title: Prediction of contact matrix for protein-protein interaction.
abstract: BACKGROUND
  Prediction of protein-protein interaction has become an important part of systems biology in reverse engineering the biological networks for better understanding the molecular biology of the cell. Although significant progress has been made in terms of prediction accuracy, most computational methods only predict whether two proteins interact but not their interacting residues-the information that can be very valuable for understanding the interaction mechanisms and designing modulation of the interaction. In this work, we developed a computational method to predict the interacting residue pairs-contact matrix for interacting protein domains, whose rows and columns correspond to the residues in the two interacting domains respectively and whose values (1 or 0) indicate whether the corresponding residues (do or do not) interact.
  
  
  RESULTS
  Our method is based on supervised learning using support vector machines. For each domain involved in a given domain-domain interaction (DDI), an interaction profile hidden Markov model (ipHMM) is first built for the domain family, and then each residue position for a member domain sequence is represented as a 20-dimension vector of Fisher scores, characterizing how similar it is as compared with the family profile at that position. Each element of the contact matrix for a sequence pair is now represented by a feature vector from concatenating the vectors of the two corresponding residues, and the task is to predict the element value (1 or 0) from the feature vector. A support vector machine is trained for a given DDI, using either a consensus contact matrix or contact matrices for individual sequence pairs, and is tested by leave-one-out cross validation. The performance averaged over a set of 115 DDIs collected from the 3 DID database shows significant improvement (sensitivity up to 85%, and specificity up to 85%), as compared with a multiple sequence alignment-based method (sensitivity 57%, and specificity 78%) previously reported in the literature.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA.
authors: Alvaro J
authors: AJ
authors: González
authors: Li
authors: L
authors: Liao
authors: Cathy H
authors: CH
authors: Wu
mesh_terms: D019295:Computational Biology / Q000379:methods; D008390:Markov Chains; D008958:Models, Molecular; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping / Q000379:methods*; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods; D060388:Support Vector Machine