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AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials

Antibiotic resistance is a major threat to public health. The development of chemo-informatic tools to guide medicinal chemistry campaigns in the efficint design of antibacterial libraries is urgently needed. We present AB-DB, an open database of all-atom force-field parameters, molecular dynamics t...

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Autores principales: Gervasoni, Silvia, Malloci, Giuliano, Bosin, Andrea, Vargiu, Attilio V., Zgurskaya, Helen I., Ruggerone, Paolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8976083/
https://www.ncbi.nlm.nih.gov/pubmed/35365662
http://dx.doi.org/10.1038/s41597-022-01261-1
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author Gervasoni, Silvia
Malloci, Giuliano
Bosin, Andrea
Vargiu, Attilio V.
Zgurskaya, Helen I.
Ruggerone, Paolo
author_facet Gervasoni, Silvia
Malloci, Giuliano
Bosin, Andrea
Vargiu, Attilio V.
Zgurskaya, Helen I.
Ruggerone, Paolo
author_sort Gervasoni, Silvia
collection PubMed
description Antibiotic resistance is a major threat to public health. The development of chemo-informatic tools to guide medicinal chemistry campaigns in the efficint design of antibacterial libraries is urgently needed. We present AB-DB, an open database of all-atom force-field parameters, molecular dynamics trajectories, quantum-mechanical properties, and curated physico-chemical descriptors of antimicrobial compounds. We considered more than 300 molecules belonging to 25 families that include the most relevant antibiotic classes in clinical use, such as β-lactams and (fluoro)quinolones, as well as inhibitors of key bacterial proteins. We provide traditional descriptors together with properties obtained with Density Functional Theory calculations. Noteworthy, AB-DB contains less conventional descriptors extracted from μs-long molecular dynamics simulations in explicit solvent. In addition, for each compound we make available force-field parameters for the major micro-species at physiological pH. With the rise of multi-drug-resistant pathogens and the consequent need for novel antibiotics, inhibitors, and drug re-purposing strategies, curated databases containing reliable and not straightforward properties facilitate the integration of data mining and statistics into the discovery of new antimicrobials.
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spelling pubmed-89760832022-04-20 AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials Gervasoni, Silvia Malloci, Giuliano Bosin, Andrea Vargiu, Attilio V. Zgurskaya, Helen I. Ruggerone, Paolo Sci Data Data Descriptor Antibiotic resistance is a major threat to public health. The development of chemo-informatic tools to guide medicinal chemistry campaigns in the efficint design of antibacterial libraries is urgently needed. We present AB-DB, an open database of all-atom force-field parameters, molecular dynamics trajectories, quantum-mechanical properties, and curated physico-chemical descriptors of antimicrobial compounds. We considered more than 300 molecules belonging to 25 families that include the most relevant antibiotic classes in clinical use, such as β-lactams and (fluoro)quinolones, as well as inhibitors of key bacterial proteins. We provide traditional descriptors together with properties obtained with Density Functional Theory calculations. Noteworthy, AB-DB contains less conventional descriptors extracted from μs-long molecular dynamics simulations in explicit solvent. In addition, for each compound we make available force-field parameters for the major micro-species at physiological pH. With the rise of multi-drug-resistant pathogens and the consequent need for novel antibiotics, inhibitors, and drug re-purposing strategies, curated databases containing reliable and not straightforward properties facilitate the integration of data mining and statistics into the discovery of new antimicrobials. Nature Publishing Group UK 2022-04-01 /pmc/articles/PMC8976083/ /pubmed/35365662 http://dx.doi.org/10.1038/s41597-022-01261-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Gervasoni, Silvia
Malloci, Giuliano
Bosin, Andrea
Vargiu, Attilio V.
Zgurskaya, Helen I.
Ruggerone, Paolo
AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials
title AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials
title_full AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials
title_fullStr AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials
title_full_unstemmed AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials
title_short AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials
title_sort ab-db: force-field parameters, md trajectories, qm-based data, and descriptors of antimicrobials
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8976083/
https://www.ncbi.nlm.nih.gov/pubmed/35365662
http://dx.doi.org/10.1038/s41597-022-01261-1
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