Abstract Cross-immunity, defined as the ability of T-cells to recognize multiple antigen peptide-major histocompatibility complexes, is a fundamental feature of adaptive immunity. However, the prediction of different peptide epitopes that can be recognized by the same T-cell receptor remains challenging. Currently, artificial intelligent (AI)-based machine learning (ML) methods can be successfully used for pattern recognition in epitope molecular space by detecting the functional similarity between peptide sequences. In this study, using literature-based experimental data, we examined ML-based binary classification models trained on small datasets to predict the activity of nine-amino-acid-long peptides. Our results suggest that the consensus function of well-established similarity matrix-based representations and structural-based descriptors of epitopes yields better performance because representation-specific noises are reduced and individual model weaknesses are partially compensated. We also sought to determine the extent to which the predictive power of the applied AIs procedure depended on the physicochemical content of the descriptor set during the training process. In addition, challenging the models, we applied them to an independent experimental dataset to examine the effects of diverse laboratory conditions on a regulated biological measurement. In summary, applying a consensus function can capture the biological complexity of cross-reactivity at the binary classification level, even when applied to relatively small datasets.
V. Resch, László Tóth, Anita Rácz et al.· Briefings in Bioinformatics· 0 citations
Background/Objectives: Methicillin-resistant Staphylococcus aureus (MRSA) is a major therapeutic challenge due to its extensive antibiotic resistance. This study examined how bacteriophage exposure affects the genome, proteome and antibiotic susceptibility of a clinical MRSA isolate. Methods: The MRSA isolate was exposed to the PYOFAG bacteriophage cocktail, and small colony variants (SCVs) of the surviving bacteria were compared with the untreated parental strain by phenotypic testing, whole-genome sequencing, comparative proteomics, and transmission electron microscopy. Results: Phage exposure induced marked remodeling in MRSA, including altered growth, colony morphology, and increased susceptibility to various antibiotics, especially β-lactams and aminoglycosides. Genomic analysis identified multiple mutations and the loss of two genomic regions, including changes in tarS, a gene linked to wall teichoic acid glycosylation, phage adsorption, and β-lactam resistance. Proteomic analysis revealed broad changes in metabolic, stress-response, and cell-envelope-associated pathways. Transmission electron microscopy showed a significant reduction in cell wall thickness after phage treatment. Conclusions: Bacteriophage exposure drives phenotypic and molecular adaptation in MRSA and may create evolutionary trade-offs that weaken resistance mechanisms. These findings support the potential of bacteriophages as both direct antibacterial agents and modulators of antibiotic susceptibility.
Otília Vágó, K. Laczi, László Orosz et al.· Antibiotics· 0 citations
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