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SurfPro: Functional Protein Design Based on Continuous Surface

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arxiv 2405.06693 v2 pith:5DVQ7VBS submitted 2024-05-07 q-bio.BM cs.LG

classification q-bio.BMcs.LG
keywords designproteinsurfprobiochemicalfunctionalsurfacecathdesired
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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.

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Cited by 1 Pith paper

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  1. Tokenizing Electron Cloud in Protein-Ligand Interaction Learning

    cs.LG 2025-05 conditional novelty 6.0 of 10

    ECBind tokenizes electron cloud densities via quantized embeddings and improves protein-ligand binding affinity prediction, especially per-structure correlations on MISATO.

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