Multiscale Modeling of Biomolecular Systems

Biological function emerges across multiple spatial and temporal scales, from atomic interactions in proteins and nucleic acids to the collective behavior of molecular assemblies in complex cellular environments. Understanding these processes increasingly requires integrative computational approaches capable of connecting molecular detail with larger-scale biological function.Recent advances in molecular simulation, integrative structural biology, high-performance computing, and artificial intelligence (AI) are rapidly transforming biomolecular modeling. Enhanced-sampling methods, hybrid multiscale simulations, and machine learning–driven approaches are improving the accuracy, scalability, and efficiency of modeling complex biomolecular systems. At the same time, the integration of computational methods with experimental techniques such as cryo-EM, cryo-ET, NMR, SAXS, and mass spectrometry is enabling more comprehensive and physiologically relevant representations of biomolecular dynamics and interactions.Despite this progress, major challenges remain in achieving robust cross-scale integration, accurately representing heterogeneous cellular environments, and balancing physical interpretability with computational efficiency. Addressing these challenges requires new theoretical frameworks, scalable algorithms, hybrid simulation methodologies, and closer integration between experimental and computational approaches.This Research Topic will focus on methodological developments and biological applications that integrate mechanistic and statistical approaches to model biomolecular dynamics, conformational change, intermolecular recognition, and function in physiologically relevant contexts. Particular emphasis will be placed on frameworks that improve predictive capability, computational efficiency, and the integration of heterogeneous experimental and simulation data.Topics of interest include, but are not limited to:• AI-enhanced molecular dynamics and enhanced-sampling methods • Multiscale simulation of proteins, nucleic acids, membranes, and biomolecular complexes • Hybrid modeling approaches integrating simulation with cryo-EM, cryo-ET, NMR, SAXS, and mass spectrometry data • Machine learning potentials, graph neural networks, and coarse-grained models for biomolecular systems • High-performance computing, distributed computing, and discrete-event simulation strategies for large-scale biomolecular modeling • Modeling of allostery, folding, intrinsic disorder, ligand binding, and conformational landscapes • Simulation-driven investigations of enzyme mechanisms, membrane biology, and molecular recognition • Integrative approaches combining experimental and computational methods to study biomolecular systems across scales • AI-driven optimization and adaptive workflows for scalable biomolecular simulation and analysis The aim of this collection is to highlight next-generation modeling frameworks that bridge molecular detail and biological function. We welcome contributions that improve predictive accuracy, facilitate interpretation of experimental data, and expand the applicability of biomolecular simulation to larger and more realistic biological systems. Topic editors: Jose Garcia, Jorge Alejandro Tapia González Source: https://www.frontiersin.org/research-topics/81924/multiscale-modeling-of-biomolecular-systems