UMPPI:通过语言模型揭示多层次蛋白质相互作用预测
Shuwen Xiong1, Jiajie Cai2, Hua Shi3
1Faculty of Applied Sciences, Macao Polytechnic University, R. de Luís Gonzaga Gomes, Macao 999078, China.
Journal of chemical information and modeling
|March 13, 2025
概括
我们开发了UMPPI,这是一个新的计算工具,用于预测蛋白质-相互作用并识别结合残留物. 这种方法有助于理解细胞过程,并加速类药物发现.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 蛋白质-的相互作用对于细胞功能和疾病的发病过程至关重要.
- 准确识别结合残留物对于功能分析和治疗开发至关重要.
- 当前的计算方法在功能集成和数据依赖方面存在局限性.
研究的目的:
- 引入UMPPI (揭示多层次蛋白质-相互作用),这是一个用于同时预测蛋白质-相互作用和结合残留物的新框架.
- 利用预训练的蛋白质语言模型ESM2进行增强的序列和结构信息编码.
- 通过整合基于序列和结构的方法来克服现有方法的局限性.
主要方法:
- 开发了一个多目标优化框架,UMPPI.
- 利用ESM2蛋白语言模型进行序列编码和潜在结构特征提取.
- 实施了一种用于同时预测二进制相互作用和结合残留物的策略.
主要成果:
- UMPPI准确地预测了二进制蛋白质-相互作用.
- UMPPI成功地确定了蛋白质和合作伙伴的关键结合残留物.
- 该框架有效地将序列信息与潜在的结构见解集成在一起.
结论:
- UMPPI提供了一个强大的计算工具来预测蛋白质-相互作用.
- 通过UMPPI识别结合残留物有助于更深入地了解的功能.
- UMPPI显示出在推进类药物发现管道方面的巨大潜力.
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