The increased resistance to the currently effective antimalarial drugs against Plasmodium falciparum has necessitated the development of new drugs for malaria treatment. Many proteins have been predicted using various means as potential drug targets for the treatment of the P. falciparum malaria infection. Meanwhile, only a few studies went on to predict the 3-dimensional (3D) structure of potential target. Therefore, this study aimed to predict potential antimalarial drug targets against the deadliest malaria parasite P. falciparum as well as to determine the 3D structure and possible inhibitors of one of the targets. We employed machine learning approach to predict suitable drug targets in P. falciparum. Five of the predicted protein targets were considered as potential drug targets as they were non-homologous to their human counterparts. Out of these, we determined the physicochemical properties, predicted the 3D structure and carried out docking-based virtual screening of P. falciparum RNA pseudouridylate synthase, putative (PfRPuSP). The PfRPuSP was one of the potential five target proteins. Homology modelling and the ab initio methods were used to predict the 3D structure of PfRPuSP. Then, a compound library of 5621 molecules was constructed from PubChem and ChEMBL databases using 5-fluorouridine as the control inhibitor. Docking-based virtual screening was performed using Autodock 4.2 and Autodock Vina to select compounds with high binding affinity. A total of 11 compounds were selected based on their binding energies from 881 compounds which were manually examined after docking. Seven of the 11 compounds that exhibited remarkable interactions with the residues in the active sites of PfRPuSP were analysed. These compounds performed favourably when compared to the control inhibitor and predicted to bind better than 5-fluorouridine. These seven compounds are suggested as new potential lead structures for antimalarial treatment.
Keywords: Antimalarial drugs; Drug target; Homology modelling; Inhibitors; Metabolic network; Virtual screening.
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