Literature DB >> 34096457

Identification of potential inhibitors for LLM of Staphylococcus aureus: structure-based pharmacophore modeling, molecular dynamics, and binding free energy studies.

Reena Kumari1, Vikram Dalal2.   

Abstract

Staphylococcus aureus causes various life-threatening diseases in humans and developed resistance to several antibiotics. Lipophilic membrane (LLM) protein regulates bacterial lysis rate and methicillin resistance level in S. aureus. To identify potential lead molecules, we performed a structure-based pharmacophore modeling by consideration of pharmacophore properties from LLM-tunicamycin complex. Further, virtual screening of ZINC database against the LLM was conducted and compounds were assessed for Lipinski and ADMET properties. Based on pharmacokinetic, and molecular docking, five potential inhibitors (ZINC000072380005, ZINC000257219974, ZINC000176045471, ZINC000035296288, and ZINC000008789934) were identified. Molecular dynamics simulation (MDS) of these five molecules was performed to evaluate the dynamics and stability of protein after binding of the ligands. Several MDS analysis like RMSD, RMSF, Rg, SASA, and PCA confirm that identified compounds exhibit higher binding affinity as compared to tunicamycin for LLM. The binding free energy analysis reveals that five compounds exhibit higher binding energy in the range of -218.76 to -159.52 kJ/mol, which is higher as compared to tunicamycin (-116.13 kJ/mol). Individual residue decomposition analysis concludes that Asn148, Asp151, Asp208, His271, and His272 of LLM play a significant role in the formation of lower energy LLM-inhibitor(s) complexes. These predicted molecules displayed pharmacological and structural properties and may be further used to develop novel antimicrobial compounds against S. aureus.Communicated by Ramaswamy H. Sarma.

Entities:  

Keywords:  LLM; Penicillin-binding protein; Staphylococcus aureus; molecular dynamics simulation; pharmacophore modeling

Year:  2021        PMID: 34096457     DOI: 10.1080/07391102.2021.1936179

Source DB:  PubMed          Journal:  J Biomol Struct Dyn        ISSN: 0739-1102


  2 in total

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2.  In silico prediction of the animal susceptibility and virtual screening of natural compounds against SARS-CoV-2: Molecular dynamics simulation based analysis.

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  2 in total

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