向一个更绿色的AlphaFold2抗体-抗原建模协议:来自CAPRI第55轮的见解
Büşra Savaş1,2, İrem Yılmazbilek1, Atakan Özsan1
1İzmir Biomedicine and Genome Center, İzmir, Turkey.
Proteins
|March 3, 2025
概括
增强的AlphaFold2采样准确预测了CAPRI55.5中的抗体复合体. 这种方法对高效和精确的蛋白质复合体建模,特别是对抗体点的建模具有前景.
科学领域:
- 计算生物学 计算生物学
- 结构生物学 结构生物学
- 生物物理学的生物物理.
背景情况:
- 相互作用预测的批判性评估 (CAPRI) 挑战对蛋白质结构预测方法进行基准测试.
- 精确预测蛋白质-蛋白质相互作用,特别是抗体-抗原复合体,仍然是结构生物学中的一个重大挑战.
研究的目的:
- 评估一个增强的AlphaFold2 (AF2) 采样协议与数据驱动对接相结合,用于预测CAPRI第55轮蛋白质复合体.
- 在复杂预测中评估减少MinnieFold采样策略和FoldX评分函数的性能.
主要方法:
- 利用基于Wallner的大规模采样方法的增强AlphaFold2 (AF2) 协议,生成每个目标的数千个模型.
- 采用了50倍减少的MinnieFold采样和针对特定目标的定制排名 (T231,T232).
- 应用了针对抗体-MHC I复合体 (T233,T234) 的数据驱动对接,并使用FoldX进行模型评分.
主要成果:
- 使用MinnieFold采样实现了对抗体-复合体 (T231) 和PP2A:TIPRL复合体 (T232) 的最高级别的预测.
- 数据驱动的对接未能为抗体-MHC I复合体 (T233,T234) 产生可接受的模型.
- 后分析表明,MinnieFold可以为T233产生中等质量的模型,而FoldX成功地为T231和T232选择了可接受/中等质量的模型.
结论:
- 增强的AF2采样,特别是MinnieFold方法,证明了准确有效地预测抗体复合物的可行性.
- 这些发现突出了绿色和精确的增强AF2采样策略的潜力,用于抗体复杂模型.
- 像FoldX这样的定制排名和评分功能对于从大型模型集中识别高质量的预测至关重要.
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