SurfPro: Functional Protein Design Based on Continuous Surface

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

Cite

Text

Song et al. "SurfPro: Functional Protein Design Based on Continuous Surface." International Conference on Machine Learning, 2024.

Markdown

[Song et al. "SurfPro: Functional Protein Design Based on Continuous Surface." International Conference on Machine Learning, 2024.](https://mlanthology.org/icml/2024/song2024icml-surfpro/)

BibTeX

@inproceedings{song2024icml-surfpro,
  title     = {{SurfPro: Functional Protein Design Based on Continuous Surface}},
  author    = {Song, Zhenqiao and Huang, Tinglin and Li, Lei and Jin, Wengong},
  booktitle = {International Conference on Machine Learning},
  year      = {2024},
  pages     = {46074-46088},
  volume    = {235},
  url       = {https://mlanthology.org/icml/2024/song2024icml-surfpro/}
}