在CASP16中对蛋白质复合体预测的评估:我们在取得进展吗?
bioRxiv : the preprint server for biology
|June 12, 2025
概括
复杂蛋白质结构的预测仍然具有挑战性,特别是对于抗体-抗原标. 虽然AlphaFold3提高了性能,但优化建模和广泛采样是顶级CASP16组的关键,突出了对模型排名和替代方法的需求.
科学领域:
- 计算生物学 计算生物学
- 结构生物信息学 结构生物信息学
- 预测蛋白质结构的方法
背景情况:
- 结构预测的批判性评估 (CASP) 对蛋白质结构预测的进展进行了基准评估.
- 预测多重体 (寡合体) 结构,特别是对于复杂的目标,如抗体-抗原复合体,仍然是一个重要的计算挑战.
- 最近的进展包括AlphaFold-Multimer (AFM) 和AlphaFold3 (AF3),它们已成为许多研究小组的核心工具.
研究的目的:
- 在CASP16竞赛中评估各种计算方法在预测多重蛋白质结构方面的性能.
- 确定蛋白质结构预测中的关键挑战和新兴趋势,重点关注复杂的目标和模型选择策略.
- 评估先进的建模技术和大规模采样对预测准确性的影响.
主要方法:
- 参与者使用了AlphaFold-Multimer (AFM) 和AlphaFold3 (AF3) 等核心建模引擎.
- 性能最好的小组采用了诸如优化多个序列对齐 (MSA) 等策略,完善建模结构 (例如使用部分序列),以及广泛的模型采样和选择.
- 特别的挑战包括第0阶段 (在没有静脉测量信息的情况下进行预测) 和第2阶段 (使用MassiveFold进行大规模采样).
主要成果:
- 超过30%的CASP16标,特别是抗体-抗原复合体,被证明是非常具有挑战性的,大多数组的成功率有限.
- 通过优化方法和采样,表现最好的公司 (例如,MULTICOM系列,Kiharalab) 在默认情况下显著改善了AFM/AF3预测.
- 科扎科夫瓦吉达组在使用非AF基方法的抗体-抗原点上表现出色,这表明了替代方法的潜力. 模型排名和选择成为关键瓶.
结论:
- CASP16在多元预测方面取得了适度的进展,这在很大程度上是由AF3和先进的采样技术推动的.
- 预测复杂的目标,有效的模型排名以及对不同组件的准确石化测量预测仍然存在重大挑战.
- 未来的研究应该专注于开发超越当前基于AF的方法的新型建模范式,并改进模型选择和排名管道.
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