发表机构
University of British Columbia; Massachusetts Institute of Technology; Carnegie Mellon University; Northeastern University; The University of Hong Kong（不列颠哥伦比亚大学; 麻省理工学院; 卡内基梅隆大学; 东北大学; 香港大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出Protein-TetSphere，通过四面体化和配准建立残基级体积表示，与表面信息融合，在口袋分类、界面预测和结合剂设计上显著提升性能。
AI 中文摘要
现有的蛋白质几何模型通常使用局部几何特征（如采样点、法线和曲率）来表示分子表面。虽然这些特征能有效捕获暴露的分子形状，但它们并未显式建模表面下方的体积组织，也未为残基级别的体积结构提供一致的坐标系。我们提出了Protein-TetSphere，一种配准的残基级体积表示方法。每条蛋白质链被四面体化，以获得与单个残基关联的局部体积区域，然后将这些区域配准到共享的固定拓扑四面体参考上，并在共同的拉普拉斯基中表示。这种配准在残基之间建立了一致的体积坐标，使得局部三维形变能够与表面和化学信息在多模态蛋白质表示中集成。我们在配体结合口袋分类、蛋白质-蛋白质界面预测和从头蛋白质结合剂设计上评估了Protein-TetSphere。在这三个任务中，Protein-TetSphere将配体结合口袋的平衡准确率从0.795提高到0.826，Pinder-Pair/Site AUROC从0.914/0.852提高到0.932/0.866，结合剂设计成功率在BoltzGen挑战集上从14.95%提高到19.90%，在ProtDBench骨架水平上从27.62%提高到32.19%。这些结果表明，配准的体积几何为分子表面提供了互补的空间信息，在蛋白质识别、相互作用和设计方面均有提升。
英文摘要
Existing protein geometry models typically represent molecular surfaces using local geometric features such as sampled points, normals, and curvature. While effective for capturing exposed molecular shape, these representations do not explicitly model the volumetric organization beneath the surface or provide a consistent coordinate system for residue-wise volumetric structure. We introduce Protein-TetSphere, a registered residue-wise volumetric representation for proteins. Each protein chain is tetrahedralized to obtain local volumetric regions associated with individual residues, which are then registered to a shared fixed-topology tetrahedral reference and represented in a common Laplacian basis. This registration establishes consistent volumetric coordinates across residues, enabling local three-dimensional deformation to be integrated with surface and chemical information in a multimodal protein representation. We evaluate Protein-TetSphere on ligand-binding pocket classification, protein--protein interface prediction, and de novo protein binder design. Across the three tasks, Protein-TetSphere improves ligand-binding pocket balanced accuracy from $0.795$ to $0.826$, Pinder-Pair/Site AUROC from $0.914/0.852$ to $0.932/0.866$, and binder-design success from $14.95\%$ to $19.90\%$ on the BoltzGen Challenge Set and from $27.62\%$ to $32.19\%$ at the ProtDBench backbone level. These results show that registered volumetric geometry provides complementary spatial information beyond molecular surfaces across protein recognition, interaction, and design.
