Vaxign-ML: Supervised Machine Learning Reverse Vaccinology Model for Improved Prediction of Bacterial Protective Antigens

MOTIVATION: Reverse vaccinology (RV) is a milestone in rational vaccine design, and machine learning (ML) has been applied to enhance the accuracy of RV prediction. However, ML-based RV still faces the challenges in prediction accuracy and program accessibility. RESULTS: This study presents Vaxign-ML, a supervised ML classification to predict bacterial protective antigens. To identify the best ML method with optimized conditions, five ML methods were tested with biological and physiochemical features extracted from well-defined training data. Nested five-fold cross-validation and leave-one-pathogen-out validation were used to ensure unbiased performance assessment and the capability to predict vaccine candidates against a new emerging pathogen. The best performing model, Vaxign-ML, was compared to three publicly available RV programs with a high-quality benchmark dataset. Vaxign-ML showed superior performance in predicting bacterial protective antigens. Vaxign-ML is deployed in a publicly available web server. AVAILABILITY: Vaxign-ML website at http://www.violinet.org/vaxign/vaxign-ml. Docker standalone Vaxign-ML available at https://hub.docker.com/r/e4ong1031/vaxign-ml and source code is available at https://github.com/VIOLINet/Vaxign-ML-docker. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.