Computational discovery of TMPRSS2 inhibitors

Description: Combining molecular simulations, active learning and experimental validation to discover inhibitors of TMPRSS2.
Duration: 2020–2025
Affiliation: Free University of Berlin
Role: Research Fellow

Overview

This project focused on discovering inhibitors of TMPRSS2, a host protease required for coronavirus entry. The work progressed from mechanistic studies of TMPRSS2 inhibition (Azouz et al., 2021) and drug repurposing strategies (Hempel et al., 2021) (Hempel et al., 2023), through large-scale community-driven drug discovery efforts (Schimunek et al., 2023), to the development of a screening framework that led to the discovery of a nanomolar inhibitor with broad coronavirus activity (Elez et al., 2025).

Key Contributions

  • Identified and characterized TMPRSS2 inhibitors with antiviral activity.
  • Demonstrated synergistic inhibition of SARS-CoV-2 cell entry using drug combinations.
  • Contributed to a patented pharmaceutical composition for COVID-19 treatment.
  • Participated in a large-scale open-science drug discovery initiative during the COVID-19 pandemic.
  • Developed an active-learning framework that reduced computational screening requirements by ~29-fold.
  • Discovered BMS-262084, a nanomolar inhibitor with broad coronavirus activity.

Background

TMPRSS2 is a host protease required for activation of coronavirus spike proteins and viral entry into human cells. Because it is a host protein rather than a viral target, it represents a promising therapeutic target with reduced risk of resistance from viral mutations. Despite its therapeutic relevance, discovering potent and selective TMPRSS2 inhibitors remains challenging. The protein exhibits conformational flexibility and experimentally screening large compound libraries is costly and time-consuming. These challenges make TMPRSS2 a strong candidate for computational drug discovery approaches that integrate molecular simulations and machine learning to prioritize compounds for experimental testing.

Methodology

  • Molecular docking
  • Molecular dynamics simulations
  • Ensemble-based virtual screening
  • Active learning
  • Target-specific scoring function
  • Experimental validation (biochemical assays and cell-based)

Collaborators

  • Free University of Berlin: Tim Hempel, Lluís Raich, Robin Winter, Tuan Le, Simon Olsson, Frank Noé
  • German Primate Center - Leibniz Institute for Primate Research: Nadine Krüger, Nicole Moor, Cheila Rocha, Stefan Pöhlmann, Markus Hoffmann
  • National Institutes of Health: Jonathan H. Shrimp, Min Shen, Matthew D. Hall
  • Cincinnati Children’s Hospital Medical Center: Nurit P. Azouz, Marc E. Rothenberg

References

2025

  1. elez_simulations_2025.jpg
    Simulations and active learning enable efficient identification of an experimentally-validated broad coronavirus inhibitor
    Katarina Elez, Tim Hempel, Jonathan H. Shrimp, Nicole Moor, Lluís Raich, and 7 more authors
    Nature Communications, 2025

2023

  1. hempel_pharmaceutical_2023.png
    Pharmaceutical Composition for Treating Covid-19 Comprising Otamixaban and at Least One of Camostat and Nafamostat
    Tim Hempel, Katarina Elez, Lluís Raich, Frank Noé, Nadine Krüger, and 2 more authors
    2023
    EP4122461A1
  2. schimunek_community_2023.jpg
    A Community Effort in SARS-CoV-2 Drug Discovery
    Johannes Schimunek, Philipp Seidl, Katarina Elez, Tim Hempel, Tuan Le, and 147 more authors
    Molecular Informatics, 2023

2021

  1. azouz_alpha_2021.jpg
    Alpha 1 Antitrypsin Is an Inhibitor of the SARS-CoV-2–Priming Protease TMPRSS2
    Nurit P. Azouz, Andrea Klingler, Victoria Callahan, Ivan Akhrymuk, Katarina Elez, and 7 more authors
    Pathogens and Immunity, 2021
  2. hempel_synergistic_2021.gif
    Synergistic Inhibition of SARS-CoV-2 Cell Entry by Otamixaban and Covalent Protease Inhibitors: Pre-Clinical Assessment of Pharmacological and Molecular Properties
    Tim Hempel, Katarina Elez, Nadine Krüger, Lluís Raich, Jonathan H. Shrimp, and 8 more authors
    Chemical Science, 2021