Dataset Information

The Potential of Systems Biology to Discover Antibacterial Mechanisms of Plant Phenolics.

ABSTRACT: Drug resistance of bacterial pathogens is a growing problem that can be addressed through the discovery of compounds with novel mechanisms of antibacterial activity. Natural products, including plant phenolic compounds, are one source of diverse chemical structures that could inhibit bacteria through novel mechanisms. However, evaluating novel antibacterial mechanisms of action can be difficult and is uncommon in assessments of plant phenolic compounds. With systems biology approaches, though, antibacterial mechanisms can be assessed without the bias of target-directed bioassays to enable the discovery of novel mechanism(s) of action against drug resistant microorganisms. This review article summarizes the current knowledge of antibacterial mechanisms of action of plant phenolic compounds and discusses relevant methodology.

SUBMITTER: Rempe CS

PROVIDER: S-EPMC5352675 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The Potential of Systems Biology to Discover Antibacterial Mechanisms of Plant Phenolics.

Rempe Caroline S CS Burris Kellie P KP Lenaghan Scott C SC Stewart C Neal CN

Frontiers in microbiology 20170316

Drug resistance of bacterial pathogens is a growing problem that can be addressed through the discovery of compounds with novel mechanisms of antibacterial activity. Natural products, including plant phenolic compounds, are one source of diverse chemical structures that could inhibit bacteria through novel mechanisms. However, evaluating novel antibacterial mechanisms of action can be difficult and is uncommon in assessments of plant phenolic compounds. With systems biology approaches, though, a ...[more]

PMID: 28360902

Similar Datasets

Project description:BackgroundThe growing discipline of structural systems pharmacology is applied prospectively in this study to predict pharmacological outcomes of antibacterial compounds in Escherichia coli K12. This work builds upon previously established methods for structural prediction of ligand binding pockets on protein molecules and utilizes and expands upon the previously developed genome scale model of metabolism integrated with protein structures (GEM-PRO) for E. coli, structurally accounting for protein complexes. Carefully selected case studies are demonstrated to display the potential for this structural systems pharmacology framework in discovery and development of antibacterial compounds.ResultsThe prediction framework for antibacterial activity of compounds was validated for a control set of well-studied compounds, recapitulating experimentally-determined protein binding interactions and deleterious growth phenotypes resulting from these interactions. The antibacterial activity of fosfomycin, sulfathiazole, and trimethoprim were accurately predicted, and as a negative control glucose was found to have no predicted antibacterial activity. Previously uncharacterized mechanisms of action were predicted for compounds with known antibacterial properties, including (1-hydroxyheptane-1,1-diyl)bis(phosphonic acid) and cholesteryl oleate. Five candidate inhibitors were predicted for a desirable target protein without any known inhibitors, tryptophan synthase ? subunit (TrpB). In addition to the predictions presented, this effort also included significant expansion of the previously developed GEM-PRO to account for physiological assemblies of protein complex structures with activities included in the E. coli K12 metabolic network.ConclusionsThe structural systems pharmacology framework presented in this study was shown to be effective in the prediction of molecular mechanisms of antibacterial compounds. The study provides a promising proof of principle for such an approach to antibacterial development and raises specific molecular and systemic hypotheses about antibacterials that are amenable to experimental testing. This framework, and perhaps also the specific predictions of antibacterials, is extensible to developing antibacterial treatments for pathogenic E. coli and other bacterial pathogens.

Dataset Information

The Potential of Systems Biology to Discover Antibacterial Mechanisms of Plant Phenolics.

Publications

The Potential of Systems Biology to Discover Antibacterial Mechanisms of Plant Phenolics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets