Co-evolution (Correlated Mutations) as An Indicator of Protein and Domain Interactions

Raja Jothi, Ph.D.
National Institutes of Health (NIH)
National Center for Biotechnology Information, Bethesda, MD

Abstract

Post-genomic advances in molecular biology have helped uncover the intricate interplay between proteins in metabolic, signaling and regulatory pathways. Identification of protein-protein interactions is an essential step towards a better understanding of various cellular processes. Various high-throughput experimental techniques such as mass spectrometry, yeast two-hybrid, and tandem affinity purification have been used to discover and generate large scale protein interaction data. In addition, several computational approaches towards predicting protein-protein interactions have been proposed in an effort to complement experimental methods. In the first part of my talk, I will discuss the protein interaction specificity problem in which the objective is to identify interaction partners between members of two families of proteins that are known to interact, e.g., ligands and receptors. I will present our algorithm MORPH [1], which explores the evolutionary tree automorphism space to predict interaction partners based on the theory of co-evolution.

Protein-protein interaction data, though extremely valuable towards molecular level nderstanding of cells, do not provide insights on interaction specificity at the domain level. Most often, it is only a fraction of a protein that directly interacts with its biological partners. Since two thirds of proteins in prokaryotes and four fifths of proteins in eukaryotes are multidomain proteins, interaction between two proteins (either stably or transiently) often involves binding of pair(s) of domains. Importantly, understanding interaction at the domain level is a critical step towards a thorough understanding of the protein-protein interaction networks and their evolution. In the second part of my talk, I will discuss our results on the co-evolutionary analysis of domain pairs in interacting proteins [2]. We used a combination of sequence and structural analysis to analyze protein-protein interactions in F1-ATPase, Sec23p/Sec24p, DNA-directed RNA polymerase and nuclear pore complexes, and found that interacting domain pair(s) for a given interaction exhibits higher level of co-evolution than the non-interacting domain pairs. Based on this finding, we developed a computational method to predict large-scale domain-domain interactions from the yeast interactome. Our results show that the prediction accuracy of the proposed method is statistically significant, and better than that of other methods. The proposed method can be used in conjunction with other methods to help identify previously unrecognized domain-domain interactions on a genomic scale, and could potentially help reduce the search space for identifying interaction (binding) sites that could be potential drug targets.

[1] Jothi et al., Predicting protein-protein interaction by searching evolutionary tree automorphism space, Bioinformatics, Suppl 1:i240-50, 2005.
[2] Jothi et al., Co-evolutionary analysis of domains in interacting proteins reveals insights into domain-domain interactions mediating protein-protein interactions, J Mol Biol, 362(4):861-75, 2006.

Speaker Bio

Dr. Raja Jothi is a Research Associate at the National Center for Biotechnology Information (NCBI), National Institutes of Health (NIH). He received a B.E. in Computer Science and Engineering from the University of Madras (1998), and a M.S. and a Ph.D. in Computer Science from the University of Texas at Dallas (2000 and 2004, respectively). His general areas of interest include Computational and Systems Biology, Bioinformatics, and Algorithms. The underlying theme of his current research is to study and understand protein and genome evolution using comparative genomics, and thus uncover the underlying organizing principles governing cellular systems to better understand biological processes. For more information on his research work, please visit http://www.rajajothi.com.