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PocketStruct:整合蛋白质口袋的结构特征来预测蛋白质-结合

Ziyang Wang, Yangkun Zheng, Ridi Wen

IEEE transactions on computational biology and bioinformatics

|December 25, 2025

概括

类药物开发从结合蛋白质口袋结构数据中受益. 这项研究表明,与仅序列模型相比,使用结构信息可以提高结合性预测,从而提高药物发现潜力.

科学领域:

计算生物学是一种计算生物学.
药物发现药物发现
结构生物信息学结构生物信息学

背景情况:

类提供低毒性和小的结合接口,使它们成为有希望的药物候选者.
预测蛋白结合对于药物开发至关重要.
当前的方法通常仅依赖于序列信息,可能缺少关键的结构洞察力.

研究的目的:

评估将蛋白质口袋结构信息整合到蛋白结合预测模型中的影响.
在统一的框架内比较不同结构编码器的性能.
在新数据上评估结构意识模型的稳定性.

主要方法:

开发了一个基于变压器的框架,共同关注整合蛋白质口袋结构数据.
系统地比较了三个口袋结构编码器:SE(3) - 变压器,ProtGVP (几何图形神经网络) 和ESM-IF1 (基于大规模结构的预训练模型).
采用严格的数据分区,严格分离培训和测试集.

主要成果:

整合口袋结构信息始终提高了与仅序列模型相比的约束预测准确性.
ProtGVP表现出特别有效的口袋表示.
基于结构的模型变体在预测对以前未见过的数据的绑定时表现出卓越的稳定性.

结论:

蛋白质口袋结构是提高-蛋白质结合预测准确性的重要特征.
像ProtGVP这样的几何深度学习模型在表示结构信息方面显示出显著的前景.
结构意识模型为药物发现应用提供了更好的概括性和稳定性.

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