曲线AGN:基于曲线的自适应图形神经网络,用于预测蛋白质-连接物结合亲和力
Jianqiu Wu1, Hongyang Chen2, Minhao Cheng3
1Research Center for Graph Computing, Zhejiang Lab, Yuhang, Hangzhou, 311121, Zhejiang, China.
BMC bioinformatics
|October 5, 2023
概括
预测蛋白质 - 配体结合亲和力是药物发现的关键. 一个新的基于曲率的自适应图形神经网络 (CurvAGN) 模型通过结合复杂的几何特征来改善预测,优于现有方法.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 机器学习 机器学习
背景情况:
- 精确预测蛋白质 - 配体结合亲和力对于合理的药物设计至关重要.
- 图形神经网络 (GNN) 是有前途的,但往往忽视了高层次的几何属性和蛋白质-连接体复合体中的异构相互作用.
- 现有的GNN主要使用基本的几何信息,如距离和角度,限制了它们的表示能力.
研究的目的:
- 开发一个先进的图形神经网络 (GNN) 模型,集成多尺度曲率信息和适应性注意力机制,用于增强蛋白质-连接体结合亲和力预测.
- 解决当前GNN在捕捉蛋白质 - 连接体复合体内的复杂几何特征和异构相互作用方面的局限性.
主要方法:
- 介绍基于曲率的自适应图形神经网络 (CurvAGN),具有编码多尺度曲率信息的曲率块.
- 在神经区块 (AGN) 中整合了自适应图的注意力机制,以结合几何 (角度,距离,曲率),长距离相互作用和图异构.
- 使用PDBbind-V2016核心数据集验证CurvAGN模型.
主要成果:
- 与现有方法相比,CurvAGN模型在预测蛋白质 - 连接体结合亲和关系方面表现优越.
- 结合多尺度曲率和适应性注意力机制显著提高了模型的预测准确性.
- 在PDBbind-V2016数据集上的实验结果证实了拟议方法的有效性.
结论:
- 基于曲率的自适应图形神经网络 (CurvAGN) 在预测蛋白质-连接体结合亲缘关系方面取得了重大进展.
- 通过CurvAGN整合更高层次的几何属性和异构相互作用,提高了GNN对分子复合物的表示能力.
- 这种方法有望通过提供更准确的结合亲和力预测来加速药物发现.
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