The NLR family of innate immune and cell death sensors

Nucleotide-binding oligomerization domain (NOD)-like receptors, also known as nucleotide-binding leucine-rich repeat receptors (NLRs), are a family of cytosolic pattern recognition receptors that detect a wide variety of pathogenic and sterile triggers. Activation of specific NLRs initiates pro- or anti-inflammatory signaling cascades and the formation of inflammasomes-multi-protein complexes that induce caspase-1 activation to drive inflammatory cytokine maturation and lytic cell death, pyroptosis. Certain NLRs and inflammasomes act as integral components of larger cell death complexes-PANoptosomes-driving another form of lytic cell death, PANoptosis. Here, we review the current understanding of the evolution, structure, and function of NLRs in health and disease. We discuss the concept of NLR networks and their roles in driving cell death and immunity. An improved mechanistic understanding of NLRs may provide therapeutic strategies applicable across infectious and inflammatory diseases and in cancer.

Targeting leukemic stem cell subpopulation in AML using phytochemicals: An in-silico and in-vitro approach

It has been indicated that leukemic stem cells (LSCs), a subset of leukaemia cells, are responsible for therapy resistance and relapse in acute myeloid leukaemia (AML). Therefore, the current study aimed to discover an LSC biomarker in AML patients and identify a natural compound that may target the same. By performing the different gene expression analyses, we identified 12 up-regulated and 192 down-regulated genes in LSCs of AML compared to normal bone marrow-derived HSCs. Further STRING interaction, GO enrichment and KEGG pathway analysis were carried out to top hub genes. Wilms' tumour-1 (WT1) transcription factor was pointed out as the top hub gene and a potential biomarker for LSCs in AML. For the targeted inhibition of WT1, we performed screening and stimulation of potential natural compounds. The results revealed Gallic acid (GA) and Chlorogenic acid (CA) as promising WT1 inhibitors. In-vitro validation of cytotoxic effects of both GA and CA on THP-1 and HL-60 cell lines suggested that both these compounds inhibited cell proliferation. Still, GA has a more cytotoxic effect compared to CA. Next, we performed cell cycle analysis and apoptosis analysis and found that both compounds arrested cells in G0/G1 phase and induced apoptosis in both cell lines. Surprisingly, a significant decrease in colony formation and cell migration was also observed. However, GA gave more promising results in all cellular assays than CA. Furthermore, we studied the mRNA expression of WT1 and BCL2, which are transcriptionally activated by it. We found that GA significantly downregulated both these genes compared to CA. Our results suggested that GA is a potential inhibitor of WT1 and might be an excellent anti-LSCs natural drug for AML patients.

MHC2AffyPred: A machine-learning approach to estimate affinity of MHC class II peptides based on structural interaction fingerprints

Understanding how MHC class II (MHC-II) binding peptides with differing lengths exhibit specific interaction at the core and extended sites within the large MHC-II pocket is a very important aspect of immunological research for designing peptides. Certain efforts were made to generate peptide conformations amenable for MHC-II binding and calculate the binding energy of such complex formation but not directed toward developing a relationship between the peptide conformation in MHC-II structures and the binding affinity (BA) (IC₅₀ ). We present here a machine-learning approach to calculate the BA of the peptides within the MHC-II pocket for HLA-DRA1, HLA-DRB1, HLA-DP, and HLA-DQ allotypes. Instead of generating ensembles of peptide conformations conventionally, the biased mode of conformations was created by considering the peptides in the crystal structures of pMHC-II complexes as the templates, followed by site-directed peptide docking. The structural interaction fingerprints generated from such docked pMHC-II structures along with the Moran autocorrelation descriptors were trained using a random forest regressor specific to each MHC-II peptide lengths (9-19). The entire workflow is automated using Linux shell and Perl scripts to promote the utilization of MHC2AffyPred program to any characterized MHC-II allotypes and is made for free access at https://github.com/SiddhiJani/MHC2AffyPred. The MHC2AffyPred attained better performance (correlation coefficient [CC] of .612-.898) than MHCII3D (.03-.594) and NetMHCIIpan-3.2 (.289-.692) programs in the HLA-DRA1, HLA-DRB1 types. Similarly, the MHC2AffyPred program achieved CC between .91 and .98 for HLA-DP and HLA-DQ peptides (13-mer to 17-mer). Further, a case study on MHC-II binding 15-mer peptides of severe acute respiratory syndrome coronavirus-2 showed very close competency in computing the IC₅₀ values compared to the sequence-based NetMHCIIpan v3.2 and v4.0 programs with a correlation of .998 and .570, respectively.

Innate sensing pathways: Defining new innate immune and inflammatory cell death pathways has shaped translational applications

The past 20 years of research has elucidated new innate immune sensing and cell death pathways with disease relevance. Future molecular characterization of these pathways and their crosstalk and functional redundancies will aid in development of therapeutic strategies.

Computational investigation of ginkgetin and theaflavin as potential inhibitors of heat shock protein 90 (Hsp90)

Heat shock protein 90 (Hsp90) is the prime molecular chaperone found to be overexpressed in cancer cells and pose as an anti-cancer therapeutic drug target for cancer chemotherapy. Even drugs are available which inhibit Hsp90, the associated side effects along with multi-drug regimen necessitate the identification of natural molecules to block the activity of Hsp90. In this present investigation, we performed virtual screening of Hsp90 inhibitors from a curated collection of natural molecules with proven pharmacological effects. This process helped in the identification of the top two scoring ligands, ginkgetin and theaflavin with favorable as well as crucial interactions with the Hsp90 ligand-binding pocket. Molecular dynamics simulations of these two natural molecules exhibited minimal fluctuations in the binding pattern of ginkgetin and theaflavin to Hsp90 which retained crucial contacts throughout the simulation time. We anticipate that ginkgetin and theaflavin could act as potent Hsp90 inhibitors which are under current investigation in our laboratory.Communicated by Ramaswamy H. Sarma.

PharmRF: A machine-learning scoring function to identify the best protein-ligand complexes for structure-based pharmacophore screening with high enrichments

Structure-based pharmacophore models are often developed by selecting a single protein-ligand complex with good resolution and better binding affinity data which prevents the analysis of other structures having a similar potential to act as better templates. PharmRF is a pharmacophore-based scoring function for selecting the best crystal structures with the potential to attain high enrichment rates in pharmacophore-based virtual screening prospectively. The PharmRF scoring function is trained and tested on the PDBbind v2018 protein-ligand complex dataset and employs a random forest regressor to correlate protein pocket descriptors and ligand pharmacophoric elements with binding affinity. PharmRF score represents the calculated binding affinity which identifies high-affinity ligands by thorough pruning of all the PDB entries available for a particular protein of interest with a high PharmRF score. Ligands with high PharmRF scores can provide a better basis for structure-based pharmacophore enumerations with a better enrichment rate. Evaluated on 10 protein-ligand systems of the DUD-E dataset, PharmRF achieved superior performance (average success rate: 77.61%, median success rate: 87.16%) than Vina docking score (75.47%, 79.39%). PharmRF was further evaluated using the CASF-2016 benchmark set yielding a moderate correlation of 0.591 with experimental binding affinity, similar in performance to 25 scoring functions tested on this dataset. Independent assessment of PharmRF on 8 protein-ligand systems of LIT-PCBA dataset exhibited average and median success rates of 57.55% and 74.72% with 4 targets attaining success rate > 90%. The PharmRF scoring model, scripts, and related resources can be accessed at https://github.com/Prasanth-Kumar87/PharmRF.

Identification of antiviral phytochemicals as a potential SARS-CoV-2 main protease (Mpro) inhibitor using docking and molecular dynamics simulations

Novel SARS-CoV-2, an etiological factor of Coronavirus disease 2019 (COVID-19), poses a great challenge to the public health care system. Among other druggable targets of SARS-Cov-2, the main protease (Mpro) is regarded as a prominent enzyme target for drug developments owing to its crucial role in virus replication and transcription. We pursued a computational investigation to identify Mpro inhibitors from a compiled library of natural compounds with proven antiviral activities using a hierarchical workflow of molecular docking, ADMET assessment, dynamic simulations and binding free-energy calculations. Five natural compounds, Withanosides V and VI, Racemosides A and B, and Shatavarin IX, obtained better binding affinity and attained stable interactions with Mpro key pocket residues. These intermolecular key interactions were also retained profoundly in the simulation trajectory of 100 ns time scale indicating tight receptor binding. Free energy calculations prioritized Withanosides V and VI as the top candidates that can act as effective SARS-CoV-2 Mpro inhibitors.

Quantum circuits with many photons on a programmable nanophotonic chip

Growing interest in quantum computing for practical applications has led to a surge in the availability of programmable machines for executing quantum algorithms1,2. Present-day photonic quantum computers3-7 have been limited either to non-deterministic operation, low photon numbers and rates, or fixed random gate sequences. Here we introduce a full-stack hardware-software system for executing many-photon quantum circuit operations using integrated nanophotonics: a programmable chip, operating at room temperature and interfaced with a fully automated control system. The system enables remote users to execute quantum algorithms that require up to eight modes of strongly squeezed vacuum initialized as two-mode squeezed states in single temporal modes, a fully general and programmable four-mode interferometer, and photon number-resolving readout on all outputs. Detection of multi-photon events with photon numbers and rates exceeding any previous programmable quantum optical demonstration is made possible by strong squeezing and high sampling rates. We verify the non-classicality of the device output, and use the platform to carry out proof-of-principle demonstrations of three quantum algorithms: Gaussian boson sampling, molecular vibronic spectra and graph similarity8. These demonstrations validate the platform as a launchpad for scaling photonic technologies for quantum information processing.

Development of cardiotoxicity model using ligand-centric and receptor-centric descriptors

Background:

Bioinformatics and statistical analysis have been employed to develop a classification model to distinguish toxic and non-toxic molecules.

Aims:

The primary objective of this study is to enumerate the cut-off values of various physico-chemical (ligand-centric) and target interaction (receptor-centric) descriptors which forms the basis for classifying cardiotoxic and non-toxic molecules. We also sought correlation of molecular docking, absorption, distribution, metabolism, excretion, and toxicology (ADMET) parameters, Lipinski rules, physico-chemical parameters, etc. of human cardiotoxicity drugs.

Methods:

A training and test set of 91 compounds were applied to linear discriminant analysis (LDA) using 2D and 3D descriptors as discriminating variables representing various molecular modeling parameters to identify which function of descriptor type is responsible for cardiotoxicity. Internal validation was performed using the leave-one-out cross-validation methodology ensuing in good results, assuring the stability of the discriminant function (DF).

Results:

The values of the statistical parameters Fisher Discriminant Analysis (FDA) and Wilk’s λ for the DF showed reliable statistical significance, as long as the success rate in the prediction for both the training and the test set attained more than 93% accuracy, 87.50% sensitivity and 94.74% specificity.

Conclusion:

The predictive model was built using a hybrid approach using organ-specific targets for docking and ADMET properties for the FDA (Food and Drug Administration) approved and withdrawn drugs. Classifiers were developed by linear discriminant analysis and the cut-off was enumerated by receiver operating characteristic curve (ROC) analysis to achieve reliable specificity and sensitivity.

Identification of potential inhibitors of coronavirus hemagglutinin-esterase using molecular docking, molecular dynamics simulation and binding free energy calculation

Abstract

The pandemic outbreak of the Corona viral infection has become a critical global health issue. Biophysical and structural evidence shows that spike protein possesses a high binding affinity towards host angiotensin-converting enzyme 2 and viral hemagglutinin-acetylesterase (HE) glycoprotein receptor. We selected HE as a target in this study to identify potential inhibitors using a combination of various computational approaches such as molecular docking, ADMET analysis, dynamics simulations and binding free energy calculations. Virtual screening of NPACT compounds identified 3,4,5-Trihydroxy-1,8-bis[(2R,3R)-3,5,7-trihydroxy-3,4-dihydro-2H-chromen-2-yl]benzo[7]annulen-6-one, Silymarin, Withanolide D, Spirosolane and Oridonin as potential HE inhibitors with better binding energy. Furthermore, molecular dynamics simulations for 100 ns time scale revealed that most of the key HE contacts were retained throughout the simulations trajectories. Binding free energy calculations using MM/PBSA approach ranked the top-five potential NPACT compounds which can act as effective HE inhibitors.

Graphic abstract

Electronic supplementary material

The online version of this article (10.1007/s11030-020-10135-w) contains supplementary material, which is available to authorized users.

A multiparametric organ toxicity predictor for drug discovery

The assessment of major organ toxicities through in silico predictive models plays a crucial role in drug discovery. Computational tools can predict chemical toxicities using the knowledge gained from experimental studies which drastically reduces the attrition rate of compounds during drug discovery and developmental stages. The purpose of in silico predictions for drug leads and anticipating toxicological endpoints of absorption, distribution, metabolism, excretion and toxicity, clinical adverse impacts and metabolism of pharmaceutically active substances has gained widespread acceptance in academia and pharmaceutical industries. With unrestricted accessibility to powerful biomarkers, researchers have an opportunity to contemplate the most accurate predictive scores to evaluate drug's adverse impact on various organs.A multiparametric model involving physico-chemical properties, quantitative structure-activity relationship predictions and docking score was found to be a more reliable predictor for estimating chemical toxicities with potential to reflect atomic-level insights. These in silico models provide informed decisions to carry out in vitro and in vivo studies and subsequently confirms the molecules clues deciphering the cytotoxicity, pharmacokinetics, and pharmacodynamics and organ toxicity properties of compounds. Even though the drugs withdrawn by USFDA at later phases of drug discovery which should have passed all the state-of-the-art experimental approaches and currently acceptable toxicity filters, there is a dire need to interconnect all these molecular key properties to enhance our knowledge and guide in the identification of leads to drug optimization phases. Current computational tools can predict ADMET and organ toxicities based on pharmacophore fingerprint, toxicophores and advanced machine-learning techniques.

Cardiotonic steroids as potential Na+/K+-ATPase inhibitors - a computational study

Cardiotonic steroids (CTS) are steroidal drugs, processed from the seeds and dried leaves of the genus Digitalis as well as from the skin and parotid gland of amphibians. The most commonly known CTS are ouabain, digoxin, digoxigenin and bufalin. CTS can be used for safer medication of congestive heart failure and other related conditions due to promising pharmacological and medicinal properties. Ouabain isolated from plants is widely utilized in in vitro studies to specifically block the sodium potassium (Na⁺/K⁺-ATPase) pump. For checking, whether ouabain derivatives are robust inhibitors of Na⁺/K⁺-ATPase pump, molecular docking simulation was performed between ouabain and its derivatives using YASARA software. The docking energy falls within the range of 8.470 kcal/mol to 7.234 kcal/mol, in which digoxigenin was found to be the potential ligand with the best docking energy of 8.470 kcal/mol. Furthermore, pharmacophore modeling was applied to decipher the electronic features of CTS. Molecular dynamics simulation was also employed to determine the conformational properties of Na⁺/K⁺-ATPase-ouabain and Na⁺/K⁺-ATPase-digoxigenin complexes with the plausible structural integrity through conformational ensembles for 100 ns which promoted digoxigenin as the most promising CTS for treating conditions of congestive heart failure patients.

Multi-Pharmacophore Modeling of Caspase-3 Inhibitors using Crystal, Dock and Flexible Conformation Schemes

AIM AND OBJECTIVE

Numerous caspase-3 drug discovery projects were found to have relied on single receptor as the template to recognize most promising small molecule candidates using docking approach. Alternatively, some researchers were contingent upon ligand-based alignment to build up an empirical relationship between ligand functional groups and caspase-3 inhibitory activity quantitatively. To connect both caspase-3 receptor details and its inhibitors chemical functionalities, this study was undertaken to develop receptor- and ligand-pharmacophore models based on different conformational schemes.

MATERIAL AND METHODS

A multi-pharmacophore modeling strategy is carried out based on three conformational schemes of pharmacophore hypothesis generation to screen caspase-3 inhibitors from database. The schemes include (i) flexible (conformations unrestricted or flexible during pharmacophore mapping), (ii) dock (conformations obtained using FlexX docking method) and (iii) crystal (extracted from multiple caspase-3-ligand complexes from PDB repository) conformations of query ligands. The pharmacophore models developed using these conformational schemes were then used to identify probable caspase-3 inhibitors from ZINC database.

RESULTS

We noticed better sensitivity with good specificity measures returned by candidate pharmacophore hypotheses across each conformation type and recognized crucial pharmacophore features that enable caspase-3 binding. Pharmacophore modeling based on flexible conformational scheme indicated that the crystal structure 3KJF (AAAADH) is the best receptor structure to perform receptor-based pharmacophore screening of caspase-3 inhibitors. When multiple crystal structures were included, the hypothesis (HAAA) is more generalized. Superimposition of multiple co-crystal ligands from various caspase-3 PDB entries in crystallographic binding mode revealed similar hypothesis (HAAA). Further, FlexX-guided dock conformations of validation dataset showed that the crystal structure 1RE1 is the best-suited for dock-based pharmacophore models. Database screening using these pharmacophore hypotheses identified N'-[6-(benzimidazol-1-yl)-5-nitro-pyrimidin-4-yl]-4 methylbenzenesulfonohydrazide and 2-nitro-N'-[5-nitro-6-[N'-(p-tolylsulfonyl)hydrazino]pyrimidin-4- yl]benzohydrazide as the probable caspase-3 inhibitors.

CONCLUSION

N'-[6-(benzimidazol-1-yl)-5-nitro-pyrimidin-4-yl]-4 methylbenzenesulfonohydrazide and 2-nitro-N'-[5-nitro-6-[N'-(p-tolylsulfonyl)hydrazino]pyrimidin-4-yl]benzohydrazide may be tested for caspase-3 inhibition. We believe that potential caspase-3 inhibitors can be recognized efficiently by adapting multi-pharmacophore models in database screening.

MDCKpred: a web-tool to calculate MDCK permeability coefficient of small molecule using membrane-interaction chemical features

Structure-based models to understand the transport of small molecules through biological membrane can be developed by enumerating intermolecular interactions of the small molecule with a biological membrane, usually a dimyristoylphosphatidylcholine (DMPC) monolayer. This ADME (absorption, distribution, metabolism, and excretion) property based on Madin-Darby Canine Kidney (MDCK) cell line demonstrates intestinal drug absorption of small molecules and correlated to human intestinal absorption which acts as a determining factor to forecast small-molecule prioritization in drug-discovery projects. We present here the development of MDCKpred web-tool which calculates MDCK permeability coefficient of small molecule based on the regression model, developed using membrane-interaction chemical features. The web-tool allows users to calculate the MDCK permeability coefficient (nm/s) instantly by providing simple descriptor inputs. The chemical-interaction features are derived from different parts of the DMPC molecule viz. head, middle, and tail regions and accounts overall intermolecular contacts of the small molecule when passively diffused through the phospholipid-rich biological membrane. The MDCKpred model is both internally (R² = .76; [Formula: see text]= .68; R_train = .87; R_test = .69) and externally (R_ext = .55) validated. Furthermore, we used natural molecules as application examples to demonstrate its utility in lead exploration and optimization projects. The MDCKpred web-tool can be accessed freely at http://www.mdckpred.in . This web-tool is designed to offer an intuitive way of prioritizing small molecules based on calculated MDCK permeabilities.

Receptor pharmacophore ensemble (REPHARMBLE): a probabilistic pharmacophore modeling approach using multiple protein-ligand complexes

Ensemble methods are gaining more importance in structure-based approaches as single protein-ligand complexes strongly influence the outcomes of virtual screening. Structure-based pharmacophore modeling based on a single protein-ligand complex with complex feature combinations is often limited to certain chemical classes. The REPHARMBLE (receptor pharmacophore ensemble) approach presented here examines the ability of an ensemble of selected protein-ligand complexes to populate pharmacophore space in the ligand binding site, rigorously assesses the importance of pharmacophore features using Poisson statistic and information theory-based entropy calculations, and generates pharmacophore models with high probabilities. In addition, an ensemble scoring function that combines all the resultant high-scoring pharmacophore models to score molecules is derived. The REPHARMBLE approach was evaluated on ten DUD-E benchmark datasets and afforded good screening performance, as measured by receiver operating characteristic, enrichment factor and Güner-Henry score. Although one of the high-scoring models achieved superior statistical results in each dataset, the ensemble scoring function balanced the shortcomings of each model and passed with close performance measures. This approach offers a reliable way of choosing the best-scoring features to build four-feature pharmacophore queries and customize a target-biased 'pharmacophore ensemble' scoring function for subsequent virtual screening.

PLHINT: A knowledge-driven computational approach based on the intermolecular H bond interactions at the protein-ligand interface from docking solutions

The tendency of docking scoring functions to generate crystal close conformations of ligands bound to protein structures face limitations in not reproducing the exact crystal intermolecular contacts in dock poses. Intermolecular H bond contacts enumerated at the protein-docked ligand interface can be used to train scoring models and improve virtual screening performance. There is a need to incorporate additional knowledge of protein-ligand H bond contacts in extension to crystal contacts from docking solutions within the reproducibility efficiency of the docking program. A computational approach PLHINT (Protein-ligand H bond interaction pattern) is presented here which extracts intermolecular H bond interactions from native-like docked ligand poses, transform into the scoring scheme and apply over the virtual screening results of database molecules. The basic premise of the PLHINT approach is to score the most observed H bond patterns with the high score to achieve high recovery rates. Tested on ten diverse DUD-E benchmark datasets, the approach has demonstrated better overall performance and ligand enrichment competency over virtual screening results generated by three genetic algorithm-based docking programs viz. AutoDock Vina, FlexAID and PLANTS. Furthermore, the approach has successfully recovered the poor and random virtual screening results with better enrichments.

Exploration of interaction zones of β-tubulin colchicine binding domain of helminths and binding mechanism of anthelmintics

Numerous studies postulated the possible modes of anthelmintic activity by targeting alternate or extended regions of colchicine binding domain of helminth β-tubulin. We present three interaction zones (zones vide -1 to -3) in the colchicine binding domain of Haemonchus contortus (a helminth) β-tubulin homology model and developed zone-wise structure-based pharmacophore models coupled with molecular docking technique to unveil the binding hypotheses. The resulted ten structure-based hypotheses were then refined to essential three point pharmacophore features that captured recurring and crucial non-covalent receptor contacts and proposed three characteristics necessary for optimal zone-2 binding: a conserved pair of H bond acceptor (HBA to form H bond with Asn226 residue) and an aliphatic moiety of molecule separated by 3.75±0.44Å. Further, an aliphatic or a heterocyclic group distant (11.75±1.14Å) to the conserved aliphatic site formed the third feature component in the zone-2 specific anthelmintic pharmacophore model. Alternatively, an additional HBA can be substituted as a third component to establish H bonding with Asn204. We discern that selective zone-2 anthelmintics can be designed effectively by closely adapting the pharmacophore feature patterns and its geometrical constraints.

Assessment of sleep studies in myotonic dystrophy

Myotonic dystrophy (MD) is the commonest adult muscular dystrophy and is associated with respiratory muscle weakness. The role of screening sleep studies is unclear in MD. We prospectively evaluated polysomnography/overnight oximetry in a group of MD patients and related this to the daytime respiratory function in an attempt to evaluate the usefulness of screening sleep studies. Twenty-five patients with type I MD [15 males; mean age (SD) 40.0 (10.9) years] who had at least one symptom suggestive of nocturnal hypoventilation were included in the study. We performed spirometry, maximal inspiratory and expiratory mouth pressures, sniff nasal inspiratory pressure, arterial blood gases and polysomnography or overnight oximetry. Excessive tiredness and sleepiness were the most common presenting symptoms. Prevalence of sleep related breathing disorder (SRBD) was 36%. FVC was found to be normal in 33% of subjects with significant SRBD. Mouth pressures were reduced more than FVC, even in patients with normal overnight oxygen saturation. Of all the daytime measures, FVC correlated best with arterial carbon dioxide tension ( r = -0.7). Sleep studies were useful to identify a small group of myotonic dystrophy patients (12%, three out of 25 in our series) with SRBD that would have been missed with routine daytime assessments. Targeted sleep monitoring in patients who are older, with multiple symptoms suggestive of SRBD, especially if they are over-weight seems to be the best way to utilize the existing resources. Home unattended oximetry was well tolerated and offers a practical screening tool in this challenging patient group where excess daytime sleepiness is often due to causes other than SRBD.

Molecular modeling of Plasmodium falciparum peptide deformylase and structure-based pharmacophore screening for inhibitors

The role of metal coordination geometry and actinonin (inhibitor) binding was examined to develop pharmacophore-based inhibitor design strategy forPlasmodium falciparumpeptide deformylase.

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.

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.

Prioritization of active antimalarials using structural interaction profile of Plasmodium falciparum enoyl-acyl carrier protein reductase (PfENR)-triclosan derivatives

An empirical relationship between the experimental inhibitory activities of triclosan derivatives and its computationally predicted Plasmodium falciparum enoyl-acyl carrier protein (ACP) reductase (PfENR) dock poses was developed to model activities of known antimalarials. A statistical model was developed using 57 triclosan derivatives with significant measures (r = 0.849, q(2) = 0.619, s = 0.481) and applied on structurally related and structurally diverse external datasets. A substructure-based search on ChEMBL malaria dataset (280 compounds) yielded only two molecules with significant docking energy, whereas eight active antimalarials (EC(50) < 100 nM, tested on 3D7 strain) with better predicted activities (pIC(50) ~ 7) from Open Access Malaria Box (400 compounds) were prioritized. Further, calculations on the structurally diverse rhodanine molecules (known PfENR inhibitors) distinguished actives (experimental IC(50) = 0.035 μM; predicted pIC(50) = 6.568) and inactives (experimental IC(50) = 50 μM; predicted pIC50 = -4.078), which showed that antimalarials possessing dock poses similar to experimental interaction profiles can be used as leads to test experimentally on enzyme assays.

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.

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.

A computational model for non-conserved mature miRNAs from the rice genome

Several computational approaches employ the high complementarity of plant miRNAs to target mRNAs as a filter to recognize miRNA. Numerous non-conserved miRNAs are known with more recent evolutionary origin as a result of target gene duplication events. We present here a computational model with knowledge inputs from reported non-conserved mature miRNAs of Oryza sativa (rice). Sequence- and structure-based approaches were used to retrieve miRNA features based on rice Argonaute protein and develop a multiple linear regression (MLR) model (r(2) = 0.996, q(2)cv = 0.989) which scored mature miRNAs as predicted by the MaturePred program. The model was validated by scoring test set (q(2) = 0.990) and computationally predicted mature miRNAs as external test set (q(2)test = 0.895). This strategy successfully enhanced the confidence of retrieving most probable non-conserved miRNAs from the rice genome. We anticipate that this computational model would recognize unknown non-conserved miRNA candidates and nurture the current mechanistic understanding of miRNA sorting to unveil the role of non-conserved miRNAs in gene silencing.

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.

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.

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.

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.

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 β-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.

The Babinski sign--a critical review

In 1896, Josph Babinski, a French neurologist, first described the best known neurologic eponym--"the Babinski sign". This sign is characterised by dorsiflexion of the big toe, by recruitment of the extensor hallucis longus muscle, on stimulating the sole of the foot. He himself emphasised from the outset the intimate relationship between this sign and the shortening movement in other leg muscles, which forms the flexion synergy of the lower limb. The Babinski sign is not a new reflex, rather it is released as a result of breakdown of the harmonious integration of the flexion and extension component of the normal defence reflex mechanism, due to pyramidal tract dysfunction. A pathological Babinski sign should be clearly distinguished from upgoing toes that may not always be a part of the flexion synergy. This article reviews the Babinski sign in detail, focusing on the historical perspectives, role of pyramidal tract dysfunction, art of elicitation and interpretation. The significance of assessing this phenomenon in the entire leg and the clinical clues that will help to dispel the myths regarding the Babinski sign has been emphasized.

Free radical scavenging active components from Cedrus deodara

An activity-directed fractionation and purification process was used to identify the antioxidant components of Cedrus deodara. Dried heartwood powder of C. deodara was first defatted with petroleum ether and then extracted with chloroform. The chloroform extract showed strong antioxidant activity on 1,1-diphenyl-2-picrylhydrazyl (DPPH) free radical. This fraction was then subjected to separation and purification using silica gel column chromatography. Three compounds with potent antioxidant activity were isolated in significant yields and identified by spectroscopic methods ((1)H NMR, (13)C NMR, IR, and MS). They were identified as (-)-matairesinol, (-)-nortrachelogenin, and a dibenzylbutyrolactollignan (4,4',9-trihydroxy-3,3'-dimethoxy-9,9'-epoxylignan). This is the first report of the occurrence of these compounds in C. deodara.

The Babinski sign--a reappraisal

In 1896, Joseph Babinski, a French neurologist, first described the best known neurologic eponym 'the Babinski sign'. This sign is characterised by dorsiflexion of the big toe and recruitment of the extensor hallucis longus muscle, on stimulating the sole of the foot. He has emphasised from the outset, the intimate relationship between this sign and the shortening movement in other leg muscles, which form the flexion synergy of the lower limb. The Babinski sign is not a new reflex, rather it is released as a result of breakdown of the harmonious integration of the flexion and extension components of the normal defence reflex mechanism, due to pyramidal tract dysfunction. A pathological Babinski sign should be clearly distinguished from upgoing toes that may not always be a part of the flexion synergy. This article reviews the Babinski sign in detail, focusing on the historical perspectives, role of pyramidal tract dysfunction and art of elicitation and interpretation. The significance of assessing this phenomenon in the entire leg, and the clinical clues that will help to dispel the myths regarding the Babinski sign, have been emphasised.

Profound hypothermic circulatory arrest in management of aortic aneurysms

A total of 15 patients having aneurysms of aorta were operated from June 1997 to December 1998 using deep hypothermic circulatory arrest as a modality of brain protection. There were 12 males and 3 females. The age ranged from 19 years to 74 years and the mean age was 44.9 years. Nine patients had aneurysms of ascending aorta (group I), one had aneurysm of ascending aorta and arch of aorta (group II), four had aneurysm of the distal aortic arch (group III) and one patient had thoracoabdominal aortic aneurysm (group IV). In group I, six patients underwent Bentall procedure, two underwent Wheat procedure and one patient had repair of pseudoaneurysm of ascending aorta. The only patient in group II had his ascending aorta and arch replaced, with reimplantation of left common carotid and innominate artery. In group III, three patients had interposition Gelseal graft and one had repair of the tear in distal aortic arch. The lone patient in group IV had interposition Gelseal graft of thoracoabdominal aorta. The hypothermic circulatory arrest was used in all of them for brain and/or spinal cord protection. Retrograde cerebral perfusion was used in two patients. There were two (13%) operative deaths. One patient died of cerebrovascular accident on eighth post-operative day and second died of inadequate surgical repair. There was one instance of left hemiparesis secondary to an infarct in right frontoparietal region. To conclude, hypothermic circulatory arrest could provide an adequate brain protection for aortic aneurysm surgery. Retrograde cerebral perfusion could be an adjuvant when the anticipated time of hypothermic circulatory arrest is likely to exceed 45 minutes.

Profile of antiepileptic pharmacotherapy in a tertiary referral center in South India: a pharmacoepidemiologic and pharmacoeconomic study

PURPOSE

To study the current pharmacotherapy practices of epilepsy and its economics in a developing country by correlating the epidemiology and economics of antiepileptic drug (AED) treatment in general epilepsy care and comprehensive epilepsy care.

METHODS

We compared the AED-use profiles, efficacy, and tolerability at entry and at last follow-up for 972 patients seen at a comprehensive epilepsy care program in South India from 1993 to 1995. The relative cost was expressed as the average percentage of the per capita gross national product (GNP/capita) each individual spent for AED treatment.

RESULTS

At entry, 562 (57.8%) subjects were receiving polytherapy; at last follow-up, 743 (76.4%) patients were receiving monotherapy, an increase of 34.3% in the use of monotherapy. One or more adverse drug reactions were reported by 28.6% of patients at entry and by 19.8% at last follow-up. The proportion of patients who were seizure free increased from 29.0 to 44.8%. Carbamazepine (CBZ) was the most frequently used AED, followed by diphenylhydantoin (DPH), valproate (VPA), and phenobarbitone (PB). The relative cost (% GNP/capita) for standard AEDs were as follows: PB, 4.4%; DPH, 7.1%; CBZ, 16.8%; and VPA, 29.5%. The average annual cost of AED treatment per patient in U.S. dollars was $64.32 at entry and $47.73 at last follow-up. Reduction in polytherapy resulted in the net annual saving of $16,128 ($16.59 per patient, or 5.4% GNP/capita).

CONCLUSIONS

The more frequent use of relatively expensive drugs like CBZ and VPA and the use of polytherapy-still quite prevalent in developing countries-has escalated the cost of AED therapy. Although in recent years AEDs have become more available in developing regions, primary and secondary care physicians have not been adequately educated about the current trends in the pharmacotherapy of epilepsy.