Computational Analysis and In silico Predictive Modeling for Inhibitors of PhoP Regulon in S. typhi on High-Throughput Screening Bioassay Dataset.
There is emergence of multidrug-resistant Salmonella enterica serotype typhi in pandemic proportions throughout the world, and therefore, there is a necessity to speed up the discovery of novel molecules having different modes of action and also less influenced by the resistance formation that would be used as drug for the treatment of salmonellosis particularly typhoid fever. The PhoP regulon is well studied and has now been shown to be a critical regulator of number of gene expressions which are required for intracellular survival of S. enterica and pathophysiology of disease like typhoid. The evident roles of two-component PhoP-/PhoQ-regulated products in salmonella virulence have motivated attempts to target them therapeutically. Although the discovery process of biologically active compounds for the treatment of typhoid relies on hit-finding procedure, using high-throughput screening technology alone is very expensive, as well as time consuming when performed on large scales. With the recent advancement in combinatorial chemistry and contemporary technique for compounds synthesis, there are more and more compounds available which give ample growth of diverse compound library, but the time and endeavor required to screen these unfocused massive and diverse library have been slightly reduced in the past years. Hence, there is demand to improve the high-quality hits and success rate for high-throughput screening that required focused and biased compound library toward the particular target. Therefore, we still need an advantageous and expedient method to prioritize the molecules that will be utilized for biological screens, which saves time and is also inexpensive. In this concept, in silico methods like machine learning are widely applicable technique used to build computational model for high-throughput virtual screens to prioritize molecules for advance study. Furthermore, in computational analysis, we extended our study to identify the common enriched structural entities among the biologically active compound toward finding out the privileged scaffold.
['Kaur|Harleen|H|', 'Ahmad|Mohd|M|', 'Scaria|Vinod|V|']
D019540:Area Under Curve D001426:Bacterial Proteins D001499:Bayes Theorem D001681:Biological Assay D019295:Computational Biology D003198:Computer Simulation D019992:Databases as Topic D057166:High-Throughput Screening Assays D008958:Models, Molecular D012372:ROC Curve D018085:Regulon D012485:Salmonella typhi
PhoP regulon Machine learning Cost-sensitive learning High-throughput screening (HTS) Maximum common substructure (MCS) Molecular descriptor Random forest Salmonella typhi