Hidden Markov models that use predicted local structure for fold recognition: Alphabets of backbone geometry

Proteins: Structure, Function and Genetics

Volumn 51, Issue 4, 2003, Pages 504-514

  Karchin, Rachel ^a   Cline, Melissa ^b   Mandel Gutfreund, Yael ^c   Karplus, Kevin ^a  

^a Santa Cruz   (United States)

^b Thermo Fisher Scientific   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Alignment; Information theory; Multitrack HMM; Neural network; Protein structure prediction; Secondary structure; Two track HMM

Indexed keywords

PROTEIN;

ARTICLE; PRIORITY JOURNAL; PROBABILITY; PROTEIN FOLDING; PROTEIN SECONDARY STRUCTURE; PROTEIN STRUCTURE; ROC CURVE; SEQUENCE ALIGNMENT; SEQUENCE HOMOLOGY;

ALGORITHMS; COMPUTATIONAL BIOLOGY; MARKOV CHAINS; PROTEIN FOLDING; PROTEIN STRUCTURE, SECONDARY; PROTEINS; REPRODUCIBILITY OF RESULTS; SEQUENCE ALIGNMENT;

EID: 0038278386     PISSN: 08873585     EISSN: None     Source Type: Journal    
DOI: 10.1002/prot.10369     Document Type: Article

References (61)

1. Rost B, Schneider R, Sander C. Protein fold recognition by prediction-based threading. J Mol Biol 1997;270:471-480.
2. Di Francesco V, Geetha V, Garnier J, Munson PJ. Fold recognition using predicted secondary structure sequences and hidden Markov models of protein folds. Proteins 1997;1:123-128.
3. Rice DW, Eisenberg D. A 3d-1d substitution matrix for protein fold recognition that includes predicted secondary structure of the sequence. J Mol Biol 1997;267:1026-1038.
4. De La Cruz X, Thornton JM. Factors limiting the performance of prediction-based fold recognition methods. Protein Sci 1999;8:750-759.
5. Kelley LA, MacCallum RM, Sternberg MJE. Enhanced genome annotation using structural profiles in the program 3D-PSSM. J Mol Biol 2000;299:501-522.
6. Hughey R, Krogh A. SAM: Sequence alignment and modeling software system. Technical report UCSC-CRL-95-7. Santa Cruz, CA: University of California, Santa Cruz, Computer Engineering; 1995.
7. Hughey R, Karplus K, Krogh A. SAM: Sequence alignment and modeling software system, version 3. Technical report UCSC-CRL-99-11. Santa Cruz, CA: University of California, Santa Cruz, Computer Engineering; 1999. Available from http://www.soe.ucsc.edu/research/compbio/sam.html.
8. Fetrow JS, Palumbo MJ, Berg G. Patterns, structures, and amino acid frequencies in structural building blocks, a protein secondary structure classification scheme. Proteins 1997;27:249-271.
9. Kabsch W, Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983;22:2577-2637.
10. Unger R, Harel D, Wherland S, Sussman J. A 3d building blocks approach to analyzing and predicting structure of proteins. Proteins 1989;5:355-373.
11. Unger R, Sussman JL. The importance of short structural motifs in protein structure analysis. J Comput Aided Mol Des 1993;7:457-472.
12. Rooman MJ, Rodriguez J, Wodak SJ. Automatic definition of recurrent local structure motifs in proteins. J Mol Biol 1990;213:327-336.
13. Rooman MJ, Rodriguez J, Wodak SJ. Relations between protein sequence and structure and their significance. J Mol Biol 1990;213:337-350.
14. Zhang X, Fetrow JS, Rennie WA, Waltz DL, Berg G. Automatic derivation of substructures yields novel structural building blocks in globular proteins. In: Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology. Bethesda, MD, USA: AAAI: 1993. p 438-446.
15. Kang HS, Kurochkina NA, Lee B. Estimation and use of protein backbone angle probabilities. J Mol Biol 1993;229:448-460.
16. Frishman D, Argos P. Knowledge-based protein secondary structure assignment. Proteins 1995;23:566-579.
17. Swindells MB, MacArthur MW, Thornton JM. Intrinsic phi,psi propensities of amino acids, derived from the coil regions of known structures. Nat Struct Biol 1995;2:596-603.
18. Thompson MJ, Goldstein RA. Predicting protein secondary structure with probabilistic schemata of evolutionarily derived information. Protein Sci 1997;6:1963-1975.
19. Bystroff C, Baker D. Prediction of local structure in proteins using a library of sequence-structure motifs. J Mol Biol 1998;281:565-577.
20. Camproux AC, Tuffery P, Chevrolat JP, Boisvieux JF, Hazout S. Hidden Markov model approach for identifying the modular framework of the protein backbone. Protein Eng 1999;12:1063-1073.
21. King SM, Johnson WC. Assigning secondary structure from protein coordinate data. Proteins 1999;35:313-320.
22. Bystroff C, Thorsson V, Baker D. HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins. J Mol Biol 2000;301:173-190.
23. de Brevern AG, Etchebest C, Hazout S. Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks. Proteins 2000;41:271-287.
24. Di Franceso V, Munson PJ, Garnier J. FORESST: fold recognition from secondary structure predictions of proteins. Bioinformatics 1999;15:131-140.
25. Hargbo J, Elofsson A. Hidden Markov models that use predicted secondary structures for fold recognition. Proteins 1999;36:68-76.
26. Orengo CA, Michie AD, Jones S, Jones DT, Swindells MB, Thornton JM. CATH - a hierarchic classification of protein domain structures. Structure 1997;5:1093-108.
27. Sander C, Schneider R. Database of homology-derived protein structures and the structural meaning of sequence alignment. Proteins 1991;9:56-68.
28. Karplus K, Sjölander K, Barrett C, Cline M, Haussler D, Hughey R, Holm L, Sander C. Predicting protein structure using hidden Markov models. Proteins 1997;Suppl 1:134-139.
29. Karplus K, Barrett C, Cline M, Diekhans M, Grate L, Hughey R. Predicting protein structure using only sequence information. Proteins 1999;Suppl 3:121-125.
30. Karplus K, Karchin R, Barrett C, Tu S, Cline M, Diekhans M, Grate L, Casper J, Hughey R. What is the value added by human intervention in protein structure prediction? Proteins 2001;Suppl 5:86-91.
31. Ramachandran GN, Ramakrishnan C, Sasisekharan V. Stereochemistry of polypeptide chain configurations. J Mol Biol 1963;7:95-99.
32. Kohonen T. Self-organizing formation of topologically correct feature maps. Biol Cybernet 1982;43:59-69.
33. Kohonen T. Self-organization and associative memory, 3rd ed. New York: Springer-Verlag; 1989.
34. MacQueen J. Some methods for classification and analysis of multivariate observations. In: Le Cam LM, Neyman J, editors. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1. Berkeley, CA: University of California Press; 1967. p 281-297.
35. Holm L, Sander C. Mapping the protein universe. Science 1996;273(5275):595-603.
36. Dunbrack R. Culling the PDB by resolution and sequence identity. 2001. http://www.fccc.edu/research/labs/dunbrack/culledpdb.html.
37. Dowe DL, Allison L, Dix TI, Hunter L, Wallace CS, Edgoose T. Circular clustering of protein dihedral angles by minimum message length. In: Pacific Symposium on Biocomputing. Singapore: World Scientific; 1996. p 242-255.
38. Wallace C, Dowe D. Intrinsic classification by MML - the snob program. In: Proceedings of the 7th Australian Joint Conference on Artificial Intelligence. Singapore: World Scientific; 1994. p 37-44.
39. Fetrow JS, Berg G. Using information theory to discover side chain rotamer classes: analysis of the effects of local backbone structure. In Pacific Symposium on Biocomputing, Mauna Lani, Hawaii: World Scientific; 1999. p 278-289.
40. Durbin R, Eddy S, Krogh A, Mitchison G. Biological sequence analysis: Probabilistic models of proteins and nucleic acids. Cambridge, UK: Cambridge University Press; 1998.
41. Rost B, Sander C, Schneider R. Redefining the goals of protein secondary structure prediction. J Mol Biol 1994;235:13-26.
42. Holm L, Sander C. Protein structure comparison by alignment of distance matrices. J Mol Biol 1993;233:123-138.
43. Wolpert DH, Wolf DR. Estimating functions of probability distributions from a finite set of samples. Phys Rev E 1995;52:6841-6854.
44. Wolpert DH, Wolf DR. Estimating functions of probability distributions from a finite set of samples; part 2: Bayes estimators for mutual information, chi-squared, covariance, and other statistics. Technical Report LA-UR-93-833,TR-93-07-047. Santa Fe, NM: Los Alamos National Lab, Santa Fe Institute; 1993.
45. Cline MS, Karplus K, Lathrop RH, Smith TF, Rogers RG Jr, Haussler D. Information-theoretic dissection of pairwise contact potentials. Proteins 2002;49:7-14.
46. Park J, Karplus K, Barrett C, Hughey R, Haussler D, Hubbard T, Chothia C. Sequence comparisons using multiple sequences detect three times as many remote homologues as pairwise methods. J Mol Biol 1998;284:1201-1210.
47. Jaakkola T, Diekhans M, Haussler D. A discriminative framework for detecting remote protein homologies. J Comput Biol 2000;7(1/2):95-114. Available from http://www.soe.ucsc.edu/research/compbio/research.html.
48. Gough J, Karplus K, Hughey R, Chothia C. Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structure. J Mol Biol 2001;313:903-919.
49. Raval A, Ghahramani Z, Wild DL. A Bayesian network model for protein fold and remote homologue recognition. Bioinformatics 2002;18:788-801.
50. Altschul S, Madden T, Schaffer A, Zhang J, Zhang Z, Miller W, Lipman D. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res 1997;25:3899-3402.
51. Schäffer AA, Aravind L, Madden TL, Shavirin S, Spouge JL, Wolf YI, Koonin E, Altschul SF. Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements. Nucleic Acids Res 2001;29:2994-3005.
52. NRP (Non-Redundant Protein) Database. Distributed on the Internet via anonymous FTP from ftp.ncifcrf.gov, under the auspices of the National Cancer Institute's Frederick Biomedical Supercomputing Center. 1998.
53. Karplus K, Karchin R, Hughey R. Calibrating E-values for hidden Markov models with reverse-sequence null models. Bioinformatics submitted for publication. 2003. preprint at http://www.soe.ucsc.edu/~-karplus/papers/e-value-bioinformatics- submitted.pdf.
54. Gribskov M, Robinson NL. Use of receiver operating characteristic (ROC) analysis to evaluate sequence matching. Comp Chem 1996;20:25-33.
55. Viterbi AJ. Error bounds for convolution codes and an asymptotically optimal decoding algorithm. IEEE Trans Inf Theory 1967;13:260-269.
56. Shindyalov IN, Bourne PE. Protein structure alignment by incremental combinatorial extension (CE) of the optimal path. Protein Eng 1998;11:739-747.
57. Cline M, Karplus K. On alignment shift and its measures. Technical Report UCSC-CRL-97-27. Santa Cruz, CA: University of California, Santa Cruz, Jack Baskin School of Engineering; 1998.
58. Cline M, Hughey R, Karplus K. Predicting reliable regions in protein sequence alignments. Bioinformatics 2002;18:306-314.
59. Griffiths-Jones S, Bateman A. The use of structure information to increase alignment accuracy does not aid homologue detection with profile HMMs. Bioinformatics 2002;18:1243-1249.
60. Mandel-Gutfreund Y, Gregoret L. On the significance of alternating patterns of polar and nonpolar residues in beta strands. J Mol Biol 2002;323:453-461.
61. Cline M. Protein sequence alignment reliability: prediction and measurement. PhD thesis. Santa Cruz, CA: University of California, Computer Science; 2000.