Authors :
Oshinkoya Suliat; Hassan Toheeb; Azeez Fatai
Volume/Issue :
Volume 8 - 2023, Issue 11 - November
Google Scholar :
https://tinyurl.com/ymbs8w53
Scribd :
https://tinyurl.com/kappzzst
DOI :
https://doi.org/10.5281/zenodo.10255193
Abstract :
Malaria is a deadly disease caused by
parasites that are transmitted to people through the
bites of infected female Anopheles mosquitoes. Of the
five species of Plasmodium, P. falciparum is the
deadliest, and findings have shown to have growing
evidence of drug-resistance mechanisms in malaria
treatments. Therefore, the identification of new drug
targets is an urgent need for the clinical management of
the disease. In this study, we employ an approach of
identifying drug leads against fructose bisphosphate
aldolase, a potent drug target in P.
falciparum.Molecular docking was carried out using
PyRx and CBDock to determine the binding affinities of
protein-ligand complexes. Two drug leads were
generated using machine learning. These drug leads
were selected based on Lipinski’s drug-likeness criteria.
The ligand 5-Chloro-1-(2-phenylethyl)-1H-indole-2,3-
dione exerted the highest binding effect on the aldolase
as compared to 1-(7,8 Dihydronaphthalen-2-ylmethyl)-
5-(piperidine-1-carbonyl)indole-2,3-dione using
molecular docking. The 5-Chloro-1-(2-phenyl ethyl)-1H-
indole-2,3-dione superior binding affinity with
bisphosphate aldolase compared to 1-(7,8-
Dihydronaphthalen-2-ylmethyl)-5-(piperidine-1-
carbonyl)indole-2,3-dione imply that it can inhibit the
bisphosphate aldolase activity in the plasmodium
falciparum.
Keywords :
BLAST; Molecular Docking; Machine Learning; QSAR; Drug Lead; Drug Target; Aldolase.
Malaria is a deadly disease caused by
parasites that are transmitted to people through the
bites of infected female Anopheles mosquitoes. Of the
five species of Plasmodium, P. falciparum is the
deadliest, and findings have shown to have growing
evidence of drug-resistance mechanisms in malaria
treatments. Therefore, the identification of new drug
targets is an urgent need for the clinical management of
the disease. In this study, we employ an approach of
identifying drug leads against fructose bisphosphate
aldolase, a potent drug target in P.
falciparum.Molecular docking was carried out using
PyRx and CBDock to determine the binding affinities of
protein-ligand complexes. Two drug leads were
generated using machine learning. These drug leads
were selected based on Lipinski’s drug-likeness criteria.
The ligand 5-Chloro-1-(2-phenylethyl)-1H-indole-2,3-
dione exerted the highest binding effect on the aldolase
as compared to 1-(7,8 Dihydronaphthalen-2-ylmethyl)-
5-(piperidine-1-carbonyl)indole-2,3-dione using
molecular docking. The 5-Chloro-1-(2-phenyl ethyl)-1H-
indole-2,3-dione superior binding affinity with
bisphosphate aldolase compared to 1-(7,8-
Dihydronaphthalen-2-ylmethyl)-5-(piperidine-1-
carbonyl)indole-2,3-dione imply that it can inhibit the
bisphosphate aldolase activity in the plasmodium
falciparum.
Keywords :
BLAST; Molecular Docking; Machine Learning; QSAR; Drug Lead; Drug Target; Aldolase.
