来自等价图形变换器的背景蛋白质和抗体编码
Sai Pooja Mahajan1, Jeffrey A Ruffolo2, Jeffrey J Gray1,2,3
1Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States.
bioRxiv : the preprint server for biology
|July 28, 2023
概括
这项研究使用掩面语言建模来预测蛋白质中最佳的氨基酸残留物,仅基于结构上下文. 该方法准确地恢复原生序列,预测蛋白质结构,并设计功能性抗体CDR循环.
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.
- 结构生物信息学 结构生物信息学
背景情况:
- 蛋白质的功能是由其结构和进化背景中的氨基酸序列决定的.
- 预测最佳的残留物标识需要理解这些复杂的关系.
研究的目的:
- 开发一种面具语言模型 (MLM),能够从结构上下文中学习蛋白质残留表征.
- 调查结构信息是否可以单独确定最佳的氨基酸身份.
- 为设计抗体互补性确定区域 (CDR) 应用MLM.
主要方法:
- 利用受自然语言处理启发的掩盖语言建模 (MLM).
- 训练模型,根据它们的结构环境来预测掩盖的氨基酸残留物.
- 使用AlphaFold2进行本机序列恢复和前向折叠预测.
- 针对CDR循环设计的抗体特定结构数据库上的微调模型.
主要成果:
- 仅仅结构上下文就足以确定蛋白质残留的身份,正如本源序列恢复所显示的那样.
- 蒙面模型准确地回顾了进化序列的邻居和形状灵活性.
- 使用MLM设计的蛋白质接口表现出野生类型的结合能.
- 对一般蛋白质结构的预训练改善了CDR H3循环设计,而微调则保留了特定的模式.
结论:
- MLM是学习蛋白质序列结构关系的强大工具.
- 这种方法可以准确预测残留物标识和蛋白质结构.
- 该方法有望设计具有所需功能的新型蛋白质和抗体CDR循环.
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