Decrypting protein surfaces by combining evolution, geometry and molecular docking

Abstract

The growing body of experimental and computational data describing how proteins interact with each other has emphasized the multiplicity of protein interactions and the complexity underlying protein surface usage and deformability. In this work, we propose new concepts and methods toward deciphering such complexity. We introduce the notion of interacting region to account for the multiple usage of a proteins surface residues by several partners and for the variability of protein interfaces coming from molecular flexibility. We predict interacting patches by crossing evolutionary, physico-chemical and geometrical properties of the protein surface with information coming from complete cross-docking (CC-D) simulations. We show that our predictions match well interacting regions and that the different sources of information are complementary. We further propose an indicator of whether a protein has a few or many partners. Our prediction strategies are implemented in the dynJET2 algorithm and assessed on a new dataset of 262 protein on which we performed CC-D. The code and the data are available at: http://www.lcqb.upmc.fr/dynJET2/.\n\nAuthor summaryThe multiplicity and versatility of protein interactions make protein surfaces complex biological objects. For instance, a protein may interact with several partners at different moments via the same region at its surface, or it may use several regions, with different shapes and evolutionary origins, to establish different types of interactions. In addition, interfaces can re-adjust depending on environmental conditions. In this work, we introduce the notion of interaction region\", in contrast to interaction site\", which accounts for the interface variability coming from molecular flexibility and for the multiple usage of the protein surface by several partners. Moreover, we use four biologically meaningful descriptors to delineate protein surface patches. Three of them, namely evolutionary conservation, physico-chemical composition and local geometry, are computed for a given protein, without any knowledge on its potential partners. The fourth property is inferred from the behavior of the protein with respect to other proteins, partners or not, in docking simulations. We show that predicted patches match well with known interacting regions, that the four descriptors are complementary and that they enable capturing most of the signal relevant to protein interactions. We further exploit the way patches are grown to precisely localize interaction regions, and to estimate whether a protein have a few or many partners.