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MBE Advance Access published online on October 26, 2005
Molecular Biology and Evolution, doi:10.1093/molbev/msj048
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© The Author 2005. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org
Accepted October 19, 2005

Research Article

Estimation of Amino Acid Residue Substitution Rates at Local Spatial Regions and Application in Protein Function Inference: A Bayesian Monte Carlo Approach

Yan Yuan Tseng 1 and Jie Liang 1*

1 Department of Bioengineering, SEO, MC-063 University of Illinois at Chicago 851 S. Morgan Street, Room 218 Chicago, IL 60607-7052, U.S.A.

* To whom correspondence should be addressed.
Jie Liang, E-mail: jliang{at}uic.edu


  Abstract

The amino acid sequences of proteins provide rich information for inferring distant phylogenetic relationships and for predicting protein functions. Estimating the rate matrix of residue substitutions from amino acid sequences is also important because the rate matrix can be used to develop scoring matrices for sequence alignment. Here we use a continuous time Markov process to model the substitution rates of residues and develop a Bayesian Markov chain Monte Carlo method for rate estimation. We validate our method using simulated artificial protein sequences. Because different local regions such as binding surfaces and the protein interior core experience different selection pressures due to functional or stability constraints, we use our method to estimate the substitution rates of local regions. Our results show that the substitution rates are very different for residues in the buried core and residues on the solvent exposed surfaces. In addition, the rest of the proteins on the binding surfaces also have very different substitution rates from residues. Based on these findings, we further develop a method for protein function prediction by surface matching using scoring matrices derived from estimated substitution rates for residues located on the binding surfaces. We show with examples that our method is effective in identifying functionally related proteins that have overall low sequence identity, a task known to be very challenging.

Keywords: Continuous time Markov process; Bayesian Markov chain Monte Carlo; amino acid substitution matrix; protein function prediction.
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