moPPIt: De Novo 基因特异结合剂与蛋白质语言模型的生成
Tong Chen1, Yinuo Zhang2, Pranam Chatterjee1,2,3
1Department of Biomedical Engineering, Duke University.
bioRxiv : the preprint server for biology
|August 12, 2024
概括
我们开发了动机特异性蛋白质-蛋白质相互作用 (PPI) 向算法moPPIt,以生成定制的结剂. 这个计算工具只使用序列信息,而不需要蛋白质结构来设计针对疾病蛋白质的特定疗法.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 精确准与疾病相关的蛋白质对于有效的治疗方法至关重要.
- 当前的方法往往需要结构信息,并与具体性作斗争.
- 开发特定基因的结合剂对于调节蛋白质功能而具有最小的非目标效应至关重要.
研究的目的:
- 引入moPPIt,这是一种用于*de novo*生成动机特异性结体的新算法.
- 为了能够设计针对特定蛋白质基因的类疗法,仅使用序列数据.
- 克服依赖结构信息的现有方法的局限性.
主要方法:
- 开发了BindEvaluator,这是一个基于变压器的模型,插入蛋白质语言模型嵌入,专注于局部图案特征.
- 训练有素的BindEvaluator对超过51万个注释的蛋白质与蛋白质相互作用 (PPI) 进行了评估.
- 集成的BindEvaluator与生成器 (PepMLM) 和基因算法优化,用于*de novo*的绑定器设计.
主要成果:
- 在预测目标结合位点方面,BindEvaluator取得了很高的准确性 (AUC>0.94在PPI上,>0.96在-蛋白对上).
- moPPIt成功地生成了通过计算设计的,这些与目标蛋白质上特定的用户定义的动图结合.
- 在具有或没有已知的结合剂的目标上证明有效,包括结构化和无序蛋白质.
结论:
- moPPIt是一种强大的计算工具,用于设计高度特定的疗法.
- 该算法可以在不依赖蛋白质结构的情况下开发有针对性的治疗方法.
- 这种方法推动了个性化医疗和有针对性的药物输送领域的发展.
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