Proteins-nanoparticles interaction modeling: a machine learning and molecular simulation mixed approach

Type de poste
Niveau d'étude minimal
Dates
Durée du poste
Contrat renouvelable
Contrat non renouvelable
Date de prise de fonction
Date de fin de validité de l'annonce
Localisation
Nom de la structure d'accueil
Adresse

CEA Saclay
91191 Gif-sur-Yvette
France

Contacts
Jean-Christophe Aude
Yves Boulard
Email du/des contacts
jean-christophe.aude@cea.fr
yves.boulard@cea.fr
Description

This thesis is part of the NUMERICS project that has received funding from the European Union's Horizon 2020 research and innovation programme. Applicants shall not have resided or carried out their main activity in France more than 12 months in the three years prior to the call deadline (2020-04-30)
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Nowdays nanoparticles (NPs) are present in various fields such as the industrial and food processes and more recently in the medical field, but also as a side product of the pollution generated by our modern society (carbon NPs, plastic,…). The exposure of the living world to these materials, notably the nano plastics, raises the question of their toxicity.

Traditionally these issues are considered at a cellular level either in vitro or in vivo. In this project we will focus on predicting and anticipating the impact at the molecular scale of an exposure to the NPs. Indeed, in the framework of the toxicity assessment, the biological object to consider is not the bare particle but the surface covered with biological molecules, also denote corona. To this purpose, we will build up on results obtained using a large scale proteomic approach with silica and titanium dioxide NPs and on an ongoing project on plastic particles. These results showed very strong differences between some physico-chemical and structural properties of adsorbed versus non-adsorbed proteins with some functional consequences. The knowledge of the physico-chemical surface properties combined with the adsorbed proteins properties allows to build predictive adsorption models of proteins onto these surfaces but also on surfaces under development.

The subject of this thesis is to develop new models, based on machine learning methods, capable of estimating the probability of an interaction between a given cellular protein and a nano-surface. These models will be improved using coarse grain molecular simulations as well as model validation purposes.

Equipe adhérente personne morale SFBI
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