Abstract: The elucidation of the structure of macromolecular interactions is essential to better understand and modulate cellular functions and pathological situations. Our team is invested in the development of improved computational prediction methods, including the prediction of binding sites and the docking of proteins [1-2]. In complement, the integration of spatial constraints from Deep Mutation Scanning (DMS) experiments provide valuable tools for disrupting interactomes through the generation of edgetic and compensatory mutants. I will present how the evolutionary properties of interface structures can be combined with machine learning algorithms [3] and DMS experiments to provide improved structural mapping and functional dissection of conserved protein interaction networks [4-5]. 

[1] Nadaradjane et al. Docking proteins and peptides under evolutionary constraints in Critical Assessment of PRediction of Interactions rounds 38 to 45. Proteins. (2020) Aug;88(8):986-998. 
[2] Quignot et al. Atomic-level evolutionary information improves protein-protein interface scoring. BioRxiv 2020. https://doi.org/10.1101/2020.10.26.355073
[3] Quignot et al. InterEvDock3: A combined template-based and free docking server with increased performance through explicit modelling of complex homologs and integration of covariation-based contact maps. Submitted
[4] Sanchez A et al. Exo1 recruits Cdc5 polo kinase to MutLγ to ensure efficient meiotic crossover formation. PNAS (2020) 117(48):30577-30588.