A phylogenetic mixture model for the identification of functionally divergent protein residues

Bioinformatics

Volumn 27, Issue 19, 2011, Pages 2655-2663

  Gaston, Daniel ^a,b   Susko, Edward ^a,c   Roger, Andrew J ^a,b  

^a DALHOUSIE UNIVERSITY   (Canada)

^b DALHOUSIE UNIVERSITY   (Canada)

^c DALHOUSIE UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACID; PROTEIN;

ARTICLE; BIOLOGICAL MODEL; CHEMISTRY; GENETICS; METABOLISM; MOLECULAR EVOLUTION; PHYLOGENY; PROTEIN BINDING;

AMINO ACIDS; EVOLUTION, MOLECULAR; MODELS, GENETIC; PHYLOGENY; PROTEIN BINDING; PROTEINS;

EID: 80053447612     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btr470     Document Type: Article

References (47)

1. Blouin,C. et al. (2005) Impact of taxon sampling on the estimation of rates of evolution at sites. Mol. Biol. Evol., 22, 784-791.
2. Brandt,B.W. et al. (2010) Multi-Harmony: detecting functional specificity from sequence alignment. Nucleic Acids Res., 38, W35-W40.
3. Caffrey,D.R. et al. (2008) Prediction of specificity-determining residues for smallmolecule kinase inhibitors. BMC Bioinformatics, 9, 49.
4. Capra,J.A. and Singh,M. (2007) Predicting functionally important residues from sequence conservation. Bioinformatics, 23, 1875-1882.
5. Capra,J.A. and Singh,M. (2008) Characterization and prediction of residues determining protein functional specificity. Bioinformatics, 24, 1473-1480.
6. Capra,J.A et al. (2009) Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure. PLoS Comput. Biol., 5, e1000585.
7. Chakrabarti,S. and Panchenko,A.R. (2009) Ensemble approach to predict specificity determinants: benchmarking and validation. BMC Bioinformatics, 10, 207.
8. Chakrabarti,S. et al. (2007) Functional specificity lies within the properties and evolutionary changes of amino acids. J. Mol. Biol., 373, 801-810.
9. Davis,J. and Goadrich,D. (2006) The relationship between precision-recall and ROC curves. In 23rd International Conference on Machine Learning (ICML), Pittsburgh, PA, USA. ACM, New York, NY.
10. de Melo-Minardi,R.C. et al. (2010) Identification of subfamily-specific sites based on active sites modeling and clustering. Bioinformatics, 26, 3075-3082.
11. Feenstra,K.A. et al. (2007) Sequence harmony: detecting functional specificity from alignments. Nucleic Acids Res., 35, W495-W498.
12. Fletcher,W. and Yang,Z. (2009) INDELible: a flexible simulator of biological sequence evolution. Mol. Biol. Evol., 26, 1879-1888.
13. Gerlt,J.A. and Babbitt,P.C. (2000) Can sequence determine function? Genome Biol., 1, reviews0005.1-0005.10.
14. Gu,X. (1999) Statistical methods for testing functional divergence after gene duplication. Mol. Biol. Evol., 16, 1664-1674.
15. Gu,X. (2001) Maxmimum-likelihood approach for gene family evolution under functional divergence. Mol. Biol. Evol., 18, 453-464.
16. Gu,X. and Vander Velden,K. (2002) DIVERGE: phylogeny-based analysis for functional-structural divergence of a protein family. Bioinformatics, 18, 500-501.
17. Henikoff,S. et al. (1997) Gene families: the taxonomy of protein paralogs and chimeras. Science, 278, 609-614.
18. Jones,D.T. et al. (1992) The rapid generation of mutation data matrices from protein sequences. Comput. Appl. Biosci., 8, 275-282.
19. Knudsen,B. and Miyamoto,M.M. (2001) A likelihood ratio test for evolutionary rate shifts and functional divergence among proteins. Proc. Natl Acad. Sci. USA, 98, 14512-14517.
20. Knudesen,B. et al. (2003) Using evolutionary rates to investigate protein functional divergence and conservation. A case study of the carbonic anhydrases. Genetics, 164, 1261-1269.
21. Kullback,S. and Leibler,R.A. (1951) On information and sufficiency. Ann. Math. Stat., 22, 79-86.
22. Lartillot,N. and Phillipe,H. (2004) A Bayesian mixture model for across-site heterogeneities in the amino-acid replacement process. Mol. Biol. Evol., 21, 1095-1109.
23. Le,S.Q. and Gascuel,O. (2008) An improved general amino acid replacement matrix. Mol. Biol. Evol., 25, 1307-1320.
24. Li,W.H. (1983) Evolution of duplicated genes. In Nei,M. and Koehn,R.K. (eds) Evolution of Genes and Proteins. Sinauer Associates, Sunderland, MA, pp. 14-37.
25. Lichtarge,O. et al. (1996) An evolutionary trace method defines binding surfaces common to protein families. J. Mol. Biol., 257, 342-358.
26. Lin,J. (1991) Divergence measures based on the shannon entropy. IEEE Trans. Informat. Theory, 37, 145-151.
27. Madabushi,S. et al. (2004) Evolutionary trace of G protein-coupled receptors reveals clusters of residues that determine global and class-specific functions. J. Biol. Chem., 279, 8126-8132.
28. Mihalek,I. et al. (2004) A family of evolution-entropy hybrid methods for ranking protein residues by importance. J. Mol. Biol., 336, 1265-1282.
29. Pawlowski,K. and Godzik,A. (2001) Surface map comparison: studying function diversity of homologous proteins. J. Mol. Biol., 309, 793-806.
30. Pirovano,W. et al. (2006) Sequence comparison by sequence harmony identifies subtype-specific functional sites. Nucleic Acids Res., 34, 6540-6548.
31. Price,M.N. et al. (2009) FastTree: computing large minimum-evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol., 26, 1641-1650.
32. Price,M.N. et al. (2010) FastTree 2 - approximately maximum-likelihood trees for large alignments. PLoS One, 5, e9490.
33. Raviscioni,M. et al. (2006) Evolutionary identification of a subtype specific functional site in the ligand binding domain of steroid receptors. Bioinformatics, 1057, 1046-1057.
34. Sael,L. et al. (2008) Rapid comparison of properties on protein surface. Proteins, 73, 1-10.
35. Sankararaman,S. et al. (2010) Active site prediction using evolutionary and structural information. Bioinformatics, 26, 617-624.
36. Schmidt,H.A. et al. (2002) TREE-PUZZLE: maximum likelihood phylogenetic analysis using quartets and parallel computing. Bioinformatics, 18, 502-504.
37. Sjölander,K. et al. (1996) Dirichlet mixtures: a method for improved detection of weak but significant protein sequence homology. Comput. Appl. Biosci., 12, 327-345.
38. Stamatakis,A. (2006) RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics, 22, 2688-2690.
39. Strope,C.L. et al. (2007) indel-Seq-Gen: a new protein family simulator incorporating domains, motifs, and indels. Mol. Biol. Evol., 24, 640-649.
40. Strope,C.L. et al. (2009) Biological sequence simulation for testing complex evolutionary hypotheses: indel-Seq-Gen version 2.0. Mol. Biol. Evol., 26, 2581-2593.
41. Susko,E. et al. (2002) Testing for differences in rates-across-sites distributions in phylogenetic trees. Mol. Biol. Evol., 19, 1514-1523.
42. Susko,E. et al. (2005) Biases in phylogenetic estimation can be caused by random sequence segments. J. Mol. Evol., 61, 351-359.
43. Wang,H.C. et al. (2008) A class frequency mixture model that adjusts for site-specific amino acid frequencies and improves inference of protein phylogeny. BMC Evol. Biol., 8, 331.
44. Whelan,S. and Goldman,N. (2001) A general empirical model of protein evolution derived from multiple protein families using a maximum-likelihood approach. Mol. Biol. Evol., 18, 691-699.
45. Yang,Z. and Rannala,B. (1997) Bayesian phylogenetic inferences using DNA sequences: a Markov chain Monte Carlo method. Mol. Biol. Evol., 14, 717-724.
46. Ye,K. et al. (2008) Multi-RELIEF: a method to recognize specificity determining residues from multiple sequence alignments using a Machine-Learning approach for feature weighting. Bioinformatics, 24, 18-25.
47. Zwickl,D.J. and Hillis,D.M. (2002) Increased taxon sampling greatly reduces phylogenetic error. Syst. Biol., 51, 588-589.