English

SurfPro: Functional Protein Design Based on Continuous Surface

Biomolecules 2024-06-19 v2 Machine Learning

Abstract

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.

Keywords

Cite

@article{arxiv.2405.06693,
  title  = {SurfPro: Functional Protein Design Based on Continuous Surface},
  author = {Zhenqiao Song and Tinglin Huang and Lei Li and Wengong Jin},
  journal= {arXiv preprint arXiv:2405.06693},
  year   = {2024}
}
R2 v1 2026-06-28T16:23:35.961Z