Ph.D. candidate
Nagmi Bano
Computational Intelligence and Bioinformatics Lab, Department of Computer Science
Title: Mg-Gluconate Outperforms Ceftriaxone and Meropenem: An ML-Guided Multitarget Study against Klebsiella pneumoniae
Nagmi Bano1, Khalid Raza1,#
1 Computational Intelligence and Bioinformatics Lab, Department of Computer Science, Jamia Millia
Islamia, New Delhi-110025, India.
nagmi2300973@st.jmi.ac.in (https://orcid.org/0000–0002–7336–8928) (Presenting Author) kraza@jmi.ac.in (https://orcid.org/0000–0002–3646–6828) (#Corresponding Author)
Abstract
The increasing burden of antimicrobial resistance (AMR) has made the treatment of lifethreatening infections such as urinary tract infections (UTIs), bloodstream infections (BSIs), and pneumonia increasingly challenging. Klebsiella pneumoniae, a major multidrug-resistant pathogen, often compromises the efficacy of frontline antibiotics. In this study, we employed a machine learning (ML)-guided drug repurposing strategy to identify a multitarget therapeutic candidate. Multitarget molecular docking of an FDA-approved DrugBank library was performed against key proteins associated with UTIs and BSIs (Penicillin-Binding Protein 3, SHV-1, CTX-M-15, and TEM-1) and pneumonia (DNA Gyrase, OmpK36, AcrB, and 16S rRNA Methyltransferase RmtB). Docking results identified Mg-Gluconate (DrugBank ID: DB13749) as a potent multitarget inhibitor, exhibiting binding scores ranging from -5.11 to -7.85 kcal/mol for UTIs-BSIs targets and -5.77 to –
12.02 kcal/mol for pneumonia targets. In comparison, Meropenem and Ceftriaxone showed significantly lower binding affinities. Interaction fingerprints of each protein with Mg-Gluconate showed diverse interactions, indicating strong multitarget potential. DFT and pharmacokinetics were performed to support the suitability of Mg-Gluconate. ML-based approaches were employed for toxicity and antimicrobial activity prediction, indicating a favorable safety profile and potential efficacy compared to standard drugs. WaterMap simulations highlighted the role of hydration sites in stabilizing protein–ligand complexes, while molecular dynamics simulations demonstrated minimal structural deviations and stable interactions across all targets. MM/GBSA calculations further confirmed favorable binding free energies and overall complex stability, making it a better candidate than Meropenem and Ceftriaxone. Our studies demonstrated that Mg-Gluconate exhibits superior multitarget binding, stability, and predicted safety, outperforming standard drugs against Klebsiella pneumoniae-associated infections. However, experimental validation is required to confirm these computational predictions.
Keywords: Drug Resistance; Klebsiella Pneumoniae; UTI-BSI Infections; Pneumonia; FDA.
Biography of the presenting author-
Nagmi Bano is currently pursuing her PhD in the Computational Intelligence and Bioinformatics Lab, Department of Computer Science, Jamia Millia Islamia, New Delhi, India, under the supervision of Dr. Khalid Raza. Her research focuses on microbiology, antimicrobial resistance, and bioinformatics, with a special emphasis on multitarget drug design to combat infectious diseases.
She is actively involved in interdisciplinary research that integrates machine learning and computational approaches to understand resistance mechanisms and identify potential therapeutic candidates. Her work aligns with global health priorities, particularly in the areas of One Health, drug discovery, and pathogen analysis.
Nagmi has contributed to the scientific community through publications in reputed international journals and continues to expand her research in infectious diseases and bioinformatics. Alongside her research, she is also engaged in teaching and mentoring students, reflecting her commitment to academic excellence and knowledge sharing.In addition to her research activities, she is engaged in academic collaborations and contributes to the scientific community through her involvement in research and scholarly communication. Her work reflects a strong commitment to advancing knowledge in microbiology and improving healthcare outcomes.
Presenting author details
Full name: Nagmi Bano
Contact number: 6394600063
Email: nagmi2300973@st.jmi.ac.in
LinkedIn: https://www.linkedin.com/in/nagmi–bano–a20a11220/
Researchgate: https://www.researchgate.net/profile/Nagmi–Bano?ev=hdr_xprf
Google Scholar: https://scholar.google.com/citations?hl=en&user=HCvLj5sAAAAJ
ORCID: https://orcid.org/0000–0002–7336–8928
Session name/ number: Session- 2. Antimicrobial resistance and treatment Category: Oral presentation