VPAgs-Dataset4ML: A Dataset to Predict Viral Protective Antigens for Machine Learning-Based Reverse Vaccinology

Zakia Salod () and Ozayr Mahomed
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Zakia Salod: Discipline of Public Health Medicine, University of KwaZulu-Natal, Durban 4051, South Africa
Ozayr Mahomed: Discipline of Public Health Medicine, University of KwaZulu-Natal, Durban 4051, South Africa

Data, 2023, vol. 8, issue 2, 1-12

Abstract: Reverse vaccinology (RV) is a computer-aided approach for vaccine development that identifies a subset of pathogen proteins as protective antigens (PAgs) or potential vaccine candidates. Machine learning (ML)-based RV is promising, but requires a dataset of PAgs (positives) and non-protective protein sequences (negatives). This study aimed to create an ML dataset, VPAgs-Dataset4ML, to predict viral PAgs based on PAgs obtained from Protegen. We performed seven steps to identify PAgs from the Protegen website and non-protective protein sequences from Universal Protein Resource (UniProt). The seven steps included downloading viral PAgs from Protegen, performing quality checks on PAgs using the standard BLASTp identity check ≤30% via MMseqs2, and computational steps running on Google Colaboratory and the Ubuntu terminal to retrieve and perform quality checks (similar to the PAgs) on non-protective protein sequences as negatives from UniProt. VPAgs-Dataset4ML contains 2145 viral protein sequences, with 210 PAgs in positive.fasta and 1935 non-protective protein sequences in negative.fasta . This dataset can be used to train ML models to predict antigens for various viral pathogens with the aim of developing effective vaccines.

Keywords: viruses; antigens; machine learning; reverse vaccinology; vaccinology; vaccines; bioinformatics (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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