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From complete cross-docking to partners identification and binding sites predictions

Abstract : Proteins ensure their biological functions by interacting with each other. Hence, characterising protein interactions is fundamental for our understanding of the cellular machinery, and for improving medicine and bioengineering. Over the past years, a large body of experimental data has been accumulated on who interacts with whom and in what manner. However, these data are highly heterogeneous and sometimes contradictory, noisy, and biased. Ab initio methods provide a means to a "blind" protein-protein interaction network reconstruction. Here, we report on a molecular cross-docking-based approach for the identification of protein partners. The docking algorithm uses a coarse-grained representation of the protein structures and treats them as rigid bodies. We applied the approach to a few hundred of proteins, in the unbound conformations, and we systematically investigated the influence of several key ingredients, such as the size and quality of the interfaces, and the scoring function. We achieved some significant improvement compared to previous works, and a very high discriminative power on some specific functional classes. We provide a readout of the contributions of shape and physico-chemical complementarity, interface matching, and specificity, in the predictions. In addition, we assessed the ability of the approach to account for protein surface multiple usages, and we compared it with a sequence-based deep learning method. This work may contribute to guiding the exploitation of the large amounts of protein structural models now available toward the discovery of unexpected partners and their complex structure characterisation.
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Submitted on : Monday, January 31, 2022 - 2:25:30 PM
Last modification on : Tuesday, September 27, 2022 - 4:42:02 AM
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Chloé Dequeker, Yasser Mohseni Behbahani, Laurent David, Elodie Laine, Alessandra Carbone. From complete cross-docking to partners identification and binding sites predictions. PLoS Computational Biology, Public Library of Science, 2022, 18 (1), pp.e1009825. ⟨10.1371/journal.pcbi.1009825⟩. ⟨hal-03549513⟩



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