SurfDesign: Effective Protein Design on Molecular Surfaces

• URL: http://arxiv.org/abs/2606.07567v1
• Date: Mon, 25 May 2026 19:53:02 GMT
• Title: SurfDesign: Effective Protein Design on Molecular Surfaces
• Abstract summary: We introduce SurfDesign, a surface-conditioned protein design framework.<n>SurfDesign employs surface-based equivariant message passing to capture surface normals, curvature, and directional geometry.<n>We show that SurfDesign consistently outperforms prior surface-conditioned and backbone-only methods on de novo binder and enzyme design benchmarks.
• Score: 75.87423747412242
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Deciphering Fingerprints of 3D Molecular Surfaces for Accurate Epitope Prediction [82.84146585955015]
  SurfBind is a surface-centric learning framework for prediction that operates directly on molecular surface representations.<n>SurfBind integrates geometric and physicochemical cues through a Transformer-based architecture with patch-level surface modeling, binder-aware cross-attention, and a hierarchical coarse-to-fine prediction paradigm.
  arXiv  Detail & Related papers  (2026-06-22T18:14:14Z)
• Surface-based Molecular Design with Multi-modal Flow Matching [64.00572241268597]
  SurfFlow is a novel surface-based generative algorithm that enables comprehensive co-design of sequence, structure, and surface for peptides.<n> evaluated on the comprehensive PepMerge benchmark, SurfFlow consistently outperforms full-atom baselines across all metrics.
  arXiv  Detail & Related papers  (2026-01-08T02:19:29Z)
• Multi-Scale Representation Learning for Protein Fitness Prediction [31.735234482320283]
  Previous methods have primarily relied on self-supervised models trained on vast, unlabeled protein sequence or structure datasets.<n>We introduce the Sequence-Structure-Surface Fitness (S3F) model - a novel multimodal representation learning framework that integrates protein features across several scales.<n>Our approach combines sequence representations from a protein language model with Geometric Vector Perceptron networks encoding protein backbone and detailed surface topology.
  arXiv  Detail & Related papers  (2024-12-02T04:28:10Z)
• SurfPro: Functional Protein Design Based on Continuous Surface [14.199812995207072]
  SurfPro is a new method to generate functional proteins given a desired surface and its associated biochemical properties. We evaluate SurfPro on a standard inverse folding benchmark CATH 4.2 and two functional protein design tasks. Our SurfPro consistently surpasses previous state-of-the-art inverse folding methods, achieving a recovery rate of 57.78% on CATH 4.2.
  arXiv  Detail & Related papers  (2024-05-07T19:09:46Z)
• Progressive Multi-Modality Learning for Inverse Protein Folding [47.095862120116976]
  We propose a novel protein design paradigm called MMDesign, which leverages multi-modality transfer learning. MMDesign is the first framework that combines a pretrained structural module with a pretrained contextual module, using an auto-encoder (AE) based language model to incorporate prior protein semantic knowledge. Experimental results, only training with the small dataset, demonstrate that MMDesign consistently outperforms baselines on various public benchmarks.
  arXiv  Detail & Related papers  (2023-12-11T10:59:23Z)
• Joint Design of Protein Sequence and Structure based on Motifs [11.731131799546489]
  We propose GeoPro, a method to design protein backbone structure and sequence jointly. GeoPro is powered by an equivariant encoder for three-dimensional (3D) backbone structure and a protein sequence decoder guided by 3D geometry. Our method discovers novel $beta$-lactamases and myoglobins which are not present in protein data bank (PDB) and UniProt.
  arXiv  Detail & Related papers  (2023-10-04T03:07:03Z)
• EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction [49.674494450107005]
  Predicting the binding sites of target proteins plays a fundamental role in drug discovery. Most existing deep-learning methods consider a protein as a 3D image by spatially clustering its atoms into voxels. This work proposes EquiPocket, an E(3)-equivariant Graph Neural Network (GNN) for binding site prediction.
  arXiv  Detail & Related papers  (2023-02-23T17:18:26Z)
• Learning Geometrically Disentangled Representations of Protein Folding Simulations [72.03095377508856]
  This work focuses on learning a generative neural network on a structural ensemble of a drug-target protein. Model tasks involve characterizing the distinct structural fluctuations of the protein bound to various drug molecules. Results show that our geometric learning-based method enjoys both accuracy and efficiency for generating complex structural variations.
  arXiv  Detail & Related papers  (2022-05-20T19:38:00Z)
• Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking [57.2037357017652]
  We tackle rigid body protein-protein docking, i.e., computationally predicting the 3D structure of a protein-protein complex from the individual unbound structures. We design a novel pairwise-independent SE(3)-equivariant graph matching network to predict the rotation and translation to place one of the proteins at the right docked position. Our model, named EquiDock, approximates the binding pockets and predicts the docking poses using keypoint matching and alignment.
  arXiv  Detail & Related papers  (2021-11-15T18:46:37Z)
• PDBench: Evaluating Computational Methods for Protein Sequence Design [2.0187324832551385]
  We present a benchmark set of proteins and propose tests to assess the performance of deep learning based methods. Our robust benchmark provides biological insight into the behaviour of design methods, which is essential for evaluating their performance and utility.
  arXiv  Detail & Related papers  (2021-09-16T12:20:03Z)
• G-VAE, a Geometric Convolutional VAE for ProteinStructure Generation [41.66010308405784]
  We introduce a joint geometric-neural networks approach for comparing, deforming and generating 3D protein structures. Our method is able to generate plausible structures, different from the structures in the training data.
  arXiv  Detail & Related papers  (2021-06-22T16:52:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.