Computational strategies to explore antimalarial thiazine alkaloid lead compounds based on an Australian marine sponge Plakortis Lita (original) (raw)
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IRJET, 2023
This research paper explores the application of computational analysis in the investigation of different ligands that target malarial parasites, with the objective of advancing the development of antimalarial drugs. This study aims to identify possible lead compounds with efficacy against malarial parasites by employing several computational tools, such as molecular docking, virtual screening, and quantitative structure-activity relationship (QSAR) modelling. The findings provided in this study provide valuable insights into the interactions between ligands and receptors, as well as the binding affinities and predictive models associated with these interactions. These results contribute significantly to the current efforts aimed at treating malaria.
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Plasmodium falciparum (P. falciparum) is the most fatal among the other Plasmodium parasites that infect humans with the malaria disease. Currently, the resistance of P. falciparum against some antifolate drugs has become a severe problem. On the other hand, xanthone and thioxanthone derivatives have been reported to have remarkable antimalarial activity. However, molecular docking studies have not evaluated thioxanthone derivative compounds as antimalarial agents. Accordingly, this research investigated the binding pose and inhibition mechanism of several thioxanthone derivatives against P. falciparum proteins DHFR (PDB ID: 1J3K) and DHODH (PDB ID: 1TV5) through molecular docking study. The compound structures were geometrically optimized using Gaussian 09 software and docked to the receptors using AutoDock4 software. The results showed that the free binding energy of thioxanthone derivatives ranged between -6.77 to -7.50 and -8.45 to -9.55 kcal mol–1 against pfDHFR and pfDHODH, re...
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Malaria is one of the most significant public health concerns in many tropical and subtropical regions of the world, with 40% of the world population exposed to malariacausing parasites. Increasing resistance of Plasmodium spp. to existing therapies has heightened alarms about malaria in the international health community. Nowadays there is a pressing need to identify and develop new drug-based antimalarial therapies. In an effort to overcome this problem, the main aim of this study was to develop simple linear discriminant-based QSAR models for the classification and prediction of antimalarial activity using some of the TOMOCOMD-CARDD fingerprints, so as to enable computational screening from virtual combinatorial datasets. In this sense a database of 1562 organic-chemicals having great structural variability; 597 of them antimalarial agents and 965 compounds having other clinical uses, was analyzed and presented as a helpful tool not only for theoretical chemist but also for other researchers in this area. These series of compounds were processed by a k-means cluster analysis in order to design training and predicting sets. Afterward, two linear classification functions were derived toward discrimination between antimalarial and non-antimalarial compounds. The models (including non-stochastic and stochastic indices) classify correctly more than 93% of compounds in both training and external prediction datasets. They showed high Matthews´ correlation coefficients; 0.889 and 0.866 for training and 0.855 and 0.857 for test set. Models predictivity were also assessed and validated by the random removal of 10% of the compounds to form a test set, for which predictions were made from the models. The overall mean of the correct classification for this process (leave-group 10% full-out cross-validation) for obtained equations with non-stochastic and stochastic quadratic fingerprints were 93.93% and 92.77%, correspondingly. The quadratic mapsbased TOMOCOMD-CARDD approach implemented in this work was successfully compared with four of the most useful models for antimalarials selection reported to date. The models developed with non-stochastic and stochastic quadratic indices were then used in a simulation of a virtual search for Ras FTase inhibitors with antimalarial activity;
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In this work, 48 thrombin inhibitors based on the structural scaffold of dabigatran were analyzed using a combination of molecular modeling techniques. We generated three-dimensional quantitative structureactivity relationship (3D-QSAR) models based on three alignments for both comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) to highlight the structural requirements for thrombin protein inhibition. In addition to the 3D-QSAR study, Topomer CoMFA model also was established with a higher leave-one-out cross-validation q 2 and a non-cross-validation r 2 , which suggest that the three models have good predictive ability. The results indicated that the steric, hydrophobic and electrostatic fields play key roles in QSAR model. Furthermore, we employed molecular docking and re-docking simulation explored the binding relationship of the ligand and the receptor protein in detail. Molecular docking simulations identified several key interactions that were also indicated through 3D-QSAR analysis. On the basis of the obtained results, two compounds were designed and predicted by three models, the biological evaluation in vitro (IC 50) demonstrated that these molecular models were effective for the development of novel potent thrombin inhibitors.
SN Applied Sciences
Malaria, a disease caused by one of the world's fatal parasites Plasmodium falciparum, is responsible for over a million death annually. P. falciparum dihydroorotate dehydrogenase (PfDHODH) is a validated target of this deadly parasite. Quantitative structure-activity relationship and molecular docking in silico methods were employed in the discovery of unique PfDHODH inhibitors from the computational design derivatives of indolyl-3-ethanone-α-thioethers through models generation via a genetic function algorithm methods. The best model indicates good power of prediction with coefficient of determination, R 2 = 0.9482, adjusted coefficient of determination (R 2 adj) = 0.9288, Leave one out cross-validation coefficient (Q 2) = 0.9201 and the external validation (R 2 pred) = 0.6467. The contribution of every descriptor in the model was investigated through finding their mean effect to (pIC 50) the activities of the compounds. With MATS5m (− 0.11725), RDF75m (− 0.12097), VE3_Dzp (0.14697), and MLFER_BH (1.08528) contributing more to the model, while AATSC8p (− 0.04833) and minHBa (0.05430) contributed the least to the model. Hence, the mean effect indicated MLFER_BH to be the most relevant descriptor, which aided the design of five derivatives of indolyl-3-ethanone-α-thioethers. All the designed antimalarial compounds were deeply docked within the binding region thereby forming several hydrogens and hydrophobic bonds leading to the generation of better binding affinity and high binding scores (− 156.181 kcal/ mol) compared to the design template (− 138.201 kcal/mol) and the standard drug (− 128.467 kcal/mol). Furthermore, all the five designed antimalarial compounds were found to be better bonded to the binding pocket of PfDHODH than other compounds reported by other researchers.