Unraveling membrane protein conformational landscapes: Enhanced sampling, membrane regulation, and artificial intelligence–assisted simulation

Abstract

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.