling in casp6 using consensus approach to template selection, sequence-structure alignment, and structure assessment. Proteins 2005;7:99-105.
22. Needleman SB, Wunsch CD. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol 1970;48:443-453.
23. Smith TF, Waterman MS. Identification of common molecular subsequences. J Mol Biol 1981;147:195-197.
24. Shindyalov I, Bourne PE. Protein structure alignment by incremental combinatorial extension (ce) of the optimal path. Protein Eng 1998;11:739-747.
25. Konagurthu AS, Whisstock JC, Stuckey PJ, Lesk AM. Mustang: a multiple structural alignment algorithm. Proteins 2006;64:559-574.
26. George Karypis. Yasspp: better kernels and coding schemes lead to improvements in svm-based secondary structure prediction. Proteins 2006;64:575-586.
27. Zhang Y, Arakaki AJ, Skolnick J. Tasser: an automated method for the prediction of protein tertiary structures in casp6. Proteins 2005; 7:91-98.
28. Rohl CA, Strauss CEM, Misura KMS, Baker D. Protein structure prediction using rosetta. Methods Enzymol 2004;383:66-93.
29. Joachims T. Text categorization with support vector machines: learning with many relevant features. In Proc. of the European Conference on Machine Learning, 1998.
30. Vladimir N. Vapnik. The Nature of statistical learning theory. New York: Springer Verlag; 1995.
31. Smola A, Scholkopf B. A tutorial on support vector regression. NeuroCOLT2, NC2-TR-1998-030, 1998.
32. Mittelman D, Sadreyev R, Grishin N. Probabilistic scoring measures for profile-profile comparison yield more accurate short seed alignments. Bioinformatics 2003;19:1531-1539.
33. Henikoff S, Henikoff JG. Amino acid subsitution matrices from protein blocks. Proc Natl Acad Sci USA 1992;89:10915-10919.
34. Lanckriet GRG, Bie TD, Cristianini N, Jordan MI, Noble WS. A statistical framework for genomic data fusion. Bioinformatics 2004; 20:2626-2635.
35. Rangwala H, Karypis G. frmsdalign: protein sequence alignment using predicted local structure information. Technical Report 07-014, Department of Computer Science, University of Minnesota, Minneapolis, 2007.
36. Ohlson T, Elofsson A. Profnet, a method to derive profile-profile alignment scoring functions that improves the alignments of distantly related proteins. BMC Bioinform 2005;6:253.
37. Schlkopf B, Burges C, Smola A, editors. Making large-scale SVM learning practical. Advances in Kernel methods - support vector learning. Cambridge: MIT Press; 1999.
38. Holm L, Sander C. Mapping the protein universe. Science 1996; 273:595-602.
39. Murzin AG, Brenner SE, Hubbard T, Chothia C. Scop: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol 1995;247:536-540.
40. Sauder JM, Arthur JW, Dunbrack RL. Large-scale comparison of protein sequence alignments with structural alignments. Proteins 2000;40:6-22.
41. Elofsson A. A study on protein sequence alignment quality. Proteins 2002;46:330-339.
42. Cline M, Hughey R, Karplus K. Predicting reliable regions in protein sequence alignments. Bioinformatics 2002;18:306-314.
43. Dan Gusfield. Algorithms on strings, trees, and sequences: computer science and computational biology. New York, NY: Cambridge University Press; 1997.
44. Rost B, Sander C. Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol 1993;232:584-599.
45. David T. Jones. Protein secondary structure prediction based on position-specific scoring matricies. J Mol Biol 1999;292:195-202.
46. Do CB, Gross SS, Batzoglou S. Contralign: discriminative training for protein sequence alignment. In Proceedings of the Tenth Annual International Conference on Computational Molecular Biology (RECOMB), 2006.
47. Do CB, Mahabashyam MSP, Batzoglou S. Probcons: probabilistic consistency-based multiple sequence alignment. Genome Res 2005;15:330-340.
48. Ohlson T, Aggarwal V, Elofsson A, Maccallum R. Improved alignment quality by combining evolutionary information, predicted secondary structure and self-organizing maps. BMC Bioinform 2006; 1:357.
49. Gerstein M, Levitt M. Comprehensive assessment of automatic structural alignment against a manual standard, the scop classification of proteins. Protein Sci 1998;7:445-456.
50. Joachims T, Galor T, Elber R. Learning to align sequences: a maximum-margin approach. New Algorithms for Macromolecular Simulation 2005;49:57-68.
51. Yu C, Joachims T, Elber R, Pillardy J. Support vector training of protein alignment models. To appear in Proceeding of the Eleventh International Conference on Research in Computational Molecular Biology (RECOMB), 2007.
52. Sun F, Fernandez-Baca D, Yu W. Inverse parametric sequence alignment. Proceedings of the international computing and combinatorics conference (COCOON), 2002.