使用PocketGen有效生成蛋白质口袋
Zaixi Zhang1,2,3, Wan Xiang Shen3, Qi Liu1,2
1State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, Anhui, China.
bioRxiv : the preprint server for biology
|March 11, 2024
概括
一个AI模型PocketGen通过生成序列和结构来设计用于药物发现的蛋白质口袋. 与传统方法相比,这种方法加速了蛋白质的设计,并提高了结合亲和力.
科学领域:
- 计算生物学是一种计算生物学.
- 蛋白质工程是一种蛋白质工程.
- 药物发现 药物发现
背景情况:
- 设计新的蛋白质结合蛋白对于推进药物发现至关重要.
- 目前基于人工智能的蛋白质设计面临着由于复杂相互作用,分子灵活性和序列结构依赖性而面临的挑战.
研究的目的:
- 介绍PocketGen,这是一个深度生成模型,用于同时对蛋白质结合部位的残留序列和原子结构生成.
- 通过使用先进的AI技术,确保蛋白质口袋设计中的序列结构一致性.
主要方法:
- PocketGen使用胆层图形变压器进行多尺度结构编码 (原子,残留物,连接体水平).
- 它包含了一个序列精制模块,其中一个结构适配器集成到蛋白质语言模型中,用于增强预测对齐.
主要成果:
- PocketGen产生高保真性蛋白质口袋,具有更好的结合亲和力和结构有效性.
- 该模型显示,在产生具有优异结合亲和力的囊中,其成功率为95%,氨基酸回收率超过64%.
结论:
- PocketGen为药物发现提供了人工智能驱动的蛋白质设计的重大进展.
- 与基于物理学的方法相比,该模型提供了更快,更有效的方法,实现了高精度和效率.
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