Analyzing the sequence-structure relationship of a library of local structural prototypes

Journal of Theoretical Biology

Volumn 256, Issue 2, 2009, Pages 215-226

  Benros, Cristina ^a   de Brevern, Alexandre G ^a,b   Hazout, Serge ^a  

^a INSERM   (France)

^b INSTITUT NATIONAL DE LA TRANSFUSION SANGUINE   (France)

Author keywords

Amino acid equivalence classes; Protein local structure; Sequence structure correlation; Structural alphabet; Structural motif library design

Indexed keywords

AMINO ACID; ASPARAGINE; GLUTAMIC ACID; HYBRID PROTEIN; PEPTIDE FRAGMENT; PEPTIDE LIBRARY; SERINE; THREONINE;

AMINO ACID; CORRELATION; MULTIVARIATE ANALYSIS; PROTEIN;

ALPHA HELIX; AMINO ACID SEQUENCE; ARTICLE; BETA TURN; CARBOXY TERMINAL SEQUENCE; FACTUAL DATABASE; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL; PROTEIN MOTIF; PROTEIN STRUCTURE; STRUCTURE ANALYSIS; THEORETICAL MODEL;

AMINO ACID MOTIFS; AMINO ACID SEQUENCE; COMPUTATIONAL BIOLOGY; MODELS, MOLECULAR; PEPTIDE LIBRARY; PROTEIN STRUCTURE, SECONDARY; SEQUENCE ANALYSIS, PROTEIN;

EID: 57749200715     PISSN: 00225193     EISSN: 10958541     Source Type: Journal    
DOI: 10.1016/j.jtbi.2008.08.032     Document Type: Article

References (67)

1. Baker D., and Sali A. Protein structure prediction and structural genomics. Science 294 (2001) 93-96
2. Benros C., de Brevern A.G., and Hazout S. Hybrid protein model (HPM): a method for building a library of overlapping local structural prototypes. Sensitivity study and improvements of the training. IEEE Workshop Neural Networks Signal Process. (2003) 53-72
3. Benros C., de Brevern A.G., Etchebest C., and Hazout S. Assessing a novel approach for predicting local 3D protein structures from sequence. Proteins 62 (2006) 865-880
4. Benros C., Martin J., Tyagi M., and de Brevern A.G. Description of the local protein structure. I. classical approaches. In: de Brevern A.G. (Ed). Recent Advances in Structural Bioinformatics vol. 1 (2007), Research Signpost 1-33
5. Berman H.M., Westbrook J., Feng Z., Gilliland G., Bhat T.N., Weissig H., Shindyalov I.N., and Bourne P.E. The protein data bank. Nucleic Acids Res. 28 (2000) 235-242
6. Bonneau R., Strauss C.E., Rohl C.A., Chivian D., Bradley P., Malmstrom L., Robertson T., and Baker D. De novo prediction of three-dimensional structures for major protein families. J. Mol. Biol. 322 (2002) 65-78
7. Bornot A., and de Brevern A.G. Protein beta-turn assignments. Bioinformation 1 (2006) 153-155
8. Bystroff C., and Baker D. Prediction of local structure in proteins using a library of sequence-structure motifs. J. Mol. Biol. 281 (1998) 565-577
9. Chou K.C. Prediction of tight turns and their types in proteins. Anal. Biochem. 286 (2000) 1-16
10. de Brevern A.G. Compartimentation chromosomique. Biofutur 225 (2002) 20-22
11. de Brevern A.G. New assessment of protein blocks. In Silico Biol. 5 (2005) 283-289
12. de Brevern A.G., and Hazout S. Hybrid protein model (HPM): a method to compact protein 3D-structures information and physicochemical properties. IEEE Comput. Soc. S S1 (2000) 49-54
13. de Brevern A.G., and Hazout S. Compacting local protein folds with a "hybrid protein model". Theor. Chem. Acc. 106 (2001) 36-47
14. de Brevern A.G., and Hazout S. 'Hybrid protein model' for optimally defining 3D protein structure fragments. Bioinformatics 19 (2003) 345-353
15. de Brevern A.G., Etchebest C., and Hazout S. Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks. Proteins 41 (2000) 271-287
16. de Brevern A.G., Valadie H., Hazout S., and Etchebest C. Extension of a local backbone description using a structural alphabet: a new approach to the sequence-structure relationship. Protein Sci. 11 (2002) 2871-2886
17. de Brevern A.G., Loirat F., Badel-Chagnon A., Andre C., Vincens P., and Hazout S. Genome compartimentation by a hybrid chromosome model (HXM). Application to saccharomyces cerevisae subtelomeres. Comput. Chem. 26 (2002) 437-445
18. de Brevern A.G., Benros C., Gautier R., Valadie H., Hazout S., and Etchebest C. Local backbone structure prediction of proteins. In Silico Biol. 4 (2004) 381-386
19. de Brevern A.G., Benros C., and Hazout S. Structural alphabet: from a local point of view to a global description of protein 3D structures. In: Yan P.V. (Ed). Bioinformatics (2005), New Research Nova Publishers 127-169
20. de Brevern A.G., Wong H., Tournamille C., Colin Y., Le Van Kim C., and Etchebest C. A structural model of a seven-transmembrane helix receptor: the duffy antigen/receptor for chemokine (DARC). Biochim. Biophys. Acta 1724 (2005) 288-306
21. de Brevern A.G., Etchebest C., Benros C., and Hazout S. "Pinning strategy": a novel approach for predicting the backbone structure in terms of protein blocks from sequence. J. Biosci. 32 (2007) 51-70
22. Dong Q.W., Wang X.L., and Lin L. Methods for optimizing the structure alphabet sequences of proteins. Comput. Biol. Med. 37 (2007) 1610-1616
23. Du P., Andrec M., and Levy R.M. Have we seen all structures corresponding to short protein fragments in the protein data bank? An update. Protein Eng. 16 (2003) 407-414
24. Dudev M., and Lim C. Discovering structural motifs using a structural alphabet: application to magnesium-binding sites. BMC Bioinf. 8 (2007) 106
25. Etchebest C., Benros C., Hazout S., and de Brevern A.G. A structural alphabet for local protein structures: improved prediction methods. Proteins 59 (2005) 810-827
26. Etchebest C., Benros C., Bornot A., Camproux A.C., and de Brevern A.G. A reduced amino acid alphabet for understanding and designing protein adaptation to mutation. Eur. Biophys. J. 36 (2007) 1059-1069
27. Fourrier L., Benros C., and de Brevern A.G. Use of a structural alphabet for analysis of short loops connecting repetitive structures. BMC Bioinf. 5 (2004) 58
28. Frishman D., and Argos P. Knowledge-based protein secondary structure assignment. Proteins 23 (1995) 566-579
29. Fuchs P., Etchebest C., and de Brevern A.G. Turns prediction. In: de Brevern A.G. (Ed). Recent Advances in Structural Bioinformatics vol. 1 (2007), Research Signpost, Trivandrum, India 37-56
30. Fuchs P.F., and Alix A.J. High accuracy prediction of beta-turns and their types using propensities and multiple alignments. Proteins 59 (2005) 828-839
31. Gonzalez-Diaz H., Vilar S., Santana L., and Uriarte E. Medicinal chemistry and bioinformatics-current trends in drugs discovery with networks topological indices. Curr. Top. Med. Chem. 7 (2007) 1015-1029
32. Gonzalez-Diaz H., Gonzalez-Diaz Y., Santana L., Ubeira F.M., and Uriarte E. Proteomics, networks and connectivity indices. Proteomics 8 (2008) 750-778
33. Gribskov M., McLachlan A.D., and Eisenberg D. Profile analysis: detection of distantly related proteins. Proc. Natl. Acad. Sci. USA 84 (1987) 4355-4358
34. Haspel N., Tsai C.J., Wolfson H., and Nussinov R. Reducing the computational complexity of protein folding via fragment folding and assembly. Protein Sci. 12 (2003) 1177-1187
35. Hazout S. Entropy-derived measures for assessing the accuracy of N-state prediction algorithms. In: de Brevern A.G. (Ed). In Recent Advances in Structural Bioinformatics (2007), Research Signpost, Trivandrum, India 395-417
36. Hotelling H. Between two sets of variates. Biometrika 28 (1936) 321-377
37. Humphrey W., Dalke A., and Schulten K. VMD: visual molecular dynamics. J. Mol. Graph. 14 (1996) 33-38 27-28
38. Hutchinson E.G., and Thornton J.M. PROMOTIF-a program to identify and analyze structural motifs in proteins. Protein Sci. 5 (1996) 212-220
39. Ihaka R., and Gentleman R. R: a language for data analysis and graphics. J. Comput. Graph. Stat. 5 (1996) 299-314
40. Inbar Y., Benyamini H., Nussinov R., and Wolfson H.J. Protein structure prediction via combinatorial assembly of sub-structural units. Bioinformatics 19 Suppl. 1 (2003) i158-i168
41. Karchin R., Cline M., Mandel-Gutfreund Y., and Karplus K. Hidden Markov models that use predicted local structure for fold recognition: alphabets of backbone geometry. Proteins 51 (2003) 504-514
42. Karplus K., Karchin R., Draper J., Casper J., Mandel-Gutfreund Y., Diekhans M., and Hughey R. Combining local-structure, fold-recognition, and new fold methods for protein structure prediction. Proteins 53 Suppl. 6 (2003) 491-496
43. Kohonen T. Self-Organizing Maps. third ed (2001), Springer, New York
44. Kolodny R., Koehl P., Guibas L., and Levitt M. Small libraries of protein fragments model native protein structures accurately. J. Mol. Biol. 323 (2002) 297-307
45. Kullback S., and Leibler R.A. On information and sufficiency. Ann. Math. Stat. 22 (1951) 79-86
46. Lesk A.M., and Rose G.D. Folding units in globular proteins. Proc. Natl. Acad. Sci. USA 78 (1981) 4304-4308
47. Micheletti C., Seno F., and Maritan A. Recurrent oligomers in proteins: an optimal scheme reconciling accurate and concise backbone representations in automated folding and design studies. Proteins 40 (2000) 662-674
48. Munteanu C.R., Gonzalez-Diaz H., Borges F., and de Magalhaes A.L. Natural/random protein classification models based on star network topological indices. J. Theor. Biol. 4 (2008) 775-783
49. Murphy L.R., Wallqvist A., and Levy R.M. Simplified amino acid alphabets for protein fold recognition and implications for folding. Protein Eng. 13 (2000) 149-152
50. Noguchi T., and Akiyama Y. PDB-REPRDB: a database of representative protein chains from the protein data bank (PDB) in 2003. Nucleic Acids Res. 31 (2003) 492-493
51. Offmann B., Tyagi M., and de Brevern A.G. Local protein structures. Curr. Bioinf. 3 (2007) 165-202
52. Pavone V., Gaeta G., Lombardi A., Nastri F., Maglio O., Isernia C., and Saviano M. Discovering protein secondary structures: classification and description of isolated alpha-turns. Biopolymers 38 (1996) 705-721
53. Pei J., and Grishin N.V. Combining evolutionary and structural information for local protein structure prediction. Proteins 56 (2004) 782-794
54. Rabiner L.R. A tutorial on hidden Markov models and selected application in speech recognition. Proc. IEEE 77 (1989) 257-286
55. Ring C.S., Kneller D.G., Langridge R., and Cohen F.E. Taxonomy and conformational analysis of loops in proteins. J. Mol. Biol. 224 (1992) 685-699
56. Rose G.D., Gierasch L.M., and Smith J.A. Turns in peptides and proteins. Adv. Protein Chem. 37 (1985) 1-109
57. Sali A., and Blundell T.L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234 (1993) 779-815
58. Sander O., Sommer I., and Lengauer T. Local protein structure prediction using discriminative models. BMC Bioinf. 7 (2006) 14
59. Schuchhardt J., Schneider G., Reichelt J., Schomburg D., and Wrede P. Local structural motifs of protein backbones are classified by self-organizing neural networks. Protein Eng. 9 (1996) 833-842
60. Shannon C. A mathematical theory of communication. Bell Syst. Tech. J. 27 (1948) 379-423
61. Tyagi M., Gowri V.S., Srinivasan N., de Brevern A.G., and Offmann B. A substitution matrix for structural alphabet based on structural alignment of homologous proteins and its applications. Proteins 65 (2006) 32-39
62. Tyagi M., Sharma P., Swamy C.S., Cadet F., Srinivasan N., de Brevern A.G., and Offmann B. Protein block expert (PBE): a web-based protein structure analysis server using a structural alphabet. Nucleic Acids Res. 34 (2006) W119-W123
63. Tyagi M., Benros C., Martin J., and de Brevern A.G. Description of the local protein structure. II. novel approaches. In: de Brevern A.G. (Ed). Recent Advances in Structural Bioinformatics vol. 1 (2007), Research signpost, Trivandrum, India 34-47
64. Tyagi M., de Brevern A.G., Srinivasan N., and Offmann B. Protein structure mining using a structural alphabet. Proteins 71 (2008) 920-937
65. Unger R., Harel D., Wherland S., and Sussman J.L. A 3D building blocks approach to analyzing and predicting structure of proteins. Proteins 5 (1989) 355-373
66. Wang J., and Wang W. A computational approach to simplifying the protein folding alphabet. Nat. Struct. Biol. 6 (1999) 1033-1038
67. Xu D., Crawford O.H., LoCascio P.F., and Xu Y. Application of PROSPECT in CASP4: characterizing protein structures with new folds. Proteins Suppl. 5 (2001) 140-148