Protein function is largely determined by molecular surface geometry and physicochemical complementarity, yet most protein design methods condition only on backbone structure. We introduce SurfDesign, a surface-conditioned protein design framework that models molecular surfaces as continuous geometric manifolds and integrates them with pretrained protein language models. SurfDesign employs surface-based equivariant message passing to capture surface normals, curvature, and directional geometry, together with a parameter-efficient fine-tuning strategy. Focusing on functional protein design, we show that SurfDesign consistently outperforms prior surface-conditioned and backbone-only methods on de novo binder and enzyme design benchmarks. We also report strong performance on inverse-folding benchmarks as a diagnostic of structural compatibility. Our results highlight manifold-aware surface representations as a principled foundation for functional protein and enzyme design. Code is available at https://github.com/smiles724/SurfDesign.

翻译：蛋白质功能在很大程度上取决于分子表面的几何形状与物理化学互补性，然而大多数蛋白质设计方法仅以主链结构为条件。我们提出SurfDesign，一种以表面为条件的蛋白质设计框架，该框架将分子表面建模为连续几何流形，并将其与预训练的蛋白质语言模型集成。SurfDesign采用基于表面的等变消息传递机制以捕捉表面法线、曲率与方向几何，并结合参数高效微调策略。聚焦于功能性蛋白质设计，我们证明SurfDesign在从头设计结合子与酶设计基准测试中，持续优于先前的表面条件方法及仅依赖主链的方法。我们还报告了在反向折叠基准测试中的优异表现，以此作为结构兼容性的诊断。我们的研究结果表明，流形感知的表面表征可为功能性蛋白质与酶设计提供基本原理。代码已开源至https://github.com/smiles724/SurfDesign。