Exploration and validation of diphosphate-based Plasmodium LytB inhibitors using computational approaches

New insights into apicoplast metabolism in blood-stage malaria parasites

The apicoplast of Plasmodium falciparum is the only source of essential isoprenoid precursors and Coenzyme A (CoA) in the parasite. Isoprenoid precursor synthesis relies on the iron-sulfur cluster (FeS) cofactors produced within the apicoplast, rendering FeS synthesis an essential function of this organelle. Recent reports provide important insights into the roles of FeS cofactors and the use of isoprenoid precursors and CoA both inside and outside the apicoplast. Here, we review the recent insights into the roles of these metabolites in blood-stage malaria parasites and discuss new questions that have been raised in light of these discoveries.

Recent advances in quantitative structure-activity relationship models of antimalarial drugs

INTRODUCTION

Due to emerging resistance to the first-line artemisinin-based antimalarials and lack of efficient vaccines and limited chemotherapeutic alternatives, there is an urgent need to develop new antimalarial compounds. In this regard, quantitative structure-activity relationship (QSAR) modeling can provide essential information about required physicochemical properties and structural parameters of antimalarial drug candidates.

AREAS COVERED

The authors provide an overview of recent advances of QSAR models covering different classes of antimalarial compounds as well as molecular docking studies of compounds acting on different antimalarial targets reported in the last 5 years (2015-2019) to explore the mode of interactions between the molecules and the receptors. We have tried to cover most of the QSAR models of antimalarials (along with results from some other related computational methods) reported during 2015-2019.

EXPERT OPINION

Many QSAR reports for antimalarial compounds are based on small number of data points. This review infers that most of the present work deals with analog-based QSAR approach with a limited applicability domain (a very few cases with wide domain) whereas novel target-based computational approach is reported in very few cases, which leads to huge voids of computational work based on novel antimalarial targets.

Classification models for predicting the antimalarial activity against Plasmodium falciparum

Support vector machine (SVM) and general regression neural network (GRNN) were used to develop classification models for predicting the antimalarial activity against Plasmodium falciparum. Only 15 molecular descriptors were used to build the classification models for the antimalarial activities of 4750 compounds, which were divided into a training set (3887 compounds) and a test set (863 compounds). For the SVM model, its prediction accuracies are 89.5% for the training set and 87.3% for the test set. For the GRNN model, the prediction accuracies for the two sets are 99.7% and 88.9%, respectively. Both SVC and GRNN models have better prediction ability than the classification model based on binary logistic regression (BLR) analysis. Compared with previously published classification models both SVC and GRNN models are satisfactory in predicting antimalarial activities of compounds with in addition of fewer descriptors.

Evaluation of a sesquiterpene as possible drug lead against gelatinases via molecular dynamics simulations

Malignant tumors can be targeted by accounting for their metastatic capabilities. Matrix metalloproteinases (MMPs) are the key players in tumor metastasis facilitating through their proteolytic activities of angiogenesis and extracellular matrix components (ECM) degradation. MMP-2 and MMP-9 being the members of a distinguished class of MMPs more commonly known as gelatinases are the prominent enzymes which are involved in different cancer progression stages. Targeting these isoforms specifically has always been a challenging task due to highly similar structural and functional features among the other members of MMPs with well preserve active sites containing catalytic zinc atom that was the only reason that none of the MMP inhibitor has been successfully marketed for the tumor pathology up till now. Therefore, non-competitive inhibitors with different structural attributed are needed to be evaluated at the molecular level for further experiments. The present study deals with the application of molecular dynamics simulation for the investigation of an alternative pathway for the inhibition of MMP-2 and MMP-9 by a sesquiterpene isolated from Polygonum barbatum which demonstrates the characteristics binding to the S1' subsite of the enzymes followed by in vitro gene expression studies. The simulation results provide information on the possible binding profile producing inhibitory effects imposed by the inhibitor to these enzymes by acquiring different structural and dynamical features. Moreover, thermodynamic quantities based on the computationally intensive thermodynamic integration approach were also obtained in terms of inhibitor binding affinity computed for the inhibitor against MMP-2 and MMP-9 that completely augmented the experimental gene expression study.Communicated by Ramaswamy H. Sarma.