Description

résumé en anglais uniquement

Compétences requises

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).

Bibliographie

1. Blanc, F. E C; Hologne, M.; Girod, M.; Demontron, M.; Chermette, H.; Walker, O., Mechanism of Ag+-Induced Folding of a Bacterial Peptide from Replica-Exchange Molecular Simulations, J. Phys. Chem. Lett, 2026, accepted
2. Chabert, V.; Hologne, M.; Seneque, O.; Crochet, A.; Walker, O.; Fromm, K. M., Model peptide studies of Ag+ binding sites from the silver resistance protein SilE. Chem comm 2017, 53 (45), 6105-6108.
3. Chabert, V.; Hologne, M.; Seneque, O.; Walker, O.; Fromm, K. M., Alpha-helical folding of SilE models upon Ag(His)(Met) motif formation. Chem comm 2018, 54 (74), 10419-10422.
4. Babel, L.; Nguyen, M.-H.; Mittelheisser, C.; Martin, M.; Fromm, K. M.; Walker, O.; Hologne, M., NMR reveals the interplay between SilE and SilB model peptides in the context of silver resistance. Chem comm 2021, 57 (70), 8726-8729.

Mots clés

dynamique moléculaire, deep learning, RMN, protéine