MutTMPredictor: Robust and accurate cascade XGBoost classifier for prediction of mutations in transmembrane proteins

Computational and Structural Biotechnology Journal

Jiangning Song¹  Dong-Jun Yu²  Arif Muhammad³  Jian Xu³  Yi-Heng Zhu³  Fang Ge³ 

[1] School of Systems and Technology, Department of Informatics and System, University of Management and Technology, Lahore, 54770, Pakistan;Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia;School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China;

关键词: Transmembrane protein;    Mutation prediction;    Disease-associated mutations;    Cascade XGBoost;    Protein evolutionary information;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown