Evidence map›Paper›PMID 39501412›Full record

ArticleBiology direct2024

Discovery of potential natural therapeutics targeting cell wall biosynthesis in multidrug-resistant Enterococcus faecalis: a computational perspective.

Km Rakhi, Monika Jain, Amit Kumar Singh, Mohd Sajid Ali, Hamad A Al-Lohedan, Jayaraman Muthukumaran

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Article in Biology direct, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Km RakhiDepartment of Biotechnology, Sharda School of Engineering and Technology, Sharda University, Greater Noida, India.
Monika JainDepartment of Biotechnology, Sharda School of Engineering and Technology, Sharda University, Greater Noida, India.
Amit Kumar SinghDepartment of Biotechnology, Sharda School of Engineering and Technology, Sharda University, Greater Noida, India.
Mohd Sajid AliDepartment of Chemistry, College of Science, King Saud University, P.O.Box 2455, Riyadh, 11451, Saudi Arabia.
Hamad A Al-LohedanDepartment of Chemistry, College of Science, King Saud University, P.O.Box 2455, Riyadh, 11451, Saudi Arabia.
Jayaraman MuthukumaranDepartment of Biotechnology, Sharda School of Engineering and Technology, Sharda University, Greater Noida, India. j.muthukumaran@sharda.ac.in.

Funding

King Saud University, Riyadh, Saudi Arabia RSPD2024R724
6 · The paper itself

Abstract

backgroundIdentifying therapeutic inhibitors of crucial enzymes involved in the peptidoglycan biosynthesis pathway is pivotal for developing new treatments against multidrug-resistant Enterococcus faecalis V583. MurM, an essential enzyme in this pathway, plays a significant role in the bacterium's cell wall synthesis, making it an attractive druggable target for novel antimicrobial strategies. This study explored the potential of natural compounds as inhibitors of MurM, aiming to discover promising drug candidates that could serve as the foundation for future therapeutic development.

methodsThe three-dimensional structure of MurM was predicted, optimized, and its binding pocket was analyzed by comparing it with related structures. Over 4,70,000 natural compounds from the COCONUT database were subjected to virtual high-throughput screening (vHTS). The top lead candidates were selected based on their Lipinski's profile, ADME profile, toxicity profile, estimated binding free energy (ΔG) and estimated inhibition constant (Ki). Interaction pattern analysis was used to evaluate the non-covalent interactions between the inhibitors and key residues in MurM's binding pocket. Molecular dynamics simulations were performed over 300 ns to assess the structural stability and impact of these inhibitors on MurM's enzyme.

resultsThree lead compounds-CNP0056520, CNP0126952, and CNP0248480-were identified and prioritized with estimated ΔG ranging from - 9.35 to -7.9 kcal/mol. Molecular dynamics simulations revealed minimal impact on MurM's overall structure and dynamics, with the candidate inhibitors forming stable protein-ligand complexes. These interactions were supported by several non-covalent interactions between the candidate inhibitors and key residues within MurM's binding pocket.

conclusionThese findings suggest that the identified natural product candidates could serve as promising inhibitors of MurM, potentially leading to novel therapeutics targeting cell wall biosynthesis in multidrug-resistant E. faecalis.

Indexed as

Cell WallDrug Resistance, Multiple, BacterialEnterococcus faecalisAnti-Bacterial AgentsBacterial ProteinsBiological ProductsDrug DiscoveryMolecular Docking SimulationMolecular Dynamics SimulationAnti-Bacterial AgentsBacterial ProteinsBiological ProductsEnterococcus faecalisMolecular dynamics and antibiotic resistanceMurMNatural compoundsVirtual screening

Identifiers

PMID39501412
PMCPMC11536910

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.