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Raphael Guerois

Researcher at Université Paris-Saclay

Publications -  141
Citations -  6919

Raphael Guerois is an academic researcher from Université Paris-Saclay. The author has contributed to research in topics: Homologous recombination & DNA repair. The author has an hindex of 41, co-authored 129 publications receiving 6001 citations. Previous affiliations of Raphael Guerois include Centre national de la recherche scientifique & French Alternative Energies and Atomic Energy Commission.

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Coevolution at protein complex interfaces can be detected by the complementarity trace with important impact for predictive docking

TL;DR: The so-called Surface COmplementarity Trace in Complex History score (SCOTCH), found to discriminate with high efficiency the structure of biological complexes, provides a basic framework to efficiently track how protein surfaces could evolve while keeping their partners in contact.
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Structure of the Histone Chaperone Asf1 Bound to the Histone H3 C-Terminal Helix and Functional Insights

TL;DR: The structure of the conserved domain of human ASF1A in complex with the C-terminal helix of histone H3 using nuclear magnetic resonance spectroscopy is solved and constitutes an essential step in the fundamental understanding of the mechanisms of nucleosome assembly by histone chaperones.
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InterEvDock2: an expanded server for protein docking using evolutionary and biological information from homology models and multimeric inputs

TL;DR: The InterEvDock2 pipeline was benchmarked on 812 complexes for which unbound homology models of the two partners and co-evolutionary information are available in the PPI4DOCK database and identified a correct model among the top 10 consensus in 29% of cases and at least one correct interface residue among 10 predicted in 91% of these cases.
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Evolution of protein interactions: From interactomes to interfaces

TL;DR: To what extent protein-protein interactions are found to be conserved within interactomes and which properties can influence their conservation are presented, and how the computational prediction of interfaces can benefit from evolutionary inputs is described.