Applications of Receptor- and Ligand-based Models in Inverse Docking Experiments: Recognition of Dihydrofolate Reductase Using 7,8-Dialkyl- 1,3-Diaminopyrrolo[3,2-f]Quinazolines.

Inverse (or reverse) docking approach which involves docking of a ligand against a set of protein structures to predict possible protein target(s), possess limitations, including inefficient empirical scoring schemes and similarities in protein active site shape and physico-chemical properties. To overcome this limitation, we combined receptor- and ligand-based methods to predict probable protein targets. We showed that the experimental protein target along with possible offtargets can be effectively retrieved if the docking energy of the reference molecule and probe molecules based scaled energy profiles were combined and clustered together. The present method was validated using 7,8-dialkyl-1,3-diaminopyrrolo[3,2-f]quinazolines that inhibit Candida albicans dihydrofolate reductase (DHFR) in vitro.

Qualitative and quantitative pharmacophore-similarity assessment of anthranilamide-based factor Xa inhibitors: applications on similar molecules with identical biological endpoints.

It is a conventional practice to exclude molecules with identical biological endpoints to avoid bias in the resulting hypothesis model. Despite the diverse chemical functionalities, the receptor interactions of such molecules are often unexplored. The present study motivates the selection of these molecules diversified by single atom or functional group compared to internal molecules as external set and helps in the understanding of corresponding effects toward receptor interactions and biological endpoints. Applied on anthranilamide-series of factor Xa analogs, the inhibitory activities were correlated (r(2) = 0.99) and validated (q(2) = 0.68) with distance-based pharmacophore descriptors using support vector machine. The selected external set molecules exhibited better prediction accuracy by securing activities less than one residual threshold. The effect on inhibitory activity was assessed by the examination of pharmacophore-similarity and its interactions with key residues of Human factor Xa enzyme using molecular docking approach. Furthermore, qualitative pharmacophore models were developed on the subset of molecular dataset divided as most actives, moderately actives and least actives, to recognize crucial activity governing pharmacophore features. The outcome of this study will bring new insights about the requirements of pharmacophore features and prioritizes its selection in the design and optimization of potent Xa inhibitors.

Molecular interaction of selected phytochemicals under the charged environment of Plasmodium falciparum chloroquine resistance transporter (PfCRT) model.

Phytochemicals of Catharanthus roseus Linn. and Tylophora indica have been known for their inhibition of malarial parasite, Plasmodium falciparum in cell culture. Resistance to chloroquine (CQ), a widely used antimalarial drug, is due to the CQ resistance transporter (CRT) system. The present study deals with computational modeling of Plasmodium falciparum chloroquine resistance transporter (PfCRT) protein and development of charged environment to mimic a condition of resistance. The model of PfCRT was developed using Protein homology/analogy engine (PHYRE ver 0.2) and was validated based on the results obtained using PSI-PRED. Subsequently, molecular interactions of selected phytochemicals extracted from C. roseus Linn. and T. indica were studied using multiple-iterated genetic algorithm-based docking protocol in order to investigate the translocation of these legends across the PfCRT protein. Further, molecular dynamics studies exhibiting interaction energy estimates of these compounds within the active site of the protein showed that compounds are more selective toward PfCRT. Clusters of conformations with the free energy of binding were estimated which clearly demonstrated the potential channel and by this means the translocation across the PfCRT is anticipated.

The effect of various atomic partial charge schemes to elucidate consensus activity-correlating molecular regions: a test case of diverse QSAR models.

The estimation of atomic partial charges of the small molecules to calculate molecular interaction fields (MIFs) is an important process in field-based quantitative structure-activity relationship (QSAR). Several studies showed the influence of partial charge schemes that drastically affects the prediction accuracy of the QSAR model and focused on the selection of appropriate charge models that provide highest cross-validated correlation coefficient ([Formula: see text] or q(2)) to explain the variation in chemical structures against biological endpoints. This study shift this focus in a direction to understand the molecular regions deemed to explain SAR in various charge models and recognize a consensus picture of activity-correlating molecular regions. We selected eleven diverse dataset and developed MIF-based QSAR models using various charge schemes including Gasteiger-Marsili, Del Re, Merck Molecular Force Field, Hückel, Gasteiger-Hückel, and Pullman. The generalized resultant QSAR models were then compared with Open3DQSAR model to interpret the MIF descriptors decisively. We suggest the regions of activity contribution or optimization can be effectively determined by studying various charge-based models to understand SAR precisely.

Prediction of protein targets of kinetin using in silico and in vitro methods: a case study on spinach seed germination mechanism.

Kinetin, a cytokinin which promotes seed germination by inhibiting the action of abscisic acid, is an important molecule known to trigger various molecular mechanisms by interacting with an array of proteins shown from experimental observations in various model organisms. We report here the prediction of most probable protein targets of kinetin from spinach proteome using in silico approaches. Inverse docking and ligand-based similarity search was performed using kinetin as molecule. The former method prioritized six spinach proteins, whereas the latter method provided a list of protein targets retrieved from several model organisms. The most probable protein targets were selected by comparing the rank list of docking and ligand similarity methods. Both of these methods prioritized chitinase as the most probable protein target (ΔG pred = 5.064 kcal/mol) supported by the experimental structure of yeast chitinase 1 complex with kinetin (PDB: 2UY5) and Gliocladium roseum chitinase complex with 3,7-dihydro-1,3,7-trimethyl-1H-purine-2,6-dione (caffeine; 3G6M) which bears a 3D similarity of 0.43 with kinetin. An in vitro study to evaluate the effect of kinetin on spinach seed germination indicated that a very low concentration of kinetin (0.5 mg/l) did not show a significant effect compared to control in inducing seed germination process. Further, higher levels of kinetin (>0.5 mg/l) constituted an antagonist effect on spinach seed germination. It is anticipated that kinetin may have a molecular interaction with prioritized protein targets synthesized during the seed germination process and reduces growth. Thus, it appears that kinetin may not be a suitable hormone for enhancing spinach seed germination in vitro.

Development of pharmacophore similarity-based quantitative activity hypothesis and its applicability domain: applied on a diverse data-set of HIV-1 integrase inhibitors.

Quantitative pharmacophore hypothesis combines the 3D spatial arrangement of pharmacophore features with biological activities of the ligand data-set and predicts the activities of geometrically and/or pharmacophoric similar ligands. Most pharmacophore discovery programs face difficulties in conformational flexibility, molecular alignment, pharmacophore features sampling, and feature selection to score models if the data-set constitutes diverse ligands. Towards this focus, we describe a ligand-based computational procedure to introduce flexibility in aligning the small molecules and generating a pharmacophore hypothesis without geometrical constraints to define pharmacophore space, enriched with chemical features necessary to elucidate common pharmacophore hypotheses (CPHs). Maximal common substructure (MCS)-based alignment method was adopted to guide the alignment of carbon molecules, deciphered the MCS atom connectivity to cluster molecules in bins and subsequently, calculated the pharmacophore similarity matrix with the bin-specific reference molecules. After alignment, the carbon molecules were enriched with original atoms in their respective positions and conventional pharmacophore features were perceived. Distance-based pharmacophoric descriptors were enumerated by computing the interdistance between perceived features and MCS-aligned 'centroid' position. The descriptor set and biological activities were used to develop support vector machine models to predict the activities of the external test set. Finally, fitness score was estimated based on pharmacophore similarity with its bin-specific reference molecules to recognize the best and poor alignments and, also with each reference molecule to predict outliers of the quantitative hypothesis model. We applied this procedure to a diverse data-set of 40 HIV-1 integrase inhibitors and discussed its effectiveness with the reported CPH model.

Pharmacophore-similarity-based QSAR (PS-QSAR) for group-specific biological activity predictions.

Recent technological breakthroughs in medicinal chemistry arena had ameliorated the perspectives of quantitative structure-activity relationship (QSAR) methods. In this direction, we developed a group-based QSAR method based on pharmacophore-similarity concept which takes into account the 2D topological pharmacophoric descriptors and predicts the group-specific biological activities. This activity prediction may assist the contribution of certain pharmacophore features encoded by respective fragments toward activity improvement and/or detrimental effects. We termed this method as pharmacophore-similarity-based QSAR (PS-QSAR) and studied the activity contribution of fragments from 3-hydroxypyridinones derivatives possessing antimalarial activities.

Implementation of pseudoreceptor-based pharmacophore queries in the prediction of probable protein targets: explorations in the protein structural profile of Zea mays.

Molecular docking plays an important role in the protein target identification by prioritizing probable druggable proteins using docking energies. Due to the limitations of docking scoring schemes, there arises a need for structure-based approaches to acquire confidence in theoretical binding affinities. In this direction, we present here a receptor (protein)-based approach to predict probable protein targets using a small molecule of interest. We adopted a reverse approach wherein the ligand pharmacophore features were used to decipher interaction complementary amino acids of protein cavities (a pseudoreceptor) and expressed as queries to match the cavities or binding sites of the protein dataset. These pseudoreceptor-based pharmacophore queries were used to estimate total probabilities of each protein cavity thereby representing the ligand binding efficiency of the protein. We applied this approach to predict 3 experimental protein targets among 28 Zea mays structural data using 3 co-crystallized ligands as inputs and compared its effectiveness using conventional docking results. We suggest that the combination of total probabilities and docking energies increases the confidence in prioritizing probable protein targets using docking methods. These prediction hypotheses were further supported by DrugScoreX (DSX) pair potential calculations and molecular dynamic simulations.

Compound prioritization from inverse docking experiment using receptor-centric and ligand-centric methods: a case study on Plasmodium falciparum Fab enzymes.

Prioritization of compounds using inverse docking approach is limited owing to potential drawbacks in its scoring functions. Classically, molecules ranked by best or lowest binding energies and clustering methods have been considered as probable hits. Mining probable hits from an inverse docking approach is very complicated given the closely related protein targets and the chemically similar ligand data set. To overcome this problem, we present here a computational approach using receptor-centric and ligand-centric methods to infer the reliability of the inverse docking approach and to recognize probable hits. This knowledge-driven approach takes advantage of experimentally identified inhibitors against a particular protein target of interest to delineate shape and molecular field properties and use a multilayer perceptron model to predict the biological activity of the test molecules. The approach was validated using flavone derivatives possessing inhibitory activities against principal antimalarial molecular targets of fatty acid biosynthetic pathway, FabG, FabI and FabZ, respectively. We propose that probable hits can be retrieved by comparing the rank list of docking, quantitative-structure activity relationship and multilayer perceptron models.

Structural insights into the theoretical model of Plasmodium falciparum NADH dehydrogenase and its interaction with artemisinin and derivatives: towards global health therapeutics.

It is a continuing quest to uncover the principal molecular targets of malarial parasites to understand the antimalarial activity and mechanism of action of artemisinin, a potent antimalarial. A series of parasite proteins are experimentally validated as potential targets, such as translationally controlled tumor protein (TCTP) and sarco/endoplasmic reticulum membrane calcium ATP-ase (SERCA). The present study addressed the development of a theoretical model of Plasmodium falciparum NADH dehydrogenase with inference from artemisinin in vivo inhibitory activity. We report here the predicted binding modes of artemisinin and its derivatives. The modeled protein resembled the structural architecture of flavoproteins and oxidoreductases, consisting of two Rossmann folds and dedicated binding sites for its cofactors. Docked poses of the ligand dataset revealed its interactions at or near the si face, indicating being activated. This may aid in generation of reactive oxygen species, thereby disrupting the membrane potential of parasite mitochondria and leading to the clearance from the blood. These observations open up new strategies for development of novel therapeutics, or improvement of existing pharmacotherapies against malaria, a major burden for global health.

Synthesis and biological evaluation of cationic fullerene quinazolinone conjugates and their binding mode with modeled Mycobacterium tuberculosis hypoxanthine-guanine phosphoribosyltransferase enzyme.

The present work reports a series of novel cationic fullerene derivatives bearing a substituted-quinazolinone moiety as a side arm. Fullerene-quinazolinone conjugates synthesized using the 1,3-dipolar cycloaddition reaction of C60 with azomethine ylides generated from the corresponding Schiff bases of substituted quinazolinone were characterized by elemental analysis, FT-IR, (1)H NMR, (13)C NMR and ESI-MS and screened for their antibacterial activity against Mycobacterium tuberculosis (H 37 Rv strain). All the compounds exhibited significant activity with the most effective having MIC in the range of 1.562-3.125 µg/mL. Compound 9f exhibited good biological activity compared to standard drugs. We developed a computational strategy based on the modeled M. tuberculosis hypoxanthine-guanine phosphoribosyltransferase (HGPRT) using homology modeling techniques and studied its binding pattern with synthesized fullerene derivatives. We then explored the surface geometry of the protein to place the cage adjacent to the active site while optimizing its quinazolinone side arm to establish H bonding with active site residues.

A Computational Approach towards the Understanding of Plasmodium falciparum Multidrug Resistance Protein 1.

The emergence of drug resistance in Plasmodium falciparum tremendously affected the chemotherapy worldwide while the intense distribution of chloroquine-resistant strains in most of the endemic areas added more complications in the treatment of malaria. The situation has even worsened by the lack of molecular mechanism to understand the resistance conferred by Plasmodia species. Recent studies have suggested the association of antimalarial resistance with P. falciparum multidrug resistance protein 1 (PfMDR1), an ATP-binding cassette (ABC) transporter and a homologue of human P-glycoprotein 1 (P-gp1). The present study deals about the development of PfMDR1 computational model and the model of substrate transport across PfMDR1 with insights derived from conformations relative to inward- and outward-facing topologies that switch on/off the transportation system. Comparison of ATP docked positions and its structural motif binding properties were found to be similar among other ATPases, and thereby contributes to NBD domains dimerization, a unique structural agreement noticed in Mus musculus Pgp and Escherichia coli MDR transporter homolog (MsbA). The interaction of leading antimalarials and phytochemicals within the active pocket of both wild-type and mutant-type PfMDR1 demonstrated the mode of binding and provided insights of less binding affinity thereby contributing to parasite's resistance mechanism.

Design, synthesis, biological evaluation, and molecular modeling of coumarin-piperazine derivatives as acetylcholinesterase inhibitors.

Acetylcholinesterase (AChE) is an important drug target for the treatment of Alzheimer's disease. A novel series of coumarin-piperazine derivatives were synthesized and their potency to inhibit human AChE enzyme (hAChE) was studied. All the final compounds were characterized by infrared, (1)H NMR, (13)C NMR, and elemental analysis. Docking experiments of the designed coumarin-piperazine derivatives were carried out in order to compare the theoretical and experimental binding affinities toward hAChE, to delineate the inhibitory mechanism. Subsequently, a structure-activity relationship (SAR) study using the molecular field method showed that the hydrophobic field and positive charge center conferred by the coumarin and piperazine moieties demonstrated an inhibitory mechanism. Among the compounds tested, 3f, 3j, and 3m were found to be the most potent inhibitors of hAChE.

Evolutionary and molecular aspects of Indian tomato leaf curl virus coat protein.

Tomato leaf curl disease (ToLCD) is manifested by yellowing of leaf lamina with upward leaf curl, leaf distortion, shrinking of the leaf surface, and stunted plant growth caused by tomato leaf curl virus (ToLCV). In the present study, using computational methods we explored the evolutionary and molecular prospects of viral coat protein derived from an isolate of Vadodara district, Gujarat (ToLCGV-[Vad]), India. We found that the amino acids in coat protein required for systemic infection, viral particle formation, and insect transmission to host cells were conserved amongst Indian strains. Phylogenetic studies on Indian ToLCV coat proteins showed evolutionary compatibility with other viral taxa. Modeling of coat protein revealed a topology similar to characteristic Geminate viral particle consisting of antiparallel ß-barrel motif with N-terminus α-helix. The molecular interaction of coat protein with the viral DNA required for encapsidation and nuclear shuttling was investigated through sequence- and structure-based approaches. We further emphasized the role of loops in coat protein structure as molecular recognition interface.

Epitope-based immunoinformatics and molecular docking studies of nucleocapsid protein and ovarian tumor domain of crimean-congo hemorrhagic Fever virus.

Crimean-Congo hemorrhagic fever virus (CCHFV), the fatal human pathogen is transmitted to humans by tick bite, or exposure to infected blood or tissues of infected livestock. The CCHFV genome consists of three RNA segments namely, S, M, and L. The unusual large viral L protein has an ovarian tumor (OTU) protease domain located in the N terminus. It is likely that the protein may be autoproteolytically cleaved to generate the active virus L polymerase with additional functions. Identification of the epitope regions of the virus is important for the diagnosis, phylogeny studies, and drug discovery. Early diagnosis and treatment of CCHF infection is critical to the survival of patients and the control of the disease. In this study, we undertook different in silico approaches using molecular docking and immunoinformatics tools to predict epitopes which can be helpful for vaccine designing. Small molecule ligands against OTU domain and protein-protein interaction between a viral and a host protein have been studied using docking tools.