Benchmarking AI-generated structural ensembles of membrane proteins against physics-based modelling

Abstract

Proteins dynamically switch between a continuum of interconverting conformational states, and understanding these structural dynamics is important for understanding protein function and for developing therapeutics. Molecular dynamics (MD) simulations can provide insight into protein conformational ensembles, but sampling rare conformational states can require substantial computational resources. The recent development of AI-based approaches for generating protein conformational ensembles, such as the Biomolecular Emulator (BioEmu), offers a potential alternative, although it remains unclear whether these approaches can accurately capture the conformational landscapes, especially for special cases such as membrane proteins. Here, we assess the ability of BioEmu to model the conformational dynamics of a model membrane protein, the bacterial rhomboid intramembrane proteases GlpG. We find that BioEmu generates a range of conformations corresponding to both open and closed states of the rhomboid lateral gate, including states associated with different stages of the catalytic cycle. These conformations broadly correspond to states sampled during microsecond-timescale MD simulations, although BioEmu does not reproduce the full conformational landscape observed using MD. BioEmu also samples substantial conformational heterogeneity within the soluble domains of rhomboids, which are highly flexible and poorly represented in experimental structures. Overall, our findings demonstrate that BioEmu can generate plausible conformational ensembles for relatively large, six-and seven-pass membrane proteins, sampling rare states at a fraction of the computational cost of conventional MD simulations. These results suggest that AI-based ensemble generation could provide an accessible approach for exploring membrane protein dynamics and complement conventional molecular modelling approaches.