aiqbench analysis
SurfDesign advances protein design by integrating continuous molecular surface geometry with pretrained language models, outperforming backbone-only approaches on de novo binder and enzyme benchmarks. By employing surface-based equivariant message passing and parameter-efficient fine-tuning, the framework establishes manifold-aware representations as a robust foundation for functional protein engineering. This work bridges computational geometry and biological function, offering a scalable solution for inverse-folding and structural compatibility diagnostics.
SurfDesign: Effective Protein Design on Molecular Surfaces
arXiv:2606.07567v1 Announce Type: cross Abstract: 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.
Abstract: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 this https URL.
Biomolecules (q-bio.BM); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
arXiv:2606.07567 [q-bio.BM]
(or arXiv:2606.07567v1 [q-bio.BM] for this version)
https://doi.org/10.48550/arXiv.2606.07567 Focus to learn more arXiv-issued DOI via DataCite
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