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卡斯特-DTA:用于预测药物目标亲和力的等价图神经网络

Rachit Kumar¹, Joseph D Romano¹, Marylyn D Ritchie¹

¹Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104.

bioRxiv : the preprint server for biology

|December 9, 2024

概括

卡斯特-DTA是一种新的药物标亲和力预测方法,利用等价图神经网络和交叉注意力来提高准确性. 这种方法增强了蛋白质结构的利用,以更好地设计和选药物.

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:

准确预测连体蛋白结合亲和力对于药物设计和开发至关重要.
蛋白质结构预测 (例如,AlphaFold) 的进步为基于结构的药物设计提供了新的机会.
现有的基于结构的方法通常不充分利用3D蛋白质结构信息.

研究的目的:

开发一种用于预测药物向亲和力 (DTA) 的新型计算方法,充分利用3D蛋白质结构信息.
提高DTA预测模型的准确性和可解释性.
为基于结构的DTA预测建立一个新的基准.

主要方法:

开发了CASTER-DTA (用于药物标亲和力的结构目标等价表示与交叉注意),这是一个联合架构,将SE(3) -等价图神经网络用于蛋白质表示和标准图神经网络用于连接体表示结合在一起.
纳入了基于注意力的机制,用于蛋白质残留物和配体原子之间的交叉相互作用,以提高可解释性.
利用SE(3) -等价图的神经网络从3D结构数据中学习强大的蛋白质表示.

主要成果:

在戴维斯和KIBA基准数据集上,CASTER-DTA在预测药物向亲和力方面取得了最先进的表现.
使用SE(3) -equivariant图形神经网络显著改善了用于DTA预测的蛋白质表示学习.
注意力机制提供了对蛋白质-配体相互作用的见解,提高了模型的解释性.

结论:

卡斯特-DTA证明了SE(3) -等价图神经网络和交叉注意力的有效性,用于准确和可解释的药物向 afinity 预测.
拟议的方法优于现有的方法,而不依赖于外部信息,如蛋白质语言模型嵌入.
这项工作为更复杂的基于结构的药物设计和选工具铺平了道路.

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