集成无监督语言模型与多视图多序列对齐,以实现高精度的链间接触预测
Zi Liu1, Yi-Heng Zhu2, Long-Chen Shen3
1School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094, China; Computer Department, Jingdezhen Ceramic University, Jingdezhen, 333403 , China.
Computers in biology and medicine
|September 25, 2023
概括
ICCPred是一种新的深度学习方法,使用氨基酸序列准确预测蛋白质链间接触. 这种方法提高了对蛋白质复杂结构和功能的理解.
科学领域:
- 计算生物学 计算生物学
- 结构生物学 结构生物学
- 生物信息学是一种生物信息学.
背景情况:
- 准确识别链间接触对于确定蛋白质复杂的3D结构和生物功能至关重要.
- 从氨基酸序列中预测链间接触的现有方法有局限性.
研究的目的:
- 开发一种新的深度学习方法,ICCPred,仅使用氨基酸序列来准确预测蛋白质复合体中的链际接触.
- 与最先进的方法相比,提高链间接触预测的准确性.
主要方法:
- 开发了ICCPred,这是一个使用深度残余网络架构的深度学习管道.
- 集成了一个预先训练的语言模型,其中包含来自不同生物视角的三个多重序列对齐 (MSA).
- 在709个非冗余蛋白质复合体上训练和评估模型.
主要成果:
- ICCPred显著提高了链间接触预测的准确性.
- 该方法的性能优于现有的最先进的方法.
- 分析显示,预训练的变压器语言模型有效地捕捉了来自MSA的共同进化多样性,提高了预测准确度.
结论:
- ICCPred提供一种基于深度学习的高度准确的方法,仅从蛋白质序列预测链际接触.
- 该方法为大规模的蛋白质-蛋白质相互作用注释和结构生物学研究提供了有价值的工具.
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