Modeling Protein Structure Using Geometric Vector Field Networks

Proteins serve as the foundation of life. Most diseases and challenges in life sciences are intimately linked to protein structures. In this paper, we propose a novel vector field network (VFN) for modeling protein structure. Unlike previous methods that extract geometric information relying heavily on hand-crafted features, VFN establishes a new geometric representation paradigm through a novel vector field operator. This vector field operator can not only eliminate the reliance on hand-crafted features, but also capture the implicit geometric relationships between residues. Thus, it enables VFN to have better generalizability and flexibility. We evaluate VFN on the protein inverse folding task. Experiment results show that VFN can significantly improve the performance of the state-of-the-art method, PiFold, by 3.0% (51.7% vs. 54.7%) in terms of the sequence recovery score, and outperform the recent solid baseline, Protein MPNN, by 8.7% (46.0% vs. 54.7%). Furthermore, we scale up VFN with all known protein structure data. Finally, the model achieves a recovery score of 57.1%, pushing the accuracy to the next level.