在CASP16中对制药蛋白-连接体姿势和亲和预测的评估
Michael K Gilson1, Jerome Eberhardt2,3, Peter Škrinjar2,3
1Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, USA.
Proteins
|October 4, 2025
概括
结构预测的第16次关键评估 (CASP16) 评估了用于药物发现的计算蛋白-配体建模. 虽然基于模板的方法表现良好,但AlphaFold 3在姿势预测方面超过了人类预测者,尽管亲和力预测的准确性仍然是一个挑战.
科学领域:
- 计算化学是一种计算化学.
- 结构生物学是结构生物学.
- 药物发现 药物发现
背景情况:
- 结构预测的批判性评估 (CASP) 是一个社区范围的努力,以评估蛋白质结构预测方法的准确性.
- CASP16的蛋白质-配体组件专注于预测类似药物的化合物与蛋白质标的结合姿势和亲和力.
研究的目的:
- 评估计算方法在预测蛋白质 - 连接体结合姿势和亲和关系方面的性能.
- 将当前的蛋白质 - 配体建模技术与自动化方法进行比较,包括深度学习方法.
主要方法:
- 30个研究小组提交了对229个蛋白质连接体姿势目标和140个亲和力目标的预测.
- 评估了基于模板的自动姿势预测方法,包括像AlphaFold 3这样的深度神经网络.
- 预测和实验结合亲缘关系之间的相关性使用肯德尔的值进行了评估.
主要成果:
- 基于模板的姿势预测方法实现了平均LDDT-PLI为0.69.
- AlphaFold 3的平均LDDT-PLI为0.8,超过了最好的CASP16参与者.
- 亲和力预测与实验数据的相关性很小 (最大肯德尔的t = 0.42),这表明得分函数的局限性.
结论:
- 计算型蛋白质-连接体建模已经进步,但挑战仍然存在,特别是在亲和力预测方面.
- 自动化方法,如AlphaFold 3,显示出显著的希望,并且可以超过人类预测器.
- 结果为计算机辅助药物设计提供了有价值的基准,并突出了未来方法开发的领域.
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