Mots-Clés

Interfering peptide, in silico modeling, peptide optimization, protein-peptide interactions

Description

Parkinson's disease (PD) is one of the major degenerative diseases for which there is no cure. LRRK2, one of the major genetic determinants of sporadic and familial forms of PD, has emerged as a promising therapeutic target. Specifically, the importance of LRRK2 phosphorylation for its physiological and pathological functioning has recently become clear. Peptides interfering with the LRRK2/PP1 interactions have been identified (Dong et al., PlosONE, 2020; PMID: 32790695; patent 19305365.9-1120).

The team "Computational Modelling of Protein Ligand Interactions" of the CNRS UMR 8251/INSERM U1133 is looking for a post-doc (18 months) to design optimized peptides to interfere with LRRK2/PP1 and that are able to pass the blood brain barrier. The post-doc will study in silico peptide-receptor interaction (hot spots, stability, specificity, sequence variants, ...). The project merges skills from five teams, including chemists, biochemists and experts in Parkinson's disease. Design will be supervised using both in vitro and in vivo control using animal models of Parkinson's disease.

Recent advances in deep learning have led to a revolution in computational structural biology (AlphaFold, RoseTTAfold). As it is now much easier to predict the structure of an unknown protein or peptide, this has major implications for the rational design of therapeutic peptides.

The applicant is expected to be familiar with protein/peptide structure modeling and analysis. Programming skills and experience in in silico protocol development are required. Prior experience in protein/peptide design or protein-protein/peptide interactions is highly desirable.

Qualifications/Experience:

PhD (or equivalent) in Structural Bioinformatics, Data analysis, Computational biology or physics.

Experience in 3D data analysis, molecular modeling and simulation.

Good knowledge of standard programming languages: python, bash or R.

Familiar with Versioning Systems (Git).

Knowledge in molecular biology. Fluent in English. Proficiency in French is desirable.

Excellent written and oral communication skills.