Data-mining of potential antitubercular activities from molecular ingredients of traditional Chinese medicines
Note that a PrePrint of this article also exists, first published March 7, 2014.
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Abstract
Background. Traditional Chinese medicine encompasses a well
established alternate system of medicine based on a broad range of
herbal formulations and is practiced extensively in the region for the
treatment of a wide variety of diseases. In recent years, several
reports describe in depth studies of the molecular ingredients of
traditional Chinese medicines on the biological activities including
anti-bacterial activities. The availability of a well-curated dataset of
molecular ingredients of traditional Chinese medicines and accurate
in-silico cheminformatics models for data mining for antitubercular
agents and computational filters to prioritize molecules has prompted us
to search for potential hits from these datasets.
Results. We used a consensus approach to predict molecules with potential antitubercular activities from a large dataset of molecular ingredients of traditional Chinese medicines available in the public domain. We further prioritized 160 molecules based on five computational filters (SMARTSfilter) so as to avoid potentially undesirable molecules. We further examined the molecules for permeability across Mycobacterial cell wall and for potential activities against non-replicating and drug tolerant Mycobacteria. Additional in-depth literature surveys for the reported antitubercular activities of the molecular ingredients and their sources were considered for drawing support to prioritization.
Conclusions. Our analysis suggests that datasets of molecular ingredients of traditional Chinese medicines offer a new opportunity to mine for potential biological activities. In this report, we suggest a proof-of-concept methodology to prioritize molecules for further experimental assays using a variety of computational tools. We also additionally suggest that a subset of prioritized molecules could be used for evaluation for tuberculosis due to their additional effect against non-replicating tuberculosis as well as the additional hepato-protection offered by the source of these ingredients.
Results. We used a consensus approach to predict molecules with potential antitubercular activities from a large dataset of molecular ingredients of traditional Chinese medicines available in the public domain. We further prioritized 160 molecules based on five computational filters (SMARTSfilter) so as to avoid potentially undesirable molecules. We further examined the molecules for permeability across Mycobacterial cell wall and for potential activities against non-replicating and drug tolerant Mycobacteria. Additional in-depth literature surveys for the reported antitubercular activities of the molecular ingredients and their sources were considered for drawing support to prioritization.
Conclusions. Our analysis suggests that datasets of molecular ingredients of traditional Chinese medicines offer a new opportunity to mine for potential biological activities. In this report, we suggest a proof-of-concept methodology to prioritize molecules for further experimental assays using a variety of computational tools. We also additionally suggest that a subset of prioritized molecules could be used for evaluation for tuberculosis due to their additional effect against non-replicating tuberculosis as well as the additional hepato-protection offered by the source of these ingredients.
Cite this as
(2014) Data-mining of potential antitubercular activities from molecular ingredients of traditional Chinese medicines. PeerJ 2:e476 https://dx.doi.org/10.7717/peerj.476
