title: Identification of specificity determining residues in peptide recognition domains using an information theoretic approach applied to large-scale binding maps.
abstract: BACKGROUND
  Peptide Recognition Domains (PRDs) are commonly found in signaling proteins. They mediate protein-protein interactions by recognizing and binding short motifs in their ligands. Although a great deal is known about PRDs and their interactions, prediction of PRD specificities remains largely an unsolved problem.
  
  
  RESULTS
  We present a novel approach to identifying these Specificity Determining Residues (SDRs). Our algorithm generalizes earlier information theoretic approaches to coevolution analysis, to become applicable to this problem. It leverages the growing wealth of binding data between PRDs and large numbers of random peptides, and searches for PRD residues that exhibit strong evolutionary covariation with some positions of the statistical profiles of bound peptides. The calculations involve only information from sequences, and thus can be applied to PRDs without crystal structures. We applied the approach to PDZ, SH3 and kinase domains, and evaluated the results using both residue proximity in co-crystal structures and verified binding specificity maps from mutagenesis studies.
  
  
  CONCLUSIONS
  Our predictions were found to be strongly correlated with the physical proximity of residues, demonstrating the ability of our approach to detect physical interactions of the binding partners. Some high-scoring pairs were further confirmed to affect binding specificity using previous experimental results. Combining the covariation results also allowed us to predict binding profiles with higher reliability than two other methods that do not explicitly take residue covariation into account.
  
  
  CONCLUSIONS
  The general applicability of our approach to the three different domain families demonstrated in this paper suggests its potential in predicting binding targets and assisting the exploration of binding mechanisms.
authors.affiliation: Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA.
authors.forename: Kevin Y
authors.initials: KY
authors.lastname: Yip
authors.forename: Lukas
authors.initials: L
authors.lastname: Utz
authors.forename: Simon
authors.initials: S
authors.lastname: Sitwell
authors.forename: Xihao
authors.initials: X
authors.lastname: Hu
authors.forename: Sachdev S
authors.initials: SS
authors.lastname: Sidhu
authors.forename: Benjamin E
authors.initials: BE
authors.lastname: Turk
authors.forename: Mark
authors.initials: M
authors.lastname: Gerstein
authors.forename: Philip M
authors.initials: PM
authors.lastname: Kim
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000378:metabolism*; D006801:Humans; D007257:Information Theory; D008958:Models, Molecular; D016296:Mutagenesis / Q000235:genetics; D010455:Peptides / Q000737:chemistry* / Q000378:metabolism*; D056510:Position-Specific Scoring Matrices; D011485:Protein Binding; D017434:Protein Structure, Tertiary; D015203:Reproducibility of Results
chemical_list: D000596:Amino Acids; D010455:Peptides