Antimicrobial resistance (AMR) remains a critical and escalating public health threat. A major contributor is the activity of multidrug-efflux pumps, including those in the small multidrug resistance (SMR) transporter family. Prototypical SMR transporters export cationic compounds by coupling the downhill gradient of two protons to substrate transport in the opposite direction. This exchange occurs via an alternating access mechanism: outward- and inward-facing states are structurally characterized, but intermediate conformations and the molecular basis of substrate specificity have remained elusive. To address these questions, we developed a specialized computational protocol combining multi-replica enhanced sampling simulations with a multi-layered machine learning strategy to accelerate slow degrees of freedom, enabling simulation of the full conformational cycle of the prototypical SMR transporter Gdx-Clo bound to its native substrate, guanidinium. Using mean-force analysis, we quantified the free-energy landscape governing protein conformational changes during transport, revealing for the first time the molecular details of the intermediate steps. These insights are crucial for understanding substrate specificity and for predicting the emergence of resistance. Computational predictions were validated with single-molecule Förster resonance energy transfer (smFRET) experiments on wild-type and mutant proteins, providing a molecular explanation for observed single-molecule time traces. Together, these findings deepen mechanistic understanding of SMR transporters and establish a generalizable simulation-based framework for studying other membrane transport proteins, especially when integrated with experimental measurements.
Trask et al. (Sun,) studied this question.