Protein missense mutations and resulting protein stability changes are important causes for many human genetic diseases. However, the accurate prediction of stability changes due to mutations remains a challenging problem. To address this problem, we have developed an unbiased effective model: PMSPcnn that is based on a convolutional neural network. We have included an anti-symmetry property to build a balanced training dataset, which improves the prediction, in particular for stabilizing mutations. Persistent homology, which is an effective approach for characterizing protein structures, is used to obtain topological features. Additionally, a regression stratification cross-validation scheme has been proposed to improve the prediction for mutations with extreme ΔΔG. For three test datasets: S^sym, p53, and myoglobin, PMSPcnn achieves a better performance than currently existing predictors. PMSPcnn also outperforms currently available methods for membrane proteins. Overall, PMSPcnn is a promising method for the prediction of protein stability changes caused by single point mutations.

蛋白质错义突变和由此产生的蛋白质稳定性变化是许多人类遗传疾病的重要原因。然而，准确预测突变引起的稳定性变化仍然是一个具有挑战性的问题。为了解决这个问题，我们开发了一种无偏有效模型：基于卷积神经网络的 PMSPcnn。我们引入了反对称属性来构建平衡的训练数据集，这改进了预测，特别是稳定突变。持久同源性是表征蛋白质结构的有效方法，用于获得拓扑特征。此外，还提出了回归分层交叉验证方案来改进对具有极端 ΔΔG 的突变的预测。对于三个测试数据集：S ^sym 、p53 和肌红蛋白，PMSPcnn 取得了比当前现有预测器更好的性能。 PMSPcnn 的性能也优于目前可用的膜蛋白方法。总体而言，PMSPcnn 是预测单点突变引起的蛋白质稳定性变化的一种有前途的方法。