AtomSurf: Surface Representation for Learning on Protein Structures
Vincent Mallet Yangyang Miao Souhaib Attaiki Bruno Correia Maks Ovsjanikov
While there has been significant progress in evaluating and comparing different representations for learning on protein data, the role of surface-based learning approaches remains not well-understood. In particular, there is a lack of direct and fair benchmark comparison between the best available surface-based learning methods against alternative representations such as graphs. Moreover, the few existing surface-based approaches either use surface information in isolation or, at best, perform global pooling between surface and graph-based architectures. 
    
In this work, we fill this gap by first adapting a state-of-the-art surface encoder for protein learning tasks. We then perform a direct and fair comparison of the resulting method against alternative approaches within the Atom3D benchmark, highlighting the limitations of pure surface-based learning. Finally, we propose an integrated approach, which allows learned feature sharing between graphs and surface representations on the level of nodes and vertices \textit{across all layers}.
    
We demonstrate that the resulting architecture achieves state-of-the-art results on all tasks in the Atom3D benchmark, while adhering to the strict benchmark protocol, as well as more broadly on binding site identification and binding pocket classification. Furthermore, we use coarsened surfaces and optimize our approach for efficiency, making our tool competitive in training and inference time with existing techniques.

Code can be found online: https://github.com/Vincentx15/atomsurf
deep learning, protein representation learning; surface methods; geometric deep learning
We propose a state of the art surface encoder for proteins and show it performs deceptively compared to graph methods. The combination of a graph method and this encoder, however, yields state-of-the-art results.