OpenMMDL - Simplifying the Complex: Building, Simulating, and Analyzing Protein-Ligand Systems in OpenMM.
Valerij Talagayev, Yu Chen, Niklas Piet Doering, Leon Obendorf, Katrin Denzinger, Kristina Puls, Kevin D. Lam, Sijie Liu, Clemens Alexander Wolf, Theresa Noonan, Marko Breznik, Petra Knaus, Gerhard Wolber
Journal of chemical information and modeling February 24, 2025 DOI: 10.1021/acs.jcim.4c02158 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Software toolkit description with application examples Peer reviewed |
|---|---|
| Keywords | Molecular-simulation Pharmaceutical-research Drug-discovery Bioinformatics |
| Citations | 16 |
| Key points | OpenMMDL provides a dedicated open-source toolkit for preparing, simulating, and analyzing protein-ligand complexes in OpenMM, enabling studies of ligand binding modes and solvent dynamics across diverse biological systems. |
Abstract
Molecular dynamics (MD) simulations have become an essential tool for studying the dynamics of biological systems and exploring protein-ligand interactions. OpenMM is a modern, open-source software toolkit designed for MD simulations. Until now, it has lacked a module dedicated to building receptor-ligand systems, which is highly useful for investigating protein-ligand interactions for drug discovery. We therefore introduce OpenMMDL, an open-source toolkit that enables the preparation and simulation of protein-ligand complexes in OpenMM, along with the subsequent analysis of protein-ligand interactions. OpenMMDL consists of three main components: OpenMMDL Setup, a graphical user interface based on Python Flask to prepare protein and simulation settings, OpenMMDL Simulation to perform MD simulations with consecutive trajectory postprocessing, and finally OpenMMDL Analysis to analyze simulation results with respect to ligand binding. OpenMMDL is not only a versatile tool for analyzing protein-ligand interactions and generating ligand binding modes throughout simulations; it also tracks and clusters water molecules, particularly those exhibiting minimal displacement from their previous coordinates, providing insights into solvent dynamics. We applied OpenMMDL to study ligand-receptor interactions across diverse biological systems, including LDN-193189 and LDN-212854 with ALK2 (kinases), nifedipine and amlodipine in Cav1.1 (ion channels), LSD in 5-HT2B (G-protein coupled receptors), letrozole in CYP19A1 (cytochrome P450 oxygenases), flavin mononucleotide binding the FMN-riboswitch (RNAs), ligand C08 bound to TLR8 (toll-like receptor), and PZM21 bound to MOR (opioid receptor), highlighting distinct functionalities of OpenMMDL. OpenMMDL is publicly available at https://github.com/wolberlab/OpenMMDL.