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使用可解释图形神经网络从序列和结构数据预测蛋白质激酶的功能状态

Ashwin Ravichandran¹, Juan C Araque¹, John W Lawson²

¹KBR Inc., Intelligent Systems Division, NASA Ames Research Center, Moffett Field, California, USA.

|December 12, 2023

概括

一个可解释图形神经网络 (GNN) 框架准确地使用结构和序列对蛋白激酶状态进行分类. 这种方法可以识别关键的功能动机,加速药物发现和蛋白质工程.

科学领域:

生物化学和结构生物学
计算生物学和生物信息学

背景情况:

蛋白激酶调节关键的细胞功能,是治疗癌症等疾病的关键目标.
对于激酶的现有结构数据是广泛的,但将结构与功能联系起来的自动化方法是有限的,这阻碍了药物发现.
开发高效的自动化技术对于推进结构生物学和治疗开发至关重要.

研究的目的:

开发一个可解释的图形神经网络 (GNN) 框架,用于仅使用三级结构和氨基酸序列来分类蛋白激酶功能状态 (活性/无活性).
利用图形神经网络和图形Grad-CAM来自动识别功能关键的残留物和接触物,而无需手动输入.
利用可解释的框架来分析跨酶子类的结构变异,并增强蛋白质工程.

主要方法:

实现图形神经网络 (GNN) 框架,利用蛋白质三级结构和氨基酸序列进行激酶状态分类.
应用梯度加权类激活映射用于图形 (图形Grad-CAM) 来识别结构重要残留物和接触物.
使用Grad-CAM地图作为矢量嵌入来识别蛋白质数据库 (PDB) 中的激酶子类之间的微妙结构差异.

主要成果:

在将酶结构分类为活性和非活性状态时,GNN模型实现了高精度 (>97%).
图表Grad-CAM成功地确定了功能关键的动图,包括水性脊柱的保存DFG和HRD动图,与现有文献一致.
该框架有效地区分了激酶子类之间的微妙结构变异,证明了其在高通量分析中的实用性.

结论:

开发的可解释的GNN框架提供了一种自动和准确的方法来确定蛋白激酶结构-功能关系.
这种方法加速了关键功能部位的识别,有助于设计有针对性的小分子疗法.
该框架具有重要的潜力,用于设计新型蛋白质,并推进酶相关疾病的药物发现工作.

关键词:

图形神经网络的神经网络蛋白质激酶是一种蛋白质激酶.结构功能关系关系.

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