AIMC Topic: Antigens, Bacterial

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A descriptor-free machine learning framework to improve antigen discovery for bacterial pathogens.

PloS one
Identifying protective antigens (PAs), i.e., targets for bacterial vaccines, is challenging as conducting in-vivo tests at the proteome scale is impractical. Reverse Vaccinology (RV) aids in narrowing down the pool of candidates through computational...

SHASI-ML: a machine learning-based approach for immunogenicity prediction in vaccine development.

Frontiers in cellular and infection microbiology
INTRODUCTION: Accurate prediction of immunogenic proteins is crucial for vaccine development and understanding host-pathogen interactions in bacterial diseases, particularly for Salmonella infections which remain a significant global health challenge...

Integrated structural proteomics and machine learning-guided mapping of a highly protective precision vaccine against mycoplasma pulmonis.

International immunopharmacology
Mycoplasma pulmonis (M. pulmonis) is an emerging respiratory infection commonly linked to prostate cancer, and it is classified under the group of mycoplasmas. Improved management of mycoplasma infections is essential due to the frequent ineffectiven...

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

Vaccine
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 extensiv...

Enhancing tuberculosis vaccine development: a deconvolution neural network approach for multi-epitope prediction.

Scientific reports
Tuberculosis (TB) a disease caused by Mycobacterium tuberculosis (Mtb) poses a significant threat to human life, and current BCG vaccinations only provide sporadic protection, therefore there is a need for developing efficient vaccines. Numerous immu...

Preclinical efficacy of a cell division protein candidate gonococcal vaccine identified by artificial intelligence.

mBio
Vaccines to curb the global spread of multidrug-resistant gonorrhea are urgently needed. Here, 26 vaccine candidates identified by an artificial intelligence-driven platform (Efficacy Discriminative Educated Network[EDEN]) were screened for efficacy ...

Application of the random forest algorithm to Streptococcus pyogenes response regulator allele variation: from machine learning to evolutionary models.

Scientific reports
Group A Streptococcus (GAS) is a globally significant bacterial pathogen. The GAS genotyping gold standard characterises the nucleotide variation of emm, which encodes a surface-exposed protein that is recombinogenic and under immune-based selection ...

A multicentre verification study of the QuantiFERON-TB Gold Plus assay.

Tuberculosis (Edinburgh, Scotland)
OBJECTIVES: The aim of this verification study was to compare the QuantiFERON-TB Gold Plus (QFT-Plus) to the QuantiFERON-TB Gold In Tube (QFT-GIT). The new QFT-Plus test contains an extra antigen tube which, according to the manufacturer additionally...

Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology.

International journal of molecular sciences
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...

Highly predictive support vector machine (SVM) models for anthrax toxin lethal factor (LF) inhibitors.

Journal of molecular graphics & modelling
Anthrax is a highly lethal, acute infectious disease caused by the rod-shaped, Gram-positive bacterium Bacillus anthracis. The anthrax toxin lethal factor (LF), a zinc metalloprotease secreted by the bacilli, plays a key role in anthrax pathogenesis ...