在CASP15中使用MULTICOM增强基于alphafold-multimer的蛋白质复合体结构预测
Jian Liu1, Zhiye Guo1, Tianqi Wu1
1Department of Electrical Engineering and Computer Science, NextGen Precision Health Institute, University of Missouri, Columbia, MO, 65211, USA.
Communications biology
|November 10, 2023
概括
该MULTICOM系统通过改进AlphaFold-Multimer的输入和输出来增强蛋白质复杂结构预测. 这种新的方法在CASP15中获得了很高的排名,在预测四级结构方面表现优于标准方法.
科学领域:
- 计算生物学 计算生物学
- 结构生物学 结构生物学
- 生物信息学是一种生物信息学.
背景情况:
- 准确预测蛋白质复杂结构对于理解生物功能至关重要.
- AlphaFold-Multimer是预测这些结构的强大工具,但需要进一步改进.
研究的目的:
- 开发和评估MULTICOM,该系统旨在提高AlphaFold-Multimer在四级结构预测中的性能.
- 为了改进输入数据并完善AlphaFold-Multimer的输出,以便更准确的蛋白质复合体建模.
主要方法:
- 开发了MULTICOM,这是一个采样多种多重序列对齐 (MSA) 和AlphaFold-Multimer模板的系统.
- 集成的传统序列对齐和基于Foldseek的结构对齐用于预测生成.
- 采用多种指标来进行排名预测,以及基于Foldseek结构对齐的方法来改进.
主要成果:
- 在CASP15.15中,MULTICOM_qa在26个服务器预测器中排名第三,MULTICOM_human在87个预测器中排名第七.
- 对于初始预测,MULTICOM_qa的TM平均得分为~0.76,比标准AlphaFold-Multimer有5.3%的改善.
- 来自MULTICOM_qa的最佳预测达到平均TM分数为~0.80,比标准AlphaFold-Multimer有8%的改善.
结论:
- 该MULTICOM系统显著提高了AlphaFold-Multimer在蛋白质复杂结构预测方面的能力.
- 基于对齐的多重结构生成 (FSAMG) 与基于序列对齐的方法相比显示出更高的性能.
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