ProteinReDiff: Complex-based ligand-binding proteins redesign by equivariant diffusion-based generative models

被引:0
|
作者
Nguyen, Viet Thanh Duy [1 ]
Nguyen, Nhan D. [2 ]
Hy, Truong Son [3 ]
机构
[1] FPT Software AI Ctr, Ho Chi Minh City, Vietnam
[2] Univ Chicago, Pritzker Sch Mol Engn, Chicago, IL 60637 USA
[3] Univ Alabama Birmingham, Dept Comp Sci, Birmingham, AL 35294 USA
来源
STRUCTURAL DYNAMICS-US | 2024年 / 11卷 / 06期
关键词
DIRECTED EVOLUTION; COMPUTATIONAL DESIGN; SCORING FUNCTION; NEURAL-NETWORK; LANGUAGE; PREDICTION;
D O I
10.1063/4.0000271
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Proteins, serving as the fundamental architects of biological processes, interact with ligands to perform a myriad of functions essential for life. Designing functional ligand-binding proteins is pivotal for advancing drug development and enhancing therapeutic efficacy. In this study, we introduce ProteinReDiff, an diffusion framework targeting the redesign of ligand-binding proteins. Using equivariant diffusion-based generative models, ProteinReDiff enables the creation of high-affinity ligand-binding proteins without the need for detailed structural information, leveraging instead the potential of initial protein sequences and ligand SMILES strings. Our evaluations across sequence diversity, structural preservation, and ligand binding affinity underscore ProteinReDiff's potential to advance computational drug discovery and protein engineering. (C) 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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页数:19
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