Mycobacterial Dihydrofolate Reductase Inhibitors Identified Using Chemogenomic Methods and In Vitro Validation

Abstract

The lack of success in target-based screening approaches to the discovery of antibacterial agents has led to reemergence of phenotypic screening as a successful approach of identifying bioactive, antibacterial compounds. A challenge though with this route is then to identify the molecular target(s) and mechanism of action of the hits. This target identification, or deorphanization step, is often essential in further optimization and validation studies. Direct experimental identification of the molecular target of a screening hit is often complex, precisely because the properties and specificity of the hit are not yet optimized against that target, and so many false positives are often obtained. An alternative is to use computational, predictive, approaches to hypothesize a mechanism of action, which can then be validated in a more directed and efficient manner. Specifically here we present experimental validation of an in silico prediction from a large-scale screen performed against Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis. The two potent anti-tubercular compounds studied in this case, belonging to the tetrahydro-1,3,5-triazin-2-amine (THT) family, were predicted and confirmed to be an inhibitor of dihydrofolate reductase (DHFR), a known essential Mtb gene, and already clinically validated as a drug target. Given the large number of similar screening data sets shared amongst the community, this in vitro validation of these target predictions gives weight to computational approaches to establish the mechanism of action (MoA) of novel screening hit.

Beyond the Hype: Deep Neural Networks Outperform Established Methods Using A ChEMBL Bioactivity Benchmark Set

“…One point should be visited, NB in Pipeline Pilot was considerable slower than the NB trained in scikit-learn (20 minutes in scikit-learn compared with 31 hours, Supplementary table 3 ). This is caused by the calculation of the background scores (see methods for details) as was done previously [28]. Calculation of z-scores requires the prediction of all ligand – protein interactions in the matrix and is a lengthy procedure regardless of the high speed of NB.…”

Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB)

“…Again we found that there was a disparity in computational model generation, utilization and sharing and little effort in bringing many different approaches together [65] such as combining machine learning with docking [66]. Recently, chemogenomic methods and experimental validation were used to identify two compounds as dihydrofolate reductase (DHFR) inhibitors [67]. Validating such computational approaches experimentally is essential, whether that is similarity searching, pharmacophores [68] or machine learning [69].…”

A Systems Biology Approach for Identifying Hepatotoxicant Groups Based on Similarity in Mechanisms of Action and Chemical Structure

“…Based on the chemical structure of the compounds, we can predict their interaction partners (protein targets) in an organism. This is done by comparing the structure of the compound to large curated literature-based databases of known compound-protein interactions such as DrugBank, ChEMBL, the Human Metabolome Database, and the Therapeutic Target Database [20][21][22][23]. In this chapter, we use the data in the ChEMBL database release 17 containing approximately 12 million data points [24,25].…”