In-silico functional and structural annotation of hypothetical protein from Klebsiella pneumonia: A potential drug target. Academic Article uri icon

Overview

abstract

  • Klebsiella pneumonia is known to cause several nosocomial infections in immunocompromised patients. It has developed resistance against a broad range of presently available antibiotics, resulting in high mortality rates in patients and declared an urgent threat. Therefore, exploration of possible novel drug targets against this opportunistic bacteria needs to be undertaken. In the present study, we performed an extensive in-silico analysis for functional and structural annotation and characterized HP CP995_08280 from K. pneumonia as a drug target and aimed to identify potent drug candidates. The functional and structural studies using several bioinformatics tools and databases predicted that HP CP995_08280 is a cytosolic protein that belongs to the β-lactamase family and shares structural similarity with FmtA protein from Staphylococcus aureus (PDB ID: 5ZH8). The structure of HP CP995_08280 was successfully modeled followed by structure-based virtual screening, docking, molecular dynamics, and Molecular mechanic/Poisson-Boltzmann surface area (MMPBSA) were performed to identify the potential compounds. We have found five potent antibacterial molecules, namely BDD 24083171, BDD 24085737, BDE 25098678, BDE 33638819, and BDE 33672484, which exhibited high binding affinity (>-7.5 kcal/mol) and were stabilized by hydrogen bonding and hydrophobic interactions with active site residues (Ser42, Lys45, Tyr126, and Asp128) of protein. Molecular dynamics and MMPBSA revealed that HP CP995_08280 - ligand(s) complexes were less dynamic and more stable than native HP CP995_08280. Hence, the present study may serve as a potential lead for developing inhibitors against drug-resistant Klebsiella pneumonia.

publication date

  • June 30, 2022

Research

keywords

  • Molecular Dynamics Simulation
  • Pneumonia

Identity

Scopus Document Identifier

  • 85134196140

Digital Object Identifier (DOI)

  • 10.1016/j.jmgm.2022.108262

PubMed ID

  • 35839717

Additional Document Info

volume

  • 116