Arghya Banik1, Kalyan Ghosh1, Umesh K Patil1, Shovanlal Gayen2. 1. Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr Harisingh Gour University, Sagar 470003, Madhya Pradesh, India. 2. Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr Harisingh Gour University, Sagar 470003, Madhya Pradesh, India. Electronic address: shovanlal.gayen@gmail.com.
Abstract
BACKGROUND: Extensive research over the past several decades, explored that the natural compounds contain different plant secondary metabolites and have the potential to inhibit breast cancer resistance protein (BCRP). PURPOSE: To identify crucial molecular fingerprints of some natural products for the inhibition of breast cancer resistance protein and also to screen out some potent natural BCRP inhibitors. STUDY DESIGN: Multiple modelling strategies were applied with three main mottos: (a) Generation of robust classification models to identify the linear and non-linear relationships among the natural compounds and the inhibition of BCRP, (b) Identification of important structural fingerprints that modulate BCRP inhibition and screening of natural database to find the probable hit molecules, (c) Comprehensive ligand-receptor interactions analysis of those against the putative breast cancer resistant protein through molecular docking analysis. METHODS: Monte Carlo optimization and SPCI analysis was used to identify important structural fingerprints. QSARCo. and swissADME analysis were used for screening and prediction of hits. Finally, docking analysis was performed for interaction study. RESULTS: In this study, some important structural fingerprints of BCRP inhibitors were identified. Additionally, eleven natural anti-cancer compounds were predicted to be active against the BCRP and also satisfy the different drug-likeliness properties. Among them, apigenin was found to have better binding affinities against the putative target as obtained from molecular docking analysis. CONCLUSION: This study is an attempt to understand about the molecular fingerprints of natural compounds for the inhibition of BCRP and also to dig out some novel natural inhibitors against BCRP.
BACKGROUND: Extensive research over the past several decades, explored that the natural compounds contain different plant secondary metabolites and have the potential to inhibit breast cancer resistance protein (BCRP). PURPOSE: To identify crucial molecular fingerprints of some natural products for the inhibition of breast cancer resistance protein and also to screen out some potent natural BCRP inhibitors. STUDY DESIGN: Multiple modelling strategies were applied with three main mottos: (a) Generation of robust classification models to identify the linear and non-linear relationships among the natural compounds and the inhibition of BCRP, (b) Identification of important structural fingerprints that modulate BCRP inhibition and screening of natural database to find the probable hit molecules, (c) Comprehensive ligand-receptor interactions analysis of those against the putative breast cancer resistant protein through molecular docking analysis. METHODS: Monte Carlo optimization and SPCI analysis was used to identify important structural fingerprints. QSARCo. and swissADME analysis were used for screening and prediction of hits. Finally, docking analysis was performed for interaction study. RESULTS: In this study, some important structural fingerprints of BCRP inhibitors were identified. Additionally, eleven natural anti-cancer compounds were predicted to be active against the BCRP and also satisfy the different drug-likeliness properties. Among them, apigenin was found to have better binding affinities against the putative target as obtained from molecular docking analysis. CONCLUSION: This study is an attempt to understand about the molecular fingerprints of natural compounds for the inhibition of BCRP and also to dig out some novel natural inhibitors against BCRP.
Authors: Linda M Mohamed; Maha M Eltigani; Marwa H Abdallah; Hiba Ghaboosh; Yousef A Bin Jardan; Osman Yusuf; Tilal Elsaman; Magdi A Mohamed; Abdulrahim A Alzain Journal: Front Chem Date: 2022-07-22 Impact factor: 5.545