通过MSA工程和广泛的模型采样和排名在CASP16中提升AlphaFold蛋白质三级结构预测
Jian Liu1, Pawan Neupane1, Jianlin Cheng1
1Department of Electrical Engineering & Computer Science, NextGen Precision Health, University of Missouri, Columbia, Missouri, 65211, United States of America.
bioRxiv : the preprint server for biology
|July 15, 2025
概括
MULTICOM4通过增强多个序列对齐 (MSA) 和采用整体质量评估来改善对具有挑战性的目标的蛋白质结构预测. 这种集成系统在CASP16中获得了顶级排名,在难以实现的蛋白质结构中表现优于标准AlphaFold3.
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:
- AlphaFold2和AlphaFold3显著提升了蛋白质三级结构的预测.
- 预测具有浅层多重序列对齐 (MSA) 或复杂域架构的蛋白质结构仍然是一个挑战.
研究的目的:
- 开发一个集成系统,MULTICOM4,以提高难以实现目标的蛋白质结构预测准确度.
- 改进蛋白质结构模型的生成和排名,使用现有工具,如AlphaFold2和AlphaFold3.
主要方法:
- MULTICOM4使用多种MSA生成技术和大规模模型采样.
- 一个整体模型质量评估 (QA) 策略结合了多种QA方法,以改进模型选择.
- 该系统结合了MSA工程和域细分,以获得更好的输入数据.
主要成果:
- 基于MULTICOM4的预测器在第16次蛋白质结构预测技术的临界评估 (CASP16) 中表现最好,在120个预测器中排名最好.
- 最好的预测器,MULTCOM,在84个CASP16域中获得了0.902的平均TM得分.
- 在73.8%的域中达到高精度 (TM-score > 0.9),在top-1预测中预测了97.6%的正确折叠 (TM-score > 0.5).
结论:
- 对于精确的蛋白质结构预测,特别是对于具有挑战性的目标,MSA工程,广泛的模型采样和整体QA至关重要.
- MULTICOM4展示了一种强大的方法来改进超越标准方法的蛋白质结构预测.
- 这些发现强调了整合性策略对于推进计算结构生物学的重要性.
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