Master internship - ED targets MD simulation

Modeling transitions between protein functional states by accelerated molecular dynamics

Context

Under the REACH regulation of chemical substances chemical substances are generally evaluated using in vivo or in vitro experimental methods following OECD guidelines. These tests can be long and expensive to perform and they are not acceptable anymore on an ethical point of view such as the acute/dermal oral toxicity for example. To help the chemical industry to limit experimentation, KREATiS offers in silico alternatives to experimentation according to the 3Rs principles (Replace – Reduce – Refine). The aim of KREATiS is to provide in silico models which are as accurate and reliable as good laboratory studies.

Website : KREATiS : https://www.kreatis.eu/

Purpose

Endocrine disruption in human and animal involve a myriad of proteins. Investigation of isolated proteins in vitro is challenging, use of cellular-based experiments hardly provides direct proof of function of a particular protein. Aiming at probing the potential for direct protein-ligand interactions, the simulations of molecular dynamics (MD) is an excellent tool. However, MD is computationally expensive, hence not applicable for screening studies.

We propose pure in silico project, where programmatic and computational effort are made to sample the conformational space of arbitrary proteins involved in endocrine disruption (ED) using Accelerated Molecular Dynamics (AMD). The results of this work will be employed to expand the applicability domain of the iSafeRat® SESAME-3D molecular docking framework.

KREATiS & CTMB in 2023 provided a proof of concept (not published), where we demonstrated the AMD steering of Estrogen Receptor Alpha between activated and antagonized functional  states. The selected internship candidate will implement (run and test) an automation generalizing the previously adopted protocol to sample target proteins conformational space. Relaying on experimental protein conformations the automated protocol will assemble the simulated systems (CHARMM), run AMD (NAMD, OpenMM, GPU nodes), analyze and subsample (in-house procs) the produced structural ensembles.

Key words

protein-ligand interactions, molecular dynamics simulations, HPC, automation

Required professional skills

We are looking for a highly motivated candidates willing to take part in a hybrid applied-fundamental  research project.

The candidate must be autonomous to work in Linux and programming automation preferably in Python.

Theoretic knowledge of molecular dynamics simulation is required, proven experience in MD will be further appreciated.

Minimum B2 or equivalent in English.

 Bibliography

[1] Melanie Schneider, Jean-Luc Pons, Gilles Labesse. 2021. “Exploring the conformational space of a receptor for drug design: An ERα case study.” J Mol Graph Model. 2021 Nov;108:107974. doi: 10.1016/j.jmgm.2021.107974. Epub 2021 Jun 29. DOI: 10.1016/j.jmgm.2021.107974.

[2] Polo C.-H. Lam, Ruben Abagyan, Maxim Totrov. 2018. “Ligand-biased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structure-based approach.” J Comput Aided Mol Des (2018) 32:187–198. DOI: 10.1007/s10822-017-0058-x.

[3] Gan, Jessie L., Dhruv Kumar, Cynthia Chen, Bryn C. Taylor, Benjamin R. Jagger, Rommie E. Amaro, and Christopher T. Lee. 2020. “Benchmarking Ensemble Docking Methods as a Scientific Outreach Project.” Preprint. Scientific Communication and Education. DOI: 10.1101/2020.10.02.324343.

[4] Amaro, Rommie E., Jerome Baudry, John Chodera, Özlem Demir, J. Andrew McCammon, Yinglong Miao, and Jeremy C. Smith. 2018. “Ensemble Docking in Drug Discovery.” Biophysical Journal 114 (10): 2271–78. DOI: 10.1016/j.bpj.2018.02.038.

If you are interested, please send us your CV, cover letter (including details about experiences and motivation for molecular modeling) and the student grades for all the courses >=BAC+3 to zlatomir.todorov@kreatis.eu.