title: Computational Analysis and In silico Predictive Modeling for Inhibitors of PhoP Regulon in S. typhi on High-Throughput Screening Bioassay Dataset.
abstract: 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.
authors: Department of Computer Science, Hamdard University, New Delhi, India. harleen_k1@rediffmail.com.|United Nations University, International Institute for Global Health (UNU-IIGH), Kuala Lumpur, Malaysia. harleen_k1@rediffmail.com.
authors: Harleen
authors: H
authors: Kaur
authors: Department of Computer Science, Hamdard University, New Delhi, India.
authors: Mohd
authors: M
authors: Ahmad
authors: GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India.
authors: Vinod
authors: V
authors: Scaria
mesh_terms: D019540:Area Under Curve; D001426:Bacterial Proteins / Q000037:antagonists & inhibitors* / Q000378:metabolism; D001499:Bayes Theorem; D001681:Biological Assay / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D019992:Databases as Topic; D057166:High-Throughput Screening Assays / Q000379:methods*; D008958:Models, Molecular; D012372:ROC Curve; D018085:Regulon / Q000235:genetics*; D012485:Salmonella typhi / Q000235:genetics*
chemical_list: D001426:Bacterial Proteins; C061064:PhoP protein, Bacteria
keywords: Cost-sensitive learning; High-throughput screening (HTS); Machine learning; Maximum common substructure (MCS); Molecular descriptor; PhoP regulon; Random forest; Salmonella typhi