# SurfPro: Functional Protein Design Based on Continuous Surface

arXiv: https://arxiv.org/abs/2405.06693 · HTML: https://mlsanity.com/paper/2405.06693/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 53 references that are in the map.
- First posted on arXiv 7 May 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [E(n) Equivariant Graph Neural Networks](https://mlsanity.com/paper/2102.09844/) (2021)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [Learning from Protein Structure with Geometric Vector Perceptrons](https://mlsanity.com/paper/2009.01411/) (2020)
- [Biological Sequence Design with GFlowNets](https://mlsanity.com/paper/2203.04115/) (2022)
- [Frame Averaging for Invariant and Equivariant Network Design](https://mlsanity.com/paper/2110.03336/) (2021)
- [Conditioning by adaptive sampling for robust design](https://mlsanity.com/paper/1901.10060/) (2019)
- [Glancing Transformer for Non-Autoregressive Neural Machine Translation](https://mlsanity.com/paper/2008.07905/) (2020)
- [Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade](https://mlsanity.com/paper/2012.15833/) (2020)
- [Model Inversion Networks for Model-Based Optimization](https://mlsanity.com/paper/1912.13464/) (2019)
- [PiFold: Toward effective and efficient protein inverse folding](https://mlsanity.com/paper/2209.12643/) (2022)
- [Structure-informed Language Models Are Protein Designers](https://mlsanity.com/paper/2302.01649/) (2023)
- [Design by adaptive sampling](https://mlsanity.com/paper/1810.03714/) (2018)
- [Importance Weighted Expectation-Maximization for Protein Sequence Design](https://mlsanity.com/paper/2305.00386/) (2023)
- [Voxel Structure-based Mesh Reconstruction from a 3D Point Cloud](https://mlsanity.com/paper/2104.10622/) (2021)

## Built on by (most cited first, 14 of 14)

- [All-atom inverse protein folding through discrete flow matching](https://mlsanity.com/paper/2507.14156/) (2025)
- [PRISM: Enhancing Protein Inverse Folding through Fine-Grained Retrieval on Structure-Sequence Multimodal Representations](https://mlsanity.com/paper/2510.11750/) (2025)
- [Tokenizing Electron Cloud in Protein-Ligand Interaction Learning](https://mlsanity.com/paper/2505.19014/) (2025)
- [PPDiff: Diffusing in Hybrid Sequence-Structure Space for Protein-Protein Complex Design](https://mlsanity.com/paper/2506.11420/) (2025)
- [Surface-based Molecular Design with Multi-modal Flow Matching](https://mlsanity.com/paper/2601.04506/) (2026)
- [Property-driven Protein Inverse Folding With Multi-Objective Preference Alignment](https://mlsanity.com/paper/2603.06748/) (2026)
- [AbFlow : End-to-end Paratope-Centric Antibody Design by Interaction Enhanced Flow Matching](https://mlsanity.com/paper/2602.07084/) (2026)
- [EnzyControl: Adding Functional and Substrate-Specific Control for Enzyme Backbone Generation](https://mlsanity.com/paper/2510.25132/) (2025)
- [DS-ProGen: A Dual-Structure Deep Language Model for Functional Protein Design](https://mlsanity.com/paper/2505.12511/) (2025)
- [AlphaFold Database Debiasing for Robust Inverse Folding](https://mlsanity.com/paper/2506.08365/) (2025)
- [SculptDrug : A Spatial Condition-Aware Bayesian Flow Model for Structure-based Drug Design](https://mlsanity.com/paper/2511.12489/) (2025)
- [Joint Design of Protein Surface and Structure Using a Diffusion Bridge Model](https://mlsanity.com/paper/2511.16675/) (2025)
- [From Snapshots to Symphonies: The Evolution of Protein Prediction from Static Structures to Generative Dynamics and Multimodal Interactions](https://mlsanity.com/paper/2603.18505/) (2026)
- [SurfDesign: Effective Protein Design on Molecular Surfaces](https://mlsanity.com/paper/2606.07567/) (2026)

## Abstract (arXiv)

How can we design proteins with desired functions? We are motivated by a chemical intuition that both geometric structure and biochemical properties are critical to a protein's function. In this paper, we propose SurfPro, a new method to generate functional proteins given a desired surface and its associated biochemical properties. SurfPro comprises a hierarchical encoder that progressively models the geometric shape and biochemical features of a protein surface, and an autoregressive decoder to produce an amino acid sequence. We evaluate SurfPro on a standard inverse folding benchmark CATH 4.2 and two functional protein design tasks: protein binder design and enzyme design. Our SurfPro consistently surpasses previous state-of-the-art inverse folding methods, achieving a recovery rate of 57.78% on CATH 4.2 and higher success rates in terms of protein-protein binding and enzyme-substrate interaction scores.