Cracking the Code: Molecular Insights into Antibiotic Resistance in Nigeria Dairy
DOI:
https://doi.org/10.54536/ajmhc.v1i1.6725Keywords:
Antibiotic Resistance, Antimicrobial Peptides, Livestock Production, Lycopene Cyclase, Machine Learning Models, Terpene SynthaseAbstract
The extensive use and misuse of antibiotics in both human medicine and livestock production has accelerated the spread of antimicrobial resistance (AMR), creating a major challenge at the human–animal–environment interface. This study employed an in silico strategy to identify and characterize antimicrobial peptide candidates associated with antibiotic resistance pathways in livestock-related organisms, with particular emphasis on dairy cattle. Genomic sequences were screened using the CAMPR3 platform to predict antimicrobial peptides derived from biosynthetic genes. Two peptides originating from Terpene Synthase and Lycopene Cyclase genes demonstrated strong predicted antibacterial potential. Among them, the peptide designated PD101 showed favorable properties, including predicted antibacterial activity, cell-penetrating capacity, and intracellular targeting potential. Its physicochemical profile suggests preferential interaction with bacterial cells, indicating a potentially low risk of host cytotoxicity. These findings demonstrate the value of machine learning–driven computational tools for prioritizing novel antimicrobial peptides and identify PD101 as a promising candidate for reducing reliance on conventional antibiotics in livestock systems. Experimental studies are nevertheless required to validate its stability, delivery efficiency, and feasibility for large-scale production.
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Copyright (c) 2026 Jamilu Garba, Kwata Veronical John, Isah Hadiza, Aminu Abdullah Mainasara, Tiamiyu Abdulbashir Ayinde, Sulaiman Ya’u Muhammad, Ismaila, Akeem Adesola, Usman Shehu Yakubu, Abdullahi Mohammed Sheikh, Barka John, Maina Babagana Bukar, Abolaji Olaitan Kabir (Author)
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