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 predictive simulation of molecules and materials has had a broad and significant impact. It nevertheless remains constrained by the cost of accurately treating electronic correlation, excited states, and complex energy landscapes. Quantum computing offers a fundamentally different computational paradigm in which quantum states are encoded and manipulated directly rather than approximated on classical hardware. Here we discuss where this approach may provide a genuine scientific advantage in chemistry, materials science, and biochemistry. Promising directions include the high-accuracy treatment of correlated active spaces, improved excited-state simulations, and accelerated exploration of combinatorial structure spaces. The central challenge is therefore not qubit scaling alone, but demonstrably chemically meaningful gains in predictive reliability. We argue that near-term value is most likely to come from disciplined workflow integration rather than wholesale replacement of classical methods. Noisy physical devices, error-mitigated utility experiments, early fault-tolerant devices, and fully fault-tolerant quantum computers offer different scientific prospects, and claims of usefulness must be tied to the specific regime being discussed. Quantum computing will become scientifically valuable when it demonstrably reduces uncertainty in computed energies, rates, spectra, or materials stability after the full costs of state preparation, measurement, error handling, and coupling to classical simulation are included.
Bruno Camino, C. R. A. Catlow, J. Buckeridge et al.· 0 citations