Structural modeling and docking analysis of canonical and novel resistance-associated missense mutations in Sudanese Escherichia coli

Abstract

Abstract Multidrug-resistant Escherichia coli represents a growing public health challenge in low-resource settings, where therapeutic options and routine genomic surveillance remain limited. Here, we present a structural bioinformatics analysis of resistance-associated missense mutations identified in Sudanese E.coli clinical isolates. Using whole-genome sequencing data from 55 isolates, we performed an integrated in silico analysis of key antibiotic target proteins to characterize missense variation and assess its potential functional consequences. Consensus pathogenicity prediction identified 19 substitutions as likely deleterious, including multiple novel, previously unreported variants clustered in ribosomal protein L22 (rplV), alongside established resistance-associated substitutions in RNA polymerase and topoisomerase IV. These results expand the mutational landscape of resistance-associated targets in Sudanese strains and highlight region-specific patterns of genetic variation. Structure-based modeling and molecular docking revealed mutation-specific effects on predicted antibiotic interactions across multiple targets, including reduced binding for selected topoisomerase inhibitors and heterogeneous effects on macrolide interactions within L22. In contrast, a gyrase variant retained predicted fluoroquinolone binding despite reduced structural stability, suggesting resistance-associated effects beyond direct drug–target interactions. Collectively, these findings underscore the contribution of non-canonical missense mutations to antimicrobial resistance in Sudanese E.coli and demonstrate the utility of computational prioritization frameworks to support resistance surveillance and guide targeted experimental validation.