让蛋白质设计更懂表面形状，提升新药靶点和酶的设计效果
SurfDesign: Effective Protein Design on Molecular Surfaces
- 将分子表面视为连续几何面，结合语言模型进行设计
- 在全新结合剂和酶设计任务中优于以往方法
- 适合需要精准表面匹配的蛋白质功能设计场景
蛋白质功能主要由分子表面几何形态和物化互补性决定，但多数蛋白质设计方法仅依赖主链结构。我们提出SurfDesign，一种基于表面条件的蛋白质设计框架，将分子表面建模为连续几何流形，并与预训练蛋白质语言模型融合。SurfDesign采用基于表面的等变消息传递机制，捕捉表面法向、曲率及方向几何特征，同时使用参数高效微调策略。聚焦功能蛋白设计，我们在从头设计结合剂和酶的任务上，一致优于先前的表面条件和仅依赖主链的方法。此外，在逆折叠基准测试中也表现出色，作为结构兼容性的诊断指标。结果表明，具备流形感知能力的表面表示是功能蛋白质与酶设计的坚实基础。代码已公开于 https://github.com/smiles724/SurfDesign。
原文摘要 · Abstract (English)
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.
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