In Silico Evaluation of Inhibitors of Plasmodium Falciparum AP2-I Transcription Factor and Plasmodium Falciparum Bromodomain Protein 1

Oladejo, David Oladoke and Oduselu, G. O and Dokunmu, Titilope M. and Isewon, Itunuoluwa and Okafor, Esther and Iweala, E. E. J. and Adebiyi, E. F. (2022) In Silico Evaluation of Inhibitors of Plasmodium Falciparum AP2-I Transcription Factor and Plasmodium Falciparum Bromodomain Protein 1. Research Article. pp. 1-16.

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Abstract

Background Recent treatment failures observed for Artemisinin-based combination therapy (ACT) have raised concerns about the efficacy of the front-line drug to treat malaria and the need to develop a new antimalarial drug regimen. Plasmodium falciparum Apicomplexan Apetala 2 Invasion (PfAP2-I) transcription factor (TF) is a protein that regulates the expression of a subset of gene families involved in Plasmodium falciparum red blood cell (RBC) invasion. PfAP2-I associates with several chromatin proteins, including the Plasmodium falciparum bromodomain protein 1 (PfBDP1) and that complex formation is associated with transcriptional regulation. Inhibiting PfAP2-I TF and PfBDP1 with small molecules represents a potential new antimalarial therapeutic target to combat drug resistance, which this study aims to achieve. Methods The 3D model structure of PfAP2-I was predicted ab initio using ITASSER and ROBETTA prediction tools and was validated using Errat and Procheck from the Save server 6.0. The crystal structure of PfBDP1 was also retrieved from Protein Data Bank (PDB) (www.rcsb.org/structure/7M97) and Computed Atlas of Surface Topography of proteins (CASTp) 3.0 and ConCavity were used to predict the active sites of the PfAP2-I and PfBDP1 3D7 structures. Pharmacophore modeling of the control ligand (3W7 from COACH server) and modeled 3D structure of PfAP2-I was carried out using the Pharmit server to obtain several compounds for docking analysis. Chimera software was used to remove the complexed ligands, and the modeled protein structure was defined as a receptor. Virtual screening and post-screening studies were conducted using AutoDock vina and LigPlot, respectively. The designed ligands’ toxicity predictions and in silico drug-likeness were performed using the Swiss ADME predictor and OSIRIS Property Explorer Results The result of the modeled protein from the ROBETTA prediction tool was prioritized based on structure validation results of 96.827 for ERRAT and 90.2% of the amino acid residues in the most favored region for the Ramachandran plot. A total of 8656 compounds obtained from nine (9) databases on the Pharmit server were used to prepare the ligand library and screened against the prepared 3D model structure of PfAP2-I, considering the active sites predicted from CASTp and ConCavity. Six (6) best hits were selected based on the binding affinity of the ligands to the active site PfAP2-I and were considered for postscreening analyses. The six best hits exhibited dock scores between -9.9 and -10.2 kcal/mol for PfAP2-I and between -8.5 and -9.4 kcal/mol for PfBDP1. The best hits also had lower binding energies in the PfAP2-I docking model when compared to the reference compound, CHEMBL3359262 (-8.4 kcal/mol) and the standard drug, chloroquine (-4.2 kcal/mol). In the PfBDP1 docking model, the reference compound, CHEMBL3359262 and the standard drug, chloroquine has binding affinities of -8.3 and -6.1 kcal/mol respectively. The compounds with the best dock scores are ZINC97139187 (-10.2 kcal/mol) for PfAP2-I and MCULE- 6567089130 (-9.4 kcal/mol) for PfBDP1. For the ADMET properties, compound ZINC97139187 had the highest drug score of 0.63, followed by compound 154861216, MCULE-6567089130 and 57405339 with drug scores of 0.58, 0.47 and 0.47 respectively (all higher than that of the standard drug - chloroquine of 0.25). Conclusions The good, estimated binding energies and drug scores observed for compound ZINC97139187 and compound MCULE-6567089130 suggest that they can be considered possible PfAP2-I and PfBDP1 inhibitors. Further pre-clinical experimental validations should be carried out to ascertain the efficacy of these predicted best hits.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA76 Computer software Q Science > QH Natural history > QH301 Biology
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science Faculty of Medicine, Health and Life Sciences > School of Biological Sciences
Depositing User:	Patricia Nwokealisi
Date Deposited:	31 Jul 2024 10:50
Last Modified:	31 Jul 2024 10:50
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/18333

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