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Understanding the Antibacterial Resistance: Computational Explorations in Bacterial Membranes

[Image: see text] Antimicrobial resistance (AMR) represents a major threat to global public health in the 21st century, dramatically increasing the pandemic expectations in the coming years. The ongoing need to develop new antimicrobial treatments that are effective against multi-drug-resistant path...

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Autores principales: Matamoros-Recio, Alejandra, Franco-Gonzalez, Juan Felipe, Forgione, Rosa Ester, Torres-Mozas, Angel, Silipo, Alba, Martín-Santamaría, Sonsoles
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2021
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7948216/
https://www.ncbi.nlm.nih.gov/pubmed/33718695
http://dx.doi.org/10.1021/acsomega.0c05590
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author Matamoros-Recio, Alejandra
Franco-Gonzalez, Juan Felipe
Forgione, Rosa Ester
Torres-Mozas, Angel
Silipo, Alba
Martín-Santamaría, Sonsoles
author_facet Matamoros-Recio, Alejandra
Franco-Gonzalez, Juan Felipe
Forgione, Rosa Ester
Torres-Mozas, Angel
Silipo, Alba
Martín-Santamaría, Sonsoles
author_sort Matamoros-Recio, Alejandra
collection PubMed
description [Image: see text] Antimicrobial resistance (AMR) represents a major threat to global public health in the 21st century, dramatically increasing the pandemic expectations in the coming years. The ongoing need to develop new antimicrobial treatments that are effective against multi-drug-resistant pathogens has led the research community to investigate innovative strategies to tackle AMR. The bacterial cell envelope has been identified as one of the key molecular players responsible for antibiotic resistance, attracting considerable interest as a potential target for novel antimicrobials effective against AMR, to be used alone or in combination with other drugs. However, the multicomponent complexity of bacterial membranes provides a heterogeneous morphology, which is typically difficult to study at the molecular level by experimental techniques, in spite of the significant development of fast and efficient experimental protocols. In recent years, computational modeling, in particular, molecular dynamics simulations, has proven to be an effective tool to reveal key aspects in the architecture and membrane organization of bacterial cell walls. Here, after a general overview about bacterial membranes, AMR mechanisms, and experimental approaches to study AMR, we review the state-of-the-art computational approaches to investigate bacterial AMR envelopes, including their limitations and challenges ahead. Representative examples illustrate how these techniques improve our understanding of bacterial membrane resistance mechanisms, hopefully leading to the development of novel antimicrobial drugs escaping from bacterial resistance strategies.
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spelling pubmed-79482162021-03-12 Understanding the Antibacterial Resistance: Computational Explorations in Bacterial Membranes Matamoros-Recio, Alejandra Franco-Gonzalez, Juan Felipe Forgione, Rosa Ester Torres-Mozas, Angel Silipo, Alba Martín-Santamaría, Sonsoles ACS Omega [Image: see text] Antimicrobial resistance (AMR) represents a major threat to global public health in the 21st century, dramatically increasing the pandemic expectations in the coming years. The ongoing need to develop new antimicrobial treatments that are effective against multi-drug-resistant pathogens has led the research community to investigate innovative strategies to tackle AMR. The bacterial cell envelope has been identified as one of the key molecular players responsible for antibiotic resistance, attracting considerable interest as a potential target for novel antimicrobials effective against AMR, to be used alone or in combination with other drugs. However, the multicomponent complexity of bacterial membranes provides a heterogeneous morphology, which is typically difficult to study at the molecular level by experimental techniques, in spite of the significant development of fast and efficient experimental protocols. In recent years, computational modeling, in particular, molecular dynamics simulations, has proven to be an effective tool to reveal key aspects in the architecture and membrane organization of bacterial cell walls. Here, after a general overview about bacterial membranes, AMR mechanisms, and experimental approaches to study AMR, we review the state-of-the-art computational approaches to investigate bacterial AMR envelopes, including their limitations and challenges ahead. Representative examples illustrate how these techniques improve our understanding of bacterial membrane resistance mechanisms, hopefully leading to the development of novel antimicrobial drugs escaping from bacterial resistance strategies. American Chemical Society 2021-02-26 /pmc/articles/PMC7948216/ /pubmed/33718695 http://dx.doi.org/10.1021/acsomega.0c05590 Text en © 2021 The Authors. Published by American Chemical Society This is an open access article published under an ACS AuthorChoice License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
spellingShingle Matamoros-Recio, Alejandra
Franco-Gonzalez, Juan Felipe
Forgione, Rosa Ester
Torres-Mozas, Angel
Silipo, Alba
Martín-Santamaría, Sonsoles
Understanding the Antibacterial Resistance: Computational Explorations in Bacterial Membranes
title Understanding the Antibacterial Resistance: Computational Explorations in Bacterial Membranes
title_full Understanding the Antibacterial Resistance: Computational Explorations in Bacterial Membranes
title_fullStr Understanding the Antibacterial Resistance: Computational Explorations in Bacterial Membranes
title_full_unstemmed Understanding the Antibacterial Resistance: Computational Explorations in Bacterial Membranes
title_short Understanding the Antibacterial Resistance: Computational Explorations in Bacterial Membranes
title_sort understanding the antibacterial resistance: computational explorations in bacterial membranes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7948216/
https://www.ncbi.nlm.nih.gov/pubmed/33718695
http://dx.doi.org/10.1021/acsomega.0c05590
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