评估De Novo深度学习模型的蛋白质-糖相互作用组的评估
Samuel W Canner1, Lei Lu2, Sho S Takeshita1
1Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, United States.
bioRxiv : the preprint server for biology
|September 15, 2025
概括
深度学习模型显示出预测蛋白质-糖相互作用的前景,但随着碳水化合物链的长度,准确性会降低. 完整的蛋白质-糖相互作用预测需要进一步开发.
科学领域:
- 计算生物学 计算生物学
- 结构生物学 结构生物学
- 生物化学 生化学
背景情况:
- 深度学习模型擅长预测蛋白质和小分子相互作用.
- 非共价蛋白-碳水化合物复合体的结构对接是一个尚未探索的领域.
- 现有的基准没有专门评估蛋白质-碳水化合物对接.
研究的目的:
- 开发一个非共价蛋白-碳水化合物对接的基准数据集.
- 评估所有原子结构预测模型在这个任务上的表现.
- 评估碳水化合物聚合物长度对预测准确性的影响.
主要方法:
- 创建了碳水化合物蛋白相互作用 (BCAPIN) 数据集的基准.
- 开发一个DockQC评估指标.
- 对包括AlphaFold3,Boltz-1,Chai-1,DiffDock和RosettaFold-All Atom在内的模型进行评估.
主要成果:
- 所有评估的模型都取得了可比的结果,在可接受的质量结构中,成功率为85%.
- 随着碳水化合物聚合物长度的增加,预测能力显著下降.
- 在人类蛋白质上评估了AlphaFold3的结合预测能力.
结论:
- 目前的全原子模型显示了蛋白质-碳水化合物相互作用预测的潜力.
- 模型性能受到碳水化合物聚合物长度的限制.
- 对于高通量,高可靠性预测蛋白糖相互作用原子的进一步进展是必要的.
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