Multiscale frameworks for exploring protein energy landscapes: advances in theory and simulation

Energy landscape theory provides a unifying framework for describing protein structure, dynamics, and function across hierarchies of spatial and temporal scales. In practice, however, protein energy landscapes are never accessed directly; they are represented through a hierarchy of theoretical models, from quantum mechanical potential energy surfaces to coarse-grained potentials of mean force. Each level of description entails a systematic reduction of degrees of freedom and a corresponding transformation of the underlying landscape. In this review, we examine how protein energy landscape representations change under successive approximations to the molecular Hamiltonian, with particular emphasis on the emergence, interpretation, and robustness of landscape concepts across scales. We argue that many simplified models succeed not because they reproduce microscopic interactions in detail, but because key topological features of the landscape, such as funnels, barriers, and competing basins, are preserved under projection. This article also clarifies the physical principles underlying coarse-graining, solvent modelling, and multilevel simulation strategies and demonstrates why energy landscape theory remains predictive despite reduced chemical resolution, highlighting its role as a unifying framework for exploring biomolecular space.