Asset Details
Do you wish to reserve the book?
Machine learning-based virtual screening and density functional theory characterisation of natural inhibitors targeting mutant PBP2x in Streptococcus pneumoniae
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Machine learning-based virtual screening and density functional theory characterisation of natural inhibitors targeting mutant PBP2x in Streptococcus pneumoniae
Do you wish to request the book?
Machine learning-based virtual screening and density functional theory characterisation of natural inhibitors targeting mutant PBP2x in Streptococcus pneumoniae
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Machine learning-based virtual screening and density functional theory characterisation of natural inhibitors targeting mutant PBP2x in Streptococcus pneumoniae
2025
Overview
Streptococcus pneumoniae
(
S. pneumoniae
) has developed resistance to β-lactam antibiotics, largely due to mutations in penicillin-binding protein 2x (PBP2x), particularly within conserved motifs such as STMK and KSG. PBP2x mutations are frequently reported in multidrug-resistant pneumococcal strains associated with pneumonia, meningitis, and septicaemia. especially in serotypes 19A, 19F, and 23F, showing reduced susceptibility to β-lactam antibiotics. These mutations in the PBP2x disrupt antibiotic binding and enzymatic functions, highlighting the need for alternative therapeutic strategies. This study focused on five clinically relevant PBP2x mutations (T338A/G/P and K547G/T) within its active site. A library of phytocompounds was screened using a machine learning model trained to identify antibacterial compounds. Top candidates were filtered based on ADMET properties, and their electronic characteristics were assessed using HOMO–LUMO analysis and electrostatic potential mapping, through density functional theory (DFT). Glucozaluzanin C, a phytochemical derived from
Elephantopus scaber
, emerged as a potential candidate. Molecular docking and dynamics simulations revealed strong binding affinity and structural integrity with all PBP2x mutants, over a 100-ns timescale. RMSD, RMSF, and hydrogen bonding analysis confirmed stable interactions, suggesting Glucozaluzanin C may effectively interact with PBP2x mutants. Overall, the study highlights an effective strategy for identifying plant-derived inhibitors against β-lactam-resistant
S. pneumoniae.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
/ 631/154
/ 631/326
/ 631/449
/ 631/535
/ Anti-Bacterial Agents - chemistry
/ Anti-Bacterial Agents - pharmacology
/ Bacterial Proteins - antagonists & inhibitors
/ Bacterial Proteins - chemistry
/ Bacterial Proteins - genetics
/ Humanities and Social Sciences
/ Ligands
/ Molecular Docking Simulation
/ Molecular dynamic simulation
/ Mutants
/ Mutation
/ Penicillin binding protein 2x
/ Penicillin-Binding Proteins - antagonists & inhibitors
/ Penicillin-Binding Proteins - chemistry
/ Penicillin-Binding Proteins - genetics
/ Penicillin-Binding Proteins - metabolism
/ Plants
/ Proteins
/ Science
/ Streptococcus pneumoniae - drug effects
/ Streptococcus pneumoniae - genetics
/ Toxicity
