在生物物理动力学上训练的蛋白质语言模型告知突变效应.
Chao Hou1, Haiqing Zhao2,3, Yufeng Shen1,4,5
1Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032.
概括
SeqDance和ESMDance是新的蛋白质语言模型,使用生物物理数据捕获蛋白质动态. 这些模型改善了蛋白质行为和突变效应的预测,优于现有的方法.
科学领域:
- 计算生物学 计算生物学
- 结构生物学 结构生物学
- 蛋白质动力学 蛋白质动力学
背景情况:
- 蛋白质结构动态对于功能和突变影响至关重要.
- 现有的深度学习模型经常忽视蛋白质动态,使用静态结构或序列.
- 捕获蛋白质动态对于准确的功能和突变效应预测至关重要.
研究的目的:
- 介绍SeqDance和ESMDance,这些新型的蛋白质语言模型包含了蛋白质动态.
- 能够预测有序和无序蛋白质的动态特性.
- 通过整合动态信息,提高突变效应的预测准确度.
主要方法:
- 训练有素的SeqDance和ESMDance对来自分子动力学和正常模式分析超过64,000种蛋白质的动态生物物理特性进行了训练.
- SeqDance利用注意力机制来模拟残留物移动,并编码动态表示.
- ESMDance建立在ESM2架构之上,利用其输出进行增强的预测.
主要成果:
- SeqDance嵌入式捕捉了丰富的蛋白质动态,使得对形状性质的转移学习成为可能.
- SeqDance预测准确地反映了突变对蛋白质折叠稳定性的影响.
- 对于缺乏进化数据的蛋白质,ESMDance在零射击突变效应预测方面明显优于ESM2.
结论:
- SeqDance和ESMDance提供了一个强大的框架,用于将蛋白质动态整合到语言模型中.
- 这些模型提供了对蛋白质行为和突变效应的更可概括的预测.
- 开发的模型通过结合关键的动态信息,推进了蛋白质深度学习领域.
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