Free energy along drug-protein binding pathways interactively sampled in virtual reality

• Published in: Scientific reports Vol. 13; no. 1; p. 16665
• Main Authors: Deeks, Helen M, Zinovjev, Kirill, Barnoud, Jonathan, Mulholland, Adrian J, van der Kamp, Marc W, Glowacki, David R
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 04.10.2023; Nature Publishing Group UK; Nature Portfolio
• Subjects: Benzamidine; Computer applications; Free energy; Humans; Ligands; Molecular Dynamics Simulation; Protein Binding; Proteins; Trypsin; Virtual Reality
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-43523-x

We describe a two-step approach for combining interactive molecular dynamics in virtual reality (iMD-VR) with free energy (FE) calculation to explore the dynamics of biological processes at the molecular level. We refer to this combined approach as iMD-VR-FE. Stage one involves using a state-of-the-art 'human-in-the-loop' iMD-VR framework to generate a diverse range of protein-ligand unbinding pathways, benefitting from the sophistication of human spatial and chemical intuition. Stage two involves using the iMD-VR-sampled pathways as initial guesses for defining a path-based reaction coordinate from which we can obtain a corresponding free energy profile using FE methods. To investigate the performance of the method, we apply iMD-VR-FE to investigate the unbinding of a benzamidine ligand from a trypsin protein. The binding free energy calculated using iMD-VR-FE is similar for each pathway, indicating internal consistency. Moreover, the resulting free energy profiles can distinguish energetic differences between pathways corresponding to various protein-ligand conformations (e.g., helping to identify pathways that are more favourable) and enable identification of metastable states along the pathways. The two-step iMD-VR-FE approach offers an intuitive way for researchers to test hypotheses for candidate pathways in biomolecular systems, quickly obtaining both qualitative and quantitative insight.