Predicting functionally important residues from sequence conservation

MOTIVATION All residues in a protein are not equally important. Some are essential for the proper structure and function of the protein, whereas others can be readily replaced. Conservation analysis is one of the most widely used methods for predicting these functionally important residues in protein sequences. RESULTS We introduce an information-theoretic approach for estimating sequence conservation based on Jensen-Shannon divergence. We also develop a general heuristic that considers the estimated conservation of sequentially neighboring sites. In large-scale testing, we demonstrate that our combined approach outperforms previous conservation-based measures in identifying functionally important residues; in particular, it is significantly better than the commonly used Shannon entropy measure. We find that considering conservation at sequential neighbors improves the performance of all methods tested. Our analysis also reveals that many existing methods that attempt to incorporate the relationships between amino acids do not lead to better identification of functionally important sites. Finally, we find that while conservation is highly predictive in identifying catalytic sites and residues near bound ligands, it is much less effective in identifying residues in protein-protein interfaces. AVAILABILITY Data sets and code for all conservation measures evaluated are available at http://compbio.cs.princeton.edu/conservation/

[1]  B. Lee,et al.  The interpretation of protein structures: estimation of static accessibility. , 1971, Journal of molecular biology.

[2]  B. Erman,et al.  Information‐theoretical entropy as a measure of sequence variability , 1991, Proteins.

[3]  C. Sander,et al.  Database of homology‐derived protein structures and the structural meaning of sequence alignment , 1991, Proteins.

[4]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[5]  Jianhua Lin,et al.  Divergence measures based on the Shannon entropy , 1991, IEEE Trans. Inf. Theory.

[6]  S. Henikoff,et al.  Amino acid substitution matrices from protein blocks. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[7]  L. L. Lloyd,et al.  Enzyme nomenclature — Recommendations of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology: Academic Press Ltd, London, UK, 1992. xiii + 862 pp. Price £40.00. ISBN 0-12-227165-3 , 1994 .

[8]  S. Henikoff,et al.  Position-based sequence weights. , 1994, Journal of molecular biology.

[9]  R. M. Williamson Information theory analysis of the relationship between primary sequence structure and ligand recognition among a class of facilitated transporters. , 1995, Journal of theoretical biology.

[10]  F. Cohen,et al.  An evolutionary trace method defines binding surfaces common to protein families. , 1996, Journal of molecular biology.

[11]  S. Karlin,et al.  Evolutionary conservation of RecA genes in relation to protein structure and function , 1996, Journal of bacteriology.

[12]  J. Thornton,et al.  Tess: A geometric hashing algorithm for deriving 3D coordinate templates for searching structural databases. Application to enzyme active sites , 1997, Protein science : a publication of the Protein Society.

[13]  Chris Sander,et al.  The HSSP database of protein structure-sequence alignments and family profiles , 1998, Nucleic Acids Res..

[14]  Sean R. Eddy,et al.  Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids , 1998 .

[15]  J. Skolnick,et al.  Method for prediction of protein function from sequence using the sequence-to-structure-to-function paradigm with application to glutaredoxins/thioredoxins and T1 ribonucleases. , 1998, Journal of molecular biology.

[16]  L. Mirny,et al.  Universally conserved positions in protein folds: reading evolutionary signals about stability, folding kinetics and function. , 1999, Journal of molecular biology.

[17]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[18]  Amos Bairoch,et al.  The ENZYME database in 2000 , 2000, Nucleic Acids Res..

[19]  R. Russell,et al.  Analysis and prediction of functional sub-types from protein sequence alignments. , 2000, Journal of molecular biology.

[20]  J M Thornton,et al.  Conservation helps to identify biologically relevant crystal contacts. , 2001, Journal of molecular biology.

[21]  M. Ondrechen,et al.  THEMATICS: A simple computational predictor of enzyme function from structure , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[22]  A. Elcock Prediction of functionally important residues based solely on the computed energetics of protein structure. , 2001, Journal of molecular biology.

[23]  Golan Yona,et al.  Within the twilight zone: a sensitive profile-profile comparison tool based on information theory. , 2002, Journal of molecular biology.

[24]  Gail J. Bartlett,et al.  Analysis of catalytic residues in enzyme active sites. , 2002, Journal of molecular biology.

[25]  W. S. Valdar,et al.  Scoring residue conservation , 2002, Proteins.

[26]  Robert B. Russell,et al.  Annotation in three dimensions , 2003 .

[27]  Gail J. Bartlett,et al.  Using a neural network and spatial clustering to predict the location of active sites in enzymes. , 2003, Journal of molecular biology.

[28]  O. Schueler‐Furman,et al.  Conserved residue clustering and protein structure prediction , 2003, Proteins.

[29]  Robert B. Russell,et al.  Annotation in three dimensions. PINTS: Patterns in Non-homologous Tertiary Structures , 2003, Nucleic Acids Res..

[30]  M. Gelfand,et al.  Automated selection of positions determining functional specificity of proteins by comparative analysis of orthologous groups in protein families , 2004, Protein science : a publication of the Protein Society.

[31]  J. Thornton,et al.  Searching for functional sites in protein structures. , 2004, Current opinion in chemical biology.

[32]  Janet M. Thornton,et al.  The Catalytic Site Atlas: a resource of catalytic sites and residues identified in enzymes using structural data , 2004, Nucleic Acids Res..

[33]  Daniel R. Caffrey,et al.  Are protein–protein interfaces more conserved in sequence than the rest of the protein surface? , 2004, Protein science : a publication of the Protein Society.

[34]  N. Ben-Tal,et al.  Comparison of site-specific rate-inference methods for protein sequences: empirical Bayesian methods are superior. , 2004, Molecular biology and evolution.

[35]  A. Panchenko,et al.  Prediction of functional sites by analysis of sequence and structure conservation , 2004, Protein science : a publication of the Protein Society.

[36]  P. Chakrabarti,et al.  Conservation and relative importance of residues across protein-protein interfaces , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[37]  Lynne Regan,et al.  Sequence variation in ligand binding sites in proteins , 2005, BMC Bioinformatics.

[38]  Ruben Abagyan,et al.  Statistical analysis and prediction of protein–protein interfaces , 2005, Proteins.

[39]  Z. Weng,et al.  Structure, function, and evolution of transient and obligate protein-protein interactions. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[40]  Itay Mayrose,et al.  ConSurf 2005: the projection of evolutionary conservation scores of residues on protein structures , 2005, Nucleic Acids Res..

[41]  Janet M. Thornton,et al.  PDBsum more: new summaries and analyses of the known 3D structures of proteins and nucleic acids , 2004, Nucleic Acids Res..

[42]  P. Bourne,et al.  Exploiting sequence and structure homologs to identify protein–protein binding sites , 2005, Proteins.

[43]  Kai Wang,et al.  Incorporating background frequency improves entropy-based residue conservation measures , 2006, BMC Bioinform..

[44]  Cathy H. Wu,et al.  Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties , 2006, BMC Bioinformatics.

[45]  Song Liu,et al.  Protein binding site prediction using an empirical scoring function , 2006, Nucleic acids research.

[46]  Thierry Paul,et al.  Quantum computation and quantum information , 2007, Mathematical Structures in Computer Science.