MechPath: Geometry-Preserving Flow Matching for Protein Mechanical Unfolding

Understanding how proteins unfold under force is important for elucidating their mechanical functions and associated mechanopathologies, such as cardiomyopathy and muscular dystrophy. Steered molecular dynamics (SMD) simulations resolve unfolding structural dynamics under physiologically relevant pulling geometries, but their high computational cost limits extensive pathway sampling. Existing generative models for protein dynamics offer a scalable alternative, yet typically construct probability paths in ambient structural spaces that do not preserve the constraints imposed by mechanical pulling. Here, we formulate unfolding trajectory generation as probability transport directly on the path geometry induced by mechanical unfolding. We parameterize each unfolding pathway by separating monotonic unfolding progress along the pulling reaction coordinate from the remaining conformational degrees of freedom. Building on this formulation, we introduce MechPath, a flow-matching framework that learns the transport in unconstrained latent tokens and maps it back to structural trajectories through a geometry-preserving realization map. This construction makes the satisfaction of the mechanical constraints an invariant of the entire generative probability path. In the absence of a public benchmark, we further establish a large-scale dataset comprising 6,750 SMD trajectories from 1,350 proteins. On this benchmark, MechPath consistently outperforms general-purpose protein dynamics generators in structural accuracy, conformational-distribution recovery, whole-pathway similarity, and underlying contact-release dynamics. The results show that incorporating the geometry of externally driven molecular processes into generative probability paths substantially improves structural pathway generation, which provides a scalable route to study molecular mechanisms underlying mechanopathologies.