Department of Control Engineering, Northeastern University, Qinhuangdao Campus, Qinhuangdao 066001, China.
Department of Control Engineering, Northeastern University, Qinhuangdao Campus, Qinhuangdao 066001, China.
Comput Biol Chem. 2023 Dec;107:107952. doi: 10.1016/j.compbiolchem.2023.107952. Epub 2023 Aug 26.
Predicting protein stability change upon variation through a computational approach is a valuable tool to unveil the mechanisms of mutation-induced drug failure and develop immunotherapy strategies. Some previous machine learning-based techniques exhibit anti-symmetric bias toward destabilizing situations, whereas others struggle with generalization to unseen examples. To address these issues, we propose a gated graph neural network-based approach to predict changes in protein stability upon mutation. The model uses message passing to encode the links between the molecular structure and property after eliminating the non-mutant structure and creating input feature vectors. While doing so, it also incorporates the coordinates of the raw atoms to provide spatial insights into the chemical systems. We test the model on the S, Myoglobin, Broom, and p53 datasets to demonstrate the generalization performance. Compared to existing approaches, our proposed method achieves improved linearity with symmetry in less time. The code for this study is available at: https://github.com/HongzhouTang/Pros-GNN.
通过计算方法预测变异时蛋白质稳定性的变化是揭示突变引起的药物失效机制和开发免疫治疗策略的有价值的工具。一些以前基于机器学习的技术对使蛋白质不稳定的情况表现出反对称的偏差，而其他技术则难以推广到未见的例子。为了解决这些问题，我们提出了一种基于门控图神经网络的方法来预测突变时蛋白质稳定性的变化。该模型使用消息传递来编码分子结构和特性之间的联系，方法是消除非突变结构并创建输入特征向量。在这样做的过程中，它还将原始原子的坐标纳入其中，为化学系统提供空间洞察力。我们在 S、肌红蛋白、扫帚和 p53 数据集上测试了该模型，以展示其泛化性能。与现有方法相比，我们提出的方法在更短的时间内实现了改进的线性对称性。这项研究的代码可在：https://github.com/HongzhouTang/Pros-GNN。
