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RESEARCH Open AccessEgyptian Journal of MedicalHuman GeneticsDawood et al. Egyptian Journal of Medical Human Genetics (2026) 27:51 https://doi.org/10.1186/s43042-026-00882-0Comprehensive bioinformatics analysis of the mecA gene and methicillin resistance determinant in MRSA: a sequence, evolutionary, and structural studyAli Adel Dawood 1* , Zayd Kays Omer 1 , Enass Waad Al-Hadidi 2 and Mawj Saddam Zabn 3 * C o r r e s p o n d e n c e : Ali Adel Dawoodaad@uomosul.edu.iq1 Al-Batool College of Medicine, University of Mosul, Mosul, Iraq2 College of Science, University of Mosul, Mosul, Iraq3 Tikrit University College of Science, Tikrit, Iraq© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit h t t p : / / c r e a t i v e c o m m o n s . o r g / l i c e n s e s / b y / 4 . 0 / . AbstractBackground Methicillin-resistant Staphylococcus aureus (MRSA) is a signif i cant clinical problem, the principal resistance determinant of which is the mecA gene. This paper uses extensive bioinformatics resources to determine Methicillin resistance gene A (mecA) on the various levels such as sequence analysis, phylogenetic reconstruction, prediction of protein structure and molecular docking.Methods We identif i ed and used National Center for Biotechnology Information, GenBank, Clustal Omega, MEGA X, SWISS-MODEL, and AutoDock Vina to retrieve and analyze twenty-f i ve mecA sequences in various strains of MRSA.Results Findings showed a high level of sequence conservation (97.2–99.8% identity) to the presence of absolutely conserved catalytic residues (Ser403, Lys406, Thr600). Phylogenetic studies conf i rmed that mecA was spread by horizontal gene transfer using Staphylococcal Cassette Chromosome mec (SCCmec) traveling elements. PBP2a was shown to have a constricted active site that was identif i ed through three-dimensional modeling, which causes low beta-lactam af f i nity. Phenotypic resistance was described by the use of molecular docking indicating a weak binding of methicillin to PBP2a (-4.2 kcal/mol) in comparison to the native Penicillin-binding protein 2 (PBP2) (-7.5 kcal/mol). The functional analysis has determined that there are three domains and new motifs of PBP2a. Comparative analysis of SCCmec showed the presence of fourteen types (I-XIV) having dif f erent sizes and resistance genes.Conclusions These results of f er a molecular understanding of resistance to methicillin and can be used as a guide to develop novel anti-MRSA drugs with potential therapeutic targets.Keywords mecA gene, MRSA, Bioinformatics analysis, PBP2a, Antibiotic resistance