Recent advancements in Cryo-EM and protein structure prediction algorithms
have made large-scale protein structures accessible, paving the way for machine
learning-based functional annotations.The field of geometric deep learning
focuses on creating methods working on geometric data. An essential aspect of
learning from protein structures is representing these structures as a
geometric object (be it a grid, graph, or surface) and applying a learning
method tailored to this representation. The performance of a given approach
will then depend on both the representation and its corresponding learning
method.
In this paper, we investigate representing proteins as $\textit{3D mesh
surfaces}$ and incorporate them into an established representation benchmark.
Our first finding is that despite promising preliminary results, the surface
representation alone does not seem competitive with 3D grids. Building on this,
we introduce a synergistic approach, combining surface representations with
graph-based methods, resulting in a general framework that incorporates both
representations in learning. We show that using this combination, we are able
to obtain state-of-the-art results across $\textit{all tested tasks}$. Our code
and data can be found online: this https URL .

通过将蛋白质表示为三维网格表面并将其与基于图的方法相结合，我们提出了一种综合性的框架，融合了表面表示和图像的学习方法，能够在所有测试任务中取得最先进的结果。