通过MSA工程和广泛的模型采样和CASP16中的排名来提高AlphaFold蛋白质三级结构预测
Jian Liu1,2, Pawan Neupane1,2, Jianlin Cheng3,4
1Department of Electrical Engineering & Computer Science, University of Missouri, Columbia, MO, USA.
Communications biology
|November 17, 2025
概括
通过改进多个序列对齐生成和模型采样,MULTICOM4提高了对具有挑战性的目标的蛋白质结构预测. 这种集成系统提高了AlphaFold预测的准确性,有助于结构生物学和药物发现.
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:
- 对于许多蛋白质来说,AlphaFold2和AlphaFold3显著提升了蛋白质结构预测.
- 预测具有有限或杂序列数据和复杂域安排的困难目标的结构仍然是一个挑战.
研究的目的:
- 开发一个集成系统,MULTICOM4,以提高对挑战单链蛋白质的蛋白质结构预测精度.
- 改进结构模型的生成和排名,使用现有的预测工具,如AlphaFold2和AlphaFold3.
主要方法:
- 采用多种多重序列对齐 (MSA) 生成策略.
- 使用大规模模型采样和整体模型质量评估 (QA) 方法.
- 综合补充质量保证方法和模型集群,以提高排名可靠性.
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批判性评估 (CASP16) 中排名最高.
- 在CASP16领域取得了高精度,平均TM得分为0.902.2.
- 与标准AlphaFold3预测器相比,表现出卓越的性能,所有域正确折叠在最好的前5预测中.
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,特别是在困难的情况下.
- 该MULTICOM4系统为模拟具有挑战性的单链蛋白提供了实用策略.
- 蛋白质结构预测的进步对结构生物学和药物发现有重大影响.
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