Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens

Bioinformatics. 2020 May 1;36(10):3185-3191. doi: 10.1093/bioinformatics/btaa119.


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 challenges in prediction accuracy and program accessibility.

Results: This study presents Vaxign-ML, a supervised ML classification to predict bacterial protective antigens (BPAgs). 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 5-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 (eXtreme Gradient Boosting) was compared to three publicly available programs (Vaxign, VaxiJen, and Antigenic), one SVM-based method, and one epitope-based method using a high-quality benchmark dataset. Vaxign-ML showed superior performance in predicting BPAgs. Vaxign-ML is hosted in a publicly accessible web server and a standalone version is also available.

Availability and implementation: Vaxign-ML website at, Docker standalone Vaxign-ML available at and source code is available at

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Antigens, Bacterial*
  • Computational Biology
  • Machine Learning
  • Software
  • Supervised Machine Learning
  • Vaccinology*


  • Antigens, Bacterial