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

Author

Listed:
  • 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
    Dasman Diabetes Institute, P.O. Box 1180, Dasman 15462, Kuwait City, Kuwait)

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.

Suggested Citation

  • Zakia Salod & Ozayr Mahomed, 2023. "VPAgs-Dataset4ML: A Dataset to Predict Viral Protective Antigens for Machine Learning-Based Reverse Vaccinology," Data, MDPI, vol. 8(2), pages 1-12, February.
    • Handle: RePEc:gam:jdataj:v:8:y:2023:i:2:p:41-:d:1072576
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