GSScore:一种新的基于Graphormer的状评分方法,用于蛋白质 - 配体对接
Linyuan Guo1,2, Jianxin Wang1,2
1School of Computer Science and Engineering, Central South University, Rd. Lu Shan Nan, 410083, Changsha, P.R. China.
Briefings in bioinformatics
|May 6, 2024
概括
新的深度学习方法GSScore准确地预测了蛋白质-连接体对接姿势. 它使用Graphormer和类似贝的图形架构来识别近原生构造,改进药物发现的计算方法.
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:
- 蛋白质-连接体相互作用 (PLIs) 对生物过程和药物开发至关重要.
- 对PLI的实验性确定是复杂而昂贵的,这推动了对诸如蛋白质-连接体对接等计算方法的需求.
- 预测对接姿势精度 (RMSD) 的现有机器学习模型需要改进得分功能.
研究的目的:
- 开发一种新的基于深度学习的评分方法,以提高预测蛋白质-连接体对接位置的根平均平方偏差 (RMSD) 的准确性.
- 引入GSScore,一种利用Graphormer和类似shell的图形架构的方法,用于更好地识别近原生对接构造.
主要方法:
- GSScore将蛋白质-连接体对接接口建模为定义的原子外内的多个双部分图形.
- 原子被表示为节点,并使用Graphormer框架与外状图形结构相结合来捕捉相互作用.
- 该方法直接在原子结构上运行,不需要额外的输入特征.
主要成果:
- 与现有方法相比,GSScore在RMSD预测准确度方面取得了显著的改进.
- 对PDBBind 2019,CASF2016和DUD-E数据集的评估显示,在RMSE,皮尔森相关系数 (R),斯皮尔曼相关系数和对接功率方面,性能有所提高.
- 图形和状图形架构有效地区分了有利的近原生和不利的非原生姿势.
结论:
- GSScore提供了一种强大的新型深度学习方法,用于精确预测蛋白质-连接体对接中的RMSD.
- 该方法捕捉微妙的结构差异的能力提高了药物发现中的计算选的可靠性.
- GSScore代表了蛋白质-连接体对接姿势评估的评分函数的重大进步.
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