title: Prediction of functional specificity determinants from protein sequences using log-likelihood ratios.
abstract: BACKGROUND
  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 non-trivial 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 a detailed analysis of two protein families (LacI/PurR and G protein alpha subunit), and compared our method with 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.
  
  
  BACKGROUND
  SPEL is freely available for non-commercial use. Its pre-compiled versions for several platforms and alignments used in this work are available at ftp://iole.swmed.edu/pub/SPEL/
authors.affiliation: Howard Hughes Medical Institute, University of Texas Southwestern Medical Center 5323 Harry Hines Boulevard, Dallas, TX 75390-9050, USA.
authors.forename: Jimin
authors.initials: J
authors.lastname: Pei
authors.forename: Wei
authors.initials: W
authors.lastname: Cai
authors.forename: Lisa N
authors.initials: LN
authors.lastname: Kinch
authors.forename: Nick V
authors.initials: NV
authors.lastname: Grishin
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D003198:Computer Simulation; D017124:Conserved Sequence; D016013:Likelihood Functions; D008954:Models, Biological; D008956:Models, Chemical; D008958:Models, Molecular; D015233:Models, Statistical; D011485:Protein Binding; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000145:classification* / Q000378:metabolism; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D012984:Software
chemical_list: D011506:Proteins