Project
Inhibiting SARS-CoV2 cell entry
Project Heads
Frank Noé
Project Members
Katarina Elez, Tim Hempel, Simon Olsson, Lluis Raich
Cooperation Partner
Stefan Pöhlmann, Markus Hoffmann (DPZ Göttingen)
Oliver Daumke (MDC Berlin)
JEDI Covid19 group: https://www.covid19.jedi.group/
Project Duration
1-2 years
Located at
FU Berlin
Description
SARS-Cov-2 initiates cell entry by first getting its Spike (S) protein activated by the TMPRSS2 protein on human cells. We model and simulate the structure of TMPRSS2 with high-throughput adaptive molecular dynamics and Markov state modeling. We develop new mathematical and machine learning methods for predicting the binding affinity of drug molecules that may inhibit TMPRSS2 activity and therefore prevent SARS-Cov-2 cell entry and replication. We collaborate with the JEDI Covid19 consortium by providing a list of candidate inhibitor molecules that will be tested in the wetlab. We collaborate with other academic partners (Daumke, Pöhlmann) to establish an in vitro screening platform and to test whether virus entry is indeed inhibited.
Keywords
Machine learning, molecular dynamics, drug design, TMPRSS2
More Information
Machine learning for molecular simulation
F Noé, A Tkatchenko, KR Müller, C Clementi
Annual review of physical chemistry 71, 361-390 (2020)
Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
F Noé, S Olsson, J Köhler, H Wu
Science 365 (6457), eaaw1147 (2019)
VAMPnets for deep learning of molecular kinetics
A Mardt, L Pasquali, H Wu, F Noé
Nature communications 9 (1), 1-11 (2018)
Project Type
University based
Project Funding