Rahul Balasaheb Aher

Understanding the structural requirements in diverse scaffolds for the inhibition of P. falciparum dihydroorotate dehydrogenase (PfDHODH) using 2D-QSAR, 3D-pharmacophore and structure-based energy- optimized pharmacophore models.

P. falciparum dihydroorotate dehydrogenase (PfDHODH) of the pyrimidine biosynthetic pathway offers a promising target for the development of antimalarial drugs in the scenario of widespread P. falciparum resistance. In this background, we have made an effort to decipher the structural requirements for the inhibition of PfDHODH using regression-based 2DQSAR, 3D-pharmacophore modeling and energy-based pharmacophoric (e-pharmacophore) studies. The 2D-QSAR and 3D-pharmacophore models were built from a structurally diverse set of 38 dihydrothiophenone derivatives, while the e-pharmacophore models were developed from two different co-crystal structures (PDB ID: 3O8A, 3I68) with varied scaffolds (benzimidazole, IC50: 22 nM and triazolopyrimidine, IC50: 56 nM) showing an inhibitory activity against the PfDHODH. The 2D-QSAR modeling study depicted the contribution of constitutional (number of oxygen atoms), spatial (molar volume), structural (number of rotatable bonds), and electronic (dipole moment) descriptors in predicting the PfDHODH inhibitory activity. The regression model showed the maximum contribution of constitutional descriptor (number of oxygen atoms representing the hydrogen bond acceptor feature) in determining the inhibitory activity. The best 3D-pharmacophore model (Hypo-1) with a correlation coefficient of 0.960 showed two hydrogen bond acceptor (HBA) and one ring aromatic (RA) features as the essential structural requirements for predicting the inhibitory activity. The e-pharmacophores derived from two different co-crystal structures highlighted the energy-based contribution of one hydrogen bond acceptor (e-HBA), one hydrogen bond donor (e-HBD) and three/four ring aromatic (e-RA) features for the inhibitory activity. The screening of external sets by the e-pharmacophores showed that both the models are capable of identifying the structurally diverse and potent compounds.

Exploring structural requirements for the inhibition of Plasmodium falciparum calcium-dependent protein kinase-4 (PfCDPK-4) using multiple in silico approaches

Plasmodial protein kinases represent one of the most important thrust areas for antimalarial drug discovery. Although kinases are difficult to target, Plasmodium calcium-dependent protein kinases (CDPKs) are attractive drug targets because they play an essential role in the life cycle of the malaria parasite while being absent in humans. Against this background, we made an attempt in the present study to decipher the structural requirements for the inhibition of P. falciparum calcium-dependent protein kinase-4 (PfCDPK-4). Here, we utilized a data set of 89 structurally diverse pyrazolopyrimidine- and imidazopyrazine derivatives for in silico studies involving quantitative structure activity relationship (QSAR), quantitative structure activity–activity relationship (QSAAR), 3D-pharmacophore, docking and molecular dynamics simulations. The QSAR model highlighted the contributions of atom-type log P for R–CH3, R4C and Al–O–Ar fragments in determining the inhibitory potency against PfCDPK-4. The model also suggested the role of lower electron density (lower number of π bonds and loan pair of electrons) and the involvement of singly bonded –NH fragments in predicting the inhibitory activity. The QSAAR model comprising both PfCDPK-1 and PfCDPK-4 activities reflected the importance of the inhibitory response (PfCDPK-1), the alkoxy-type oxygen fragment (Al–O–Ar), the secondary alkyl fragment (R–CH2–X), the singly bonded oxygen atom (–O–) and the total hydrophobic surface area in predicting the inhibitory activity against the PfCDPK-4 target. Pharmacophore models were developed to identify the requisite groups essential for the activity and to classify the PfCDPK-4 inhibitors into more active and less active classes. The best pharmacophore model (hypothesis-1) showed four pharmacophoric features, namely, two hydrogen bond acceptors (HBA), one hydrogen bond donor (HBD) and one hydrophobic (HYPHOB) feature with a correlation coefficient of 0.933. The model showed 85.71% and 75.41% classification accuracies for the training and test sets, respectively. The docking study revealed that the highest active PfCDPK-4 inhibitor interacts with the ATP pocket residues of Lys78, Glu197, Asp148 and Tyr150 through hydrogen bonding and ionic interactions. Stable H-bond (Glu154, Asp148, Tyr150) and hydrophobic (Val84, Ala97, Tyr150, Ile214) interactions observed during 10 ns simulation may be responsible for compound 72 possessing the highest inhibitory activity among all the pyrazolopyrimidine derivatives. The molecular dynamics studies also showed that the protein complex of the most active inhibitor is more stable than the complex of the least active inhibitor in the dynamic environment.

First report on two-fold classification of Plasmodium falciparum carbonic anhydrase inhibitors using QSAR modeling approaches.

Quantitative structure-activity relationship (QSAR)-based classification approach is one of the important chemometric tools in drug discovery process for categorizing the target protein inhibitors into more active and less active classes. In this background, we have presented here a novel approach of two-fold QSAR-based classification modeling for the Plasmodium falciparum carbonic anhydrase (PfCA) inhibitors using 2D-QSAR and linear discriminant analysis (LDA) methods. The logic of applying this concept is to ensure more accurate classification of compounds and to draw some concrete conclusion about structure-activity relations for further work, in absence of 3D-protein structure and lack of sufficient experimental data using the PfCA target. The 2D-QSAR modeling analysis suggested the importance of electrotopological, electronic, extended topochemical atom, and spatial (Jurs) indices for modeling the inhibitory activity against PfCA. The LDA model analysis showed that spatial (Jurs), electrotopological and thermodynamic indices were the discriminating features to differentiate the inhibitors into more active and less active groups. The classification ability of both the models for training and test sets was checked by different qualitative validation parameters such as sensitivity, specificity, accuracy, recall, precision, F-measure and G-means. The classification results revealed that the developed models were significant in classifying the more active inhibitors as compared to the less active inhibitors of both training and test sets. The structural features unveiled from these two models could be utilized for the selection of more active compounds against PfCA in the database screening process.

Classification SAR modeling of diverse quinolone compounds for antimalarial potency against Plasmodium falciparum.

Both a development of resistance to artemisinin monotherapy and lack of effective vaccine against malaria have created the urgent need for the development of new and efficient antimalarial agents. In this background, we have developed here a linear discriminant analysis (LDA) model and a few 3D-pharmacophore models for the classification of diverse quinolone compounds based on their antimalarial potency against Plasmodium falciparum. The discriminant model shows 70% correct classification for the test set compounds into higher active and lower active analogues. The best pharmacophore model (Hypo-1) with a correlation coefficient of 0.83 shows one hydrogen bond acceptor (HBA) and two ring aromatic (RA) features as the essential structural requirements for antimalarial activity against P falciparum. Both the models may act as in silico filters for a virtual screening and could be utilized for the selection of higher active molecules falling within the applicability of the models.

Design of antimalarial transmission blocking agents: Pharmacophore mapping of ligands active against stage-V mature gametocytes of Plasmodium falciparum

Abstract The discovery of transmission-blocking (T-B) agents is crucial for preventing and complete removal of malaria infection. However, most of the existing antimalarials are only active against the asexual stages of Plasmodium parasite, but ineffective against the sexual stage (gametocytes). In this background, we have developed pharmacophore models against the stage-V mature gametocytes of P. falciparum parasites. The pharmacophore model (Hypo-1) showed five pharmacophoric features namely, one hydrogen bond donor (HBD), one hydrophobic aliphatic (HYAl), one ring aromatic (RA), and two hydrophobic aromatic (HYAr) essential for the anti-gametocytic activity. The amino, methyl, fused phenyl ring of the quinazoline heterocycle, two phenyl rings of biphenyl moiety (HBD, HYAl, HYAr1, HYAr2 and RA) are the crucial features responsible for the non-specific anti-gametocytic activity (PfG). Subsequently, the model (Hypo-2) developed against the stage-V female gametocytes (PffG) showed the contribution of three pharmacophoric features namely, two hydrogen bond acceptor (HYA) and one RA required for the anti-gametocytic activity. The sulfhydryl, imine and pyridyl groups are observed to be essential for anti-gametocytic activity against female gametocytes. Both the models (PfG and PfGG) showed the classification accuracies of 78.26 and 71.64% for training set compounds and 60.80 and 60.18% for the test set compounds, respectively, for classification of compounds into higher and lower active classes. Also, both the models were found to retain the higher active compounds (IC50 <100 nM) in top 1% of total compounds (actives and decoys) as observed after screening the decoy set compounds. Communicated by Ramaswamy H Sarma

Exploring the structural requirements in multiple chemical scaffolds for the selective inhibition of Plasmodium falciparum calcium-dependent protein kinase-1 (PfCDPK-1) by 3D-pharmacophore modelling, and docking studies

Abstract Current research on antimalarial protein kinases has provided an opportunity to design kinase-based antimalarial drugs. We have developed a common feature-based pharmacophore model from a set of multiple chemical scaffolds including derivatives of 3,6-imidazopyridazines, pyrazolo[2,3-d]pyrimidines and imidazo[1,5-a]pyrazines, in order to incorporate the maximum structural diversity information in the model for the Plasmodium falciparum calcium-dependent protein kinase-1 (PfCDPK-1) target. The best pharmacophore model (Hypo-1) with the essential features of two hydrogen bond donors (HBD), one hydrophobic aromatic (HYAr) and one ring aromatic (RA) showed the classification accuracies of 86.27%, 78.43% and 100.00% in labelling the training and test set (test set-1 and test set-2) compounds into more active and less active classes. In order to identify the crucial interaction between multiple scaffold ligands and the target protein, we first developed the homology model using a template structure of P. bergheii (PbCDPK1; PDB ID: 3Q5I), and thereafter performed the docking studies. The residues such as Lys85, Phe147, Tyr148, Leu198, Val211, and Asp212 were found to be the most important interacting residues for possessing PfCDPK-1 inhibitory activity.

QSAR with ETA indices: Insecticidal activity of plant derived compounds against zika virus vector Aedes aegypti

Dengue, zika and chikungunya have severe public health concerns in several countries. Human modification of the natural environment continues to create habitats in which mosquitoes, vectors of a wide variety of human and animal pathogens, thrive which can bring about enormous negative impact on public health if not controlled properly. Quantitative Structure–Activity Relationship (QSAR) modeling was applied in this work with the aim to explore features contributing to promising larvicidal and insecticidal property against the vector Aedes aegypti (Diptera:Culicidae). A dataset of 62 plant derived compounds obtained from the previous literatures was used in this present study where Genetic Algorithm (GA) was used for model development employing Double Cross Validation (DCV) tool. Simple topological descriptors like Extended Topochemical Atom (ETA) indices developed by the present authors’ group were used for model development. A number of models were generated by the GA method and the descriptors obtained were pooled for Best Subset Selection method (BSS). Further, the best model obtained from BSS was used for Partial Least Square (PLS) regression to obtain the final model. The model was validated extensively using different validation metrics to check the robustness and predictivity of the model for regulatory acceptance and enhancing confidence in QSAR predictions. Based on the insights obtained from the PLS model, we can conclude that presence of hydrogen bond acceptor atoms, presence of multiple bonds as well as sufficient lipophilicity and limited polar surface area play crucial roles in regulating the activity of the compounds.