Recent advances in artificial intelligence and deep generative models have greatly accelerated the generation of protein conformational ensembles. Moving beyond isolated structural states to reconstruct continuous, physically realistic transition pathways further provides mechanistic understanding to guide molecular design but remains a significant challenge. This review examines emerging computational approaches for modeling protein conformational transitions, from classical molecular dynamics simulations to deep learning-based latent space interpolation. Recent developments in capturing natural molecular motions are discussed, particularly via internal coordinate representations and physics-informed models. We also examine the complexity of modeling highly dynamic systems, such as intrinsically disordered proteins, and the role of the cellular environment. Integrating experimental data with simulated structural ensembles represents a promising direction for future methodologies. Nonetheless, key challenges remain, including maintaining physical realism throughout transition trajectories, capturing environment-dependent energy landscapes, and predicting dynamically consistent pathways with accurate transition kinetics.
Allostery is increasingly recognized as a transition-aware process in which local perturbations reshape conformational ensembles monitored by the reorganization of residue-residue contacts, leading to biased functions. This review highlights ensemble-comparison approaches to dissecting allosteric mechanisms, including those based on simulations, integration of simulation and experiment, and emerging machine learning-based predictions. These approaches provide mechanistic views of allostery that link ensemble redistribution to functional regulation. We argue that useful allosteric models should explain not only which residues connect distal sites but also which residue-residue contacts are reorganized, which regions respond cooperatively, and how these changes alter function. Understanding detailed allosteric mechanisms may help identify new allosteric pockets and guide the design of novel modulators to fine-tune biological processes for therapeutic benefit.
Membrane proteins undergo functionally essential conformational transitions that couple local chemical events and changes in the membrane environment to large-scale structural rearrangements. These transitions are often difficult to capture with conventional molecular dynamics (MD) simulations, particularly in complex protein–membrane systems. This mini-review discusses how recent MD studies have resolved transition pathways and conformational intermediates and revealed regulatory mechanisms involving substrates, ions, lipids, and membrane mechanics. A recurring theme is that membrane protein function is determined not only by the stability of experimentally resolved endpoint structures but also by the accessibility of intermediate states and the pathways connecting them. We further highlight how enhanced sampling, machine learning, complementary simulation approaches, and experimental constraints are increasingly being combined to construct more robust mechanistic models. Together, these advances are moving MD from retrospective interpretation toward predictive models of membrane protein function.
