Multiscale conformational sampling of multidomain fusion proteins by a physics informed diffusion model.

Multidomain fusion proteins, such as bispecific antibodies, rely on highly flexible linker regions for their therapeutic efficacy. Characterizing these vast conformational ensembles is crucial for rational drug design; however, while all-atom molecular dynamics (MD) is the traditional gold standard, its immense computational cost makes simulating large-scale domain motions prohibitive. Recently, deep generative diffusion models have emerged as a rapid alternative for sampling protein dynamics. Yet, being trained primarily on massive databases of structured, static domains, these generic models often lack the biophysical constraints required to thoroughly sample the large-scale dynamics of highly flexible multidomain architectures. To overcome this, we leverage microsecond MD trajectories of a multidomain protein construct with various linkers to train a multiscale diffusion framework utilizing an Equivariant Graph Neural Network (EGNN). To efficiently model the dynamics of the large molecular complexes, we employ a coarse-grained spatial graph that condenses rigid domains into center-of-mass anchors while preserving explicit backbone resolution for the flexible linker. By further integrating foundational rules in biophysics directly into both the training objective and the inference process, our model generates high-fidelity conformational ensembles that reproduce the thermodynamic distributions of long-timescale MD. This physics-informed approach provides a mathematically stable, highly scalable platform for the rapid multiscale characterization of flexible biologics, significantly accelerating the rational design of fusion protein therapeutics.