精确预测单个氨基酸变体的功能效应,使用深度学习
Houssemeddine Derbel1, Zhongming Zhao2, Qian Liu1,3
1Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, Las Vegas, NV 89154, USA.
Computational and structural biotechnology journal
|December 11, 2023
概括
使用深度学习,Rep2Mut-V2准确地预测了氨基酸变体的功能影响. 这种计算方法增强了蛋白质工程和人类疾病研究的变异解释,降低了实验成本.
科学领域:
- 蛋白质组学和计算生物学
- 蛋白质工程和临床医学
- 生物信息学中的深度学习应用
背景情况:
- 评估氨基酸变异效应对于蛋白质组学,临床医学和蛋白质工程至关重要.
- 高通量实验提供了全面的变体分析,但成本高昂.
- 现有的计算方法依赖于进化保护,限制了准确性.
研究的目的:
- 引入Rep2Mut-V2,一种用于预测蛋白质变异的功能影响的新型深度学习模型.
- 利用从变压器模型中学习到的表示来提高预测准确性.
- 为昂贵的高通量实验提供成本效益高的计算替代方案.
主要方法:
- 开发了Rep2Mut-V2,这是一个使用基于变压器的学习表示的深度学习模型.
- 在38个蛋白质数据集上评估Rep2Mut-V2,其中包括118,933个单氨基酸变体.
- 将Rep2Mut-V2的性能与六种最先进的方法进行比较,包括ESM,DeepSequence和EVE.
主要成果:
- Rep2Mut-V2显著提高了27种类型的功能效应测量的预测准确性.
- 在38个蛋白质数据集中获得了0.7的Spearman相关系数.
- 超越现有的最先进的方法,包括ESM和DeepSequence,特别是在有限的培训数据下.
结论:
- Rep2Mut-V2为蛋白质编码序列中的单氨基酸变体的功能影响提供了准确的预测.
- 该模型显示了扩展高通量实验分析,降低成本的潜力.
- Rep2Mut-V2可以显著帮助解释人类疾病研究中的变异.
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