VacSol-ML(ESKAPE): Machine learning empowering vaccine antigen prediction for ESKAPE pathogens

• Published in: Vaccine 2024-09, Vol.42 (22), p.126204, Article 126204
• Main Authors: Nasir, Samavi, Anwer, Farha, Ishaq, Zaara, Saeed, Muhammad Tariq, Ali, Amjad
• Format: Article
• Language: English
• Subjects: Acinetobacter baumannii - immunology; Algorithms; Antibiotic resistance; Antibiotics; Antigens; Antigens, Bacterial - immunology; Artificial intelligence; Bacteria; Bacterial infections; Bacterial Vaccines - immunology; Bioinformatics; Biological activity; Biological properties; Computational Biology - methods; Datasets; Enterobacter - genetics; Enterobacter - immunology; Enterococcus faecium - genetics; Enterococcus faecium - immunology; ESAKPE; Humans; Klebsiella; Klebsiella pneumoniae - genetics; Klebsiella pneumoniae - immunology; Klebsiella pneumoniae - pathogenicity; Learning algorithms; Machine Learning; Medical innovations; Morbidity; Mortality; Neural networks; Nosocomial infection; Nosocomial infections; Optimization techniques; Pathogens; Peptides; Physicochemical properties; Proteins; Pseudomonas aeruginosa - genetics; Pseudomonas aeruginosa - immunology; Pseudomonas aeruginosa - pathogenicity; Python; Reverse vaccinology; Staphylococcus aureus - genetics; Staphylococcus aureus - immunology; Supervised machine learning; Vaccine; Vaccine development; Vaccines; Vaccinology - methods; Virulence
• ISSN: 0264-410X; 1873-2518; 1873-2518
• DOI: 10.1016/j.vaccine.2024.126204

The ESKAPE family, comprising Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp., poses a significant global threat due to their heightened virulence and extensive antibiotic resistance. These pathogens contribute largely to the prevalence of nosocomial or hospital-acquired infections, resulting in high morbidity and mortality rates. To tackle this healthcare problem urgent measures are needed, including development of innovative vaccines and therapeutic strategies. Designing vaccines involves a complex and resource-intensive process of identifying protective antigens and potential vaccine candidates (PVCs) from pathogens. Reverse vaccinology (RV), an approach based on genomics, made this process more efficient by leveraging bioinformatics tools to identify potential vaccine candidates. In recent years, artificial intelligence and machine learning (ML) techniques has shown promise in enhancing the accuracy and efficiency of reverse vaccinology. This study introduces a supervised ML classification framework, to predict potential vaccine candidates specifically against ESKAPE pathogens. The model's training utilized biological and physicochemical properties from a dataset containing protective antigens and non-protective proteins of ESKAPE pathogens. Conventional autoencoders based strategy was employed for feature encoding and selection. During the training process, seven machine learning algorithms were trained and subjected to Stratified 5-fold Cross Validation. Random Forest and Logistic Regression exhibited best performance in various metrics including accuracy, precision, recall, WF1 score, and Area under the curve. An ensemble model was developed, to take collective strengths of both the algorithms. To assess efficacy of our final ensemble model, a high-quality benchmark dataset was employed. VacSol-ML(ESKAPE) demonstrated outstanding discrimination between protective vaccine candidates (PVCs) and non-protective antigens. VacSol-ML(ESKAPE), proves to be an invaluable tool in expediting vaccine development for these pathogens. Accessible to the public through both a web server and standalone version, it encourages collaborative research. The web-based and standalone tools are available at http://vacsolml.mgbio.tech/.