Modelling piperide-based derivatives as potential inhibitors of Plasmodium falciparum lactate dehydrogenase: QSAR and docking studies

Plasmodium falciparum lactate dehydrogenase (pLDH), a protein receptor with Protein Data Bank (PDB) code 1CET, was used as a molecular target for docking studies with 11 sets of piperidine-based derivatives. Modelling and geometry optimisation using density functional theory (DFT) were performed on...

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Bibliographic Details
Published in:	Scientific African Vol. 25; p. e02320
Main Authors:	Afolabi, Habeeb Abiodun, Busari, Ajani, Alabi, Abdul Azeez T., Maradesa, Aisha T., Adeleke, Solomon, Bayo, Abdulkarim Sikiru, Imran, Musa Olalekan, Francis, Saduwa, Adegoke, Nurudeen A.
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01-09-2024 Elsevier
Subjects:	Docking Kernel Plasmodium falciparum Regression model Spline, inhibitory concentration Plasmodium falciparum Spline, inhibitory concentration Docking Regression model Kernel
Online Access:	Get full text
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Summary:	Plasmodium falciparum lactate dehydrogenase (pLDH), a protein receptor with Protein Data Bank (PDB) code 1CET, was used as a molecular target for docking studies with 11 sets of piperidine-based derivatives. Modelling and geometry optimisation using density functional theory (DFT) were performed on these sets of molecules to predict and calculate the molecular descriptors and properties responsible for the bioactivity of the molecules during interaction with the protein receptor. The values obtained for the descriptors were in accordance with Lipinski's rule. The highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies result in orbital energies (band gaps) with more stable complex formation when reacted with the protein receptor. To predict the biological activities of the formed complexes, quantitative structural activity relationship (QSAR) models were developed using linear regression methods: multiple linear regression (MLR) and robust linear regression (RLM), and nonlinear regression methods: kernel regression (KRM) and spline regression (SRM). The nonlinear models provided a better fit than the linear models did. The KRM outperformed the SRM because of its better efficiency at a lower bandwidth (h = 0.6), although both models seemed to have better fits as the number of bandwidths increased. In addition, docking and scoring results of the compounds outperformed the standard drug (chloroquine) with binding affinity ranged from -7.5 to -8.5 kcal/mol (cf -5.8 kcal/mol for chloroquine).
ISSN:	2468-2276 2468-2276
DOI:	10.1016/j.sciaf.2024.e02320