Incorporating information on predicted solvent accessibility to the co-evolution-based study of protein interactions

Mol Biosyst. 2013 Jan 27;9(1):70-6. doi: 10.1039/c2mb25325a. Epub 2012 Oct 26.


A widespread family of methods for studying and predicting protein interactions using sequence information is based on co-evolution, quantified as similarity of phylogenetic trees. Part of the co-evolution observed between interacting proteins could be due to co-adaptation caused by inter-protein contacts. In this case, the co-evolution is expected to be more evident when evaluated on the surface of the proteins or the internal layers close to it. In this work we study the effect of incorporating information on predicted solvent accessibility to three methods for predicting protein interactions based on similarity of phylogenetic trees. We evaluate the performance of these methods in predicting different types of protein associations when trees based on positions with different characteristics of predicted accessibility are used as input. We found that predicted accessibility improves the results of two recent versions of the mirrortree methodology in predicting direct binary physical interactions, while it neither improves these methods, nor the original mirrortree method, in predicting other types of interactions. That improvement comes at no cost in terms of applicability since accessibility can be predicted for any sequence. We also found that predictions of protein-protein interactions are improved when multiple sequence alignments with a richer representation of sequences (including paralogs) are incorporated in the accessibility prediction.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computational Biology / methods*
  • Evolution, Molecular
  • Models, Molecular
  • Phylogeny
  • Protein Interaction Domains and Motifs*
  • Protein Interaction Mapping
  • Proteins / chemistry*
  • Proteins / genetics
  • Proteins / metabolism
  • Sequence Alignment
  • Sequence Analysis, Protein


  • Proteins