PPB-Affinity:基于AI的蛋白质药物发现的蛋白质-蛋白质结合关系数据集
Huaqing Liu1, Peiyi Chen1, Xiaochen Zhai2
1Artificial Intelligence Innovation Center, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, 510700, China.
Scientific data
|December 3, 2024
概括
研究人员创建了最大的公开蛋白质-蛋白质结合 (PPB) 亲和力数据集,以帮助药物发现. 这一数据集和一个基准深度学习模型将加速大分子药物的选.
科学领域:
- 生物化学 生化学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 蛋白质-蛋白质结合 (PPB) 亲和力预测对于大分子药物发现至关重要.
- 有深度学习模型可以预测由于突变而导致的亲和力变化,但不能预测绝对PPB亲和力.
- 缺乏公开可用的数据集阻碍了研究预测PPB亲和力的研究.
研究的目的:
- 引入一个全面的,大规模的,公开可用的数据集,用于蛋白质与蛋白质的结合亲和力 (PPB-Affinity).
- 为了解决预测PPB亲缘关系的数据稀缺问题.
- 促进大分子药物发现和查方面的进步.
主要方法:
- 编制了一套新型数据集 (PPB-Affinity),包含蛋白质-蛋白质复杂晶体结构,突变数据和结合亲和力.
- 包括关于受体和连接体蛋白质链的详细信息.
- 开发一个使用PPB-Affinity数据集的深度学习基准模型.
主要成果:
- 创建最大的公开可用的数据集,专门用于蛋白质-蛋白质结合亲和力.
- PPB-Affinity数据集包括蛋白质复合体的结构和亲和数据.
- 为PPB亲和力预测建立了一个深度学习基准模型.
结论:
- PPB-Affinity数据集对研究界来说是一个重要的资源.
- 预计这一数据集将加速对潜在的大分子药物的选.
- 该基准模型为PPB亲和力预测的未来研究提供了基础.
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