A hidden Markov model for predicting protein interfaces.

Cao Nguyen, Katheleen J Gardiner, Krzysztof J Cios
Author Information
  1. Cao Nguyen: Department of Computer Science and Engineering, University of Colorado at Denver and Health Sciences, Denver, CO 80217, USA. dcnguyen@ouray.cudenver.edu

Abstract

Protein-protein interactions play a defining role in protein function. Identifying the sites of interaction in a protein is a critical problem for understanding its functional mechanisms, as well as for drug design. To predict sites within a protein chain that participate in protein complexes, we have developed a novel method based on the Hidden Markov Model, which combines several biological characteristics of the sequences neighboring a target residue: structural information, accessible surface area, and transition probability among amino acids. We have evaluated the method using 5-fold cross-validation on 139 unique proteins and demonstrated precision of 66% and recall of 61% in identifying interfaces. These results are better than those achieved by other methods used for identification of interfaces.

Grants

  1. HD047671/NICHD NIH HHS
  2. HD49460/NICHD NIH HHS

MeSH Term

Algorithms
Amino Acids
Binding Sites
Computational Biology
Databases, Protein
Markov Chains
Models, Molecular
Multiprotein Complexes
Protein Conformation
Proteins

Chemicals

Amino Acids
Multiprotein Complexes
Proteins

Word Cloud

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