Learning conformational ensembles of proteins based on backbone geometry
This work addresses the need for faster and more applicable protein conformation sampling, particularly for de novo proteins where evolutionary data is limited, though it is incremental as it builds on existing generative modeling approaches.
The authors tackled the problem of efficiently sampling protein conformations from the Boltzmann distribution by proposing BBFlow, a flow matching model based solely on backbone geometry, which achieves competitive accuracy while being orders of magnitude faster than current state-of-the-art methods.
Deep generative models have recently been proposed for sampling protein conformations from the Boltzmann distribution, as an alternative to often prohibitively expensive Molecular Dynamics simulations. However, current state-of-the-art approaches rely on fine-tuning pre-trained folding models and evolutionary sequence information, limiting their applicability and efficiency, and introducing potential biases. In this work, we propose a flow matching model for sampling protein conformations based solely on backbone geometry - BBFlow. We introduce a geometric encoding of the backbone equilibrium structure as input and propose to condition not only the flow but also the prior distribution on the respective equilibrium structure, eliminating the need for evolutionary information. The resulting model is orders of magnitudes faster than current state-of-the-art approaches at comparable accuracy, is transferable to multi-chain proteins, and can be trained from scratch in a few GPU days. In our experiments, we demonstrate that the proposed model achieves competitive performance with reduced inference time, across not only an established benchmark of naturally occurring proteins but also de novo proteins, for which evolutionary information is scarce or absent. BBFlow is available at https://github.com/graeter-group/bbflow.