在CASP15中,zPoseScore模型用于准确和强大的蛋白质-连接体对接姿势得分
Tao Shen1, Fuxu Liu1, Zechen Wang2
1Shanghai Zelixir Biotech Company Ltd., Shanghai, China.
Proteins
|August 22, 2023
概括
我们开发了zPoseScore,这是一个深度学习模型,用于预测蛋白质-连接体复合体. 这种先进的工具准确地得分和排列连接体的姿势,有助于小分子药物发现.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物学是结构生物学.
- 药物发现 药物发现
背景情况:
- 准确预测蛋白质 - 连接体复杂结构对于药物发现至关重要.
- 现有的方法在精确评分和排名潜在药物候选物质时面临挑战.
研究的目的:
- 介绍zPoseScore,一个新的深度学习模型,用于增强蛋白质 - 配体复合体预测.
- 为了提高联结体姿势评分和排名在计算药物设计中的准确性.
主要方法:
- 开发了zPoseScore,这是一个使用重新设计的"zFormer"模块的深度学习框架,灵感来自AlphaFold2.2.
- 使用先进的数据增强和采样生成了六个培训和评估数据集.
- 启用了原子级特征编码和融合,用于蛋白质 - 配体相互作用.
主要成果:
- 在CASF-2016基准数据集上实现了高性能,Pearson的相关系数为0.783和0.659.
- 在CASP15.15中获得了0.558的平均lDDT得分,用于新的预测.
- 证明了准确的结合部位预测和侧链方向的重要性.
结论:
- zPoseScore在蛋白质 - 配体姿势预测方面展示了最先进的性能.
- 该模型为加速小分子药物发现提供了有价值的工具.
- 准确预测结合部位和连接体构成是成功药物设计的关键.
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