Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology.
Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO® vaccine against Neisseria meningitidis serogroup B. RV studies have begun to incorporate machine learning (ML) techniques to distinguish bacterial protective antigens (BPAs) from non-BPAs. This research contributes significantly to the RV field by using permutation analysis to demonstrate that a signal for protective antigens can be curated from published data. Furthermore, the effects of the following on an ML approach to RV were also assessed: nested cross-validation, balancing selection of non-BPAs for subcellular localization, increasing the training data, and incorporating greater numbers of protein annotation tools for feature generation. These enhancements yielded a support vector machine (SVM) classifier that could discriminate BPAs (n = 200) from non-BPAs (n = 200) with an area under the curve (AUC) of 0.787. In addition, hierarchical clustering of BPAs revealed that intracellular BPAs clustered separately from extracellular BPAs. However, no immediate benefit was derived when training SVM classifiers on data sets exclusively containing intra- or extracellular BPAs. In conclusion, this work demonstrates that ML classifiers have great utility in RV approaches and will lead to new subunit vaccines in the future.
Faculty of Medicine, University of Southampton, Southampton SO17 1BJ, UK. a.heinson@soton.ac.uk.
Ashley I
AI
Heinson
Faculty of Medicine, University of Southampton, Southampton SO17 1BJ, UK. y.p.gunawardana@soton.ac.uk.
Yawwani
Y
Gunawardana
Faculty of Medicine, University of Southampton, Southampton SO17 1BJ, UK. bastiaanmoesker@gmail.com.
Bastiaan
B
Moesker
London School of Hygiene and Tropical Medicine (LSHTM), Department of Pathogen Molecular BiologyLondon WC1E 7HT, UK. carmen.denman@gmail.com.
Carmen C Denman
CC
Hume
Solutions, University of Southampton, Southampton SO17 1BJ, UK. e.vataga@soton.ac.uk.
Elena
E
Vataga
Public Health England, National Infection Service, Porton Down Salisbury, SP4 0JG, UK. yper.hall@phe.gov.uk.
Yper
Y
Hall
The Jenner Institute, University of Oxford, Oxford OX3 7DQ, UK. elena.stylianou@ndm.ox.ac.uk.
Elena
E
Stylianou
The Jenner Institute, University of Oxford, Oxford OX3 7DQ, UK. helen.mcshane@ndm.ox.ac.uk.
Helen
H
McShane
Public Health England, National Infection Service, Porton Down Salisbury, SP4 0JG, UK. ann.rawkins@phe.gov.uk.
Ann
A
Williams
Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK. mn@ecs.soton.ac.uk.
Mahesan
M
Niranjan
Faculty of Medicine, University of Southampton, Southampton SO17 1BJ, UK. c.h.woelk@soton.ac.uk.
Christopher H
CH
Woelk
D000942:Antigens, Bacterial / Q000235:genetics / Q000276:immunology*; D019540:Area Under Curve; D001426:Bacterial Proteins / Q000235:genetics / Q000276:immunology; D001428:Bacterial Vaccines / Q000235:genetics / Q000276:immunology*; D019295:Computational Biology / Q000379:methods*; D018604:Epitope Mapping; D000939:Epitopes / Q000235:genetics / Q000276:immunology; D006801:Humans; D000069550:Machine Learning; D016296:Mutagenesis; D012372:ROC Curve; D060388:Support Vector Machine; D022223:Vaccines, Subunit / Q000235:genetics / Q000276:immunology*
D000942:Antigens, Bacterial; D001426:Bacterial Proteins; D001428:Bacterial Vaccines; D000939:Epitopes; D022223:Vaccines, Subunit
bacterial pathogen; bacterial protective antigen; machine learning; reverse vaccinology; support vector machine