02393nas a2200289   4500000000100000008004100001260001600042653001900058653003100077653001200108653002500120653004000145100002300185700002000208700001900228700001700247700002900264700002300293700002500316700002000341245012000361856015300481300000900634490000700643520143900650022001402089       2025                            d  bElsevier BV10aBioinformatics10aIn silico characterization10aLeprosy10aMycobacterium leprae10aTherapeutic and diagnostic purposes1 ade Araújo Silva R1 aSilva Arruda IE1 aSilva Alves MC1 ade Melo ALTM1 ade Albuquerque Marinho F1 aSoares Sobrinho JL1 ade Queiroz Balbino V1 aMoutinho-Melo C00aBioinformatics and omics revolutionizing leprosy research: Unveiling mechanisms and driving therapeutic innovations  uhttps://www.sciencedirect.com/science/article/pii/S2590097825000266/pdfft?md5=caad13e314a79f8b6bfc1096c3fdd499&pid=1-s2.0-S2590097825000266-main.pdf  a1-110 v263 a<p>The integration of bioinformatics and omics technologies has revolutionized leprosy research, providing insights into&nbsp;<em>Mycobacterium leprae</em>&nbsp;(<em>M. leprae</em>) biology. In this context, the present review analyzes two decades (2001–2021) of research using computational approaches to elucidate molecular mechanisms, identify biomarkers, and support drug discovery for leprosy. The search was conducted in the Web of Science database and found 30 studies, of which 23 met the inclusion criteria with a focus on genomic, proteomic and immunoinformatics applications targeting leprosy. Key advances include the identification of unique antigenic proteins, prediction of drug resistance mechanisms, and the development of&nbsp;<em>in silico</em>&nbsp;tools for diagnostics and therapeutic targeting. Comparative genomic studies have identified genes unique to&nbsp;<em>M. leprae</em>, such as ML2613, that may serve as potential therapeutic targets. Furthermore, bioinformatics has been used to identify biomarkers such as the recombinant antigen rMLP15, which has been shown to be effective in the diagnosis and differentiation between paucibacillary and multibacillary patients. Therefore, the present study highlights the role of bioinformatics in driving innovation for leprosy and underscores the need for continued investment in computational approaches to improve diagnostics and treatment strategies.</p>
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