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"Guaranteed Optimization for Computational Protein Design and Applications to Bio-nanotechnology", David Simoncini, colloque de l'IRIT

le 20 novembre 2018

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David Simoncini, maître de conférences dans l'équipe REVA, nous présentera ses travaux intitulés "Guaranteed Optimization for Computational Protein Design and Applications to Bio-nanotechnology".

Abstract: Computational Protein Design (CPD) refers to the problem of finding a protein sequence that maximizes the stability of a protein structure, or the affinity of a protein with a binding partner. At the core of CPD, lies a compact formulation of the Global Minimum Energy Conformation problem. This formulation assumes a rigid protein structure, captures amino-acid conformation changes in a discrete search space and relies on a pairwise decomposable description of the energy. Despite its intrinsic computational hardness, CPD has started to yield several completely new functional molecules in the last decade. If deterministic algorithms give a guaranteed access to optimal designed sequences, the NP-hardness of the problem limits them to small designs. Stochastic methods are therefore often preferred. In this talk, we will present how we used constraint programming to tackle large computational protein design problems and give a few examples of applications in bio-nanotechnology.
Mis à jour le 13 novembre 2018