3D QSAR, pharmacophore and molecular docking studies of known inhibitors and designing of novel inhibitors for M18 aspartyl aminopeptidase of Plasmodium falciparum

Abstract

BackgroundThe Plasmodium falciparum M18 Aspartyl Aminopeptidase (PfM18AAP) is only aspartyl aminopeptidase which is found in the genome of P. falciparum and is essential for its survival. The PfM18AAP enzyme performs various functions in the parasite and the erythrocytic host such as hemoglobin digestion, erythrocyte invasion, parasite growth and parasite escape from the host cell. It is a valid target to develop antimalarial drugs. In the present work, we employed 3D QSAR modeling, pharmacophore modeling, and molecular docking to identify novel potent inhibitors that bind with M18AAP of P. falciparum.ResultsThe PLSR QSAR model showed highest value for correlation coefficient r2 (88 %) and predictive correlation coefficient (pred_r2) =0.6101 for external test set among all QSAR models. The pharmacophore modeling identified DHRR (one hydrogen donor, one hydrophobic group, and two aromatic rings) as an essential feature of PfM18AAP inhibitors. The combined approach of 3D QSAR, pharmacophore, and structure-based molecular docking yielded 10 novel PfM18AAP inhibitors from ChEMBL antimalarial library, 2 novel inhibitors from each derivative of quinine, chloroquine, 8-aminoquinoline and 10 novel inhibitors from WHO antimalarial drugs. Additionally, high throughput virtual screening identified top 10 compounds as antimalarial leads showing G-scores -12.50 to -10.45 (in kcal/mol), compared with control compounds(G-scores -7.80 to -4.70) which are known antimalarial M18AAP inhibitors (AID743024). This result indicates these novel compounds have the best binding affinity for PfM18AAP.ConclusionThe 3D QSAR models of PfM18AAP inhibitors provided useful information about the structural characteristics of inhibitors which are contributors of the inhibitory potency. Interestingly, In this studies, we extrapolate that the derivatives of quinine, chloroquine, and 8-aminoquinoline, for which there is no specific target has been identified till date, might show the antimalarial effect by interacting with PfM18AAP.Electronic supplementary materialThe online version of this article (doi:10.1186/s12900-016-0063-7) contains supplementary material, which is available to authorized users.

A systematic review: Application of in silico models for antimalarial drug discovery

“…The published in silico models were mostly applied for lead identification/optimization (13 articles) of the known Plasmodium targets (13 targets). Most articles are specific for P. falciparum targets (Campbell et al, 2014;Kumari et al, 2016;LaMonte et al, 2017;Lhouvum et al, 2013;MacDonald and Boyd, 2015;Raza et al, 2017;Ren et al, 2016;Sharma et al, 2016); 3 articles involved target for P. vivax (MacDonald et al, 2015;Rout et al, 2016;Bouillon et al, 2013) and 1 article involved target for liver stage of Plasmodium (Sullivan et al, 2015). Five articles were applied in silico modeling for target identification/validation (Lhouvum et al, 2013;Mehra et al, 2015;Paul et al, 2015;Rout et al, 2015;Pandey et al, 2014).…”

“…Five articles were applied in silico modeling for target identification/validation (Lhouvum et al, 2013;Mehra et al, 2015;Paul et al, 2015;Rout et al, 2015;Pandey et al, 2014). Both structure-based (Bouillon et al, 2013;LaMonte et al, 2017;MacDonald and Boyd, 2015;Raza et al, 2017;Ren et al, 2016;Rout and Mahapatra, 2016;Sullivan et al, 2015) and ligandbased (Villalobos et al, 2013;Kumari et al, 2016;Ren et al, 2016;Sharma et al, 2016) approaches were applied to obtain lead antimalarial candidates. Two articles also assessed ADMET properties (Ren et al, 2016;Rout and Mahapatra, 2016).…”

“…Lead identification/optimization involved identification of potent antimalarial candidate molecules which are inhibitors of known specific Plasmodium targets (13 targets) encoded by the malaria parasite genome. These included: inosine monophosphate dehydrogenase (IMPDH) (Raza et al, 2017), proteasome (LaMonte et al, 2017), lipid kinase PI(4)K (phosphatidylinositol-4-OH kinase) (Ren et al, 2016), heat shock regulated A (HrA or DegP) (Sharma et al, 2016), P. falciparum M18 (Kumari et al, 2016), P. falciparum aspartyl aminopeptidase (PfM18AAP) (Kumari et al, 2016), leucine amino peptidase (M17 LAP) (Rout and Mahapatra, 2016), P. falciparum glycogen synthase kinase-3 (PfGSK-3), P. falciparum CTP synthetase (PfCTPS), choline kinase (PfCK), and glutathione S-transferase (PfGST) (Verma et al, 2015), Plasmodium peptidyl-prolyl cis/trans isomerase (MacDonald and Boyd, 2015), Plasmodium phosphatases, cytochrome bc1 (Jimenez et al, 2013), and P. vivax subtilin-like serine protease (PvSUB1) (Bouillon et al, 2013). Application of the modeling for target identification/validation included Nmyristoyltransferase (NMT) (Paul et al, 2015), Plasmodium phosphatases (Campbell et al, 2014;Pandey et al, 2014), and PFI1625c metalloprotease (Lhouvum et al, 2013).…”

“…Various commercial and public databases of proteins and compounds (ligands) were used as sources of computer-based modeling. A multitude of databases of compounds were used as sources of chemical structures in one or more of the highlighted studies including ZINC (Rout et al, 2015;Rout and Mahapatra, 2016;Verma et al, 2015), ChEMBL-NTD (Sullivan et al, 2015), DrugBank (Ren et al, 2016;Rout et al, 2015;Rout and Mahapatra, 2016), PubChem (Kumari et al, 2016;Rout and Mahapatra, 2016), and ChemSpider (Rout and Mahapatra, 2016). For antimalarial protein targets, the databases applied included PlasmoDB 9.2 (Lhouvum et al, 2013;Pandey et al, 2014;Rout et al, 2015;Sharma et al, 2016), PFAM (Pandey et al, 2014;Rout et al, 2015), CDD [29], ZINC (Lhouvum et al, 2013;Pandey et al, 2014), and InterPro (Campbell et al, 2014;Rout et al, 2015).…”

Combined 3D-QSAR and Molecular Docking Study on benzo[h][1,6]naphthyridin-2(1H)-one Analogs as mTOR Inhibitors

“…In recent years, three-dimensional quantitative structure-activity relationship (3D-QSAR) study, which is a kind of statistical method combined with 3D structural information, physicochemical properties and activity relationship of the molecular, has a widely application in the development of various types of drugs [18][19][20] . For nearly a decade, the QSAR and molecular docking study of PI3K/Akt/mTOR pathway small molecule inhibitors have made great progress in drugs development, many novel mTOR inhibitors were used in clinical treatment [21,22] .…”