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

Abstract

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 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.

References in corpus (9)

• Sampling molecular conformations and dynamics in a multi-user virtual reality framework
• Training neural nets to learn reactive potential energy surfaces using interactive quantum chemistry in virtual reality
• Recent progress in molecular simulation methods for drug binding kinetics
• Interactive molecular dynamics in virtual reality from quantum chemistry to drug binding: An open-source multi-person framework
• Autonomous robotic nanofabrication with reinforcement learning
• Interactive molecular dynamics in virtual reality for accurate flexible protein-ligand docking
• Finding Multiple Reaction Pathways of Ligand Unbinding
• Visualizing biomolecular electrostatics in virtual reality with UnityMol-APBS
• Training atomic neural networks using fragment-based data generated in virtual reality

Cited by in corpus (2)

• Virtual Reality for Understanding Artificial-Intelligence-driven Scientific Discovery with an Application in Quantum Optics
• AI-Guided Molecular Simulations in VR: Exploring Strategies for Imitation Learning in Hyperdimensional Molecular Systems