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

Gervasoni S.;Malloci G.
;
Bosin A.;Vargiu A. V.;Ruggerone P.
2022-01-01

Abstract

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.
2022
2022
Inglese
9
1
148
12
Esperti anonimi
internazionale
scientifica
Anti-Infective Agents
Molecular Dynamics Simulation
Gervasoni, S.; Malloci, G.; Bosin, A.; Vargiu, A. V.; Zgurskaya, H. I.; Ruggerone, P.
1.1 Articolo in rivista
info:eu-repo/semantics/article
1 Contributo su Rivista::1.1 Articolo in rivista
262
6
open
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