Nouvelles approches théoriques pour séparer les phénomènes de repliement et d'interaction dans le cadre d'une interaction protéine/métal par RMN et apprentissage profond // Disentangling protein folding from interaction by NMR and deep learning approaches

résumé en anglais uniquement
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Nuclear Magnetic Resonance (NMR) is an exquisite spectroscopic method that has shown its ability to explore molecular structures as well as their dynamics. In the case of protein studies, the rich NMR spectra constitute a fingerprint that probe protein structure (if any) and dynamics. NMR is also useful in the context of protein/metal interactions where the chemical shifts observables may be combined with NMR spin relaxation to track structural changes and interaction events. Unfortunately, these events are time averaged and do not allow a step-by-step analysis of the different outcome occurring upon metal binding. As a proof of concepts, we will use different short length peptides that are unstructured in their free states and adopt an helical structure upon silver ions binding.
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Début de la thèse : 01/10/2026
WEB : https://nmrbiolchem.univ-lyon1.fr/home

Public funding alone (i.e. government, region, European, international organization research grant)

Concours pour un contrat doctoral

Université Claude Bernard Lyon 1

206 Chimie de Lyon

We are seeking a motivated candidate with scientific curiosity, programming skills, and the desire to pursue interdisciplinary research. The successful candidate should have completed (or be in stage of completion) an M.Sc. degree in bioinformatics, physical chemistry or related fields. Familiarity with MD simulations, machine learning, and/or NMR would be strongly appreciated. We note that the candidate will be tasked with computational developments and analyses (programming, modelling) and will not be primarily in charge of experimental data collection (unless they express an interest for it).
We are seeking a motivated candidate with scientific curiosity, programming skills, and the desire to pursue interdisciplinary research. The successful candidate should have completed (or be in stage of completion) an M.Sc. degree in bioinformatics, physical chemistry or related fields. Familiarity with MD simulations, machine learning, and/or NMR would be strongly appreciated. We note that the candidate will be tasked with computational developments and analyses (programming, modelling) and will not be primarily in charge of experimental data collection (unless they express an interest for it).