Dataset Information

Tree-Based QSAR Model for Drug Repurposing in the Discovery of New Antibacterial Compounds Against Escherichia coli.

ABSTRACT: Drug repurposing appears as an increasing popular tool in the search of new treatment options against bacteria. In this paper, a tree-based classification method using Linear Discriminant Analysis (LDA) and discrete indexes was used to create a QSAR (Quantitative Structure-Activity Relationship) model to predict antibacterial activity against Escherichia coli. The model consists on a hierarchical decision tree in which a discrete index is used to divide compounds into groups according to their values for said index in order to construct probability spaces. The second step consists in the calculation of a discriminant function which determines the prediction of the model. The model was used to screen the DrugBank database, identifying 134 drugs as possible antibacterial candidates. Out of these 134 drugs, 8 were antibacterial drugs, 67 were drugs approved for different pathologies and 55 were drugs in experimental stages. This methodology has proven to be a viable alternative to the traditional methods used to obtain prediction models based on LDA and its application provides interesting new drug candidates to be studied as repurposed antibacterial treatments. Furthermore, the topological indexes Nclass and Numhba have proven to have the ability to group active compounds effectively, which suggests a close relationship between them and the antibacterial activity of compounds against E. coli.

SUBMITTER: Suay-Garcia B

PROVIDER: S-EPMC7760995 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Tree-Based QSAR Model for Drug Repurposing in the Discovery of New Antibacterial Compounds Against <i>Escherichia coli</i>.

Suay-Garcia Beatriz B Falcó Antonio A Bueso-Bordils J Ignacio JI Anton-Fos Gerardo M GM Pérez-Gracia M Teresa MT Alemán-López Pedro A PA

Pharmaceuticals (Basel, Switzerland) 20201128 12

Drug repurposing appears as an increasing popular tool in the search of new treatment options against bacteria. In this paper, a tree-based classification method using Linear Discriminant Analysis (LDA) and discrete indexes was used to create a QSAR (Quantitative Structure-Activity Relationship) model to predict antibacterial activity against Escherichia coli. The model consists on a hierarchical decision tree in which a discrete index is used to divide compounds into groups according to their v ...[more]

PMID: 33260726

Similar Datasets

Project description:Antibiotic-resistant infections caused by gram-negative bacteria are a major healthcare concern. Repurposing drugs circumvents the time and money limitations associated with developing new antimicrobial agents needed to combat these antibiotic-resistant infections. Here we identified the off-patent antifungal agent, ciclopirox, as a candidate to repurpose for antibiotic use. To test the efficacy of ciclopirox against antibiotic-resistant pathogens, we used a curated collection of Acinetobacter baumannii, Escherichia coli, and Klebsiella pneumoniae clinical isolates that are representative of known antibiotic resistance phenotypes. We found that ciclopirox, at 5-15 µg/ml concentrations, inhibited bacterial growth regardless of the antibiotic resistance status. At these same concentrations, ciclopirox reduced growth of Pseudomonas aeruginosa clinical isolates, but some of these pathogens required higher ciclopirox concentrations to completely block growth. To determine how ciclopirox inhibits bacterial growth, we performed an overexpression screen in E. coli. This screen revealed that galE, which encodes UDP-glucose 4-epimerase, rescued bacterial growth at otherwise restrictive ciclopirox concentrations. We found that ciclopirox does not inhibit epimerization of UDP-galactose by purified E. coli GalE; however, ?galU, ?galE, ?rfaI, or ?rfaB mutant strains all have lower ciclopirox minimum inhibitory concentrations than the parent strain. The galU, galE, rfaI, and rfaB genes all encode enzymes that use UDP-galactose or UDP-glucose for galactose metabolism and lipopolysaccharide (LPS) biosynthesis. Indeed, we found that ciclopirox altered LPS composition of an E. coli clinical isolate. Taken together, our data demonstrate that ciclopirox affects galactose metabolism and LPS biosynthesis, two pathways important for bacterial growth and virulence. The lack of any reported fungal resistance to ciclopirox in over twenty years of use in the clinic, its excellent safety profiles, novel target(s), and efficacy, make ciclopirox a promising potential antimicrobial agent to use against multidrug-resistant problematic gram-negative pathogens.

Project description:This study aimed to investigate the antibacterial mechanism of cefiderocol (CFDC) using data-independent acquisition quantitative proteomics combined with cellular and molecular biological assays. Numerous differentially expressed proteins related to the production of NADH, reduced cofactor flavin adenine dinucleotide (FADH2), NADPH and reactive oxygen species (ROS), iron-sulfur cluster binding, and iron ion homeostasis were found to be upregulated by CFDC. Furthermore, parallel reaction monitoring analysis validated these results. Meanwhile, we confirmed that the levels of NADH, ROS, H2O2, and iron ions were induced by CFDC, and the sensitivity of Escherichia coli to CFDC was inhibited by the antioxidant vitamin C, N-acetyl-l-cysteine, and deferoxamine. Moreover, deferoxamine also suppressed the H2O2 stress induced by CFDC. In addition, knockout of the NADH-quinone oxidoreductase genes (nuoA, nuoC, nuoE, nuoF, nuoG, nuoJ, nuoL, nuoM) in the respiratory chain attenuated the sensitivity of E. coli to CFDC far beyond the effects of cefepime and ceftazidime; in particular, the E. coli BW25113 ΔnuoJ strain produced 60-fold increases in MIC to CFDC compared to that of the wild-type E. coli BW25113 strain. The present study revealed that CFDC exerts its antibacterial effects by inducing ROS stress by elevating the levels of NADH and iron ions in E. coli. IMPORTANCE CFDC was the first FDA-approved siderophore cephalosporin antibiotic in 2019 and is known for its Trojan horse tactics and broad antimicrobial activity against Gram-negative bacteria. However, its antibacterial mechanism is not fully understood, and whether it has an impact on in vivo iron ion homeostasis remains unknown. To comprehensively reveal the antibacterial mechanisms of CFDC, data-independent acquisition quantitative proteomics combined with cellular and molecular biological assays were performed in this study. The findings will further facilitate our understanding of the antibacterial mechanism of CFDC and may provide a theoretical foundation for controlling CFDC resistance in the future.

Dataset Information

Tree-Based QSAR Model for Drug Repurposing in the Discovery of New Antibacterial Compounds Against Escherichia coli.

Publications

Tree-Based QSAR Model for Drug Repurposing in the Discovery of New Antibacterial Compounds Against <i>Escherichia coli</i>.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets