Bioinformatic analysis of natural compounds for the inhibition of the Ftsz protein of Mycobacterium tuberculosis 9280

Abstract Description Tuberculosis is an infectious disease caused by the bacteria Mycobacterium tuberculosis, which, according to the World Health Organization, has affected millions of people and even strains have appeared that are resistant to the drugs used for the treatment of tuberculosis. Mycobacterium tuberculosis has a slow reproduction cycle and there are multiple factors that help the mycobacterium to be able to duplicate, one of these factors is the formation of the “Z” ring that is regulated by various proteins, including the FtsZ protein that plays a fundamental role in the formation of said ring during the replication process. The FtsZ protein can be a therapeutic target in the treatment of tuberculosis, by inhibiting cell replication Molecular docking is a bioinformatics method that allows us to predict and computationally calculate the most favorable interaction position between a ligand and a target (usually protein) from their three-dimensional representations In the present research, the molecular docking technique is performed, using specific software such as PyMOL and AutoDock Tools-MGTools for molecular docking with a focus on protein-ligand interaction applied to the discovery of natural compounds to inhibit the FtsZ protein of Mycobacterium tuberculosis. The 20 molecules with minor energy less than or equal to that of the ligand were chosen, which is -8.7 kCal and therefore are those with the highest affinity. Funding Sources Supported by IPN grant number 20221842 Topic Categories Computational and Systems Immunology (COMP)

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Publication Details

Journal
The Journal of Immunology
Published
2025-11-01
DOI
https://doi.org/10.1093/jimmun/vkaf283.2703
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

Bioinformatic analysis of natural compounds for the inhibition of the Ftsz protein of Mycobacterium tuberculosis 9280

Alessandro Villa, Edgar Eduardo Lara-Ramirez, Juan Ernesto López-Ramos, Tania Amaro-Sanchéz et al.
The Journal of Immunology
Computational Drug Discovery Methods
article

Bioinformatic analysis of natural compounds for the inhibition of the Ftsz protein of Mycobacterium tuberculosis 9280

Alessandro Villa, Edgar Eduardo Lara-Ramirez, Juan Ernesto López-Ramos, Tania Amaro-Sanchéz, Claudia Hernández-Salas
article en

Abstract

Abstract Description Tuberculosis is an infectious disease caused by the bacteria Mycobacterium tuberculosis, which, according to the World Health Organization, has affected millions of people and even strains have appeared that are resistant to the drugs used for the treatment of tuberculosis. Mycobacterium tuberculosis has a slow reproduction cycle and there are multiple factors that help the mycobacterium to be able to duplicate, one of these factors is the formation of the “Z” ring that is regulated by various proteins, including the FtsZ protein that plays a fundamental role in the formation of said ring during the replication process. The FtsZ protein can be a therapeutic target in the treatment of tuberculosis, by inhibiting cell replication Molecular docking is a bioinformatics method that allows us to predict and computationally calculate the most favorable interaction position between a ligand and a target (usually protein) from their three-dimensional representations In the present research, the molecular docking technique is performed, using specific software such as PyMOL and AutoDock Tools-MGTools for molecular docking with a focus on protein-ligand interaction applied to the discovery of natural compounds to inhibit the FtsZ protein of Mycobacterium tuberculosis. The 20 molecules with minor energy less than or equal to that of the ligand were chosen, which is -8.7 kCal and therefore are those with the highest affinity. Funding Sources Supported by IPN grant number 20221842 Topic Categories Computational and Systems Immunology (COMP)

The Journal of ImmunologyVol. 214(Supplement_1)
Universidad Autónoma de Zacatecas "Francisco García Salinas" (MX), Instituto Politécnico Nacional (MX)
Openalex Percentile: Top 64%
Computational Drug Discovery Methods
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