Physics-based models of biomolecular systems that explicitly represent biomolecular structure and mechanics, such as atomistic molecular dynamics simulations are well-established because experimental data has been available to iteratively improve and validate models. Now, simulations of the biological mesoscale are growing in importance because of the improvements in experimental tools to visualise this regime. This includes techniques such as cryo-electron microscopy and tomography, microscopies that follow individual proteins in their cellular contexts, in situ scattering to follow the dynamic evolution of biomolecular assembly, and -omics tools. Together, these approaches alone and in combination have revealed the importance of interactomes that bridge multiple scales. Here we describe the theoretical, computational and cultural challenges that need to be overcome to gain an understanding of the biological mesoscale and offer potential solutions. This commentary is the result of a joint CECAM/CCPBioSim discussion workshop on how the community should address the challenges of biomolecular simulations at the mesoscale held in Trento, Italy in the summer of 2024. The aim is to provide a broad overview of the tools and techniques relevant to the biological mesoscale, and to signpost the reader to more detailed discussions within the cited literature.
Sarah Harris, Gianluca Lattanzi, Angelo Rosa et al.· Biophysical Journal· 0 citations
The kinetics of protein-ligand binding systems are increasingly recognized as a key determinant of drug efficacy, yet remain far harder to compute than binding affinities. Existing kinetics methods either bias the dynamics along a collective variable (CV), demanding careful system-specific CV design, or use path sampling, which keeps the dynamics unbiased but can struggle to converge rates out of deep free-energy wells and often relies on hand-engineered descriptors. By combining the `best of both worlds', we propose a method to compute accurate kinetics for general ligand-unbinding problems at modest computational expense and minimal fine tuning, building on the AI for Molecular Mechanism Discovery (AIMMD) path sampling framework. To avoid the need for feature engineering, we opt for modelling the committor with a single descriptor-free, equivariant graph neural network shared across all systems. We also partially flatten deep bound-state wells with a static, basin-restricted bias potential. This improves convergence by lifting the path sampling state boundary out of regions, where the committor is hard to learn, while leaving the reactive region strictly unbiased. Across host-guest and protein-ligand systems spanning roughly 17 orders of magnitude in residence time, the method robustly recovers rates in line with reference and experimental values. Simultaneously, and without further sampling, it also reconstructs the underlying unbinding mechanisms. We additionally find that accurate rates do not require globally accurate committor models, allowing for efficient kinetics estimation even in a low-data training regime. Requiring little system-specific setup, our approach offers an efficient and broadly generalizable route to binding kinetics, and its shared committor architecture lays crucial groundwork for probing structure-kinetics relationships across ligand series in drug discovery.