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June 2, Tuesday
12:00 – 14:00

We study statistical properties of interacting protein interfaces and predict two strong, related effects: (i) statistically enhanced self-attraction of proteins; (ii) statistically enhanced attraction of proteins with similar structures. The effects originate in the fact that the probability to find a pattern self-match between two identical, interacting protein interfaces is always higher compared with the probability for a pattern match between two different, promiscuous protein interfaces. This theoretical finding explains statistical prevalence of homodimers in protein-protein interaction networks reported earlier. Further, our findings are confirmed by the analysis of curated database of protein complexes that showed highly statistically significant overrepresentation of dimers formed by structurally similar proteins with highly divergent sequences (“superfamily heterodimers”). We predict that significant fraction of heterodimers evolved from homodimers with the negative design evolutionary pressure applied against promiscuous homodimer formation. This is achieved through the formation of highly specific contacts formed by charged residues as demonstrated both in model and real superfamily heterodimers. In addition we introduce the notion of structural correlations of amino acid interface density. We predict that protein interfaces with enhanced structural correlations are statistically more promiscuous as compared with proteins possessing a lower degree of interface structural correlations.

References:

1. D. B. Lukatsky and E. I. Shakhnovich, Statistically Enhanced Promiscuity of Structurally Correlated Patterns, Phys. Rev. E 77, 020901(R) (2008). 2. D. B. Lukatsky, B. E. Shakhnovich, J. Mintseris, and E. I. Shakhnovich, Structural Similarity Enhances Interaction Propensity of Proteins, J. Mol. Biol. 365, 1596 (2007). 3. D. B. Lukatsky, K. B. Zeldovich, and E. I. Shakhnovich, Statistically Enhanced Self-Attraction of Random Patterns, Phys. Rev. Lett. 97, 178101 (2006). .