GNNSeq:一个基于序列的图形神经网络,用于预测蛋白质-连接物结合亲和力
Somanath Dandibhotla1, Madhav Samudrala2, Arjun Kaneriya3
1Department of Computer Science, College of Engineering and Computing, George Mason University, Fairfax, VA 22030, USA.
Pharmaceuticals (Basel, Switzerland)
|March 27, 2025
概括
一个新的混合模型GNNSeq,只使用序列数据,准确地预测了蛋白质-连接体结合亲和力. 这种高效的方法有助于药物发现,使大规模的虚拟查和识别潜在的候选药物.
科学领域:
- 计算化学和化学信息学
- 机器学习在药物发现中的作用
- 生物信息学和结构生物学
背景情况:
- 准确预测蛋白质 - 配体结合亲和力对于有效的药物发现至关重要.
- 现有的基于序列的模型往往缺乏准确性和稳定性,限制了它们的概括性.
- 需要的模型不需要预先对接的复合体或结构数据.
研究的目的:
- 开发GNNSeq,这是一个新的混合机器学习模型,用于预测蛋白质-带结合亲和力.
- 通过完全利用序列特征来克服现有模型的局限性.
- 为了提高结合性亲和预测的准确性,稳定性和通用性.
主要方法:
- GNNSeq集成了一个图形神经网络 (GNN) 与随机森林 (RF) 和XGBoost.
- 该模型从蛋白质和配体序列中提取分子特征和序列模式.
- 基于内核的独特的上下文切换设计优化了效率,并动态调整了功能权重.
主要成果:
- 在PDBbind v.2020精炼集上,GNNSeq实现了0.784的皮尔森相关系数 (PCC),在PDBbind v.2016核心集上达到0.84.
- 对DUDE-Z数据集的外部验证显示平均AUC为0.74.
- 结合GNNSeq的混合模型达到0.97的PCC,训练时间高效 (在约1.5小时内完成5000多个复合体).
结论:
- GNNSeq提供了一个高效和可扩展的解决方案,用于结合亲和力预测.
- 该模型显示了更好的准确性和通用性,促进了大规模的虚拟选.
- 通过基于服务器的GUI,GNNSeq是公开可用的,用于具有成本效益的命中识别.
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