SurfPro: Functional Protein Design Based on Continuous Surface

Zhenqiao Song, Tinglin Huang, Lei Li, Wengong Jin
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:46074-46088, 2024.

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 this Paper

BibTeX
@InProceedings{pmlr-v235-song24c, title = {{S}urf{P}ro: Functional Protein Design Based on Continuous Surface}, author = {Song, Zhenqiao and Huang, Tinglin and Li, Lei and Jin, Wengong}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {46074--46088}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/song24c/song24c.pdf}, url = {https://proceedings.mlr.press/v235/song24c.html}, 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} }
Endnote
%0 Conference Paper %T SurfPro: Functional Protein Design Based on Continuous Surface %A Zhenqiao Song %A Tinglin Huang %A Lei Li %A Wengong Jin %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-song24c %I PMLR %P 46074--46088 %U https://proceedings.mlr.press/v235/song24c.html %V 235 %X 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
APA
Song, Z., Huang, T., Li, L. & Jin, W.. (2024). SurfPro: Functional Protein Design Based on Continuous Surface. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:46074-46088 Available from https://proceedings.mlr.press/v235/song24c.html.

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