推断蛋白质变体对蛋白质与蛋白质相互作用的影响,使用可解释的变压器表示
Zhe Liu1, Wei Qian1, Wenxiang Cai1
1Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Research (Washington, D.C.)
|September 13, 2023
概括
我们开发了MIPPI,这是一种深度学习模型,可以预测遗传变异如何影响蛋白质相互作用. 这种工具有助于识别引起疾病的突变,并了解它们的功能影响.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 分子生物学分子生物学
背景情况:
- 了解遗传变异对蛋白质-蛋白质相互作用 (PPI) 的影响对于疾病研究至关重要.
- 现有的方法经常预测结合性亲和力变化,但缺乏对变异影响类型的直接预测.
- 关于PPI后果的实验数据有限,需要先进的预测模型.
研究的目的:
- 引入MIPPI,这是一个可解释的深度学习模型,用于预测PPI的变异效应.
- 利用序列数据和相互作用数据库进行准确的变异影响预测.
- 为了证明MIPPI在识别复杂疾病中的致病突变中的实用性.
主要方法:
- 开发了一个端到端,基于变压器的深度学习模型 (MIPPI).
- 训练有素的MIPPI使用IMEx的相互作用数据来预测变体影响类型 (增加,减少,干扰,无影响).
- 对模型解释性和与相互作用的氨基酸的相关性分析了注意力权重.
主要成果:
- MIPPI准确地预测了变体对蛋白质-蛋白质相互作用的影响.
- 通过注意重量分析实现了模型解释性,将预测与特定的氨基酸联系起来.
- MIPPI成功地优先考虑了神经发育障碍中的de novo突变,并确定了潜在的致病变体.
- 实验验证证证实了所选变体的功能影响.
结论:
- MIPPI是一种多功能,强大,可解释的模型,用于预测对PPI的突变影响.
- 该模型有助于发现临床可行的变体,并增强对疾病机制的理解.
- 在复杂疾病中,MIPPI提供了一种强大的工具,用于推进遗传变异解释.
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