Bioinformatics Advance Access published online on November 8, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti766
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1 Department of Biochemistry, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA
* To whom correspondence should be addressed.
Motivation: A number of methods have been developed to predict functional specificity determinants in protein families based on sequence information. Most of these methods rely on pre-defined functional subgroups. Manual subgroup definition is difficult because of the limited number of experimentally characterized subfamilies with differing specificity, while automatic subgroup partitioning using computational tools is a nontrivial task and does not always yield ideal results. Results: We propose a new approach SPEL (Specificity Positions by Evolutionary Likelihood) to detect positions that are likely to be functional specificity determinants. SPEL, which does not require subgroup definition, takes a multiple sequence alignment of a protein family as the only input, and assigns a P-value to every position in the alignment. Positions with low P-values are likely to be important for functional specificity. An evolutionary tree is reconstructed during the calculation, and P-value estimation is based on a random model that involves evolutionary simulations. Evolutionary log-likelihood is chosen as a measure of amino acid distribution at a position. To illustrate the performance of the method, we carried out detailed analysis of two protein families (LacI/PurR and G protein Availability: SPEL is freely available for noncommercial use. Its pre-compiled versions for several platforms, alignments used in this work and supplementary materials are available at ftp://iole.swmed.edu/pub/SPEL/.
Received August 9, 2005
Revised November 3, 2005
Accepted November 3, 2005
Article
Prediction of functional specificity determinants from protein sequences using log-likelihood ratios
2 Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA
3 Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA; Department of Biochemistry, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA
Nick V. Grishin, E-mail: grishin{at}chop.swmed.edu
Abstract 
subunit), and compared our method to two existing methods (evolutionary trace and mutual information-based). All three methods were also compared on a set of protein families with known ligand-bound structures.
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