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UID:27832-1743152400-1743159600@www.loria.fr
SUMMARY:Soutenance HDR de Hamed Khakzad
DESCRIPTION:La soutenance HDR de Hamed Khakzad\, intitulée « Machine Learning-Driven Integrative Structural Biology and Protein Design« \, aura lieu le vendredi 28 mars à 9h\, dans l’amphithéâtre du Loria. \nAbstract\nBiological macromolecules interact to form molecular complexes\, enabling them to perform their functions. Understanding the structure\, interactions\, and functions of these complexes is a central goal of structural biology. In recent years\, advances in deep learning have led to remarkable breakthroughs in addressing key challenges in this field through data-driven approaches. However\, macromolecules are inherently dynamic\, and their conformational heterogeneity directly affects their structure and function. While accounting for this flexibility is crucial\, most current data-driven methods focus on static structures\, often leading to inaccurate predictions in cases where flexibility plays a key role. My research focuses on developing novel\, dynamic-aware\, data-driven approaches to improve the prediction of macromolecular interactions and functions\, specifically targeting host-pathogen interactions to elucidate antimicrobial resistance. Additionally\, I aim to leverage these models for the design of novel functional proteins to fight against pathogens.\n\nJury\n\nAlessandra CARBONE\, Sorbonne Université (Reviewer)\nFrédéric CAZALS\, Inria Centre Université Côte d’Azur (Reviewer)\nWim VRANKEN\, Vrije Universiteit Brussel (Reviewer)\nNadia IZADI-PRUNEYRE\, Institut Pasteur (Jury Member)\nFabio PIETRUCCI\, Sorbonne Université (Jury Member)\nAbdullah KAHRAMAN\, FHNW-Zurich (Jury Member)
URL:https://www.loria.fr/event/soutenance-hdr-de-hamed-khakzad/
CATEGORIES:HDR
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