College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China.
College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China.
Structure. 2024 Jun 6;32(6):838-848.e3. doi: 10.1016/j.str.2024.02.016. Epub 2024 Mar 19.
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, 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导致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的模型：PMSPcnn，它基于卷积神经网络。我们包含了一个反对称性质来构建一个平衡的训练数据集，这提高了预测能力，特别是对于稳定突变。持久同源性是一种用于描述蛋白质结构的有效方法，用于获取拓扑特征。此外，还提出了一种回归分层交叉验证方案，以提高对具有极端ΔΔG 的突变的预测。对于三个测试数据集：S、p53 和肌红蛋白，PMSPcnn 的性能优于目前现有的预测器。PMSPcnn 也优于目前用于膜蛋白的方法。总的来说，PMSPcnn 是一种很有前途的预测单点突变引起的蛋白质稳定性变化的方法。
