跨膜蛋白具有关键的生物学功能,并在包括细胞信号传导在内的多种细胞过程中发挥作用。分子和离子跨膜运输。大约60%的跨膜蛋白被认为是药物靶标。这些蛋白质的错义突变会导致许多不同的疾病和紊乱,如神经退行性疾病和囊性纤维化。然而,关于跨膜蛋白突变的研究有限。在这项工作中,我们首先设计了一种新的特征编码方法,称为权重衰减位置特定评分矩阵(WAPSSM),它建立在蛋白质进化信息的基础上。然后,我们提出了一种新的突变预测算法(级联XGBoost)通过利用从共识预测因子和gcForest学习的想法。多级实验说明了WAPSSM和级联XGBoost算法的有效性。最后,基于WAPSSM和其他三种类型的功能,结合级联XGBoost算法,我们开发了一种新的跨膜蛋白突变预测因子,名为MutTMPredictor。我们将MutTMPredictor的性能与七个数据集上的几个现有预测指标进行了基准测试。在546个突变数据集上,MutTMPredictor的精度(ACC)为0.9661,马修相关系数(MCC)为0.8950。在67,584个数据集上,MutTMPredictor的MCC为0.7523,曲线下面积(AUC)为0.8746,分别比现有最佳预测指标(fathmm)高0.1625和0.0801。此外,MutTMPredictor在Pred-MutHTP数据集上的表现也优于两个特定的预测因子。结果表明,MutTMPredictor可用作预测和优先考虑跨膜蛋白错义突变的有效方法。MutTMPredictor网络服务器和数据集可在http://csbio免费访问。njust。edu.cn/bioinf/muttmpredictor/学术用途。
Transmembrane proteins have critical biological functions and play a role in a multitude of cellular processes including cell signaling, transport of molecules and ions across membranes. Approximately 60% of transmembrane proteins are considered as drug targets. Missense mutations in such proteins can lead to many diverse diseases and disorders, such as neurodegenerative diseases and cystic fibrosis. However, there are limited studies on mutations in transmembrane proteins. In this work, we first design a new feature encoding method, termed weight attenuation position-specific scoring matrix (WAPSSM), which builds upon the protein evolutionary information. Then, we propose a new mutation prediction algorithm (cascade XGBoost) by leveraging the idea learned from consensus predictors and gcForest. Multi-level experiments illustrate the effectiveness of WAPSSM and cascade XGBoost algorithms. Finally, based on WAPSSM and other three types of features, in combination with the cascade XGBoost algorithm, we develop a new transmembrane protein mutation predictor, named MutTMPredictor. We benchmark the performance of MutTMPredictor against several existing predictors on seven datasets. On the 546 mutations dataset, MutTMPredictor achieves the accuracy (ACC) of 0.9661 and the Matthew\'s Correlation Coefficient (MCC) of 0.8950. While on the 67,584 dataset, MutTMPredictor achieves an MCC of 0.7523 and area under curve (AUC) of 0.8746, which are 0.1625 and 0.0801 respectively higher than those of the existing best predictor (fathmm). Besides, MutTMPredictor also outperforms two specific predictors on the Pred-MutHTP datasets. The results suggest that MutTMPredictor can be used as an effective method for predicting and prioritizing missense mutations in transmembrane proteins. The MutTMPredictor webserver and datasets are freely accessible at http://csbio.njust.edu.cn/bioinf/muttmpredictor/ for academic use.