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.
Research square
|June 30, 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. 该系统在CASP16中实现了最高性能,超过了标准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:
- 阿尔法Fold2和阿尔法Fold3擅长预测单链蛋白质结构.
- 对于缺乏多重序列对齐 (MSA) 或复杂架构的蛋白质,预测它们的结构仍然很困难.
研究的目的:
- 开发一个集成系统,MULTICOM4,以提高对具有挑战性的目标的蛋白质结构预测准确度.
- 改进AlphaFold2和AlphaFold3.3的蛋白质模型的生成和排名.
主要方法:
- MULTICOM4使用多种MSA生成策略和广泛的模型采样.
- 整体质量评估 (QA) 方法结合了多种QA方法,以改善模型排名.
- 该系统在蛋白质结构预测技术的批判性评估 (CASP16) 中进行了评估.
主要成果:
- 在CASP16中,MULTICOM4被列为表现最好的一员,其表现优于标准的AlphaFold3预测器.
- 最好的MULTICOM4预测器在84个CASP16域中获得了0.902的平均TM得分.
- 在73.8%的域中实现了高精度 (TM-score > 0.9),在top-1预测中实现了97.6%的正确折叠 (TM-score > 0.5).
结论:
- 对于精确的蛋白质结构建模,MSA工程和广泛的模型采样至关重要.
- 集成质量保证方法和模型集群提高了结构预测排名的可靠性.
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