ALPHASURF: ON-THE-FLY SURFACE COMPUTATIONS FOR PROTEIN REPRESENTATION LEARNING

Several protein surfaces have been proposed, notably for visualization purposes. Machine learning methods have incorporated these surfaces as protein representations, resulting in strong performance, at the cost of heavy computations. In this paper, we show that this burden can be avoided by introducing AlphaSurf, a coarse meshing method tailored for learning on proteins that leverages $\alpha$-complexes. We characterize the surfaces theoretically, including their topological relation to the SAS surface, and evaluate them experimentally on downstream tasks. Our method runs on-the-fly during training while maintaining performance. This enables use cases previously out of reach for surface-based methods: large-scale training as well as coordinate-noise data augmentation and training on molecular dynamics frames which improves performance. The code is available here https://anonymous.4open.science/r/AlphaSurf-C5A1/README.md