Noncompetitive inhibitors of sarco/endoplasmic reticulum Ca(2+)-ATPase (SERCA) orthologue (PfATP6) of P. falciparum have important therapeutic value in the treatment of malaria. Artemisinin and its analogues are one such class of inhibitors which bind to a hydrophobic pocket located in the transmembrane region of PfATP6 near the biomembrane surface and interfere with calcium transport. The 3D structure of PfATP6 was modeled by homology modeling. A library consisting of 150 artemisinin analogues has been designed. Their molecular interactions and binding affinities with modeled PfATP6 protein have been studied using the docking, molecular mechanics based on generalized Born/surface area (MM-GBSA) solvation model and multi-ligand bimolecular association with energetics (eMBrAcE). Structure activity relationship models were developed between the antimalarial activity (log RA) and molecular descriptors like docking score and binding free energy. For both the cases the r(2) was in the range of 0.538-0.688 indicating good data fit and r(2)(cv) was in the range of 0.525-0.679 indicating that the predictive capabilities of the models were acceptable. Besides, a scheme similar to linear response was used to develop free energy of binding (FEB) relationship based on electrostatic (∆G(ele)), van der Waal (∆G(vdW)) and surface accessible surface area (SASA), which can express the activity of these artemisinin derivatives. It has been seen that ∆G(vdW) has most significant correlation to the activity (log RA) and electrostatic energy (∆G(ele)) has less significant correlation. It indicates that the binding of these artemisinin derivatives to PfATP6 is almost hydrophobic. Low levels of root mean square error for the majority of inhibitors establish the docking, Prime/MM-GBSA and eMBrAcE based prediction model is an efficient tool for generating more potent and specific inhibitors of PfATP6 by testing rationally designed lead compound based on artemisinin derivatization.
Epipodophyllotoxins are the most important anticancer drugs used in chemotherapy for various types of cancers. To further, improve their clinical efficacy a large number of epipodophyllotoxin derivatives have been synthesized and tested over the years. In this study, a quantitative structure-activity relationship (QSAR) model has been developed between percentage of cellular protein-DNA complex formation and structural properties by considering a data set of 130 epipodophyllotoxin analogues. A systematic stepwise searching approach of zero tests, missing value test, simple correlation test, multicollinearity test, and genetic algorithm method of variable selection was used to generate the model. A statistically significant model ( r( train)2 = 0.721; q cv2 = 0.678) was obtained with descriptors such as solvent-accessible surface area, heat of formation, Balaban index, number of atom classes, and sum of E-state index of atoms. The robustness of the QSAR models was characterized by the values of the internal leave-one-out cross-validated regression coefficient ( q cv2) for the training set and r( test)2 for the test set. The root mean square error between the experimental and predicted percentage of cellular protein–DNA complex formation (PCPDCF) was 0.194 and r( test)2 = 0.689, revealing good predictability of the QSAR model.
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