Learning conformational ensembles of proteins based on backbone geometry

The introduction of BBFlow marks a significant advancement in protein conformation sampling, addressing the limitations of existing methods that rely heavily on evolutionary sequence data and pre-trained models. Current state-of-the-art approaches often involve extensive fine-tuning and can be prohibitively slow due to the complexities of Molecular Dynamics simulations. BBFlow, however, utilizes a novel flow matching model that focuses exclusively on the geometry of the protein backbone, allowing it to operate with remarkable efficiency. In experimental validations, BBFlow demonstrated competitive performance across established benchmarks for both naturally occurring and de novo proteins, achieving results in a fraction of the time compared to traditional methods. This capability not only enhances the speed of protein modeling but also broadens the applicability of the model to multi-chain proteins, making it a versatile tool for researchers. By eliminating the need for evolutionary in…