T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm

Tianhang Chen; Xiangeng Wang; Yanyi Chu; Yanjing Wang; Mingming Jiang; Dong-Qing Wei; Yi Xiong

doi:10.3389/fmicb.2020.580382

T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm

Front Microbiol. 2020 Sep 24:11:580382. doi: 10.3389/fmicb.2020.580382. eCollection 2020.

Authors

Tianhang Chen¹, Xiangeng Wang^{1

2}, Yanyi Chu^{1

3}, Yanjing Wang¹, Mingming Jiang¹, Dong-Qing Wei^{1

3}, Yi Xiong¹

Affiliations

¹ State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
² Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China.
³ Peng Cheng Laboratory, Shenzhen, China.

Abstract

Type IV secreted effectors (T4SEs) can be translocated into the cytosol of host cells via type IV secretion system (T4SS) and cause diseases. However, experimental approaches to identify T4SEs are time- and resource-consuming, and the existing computational tools based on machine learning techniques have some obvious limitations such as the lack of interpretability in the prediction models. In this study, we proposed a new model, T4SE-XGB, which uses the eXtreme gradient boosting (XGBoost) algorithm for accurate identification of type IV effectors based on optimal features based on protein sequences. After trying 20 different types of features, the best performance was achieved when all features were fed into XGBoost by the 5-fold cross validation in comparison with other machine learning methods. Then, the ReliefF algorithm was adopted to get the optimal feature set on our dataset, which further improved the model performance. T4SE-XGB exhibited highest predictive performance on the independent test set and outperformed other published prediction tools. Furthermore, the SHAP method was used to interpret the contribution of features to model predictions. The identification of key features can contribute to improved understanding of multifactorial contributors to host-pathogen interactions and bacterial pathogenesis. In addition to type IV effector prediction, we believe that the proposed framework can provide instructive guidance for similar studies to construct prediction methods on related biological problems. The data and source code of this study can be freely accessed at https://github.com/CT001002/T4SE-XGB.

Keywords: SHAP (SHapley additive exPlanations); extreme gradient boosting; feature secelction; interpretable analysis; type IV secreted effector.