A model of the complex between cyclin-dependent kinase 5 and the activation domain of neuronal Cdk5 activator

Molecular Docking and Dynamics Simulation Analysis of Thymoquinone and Thymol Compounds from Nigella sativa L. that Inhibit Cag A and Vac A Oncoprotein of Helicobacter pylori: Probable Treatment of H. pylori Infections

BACKGROUND

Helicobacter pylori infection is accountable for most of the peptic ulcer and intestinal cancers. Due to the uprising resistance towards H. pylori infection through the present and common proton pump inhibitors regimens, the investigation of novel candidates is the inevitable issue. Medicinal plants have always been a source of lead compounds for drug discovery. The research of the related effective enzymes linked with this gram-negative bacterium is critical for the discovery of novel drug targets.

OBJECTIVE

The aim of the study is to identify the best candidate to evaluate the inhibitory effect of thymoquinone and thymol against H. pylori oncoproteins, Cag A and Vac A in comparison to the standard drug, metronidazole by using a computational approach.

MATERIALS AND METHODS

The targeted oncoproteins, Cag A and Vac A were retrieved from RCSB PDB. Lipinski's rule and ADMET toxicity profiling were carried out on the phytoconstituents of the N. sativa. The two compounds of N. sativa were further analyzed by molecular docking and MD simulation studies. The reported phytoconstituents, thymoquinone and thymol present in N. sativa were docked with H. pylori Cag A and Vac A oncoproteins. Structures of ligands were prepared using ChemDraw Ultra 10 software and then changed into their 3D PDB structures using Molinspiration followed by energy minimization by using software Discovery Studio client 2.5.

RESULTS

The docking results revealed the promising inhibitory potential of thymoquinone against Cag A and Vac A with docking energy of -5.81 kcal/mole and -3.61kcal/mole, respectively. On the contrary, the inhibitory potential of thymol against Cag A and Vac A in terms of docking energy was -5.37 kcal/mole and -3.94kcal/mole as compared to the standard drug, metronidazole having docking energy of -4.87 kcal/mole and -3.20 kcal/mole, respectively. Further, molecular dynamic simulations were conducted for 5ns for optimization, flexibility prediction, and determination of folded Cag A and Vac A oncoproteins stability. The Cag A and Vac A oncoproteins-TQ complexes were found to be quite stable with the root mean square deviation value of 0.2nm.

CONCLUSION

The computational approaches suggested that thymoquinone and thymol may play an effective pharmacological role to treat H. pylori infection. Hence, it could be summarized that the ligands thymoquinone and thymol bound and interacted well with the proteins Cag A and Vac A as compared to the ligand MTZ. Our study showed that all lead compounds had good interaction with Cag A and Vac A proteins and suggested them to be a useful target to inhibit H. pylori infection.

In Silico Design, Synthesis and Evaluation of Novel Series of Benzothiazole- Based Pyrazolidinediones as Potent Hypoglycemic Agents

Background:

The discovery of novel ligand binding domain (LBD) of peroxisome proliferator- activated receptor γ (PPARγ) has recently attracted attention to few research groups in order to develop more potent and safer antidiabetic agents.

Objective:

This study is focused on docking-based design and synthesis of novel compounds combining benzothiazole and pyrazolidinedione scaffold as potential antidiabetic agents.

Methods:

Several benzothiazole-pyrazolidinedione hybrids were synthesized and tested for their in vivo anti-hyperglycemic activity. Interactions profile of title compounds against PPARγ was examined through molecular modelling approach.

Results:

All tested compounds exhibited anti-hyperglycemic activity similar or superior to the reference drug Rosiglitazone. Introducing chlorine atom and alkyl group at position-6 and -5 respectively on benzothiazole core resulted in enhancing the anti-hyperglycemic effect. Docking study revealed that such groups demonstrated favorable hydrophobic interactions with novel LBD Ω- pocket of PPARγ protein.

Conclusion:

Among the tested compounds, N-(6-chloro-5-methylbenzo[d]thiazol-2-yl-4-(4((3,5- dioxopyrazolidin-4-ylidene)methyl)phenoxy)butanamide 5b was found to be the most potent compound and provided valuable insights to further develop novel hybrids as anti-hyperglycemic agents.

An Insightful 10-year Recollection Since the Emergence of the 5-steps Rule

OBJECTIVE

One of the most challenging and also the most difficult problems is how to formulate a biological sequence with a vector but considerably keep its sequence order information.

METHODS

To address such a problem, the approach of Pseudo Amino Acid Components or PseAAC has been developed.

RESULTS AND CONCLUSION

It has become increasingly clear via the 10-year recollection that the aforementioned proposal has been indeed very powerful.

Identification and characterization of WD40 superfamily genes in peach

The WD40 transcription factor family is a superfamily found in all eukaryotes that plays important roles in regulating growth and development. To our knowledge, to date, WD40 superfamily genes have been identified and characterized in several plant species, but little information is available on the WD40 superfamily genes in peach. In this study, we identified 220 members of the WD40 superfamily in the peach genome, and these members were further classified into five subfamilies based on phylogenetic comparison with those in Arabidopsis. The members within each subfamily had conserved motifs and gene structures. The WD40 genes were unevenly distributed on chromosomes 1 to 8 of the peach genome. Additionally, 58 pairs of paralog WD40 members were found on eight chromosomes in peach, and 242 pairs of orthologous WD40 genes in peach and Arabidopsis were matched. The 54 selected putative WD40 genes in peach had diverse expression patterns in red-fleshed and white-fleshed peach fruits at five developmental stages. Prupe.6G211800.1 was located only on the cytomembrane, while Prupe.1G428200.1 and Prupe.I003200.1 were located on both the cytomembrane and in the nucleus; Prupe.1G558700.1 was densely localized around the nuclear rim but relatively faintly localized in the nucleoplasm; Prupe.5G116300.1 was located in the nucleus and cytomembrane with strong signals but showed weak signals in the cytoplasm; and Prupe.8G212400.1 and Prupe.1G053600.1 were located mainly in the nuclear envelope and cytomembrane but relatively faintly in the nucleoplasm. This study provides a foundation for the further functional verification of WD40 genes in peach.

New insight into poly (3-hydroxybutyrate) production by Azomonas macrocytogenes isolate KC685000: large scale production, kinetic modeling, recovery and characterization

About 24 h incubation of Azomonas (A.) macrocytogenes isolate KC685000 in 14L fermenter produced 22% poly (3-hydroxybutyrate) (PHB) per cell dry weight (CDW) biopolymer using 1 vvm aeration, 10% inoculum size, and initial pH of 7.2. To control the fermentation process, Logistic and Leudeking-Piret models were used to describe the cell growth and PHB production, respectively. These two models were in good agreement with the experimental data confirming the growth associated nature of PHB production. The best method for recovery of PHB was chemical digestion using sodium hypochlorite alone. The characterization of the produced polymer was carried out using FT-IR, ¹HNMR spectroscopy, gel permeation chromatography and transmission electron microscope. The analysis of the nucleotide sequences of PHA synthase enzyme revealed class III identity. The putative tertiary structure of PHA synthase enzyme was analyzed using Modular Approach to Structural class prediction software, Tied Mixture Hidden Markov Model server, and Swiss model software. It was deduced that PHA synthases' structural class was multidomain protein (α/β) containing a conserved cysteine residue and lipase box as characteristic features of α/β hydrolase super family. Taken together, all the results of molecular characterization and transmission electron microscope images supported that the PHB formation was attained by the micelle model. To the best of our knowledge, this is the first report on production of growth associated PHB polymer using A. macrocytogenes isolate KC685000, and its class III PHA synthase.

Metabolism of Oxalate in Humans: A Potential Role Kynurenine Aminotransferase/Glutamine Transaminase/Cysteine Conjugate Beta-lyase Plays in Hyperoxaluria

Hyperoxaluria, excessive urinary oxalate excretion, is a significant health problem worldwide. Disrupted oxalate metabolism has been implicated in hyperoxaluria and accordingly, an enzymatic disturbance in oxalate biosynthesis can result in the primary hyperoxaluria. Alanine glyoxylate aminotransferase-1 and glyoxylate reductase, the enzymes involving glyoxylate (precursor for oxalate) metabolism, have been related to primary hyperoxalurias. Some studies suggest that other enzymes such as glycolate oxidase and alanine glyoxylate aminotransferase-2 might be associated with primary hyperoxaluria as well, but evidence of a definitive link is not strong between the clinical cases and gene mutations. There are still some idiopathic hyperoxalurias, which require a further study for the etiologies. Some aminotransferases, particularly kynurenine aminotransferases, can convert glyoxylate to glycine. Based on biochemical and structural characteristics, expression level, subcellular localization of some aminotransferases, a number of them appear able to catalyze the transamination of glyoxylate to glycine more efficiently than alanine glyoxylate aminotransferase-1. The aim of this minireview is to explore other undermining causes of primary hyperoxaluria and stimulate research toward achieving a comprehensive understanding of underlying mechanisms leading to the disease. Herein, we reviewed all aminotransferases in the liver for their functions in glyoxylate metabolism. Particularly, kynurenine aminotransferase-I and III were carefully discussed regarding their biochemical and structural characteristics, cellular localization, and enzyme inhibition. Kynurenine aminotransferase-III is, so far, the most efficient putative mitochondrial enzyme to transaminate glyoxylate to glycine in mammalian livers, might be an interesting enzyme to look over in hyperoxaluria etiology of primary hyperoxaluria and should be carefully investigated for its involvement in oxalate metabolism.

Novel antiviral drug discovery strategies to tackle drug-resistant mutants of influenza virus strains

INTRODUCTION

The emergence of drug-resistant influenza virus strains highlights the need for new antiviral therapeutics to combat future pandemic outbreaks as well as continuing seasonal cycles of influenza. Areas covered: This review summarizes the mechanisms of current FDA-approved anti-influenza drugs and patterns of resistance to those drugs. It also discusses potential novel targets for broad-spectrum antiviral drugs and recent progress in novel drug design to overcome drug resistance in influenza. Expert opinion: Using the available structural information about drug-binding pockets, research is currently underway to identify molecular interactions that can be exploited to generate new antiviral drugs. Despite continued efforts, antivirals targeting viral surface proteins like HA, NA, and M2, are all susceptible to developing resistance. Structural information on the internal viral polymerase complex (PB1, PB2, and PA) provides a new avenue for influenza drug discovery. Host factors, either at the initial step of viral infection or at the later step of nuclear trafficking of viral RNP complex, are being actively pursued to generate novel drugs with new modes of action, without resulting in drug resistance.

Hydrogen bond analysis of the EGFR-ErbB3 heterodimer related to non-small cell lung cancer and drug resistance

Lung cancer is the predominant cause of cancer deaths on a worldwide scale. A mutation in the epidermal growth factor receptor (EGFR) can cause non-small cell lung cancer (NSCLC). The L858R one-point mutation in exon 21 in EGFR is the most prevalent in NSCLC. For over 60% of EGFR-muted NSCLC, another mutation T790M can cause drug resistance. In this paper, we consider EGFR and ErbB3 heterodimers involving three structures of EGFR, wild-type, with L858R mutation, and with L858R and T790M mutations. We perform molecular dynamics (MD) simulations to analyze hydrogen bonds in all three instances. The hydrogen bonds contribute to the conformational stability of the protein and molecular recognition. Several other parameters are also investigated in the present study, which reveals significant changes in the dimer at different levels of mutation. The knowledge and results obtained from this study lead to useful insight into the mechanism of NSCLC drug resistance.

Construction and analysis of dysregulated lncRNA-associated ceRNA network in colorectal cancer

Colorectal cancer (CRC) is one of the most frequently diagnosed digestive system cancer. The aim of the present study was to investigate the interactions among messenger RNAs (mRNAs), microRNAs (miRNAs), and long noncoding RNAs (lncRNAs) in CRC to reveal the mechanisms of CRC. Differentially expressed genes (DEGs) were identified from public gene expression data sets. One thousand eighty-one common dysregulated mRNAs in two data sets were identified. Gene function analysis and protein-protein interaction network analysis indicated that these DEGs might play important roles in CRC. LINC00365 was selected through coding- noncoding network analysis and its expression was validated upregulated in 22 paired clinical samples and four CRC cell lines. A competing endogenous RNA network composed of 70 miRNAs, nine mRNAs, and LINC00365 was constructed. Eight of nine mRNAs were validated upregulated in The Cancer Genome Atlas data set. Our results suggested that LINC00365 was an oncogene in CRC and it could regulate the expression of several mRNAs through sponging miRNAs.

The Emerging Role of Cyclin-Dependent Kinases (CDKs) in Pancreatic Ductal Adenocarcinoma

The family of cyclin-dependent kinases (CDKs) has critical functions in cell cycle regulation and controlling of transcriptional elongation. Moreover, dysregulated CDKs have been linked to cancer initiation and progression. Pharmacological CDK inhibition has recently emerged as a novel and promising approach in cancer therapy. This idea is of particular interest to combat pancreatic ductal adenocarcinoma (PDAC), a cancer entity with a dismal prognosis which is owed mainly to PDAC's resistance to conventional therapies. Here, we review the current knowledge of CDK biology, its role in cancer and the therapeutic potential to target CDKs as a novel treatment strategy for PDAC.

Exploration of synthetic multifunctional amides as new therapeutic agents for Alzheimer's disease through enzyme inhibition, chemoinformatic properties, molecular docking and dynamic simulation insights

A new series of multifunctional amides has been synthesized having moderate enzyme inhibitory potentials and mild cytotoxicity. 2-Furyl(1-piperazinyl)methanone (1) was coupled with 3,5-dichloro-2-hydroxybenzenesulfonyl chloride (2) to form {4-[(3,5-dichloro-2-hydroxyphenyl)sulfonyl]-1-piperazinyl}(2-furyl)methanone (3). Different elecrophiles were synthesized by the reaction of various un/substituted anilines (4a-o) with 2-bromoacetylbromide (5), 2‑bromo‑N-(un/substituted-phenyl)acetamides (6a-o). Further, equimolar ratios of 3 and 6a-o were allowed to react in the presence of K₂CO₃ in acetonitrile to form desired multifunctional amides (7a-o). The structural confirmation of all the synthesized compounds was carried out by their EI-MS, IR, ¹H NMR and ¹³C NMR spectral data. Enzyme inhibition activity was performed against acetyl and butyrylcholinestrase enzymes, whereby 7e showed very good activity having IC₅₀ value of 5.54 ± 0.03 and 9.15 ± 0.01 μM, respectively, relative to eserine, a reference standard. Hemolytic activity of the molecules was checked to asertain their cytotoxicity towards red blood cell membrance and it was observed that most of the compounds were not toxic up to certain range. Moreover, chemoinformatic protepties and docking simulation results also showed the significance of 7e as compared to other compounds. Based on in vitro and in silico analysis 7e could be used as a template for the development of new drugs against Alzheimer's disease.

A Comprehensive In Silico Method to Study the QSTR of the Aconitine Alkaloids for Designing Novel Drugs

A combined in silico method was developed to predict potential protein targets that are involved in cardiotoxicity induced by aconitine alkaloids and to study the quantitative structure⁻toxicity relationship (QSTR) of these compounds. For the prediction research, a Protein-Protein Interaction (PPI) network was built from the extraction of useful information about protein interactions connected with aconitine cardiotoxicity, based on nearly a decade of literature and the STRING database. The software Cytoscape and the PharmMapper server were utilized to screen for essential proteins in the constructed network. The Calcium-Calmodulin-Dependent Protein Kinase II alpha (CAMK2A) and gamma (CAMK2G) were identified as potential targets. To obtain a deeper insight on the relationship between the toxicity and the structure of aconitine alkaloids, the present study utilized QSAR models built in Sybyl software that possess internal robustness and external high predictions. The molecular dynamics simulation carried out here have demonstrated that aconitine alkaloids possess binding stability for the receptor CAMK2G. In conclusion, this comprehensive method will serve as a tool for following a structural modification of the aconitine alkaloids and lead to a better insight into the cardiotoxicity induced by the compounds that have similar structures to its derivatives.

Structural insights of dipeptidyl peptidase-IV inhibitors through molecular dynamics-guided receptor-dependent 4D-QSAR studies

Dipeptidyl peptidase-IV (DPP-IV) inhibitors are promising antidiabetic agents. Currently, several DPP-IV inhibitors have been approved for therapeutic use in diabetes mellitus. Receptor-dependent 4D-QSAR is comparatively a new approach which uses molecular dynamics simulations to generate conformational ensemble profiles of compounds representing a dynamic state of compounds at a target's binding site. This work describes a receptor-dependent 4D-QSAR study on triazolopiperazine derivatives. QSARINS multiple linear regression method was adopted to generate 4D-QSAR models. A model with 9 variables was found to have better predictive accuracy with [Formula: see text], [Formula: see text] (leave-one-out) = 0.592 and [Formula: see text] predicted = 0.597. The location of these 9 variables at the binding site of DPP-IV revealed the importance of the residues Val711, Tyr662, Tyr666, Val202, Asp200 and Thr199 in making critical interactions with DPP-IV inhibitors. The study of these critical interactions revealed the structural features required in DPP-IV inhibitors. Thus, in this study the importance of a halogen substituent on a phenyl ring, the extent of substitution on the triazolopiperazine ring, the presence of an ionizable amino group and the presence of a hydrophobic substituent that can bind deeper in binding pocket of DPP-IV were revealed.

iROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition

DNA replication, occurring in all living organisms and being the basis for biological inheritance, is the process of producing two identical replicas from one original DNA molecule. To in-depth understand such an important biological process and use it for developing new strategy against genetics diseases, the knowledge of duplication origin sites in DNA is indispensible. With the explosive growth of DNA sequences emerging in the postgenomic age, it is highly desired to develop high throughput tools to identify these regions purely based on the sequence information alone. In this paper, by incorporating the dinucleotide position-specific propensity information into the general pseudo nucleotide composition and using the random forest classifier, a new predictor called iROS-gPseKNC was proposed. Rigorously cross-validations have indicated that the proposed predictor is significantly better than the best existing method in sensitivity, specificity, overall accuracy, and stability. Furthermore, a user-friendly web-server for iROS-gPseKNC has been established at http://www.jci-bioinfo.cn/iROS-gPseKNC, by which users can easily get their desired results without the need to bother the complicated mathematics, which were presented just for the integrity of the methodology itself.

Small molecular floribundiquinone B derived from medicinal plants inhibits acetylcholinesterase activity

Being a neurodegenerative disorder, Alzheimer's disease (AD) is the one of the most terrible diseases. And acetylcholinesterase (AChE) is considered as an important target for treating AD. Acetylcholinesterase inhibitors (AChEI) are considered to be one of the effective drugs for the treatment of AD. The aim of this study is to find a novel potential AChEI as a drug for the treatment of AD. In this study, instead of using the synthetic compounds, we used those extracted from plants to investigate the interaction between floribundiquinone B (FB) and AChE by means of both the experimental approach such as fluorescence spectra, ultraviolet-visible (UV-vis) absorption spectrometry, circular dichroism (CD) and the theoretical approaches such as molecular docking. The findings reported here have provided many useful clues and hints for designing more effective and less toxic drugs against Alzheimer's disease.

Molecular dynamics and protein interaction studies of lipopeptide (Iturin A) on α- amylase of Spodoptera litura

The small mottled willow moth (Spodoptera litura) is one of the best-known agricultural pest insects. To understand the insecticidal activity, we have selected iturin A compound produced by Bacillus amyloliquefaciens RHNK22 which showed the strongest and most common inhibitory effect on the Spodoptera litura protein. In this work we have identified the action of iturin A on α- amylase is a major digestive enzyme of Spodoptera litura using docking studies. A 3D model of α- amylase from Spodoptera litura was generated using 2HPH as a template with the help of Modeller7v7. With the aid of the molecular mechanics and molecular dynamics methods, the final model is obtained and is further checked by Procheck and Verify 3D graph programs, which showed that the final refined model is reliable. With this model, a adjustable docking study was performed with iturin A using GOLD software. The results indicated that ARG 18, THR15, LEU42 in α- amylase are important determinant residues in binding as they have strong hydrogen bonding interactions with iturin A. These hydrogen binding interactions play an important role for the stability of the complex.

3D pharmacophore-based virtual screening, docking and density functional theory approach towards the discovery of novel human epidermal growth factor receptor-2 (HER2) inhibitors

Human epidermal growth factor receptor 2 (HER2) is one of the four members of the epidermal growth factor receptor (EGFR) family and is expressed to facilitate cellular proliferation across various tissue types. Therapies targeting HER2, which is a transmembrane glycoprotein with tyrosine kinase activity, offer promising prospects especially in breast and gastric/gastroesophageal cancer patients. Persistence of both primary and acquired resistance to various routine drugs/antibodies is a disappointing outcome in the treatment of many HER2 positive cancer patients and is a challenge that requires formulation of new and improved strategies to overcome the same. Identification of novel HER2 inhibitors with improved therapeutics index was performed with a highly correlating (r=0.975) ligand-based pharmacophore model (Hypo1) in this study. Hypo1 was generated from a training set of 22 compounds with HER2 inhibitory activity and this well-validated hypothesis was subsequently used as a 3D query to screen compounds in a total of four databases of which two were natural product databases. Further, these compounds were analyzed for compliance with Veber's drug-likeness rule and optimum ADMET parameters. The selected compounds were then subjected to molecular docking and Density Functional Theory (DFT) analysis to discern their molecular interactions at the active site of HER2. The findings thus presented would be an important starting point towards the development of novel HER2 inhibitors using well-validated computational techniques.

The effect of glycosylation on the transferrin structure: A molecular dynamic simulation analysis

Transferrins have been defined by the highly cooperative binding of iron and a carbonate anion to form a Fe-CO3-Tf ternary complex. As such, the layout of the binding site residues affects transferrin function significantly; In contrast to N-lobe, C-lobe binding site of the transferrin structure has been less characterized and little research which surveyed the interaction of carbonate with transferrin in the C-lobe binding site has been found. In the present work, molecular dynamic simulation was employed to gain access into the molecular level understanding of carbonate binding site and their interactions in each lobe. Residues responsible for carbonate binding of transferrin structure were pointed out. In addition, native human transferrin is a glycoprotein that two N-linked complex glycan chains located in the C-lobe. Usually, in the molecular dynamic simulation for simplifying, glycan is removed from the protein structure. Here, we explore the effect of glycosylation on the transferrin structure. Glycosylation appears to have an effect on the layout of the binding site residue and transferrin structure. On the other hand, sometimes the entire transferrin formed by separated lobes that it allows the results to be interpreted in a straightforward manner rather than more parameters required for full length protein. But, it should be noted that there are differences between the separated lobe and full length transferrin, hence, a comparative analysis by the molecular dynamic simulation was performed to investigate such structural variations. Results revealed that separation in C-lobe caused a significant structural variation in comparison to N-lobe. Consequently, the separated lobes and the full length one are different, showing the importance of the interlobe communication and the impact of the lobes on each other in the transferrin structure.

Identification of CD4+ T-cell epitope and investigation of HLA distribution for the immunogenic proteins of Burkholderia pseudomallei using in silico approaches - A key vaccine development strategy for melioidosis

Melioidosis is a serious infectious diseases affecting multi-organ system in humans with high mortality rate. The disease is caused by the bacterium, Burkholderia pseudomallei and it is intrinsically resistant to many antibiotics. Thus, there is an urgent need for protective vaccine against B. pseudomallei; which may reduce morbidity and mortality in endemic areas. The identification of peptides that bind to major histocompatibility complex II class helps in understanding the nature of immune response and identifying T-cell epitopes for the design of new vaccines. Previous studies indicate that, ompA, bipB, fliC and groEL proteins of B. pseudomallei stimulate CD4+ T-cell immune response and act as protective immunogens. However, the data for CD4+ T-cell epitopes of these immunogenic proteins are very limited. Hence, in this present study we attempted to identify CD4+ T-cell epitopes in B. pseudomallei immunogenic proteins using in silico approaches. We did population coverage analysis for these identified epitopic core sequences to identify individuals in endemic areas expected to respond to a given set of these epitopes on the basis of HLA genotype frequencies. We observed that eight epitopic core sequences, two from each immunogenic protein, were associated with the maximum number of HLA-DR binding alleles. These eight peptides are found to be immunogenic in more than 90% of population in endemic areas considered. Thus, these eight peptides containing epitopic core sequences may act as probable vaccine candidates and they may be considered for the development of epitope-based vaccines for melioidosis.

In silico identification of putative bifunctional Plk1 inhibitors by integrative virtual screening and structural dynamics approach

Polo like kinase (Plk1) is a master regulator of cell cycle and considered as next generation antimitotic target in human. As Plk1 predominantly expresses in the dividing cells with a much higher expression in cancerous cells, it serves as a discriminative target for cancer therapeutics. Here we implied a novel and promising integrative strategy to identify "bifunctional" Plk1 inhibitors that compete simultaneously with ATP and substrate for their binding sites. We integrated structure-based virtual screening (SBVS) and molecular dynamics simulations with emphasis on unique structural properties of Plk1. Through screening of 20,000 compounds, nearly ~2000 hits were enriched and subjected to SBVS against ATP and substrate binding sites of Plk1. Subsequently, on the basis of their binding abilities to Plk1 kinase and polo box domains, filtration of candidate hits resulted in the isolation of 26 compounds. By exclusion of close analogs or isomers, 10 unique compounds were selected for detailed study. A representative compound was subjected to molecular dynamics simulation assay to have deep structural insights and to gauge critical structural crunch for inhibitor binding against kinase and polo box domains. Our integrative approach may complement high-throughput screening and identify bifunctional Plk1 inhibitors that may contribute in selective targeting of Plk1 to elicit desired biological process.

Identification of Glucose-Binding Pockets in Human Serum Albumin Using Support Vector Machine and Molecular Dynamics Simulations

Human Serum Albumin (HSA) has been suggested to be an alternate biomarker to the existing Hemoglobin-A1c (HbA1c) marker for glycemic monitoring. Development and usage of HSA as an alternate biomarker requires the identification of glycation sites, or equivalently, glucose-binding pockets. In this work, we combine molecular dynamics simulations of HSA and the state-of-art machine learning method Support Vector Machine (SVM) to predict glucose-binding pockets in HSA. SVM uses the three dimensional arrangement of atoms and their chemical properties to predict glucose-binding ability of a pocket. Feature selection reveals that the arrangement of atoms and their chemical properties within the first 4Å from the centroid of the pocket play an important role in the binding of glucose. With a 10-fold cross validation accuracy of 84 percent, our SVM model reveals seven new potential glucose-binding sites in HSA of which two are exposed only during the dynamics of HSA. The predictions are further corroborated using docking studies. These findings can complement studies directed towards the development of HSA as an alternate biomarker for glycemic monitoring.

Identification of potential CCR5 inhibitors through pharmacophore-based virtual screening, molecular dynamics simulation and binding free energy analysis

Employing the combined strategy to identify novel CCR5 inhibitors and provide a basis for rational drug design.

Modelling the molecular mechanism of protein-protein interactions and their inhibition: CypD-p53 case study

Cyclophilin D (CypD) is an important regulatory protein involved in mitochondrial membrane permeability transition and cell death. Further, the mitochondrial CypD-p53 axis is an important contributor to necroptosis, a form of programmed necrosis, involved in various cardiovascular and neurological disorders. The CypD ligand, Cyclosporin A (CsA), was identified as an inhibitor of this interaction. In this study, using computational methods, we have attempted to model the CypD-p53 interaction in order to delineate their mode of binding and also to disclose the molecular mechanism, by means of which CsA interferes with this interaction. It was observed that p53 binds at the CsA-binding site of CypD. The knowledge obtained from this modelling was employed to identify novel CypD inhibitors through structure-based methods. Further, the identified compounds were tested by a similar strategy, adopted during the modelling process. This strategy could be applied to study the mechanism of protein-protein interaction (PPI) inhibition and to identify novel PPI inhibitors.

ProTSAV: A protein tertiary structure analysis and validation server

Quality assessment of predicted model structures of proteins is as important as the protein tertiary structure prediction. A highly efficient quality assessment of predicted model structures directs further research on function. Here we present a new server ProTSAV, capable of evaluating predicted model structures based on some popular online servers and standalone tools. ProTSAV furnishes the user with a single quality score in case of individual protein structure along with a graphical representation and ranking in case of multiple protein structure assessment. The server is validated on ~64,446 protein structures including experimental structures from RCSB and predicted model structures for CASP targets and from public decoy sets. ProTSAV succeeds in predicting quality of protein structures with a specificity of 100% and a sensitivity of 98% on experimentally solved structures and achieves a specificity of 88%and a sensitivity of 91% on predicted protein structures of CASP11 targets under 2Å.The server overcomes the limitations of any single server/method and is seen to be robust in helping in quality assessment. ProTSAV is freely available at http://www.scfbio-iitd.res.in/software/proteomics/protsav.jsp.

A bioinformatic approach to check the spatial epitope structure of an immunogenic protein coded by DNA vaccine plasmids

In this study, we used an approach to check the Hemagglutinin antigen-antibodies interactions after fusion of one or two gene segments to Hemagglutinin gene in some influenza DNA vaccines. We designed different DNA vaccine constructs containing Hemagglutinin 9 (H9) gene fused to four or eight 29 amino acids of C3d (4/8P29C3d) and/or 3, 4 domains of the Fc part of IgY (FcIgY) coding sequences. As there are receptors for P29C3d and FcIgY on the immune cells, fused H9 are targeted to these cells. Three dimensional (3D) structures of the DNA vaccine coded proteins were modeled and docked with two antibodies (1KEN, 1QFU) to evaluate the effect of the H9 gene fusion to the other gene segments (4, 8 P29C3d and FcIgY) on the interaction of two H9 spatial epitopes. Also, we docked DNA vaccine proteins containing Fc IgY to its receptor (CHIR AB1) and compare interaction affinity of Fc IgY alone with affinity of DNA vaccines containing Fc IgY. The average of 1KEN and 1QFU interface scores were 94.89 and 93.09% of H9 DNA vaccine-antibodies interface scores, respectively. These percentages showed a little change in the H9 immunogenic parts. Also, because of spatial freedom of H9 part in all DNA vaccine proteins, added parts may not interfere with antibody-antigen interactions. Once, H9+FcIgY and CHIR AB1 affinity decreased in comparison with affinity of Fc IgY alone and CHIR AB1, affinity of H9+8P29C3d+FcIgY and CHIR AB1 increased to 132%. So, this would be expectable that despite of loss of affinity in H9 and its antibodies in the H9+8P29C3d+FcIgY, dramatic increase of Fc IgY and CHIR AB1 affinity in this group, could repair the loss of H9 affinity and may lead to a better immunogenicity.

Computational assay of Zanamivir binding affinity with original and mutant influenza neuraminidase 9 using molecular docking

Based upon molecular docking, this study aimed to find notable in silico neuraminidase 9 (NA9) point mutations of the avian influenza A H7N9 virus that possess a Zanamivir resistant property and to determine the lead compound capable of inhibiting these NA9 mutations. Seven amino acids (key residues) at the binding site of neuraminidase 9 responsible for Zanamivir-NA9 direct interactions were identified and 72 commonly occurring mutant NA9 versions were created using the Sybyl-X 2.0 software. The docking scores obtained after Zanamivir was bound to all mutant molecules of NA9 revealed 3 notable mutations R292W, R118P, and R292K that could greatly reduce the binding affinity of the medicine. These 3 mutant NA9 versions were then bound to each of 154 different molecules chosen from 5 groups of compounds to determine which molecule(s) might be capable of inhibiting mutant neuraminidase 9, leading to the discovery of the lead compound of potent mutant NA9 inhibitors. This compound, together with other mutations occurring to NA9 identified in the study, would be used as data for further research regarding neuraminidase inhibitors and synthesizing new viable medications used in the fight against the virus.

Folding and binding energy of a calmodulin-binding cell antiproliferative peptide

We carry out a computational study of a calmodulin-binding peptide shown to be effective in reducing cell proliferation. We find several folded states for two short variants of different length of the peptide and determine the location of the binding site on calmodulin, the binding free energy for the different conformers and structural details that play a role in optimal binding. Binding to a hydrophobic pocket in calmodulin occurs via an anchoring phenylalanine residue of the natively disordered peptide, and is enhanced when a neighbouring hydrophobic residue acts as a co-anchor. The shorter sequence possesses better binding to calmodulin, which is encouraging in terms of the development of non-peptide analogues as therapeutic agents.

A comparative study of structural and conformational properties of casein kinase-1 isoforms: insights from molecular dynamics and principal component analysis

Wnt signaling pathway regulates several developmental processes in human; however recently this pathway has been associated with development of different types of cancers. Casein kinase-1 (CK1) constitutes a family of serine-threonine protein kinase; various members of this family participate in Wnt signal transduction pathway and serve as molecular switch to this pathway. Among the known six isoforms of CK1, in human, at least three isoforms (viz. alpha, delta and epsilon) have been reported as oncogenic. The development of common therapeutics against these kinases is an arduous task; unless we have the detailed information of their tertiary structures and conformational properties. In the present work, the dynamical and conformational properties for each of three isoforms of CK1 are explored through molecular dynamics (MD) simulations. The conformational space distribution of backbone atoms is evaluated using principal component analysis of MD data, which are further validated on the basis of potential energy surface. Based on these analytics, it is suggested that conformational subspace shifts upon binding to ligands and guides the kinase action of CK1 isoforms. Further, this paper as a first effort to concurrently study all the three isoforms of CK1 provides structural basis for development of common anticancer therapeutics against three isoforms of CK1.

QSAR prediction of HIV-1 protease inhibitory activities using docking derived molecular descriptors

In this study, application of a new hybrid docking-quantitative structure activity relationship (QSAR) methodology to model and predict the HIV-1 protease inhibitory activities of a series of newly synthesized chemicals is reported. This hybrid docking-QSAR approach can provide valuable information about the most important chemical and structural features of the ligands that affect their inhibitory activities. Docking studies were used to find the actual conformations of chemicals in active site of HIV-1 protease. Then the molecular descriptors were calculated from these conformations. Multiple linear regression (MLR) and least square support vector machine (LS-SVM) were used as QSAR models, respectively. The obtained results reveal that statistical parameters of the LS-SVM model are better than the MLR model, which indicate that there are some non-linear relations between selected molecular descriptors and anti-HIV activities of interested chemicals. The correlation coefficient (R), root mean square error (RMSE) and average absolute error (AAE) for LS-SVM are: R=0.988, RMSE=0.207 and AAE=0.145 for the training set, and R=0.965, RMSE=0.403 and AAE=0.338 for the test set. Leave one out cross validation test was used for assessment of the predictive power and validity of models which led to cross-validation correlation coefficient QUOTE of 0.864 and 0.850 and standardized predicted relative error sum of squares (SPRESS) of 0.553 and 0.581 for LS-SVM and MLR models, respectively.

CDK5 activator protein p25 preferentially binds and activates GSK3β

Glycogen synthase kinase 3β (GSK3β) and cyclin-dependent kinase 5 (CDK5) are tau kinases and have been proposed to contribute to the pathogenesis of Alzheimer's disease. The 3D structures of these kinases are remarkably similar, which led us to hypothesize that both might be capable of binding cyclin proteins--the activating cofactors of all CDKs. CDK5 is normally activated by the cyclin-like proteins p35 and p39. By contrast, we show that GSK3β does not bind to p35 but unexpectedly binds to p25, the calpain cleavage product of p35. Indeed, overexpressed GSK3β outcompetes CDK5 for p25, whereas CDK5 is the preferred p35 partner. FRET analysis reveals nanometer apposition of GSK3β:p25 in cell soma as well as in synaptic regions. Interaction with p25 also alters GSK3β substrate specificity. The GSK3β:p25 interaction leads to enhanced phosphorylation of tau, but decreased phosphorylation of β-catenin. A partial explanation for this situation comes from in silico modeling, which predicts that the docking site for p25 on GSK3β is the AXIN-binding domain; because of this, p25 inhibits the formation of the GSK3β/AXIN/APC destruction complex, thus preventing GSK3β from binding to and phosphorylating β-catenin. Coexpression of GSK3β and p25 in cultured neurons results in a neurodegeneration phenotype that exceeds that observed with CDK5 and p25. When p25 is transfected alone, the resulting neuronal damage is blocked more effectively with a specific siRNA against Gsk3β than with one against Cdk5. We propose that the effects of p25, although normally attributed to activate CDK5, may be mediated in part by elevated GSK3β activity.

Bioassay directed isolation and biological evaluation of compounds isolated from Rubus fairholmianus Gard

The in vitro and in silico analysis of Rubus fairholmianus acetone extract for antioxidant, antiproliferative, and anti-inflammatory activity led to the isolation of six compounds. Amongst all the six isolated compounds tested, 1-(2-hydroxyphenyl)-4-methylpentan-1-one (compound 1) and 2-[(3-methylbutoxy) carbonyl] benzoic acid (compound 2) were found to be more active in inhibiting BRCA and COX target proteins, which also showed the better results for DPPH and ABTS radical scavenging assays. The promising results of this investigation emphasize the importance of using R. fairholmianus in the treatment of radical generated disorders mainly cancer and other inflammatory diseases.

iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking

Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.

Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection

Motivation: Owing to its importance in both basic research (such as molecular evolution and protein attribute prediction) and practical application (such as timely modeling the 3D structures of proteins targeted for drug development), protein remote homology detection has attracted a great deal of interest. It is intriguing to note that the profile-based approach is promising and holds high potential in this regard. To further improve protein remote homology detection, a key step is how to find an optimal means to extract the evolutionary information into the profiles.

Results: Here, we propose a novel approach, the so-called profile-based protein representation, to extract the evolutionary information via the frequency profiles. The latter can be calculated from the multiple sequence alignments generated by PSI-BLAST. Three top performing sequence-based kernels (SVM-Ngram, SVM-pairwise and SVM-LA) were combined with the profile-based protein representation. Various tests were conducted on a SCOP benchmark dataset that contains 54 families and 23 superfamilies. The results showed that the new approach is promising, and can obviously improve the performance of the three kernels. Furthermore, our approach can also provide useful insights for studying the features of proteins in various families. It has not escaped our notice that the current approach can be easily combined with the existing sequence-based methods so as to improve their performance as well.

Availability and implementation: For users’ convenience, the source code of generating the profile-based proteins and the multiple kernel learning was also provided at

http://bioinformatics.hitsz.edu.cn/main/∼binliu/remote/

Contact:bliu@insun.hit.edu.cn or bliu@gordonlifescience.org

Supplementary information:Supplementary data are available at Bioinformatics online.

Find novel dual-agonist drugs for treating type 2 diabetes by means of cheminformatics

The high prevalence of type 2 diabetes mellitus in the world as well as the increasing reports about the adverse side effects of the existing diabetes treatment drugs have made developing new and effective drugs against the disease a very high priority. In this study, we report ten novel compounds found by targeting peroxisome proliferator-activated receptors (PPARs) using virtual screening and core hopping approaches. PPARs have drawn increasing attention for developing novel drugs to treat diabetes due to their unique functions in regulating glucose, lipid, and cholesterol metabolism. The reported compounds are featured with dual functions, and hence belong to the category of dual agonists. Compared with the single PPAR agonists, the dual PPAR agonists, formed by combining the lipid benefit of PPARα agonists (such as fibrates) and the glycemic advantages of the PPARγ agonists (such as thiazolidinediones), are much more powerful in treating diabetes because they can enhance metabolic effects while minimizing the side effects. This was observed in the studies on molecular dynamics simulations, as well as on absorption, distribution, metabolism, and excretion, that these novel dual agonists not only possessed the same function as ragaglitazar (an investigational drug developed by Novo Nordisk for treating type 2 diabetes) did in activating PPARα and PPARγ, but they also had more favorable conformation for binding to the two receptors. Moreover, the residues involved in forming the binding pockets of PPARα and PPARγ among the top ten compounds are explicitly presented, and this will be very useful for the in-depth conduction of mutagenesis experiments. It is anticipated that the ten compounds may become potential drug candidates, or at the very least, the findings reported here may stimulate new strategies or provide useful insights for designing new and more powerful dual-agonist drugs for treating type 2 diabetes.

A rational approach to selective pharmacophore designing: an innovative strategy for specific recognition of Gsk3β

We propose a novel cheminformatics approach that combines structure and ligand-based design to identify target-specific pharmacophores with well-defined exclusion ability. Our strategy includes the prediction of selective interactions, developing structure, and knowledge-based selective pharmacophore models, followed by database screening and molecular docking. This unique strategy was employed in addressing the off-target toxicity of Gsk3β and CDKs. The connections of Gsk3β in eukaryotic cell apoptosis and the extensive potency of Gsk3β inhibitors to block cell death have made it a potential drug-discovery target for many grievous human disorders. Gsk3β is phylogenetically very closely related to the CDKs, such as CDK1 and CDK2, which are suggested to be the off-target proteins of Gsk3β inhibitors. Here, we have employed novel computational approaches in designing the ligand candidates that are potentially inhibitory against Gsk3β, with well-defined the exclusion ability to CDKs. A structure-ligand -based selective pharmacophore was modeled. This model was used to retrieve molecules from the zinc database. The hits retrieved were further screened by molecular docking and protein–ligand interaction fingerprints. Based on these results, four molecules were predicted as selective Gsk3β antagonists. It is anticipated that this unique approach can be extended to investigate any protein–ligand specificity.

Structural origins for the loss of catalytic activities of bifunctional human LTA4H revealed through molecular dynamics simulations

Human leukotriene A4 hydrolase (hLTA4H), which is the final and rate-limiting enzyme of arachidonic acid pathway, converts the unstable epoxide LTA4 to a proinflammatory lipid mediator LTB4 through its hydrolase function. The LTA4H is a bi-functional enzyme that also exhibits aminopeptidase activity with a preference over arginyl tripeptides. Various mutations including E271Q, R563A, and K565A have completely or partially abolished both the functions of this enzyme. The crystal structures with these mutations have not shown any structural changes to address the loss of functions. Molecular dynamics simulations of LTA4 and tripeptide complex structures with functional mutations were performed to investigate the structural and conformation changes that scripts the observed differences in catalytic functions. The observed protein-ligand hydrogen bonds and distances between the important catalytic components have correlated well with the experimental results. This study also confirms based on the structural observation that E271 is very important for both the functions as it holds the catalytic metal ion at its location for the catalysis and it also acts as N-terminal recognition residue during peptide binding. The comparison of binding modes of substrates revealed the structural changes explaining the importance of R563 and K565 residues and the required alignment of substrate at the active site. The results of this study provide valuable information to be utilized in designing potent hLTA4H inhibitors as anti-inflammatory agents.

Design novel dual agonists for treating type-2 diabetes by targeting peroxisome proliferator-activated receptors with core hopping approach

Owing to their unique functions in regulating glucose, lipid and cholesterol metabolism, PPARs (peroxisome proliferator-activated receptors) have drawn special attention for developing drugs to treat type-2 diabetes. By combining the lipid benefit of PPAR-alpha agonists (such as fibrates) with the glycemic advantages of the PPAR-gamma agonists (such as thiazolidinediones), the dual PPAR agonists approach can both improve the metabolic effects and minimize the side effects caused by either agent alone, and hence has become a promising strategy for designing effective drugs against type-2 diabetes. In this study, by means of the powerful “core hopping” and “glide docking” techniques, a novel class of PPAR dual agonists was discovered based on the compound GW409544, a well-known dual agonist for both PPAR-alpha and PPAR-gamma modified from the farglitazar structure. It was observed by molecular dynamics simulations that these novel agonists not only possessed the same function as GW409544 did in activating PPAR-alpha and PPAR-gamma, but also had more favorable conformation for binding to the two receptors. It was further validated by the outcomes of their ADME (absorption, distribution, metabolism, and excretion) predictions that the new agonists hold high potential to become drug candidates. Or at the very least, the findings reported here may stimulate new strategy or provide useful insights for discovering more effective dual agonists for treating type-2 diabetes. Since the “core hopping” technique allows for rapidly screening novel cores to help overcome unwanted properties by generating new lead compounds with improved core properties, it has not escaped our notice that the current strategy along with the corresponding computational procedures can also be utilized to find novel and more effective drugs for treating other illnesses.

Novel inhibitor design for hemagglutinin against H1N1 influenza virus by core hopping method

The worldwide spread of H1N1 avian influenza and the increasing reports about its resistance to the current drugs have made a high priority for developing new anti-influenza drugs. Owing to its unique function in assisting viruses to bind the cellular surface, a key step for them to subsequently penetrate into the infected cell, hemagglutinin (HA) has become one of the main targets for drug design against influenza virus. To develop potent HA inhibitors, the ZINC fragment database was searched for finding the optimal compound with the core hopping technique. As a result, the Neo6 compound was obtained. It has been shown through the subsequent molecular docking studies and molecular dynamic simulations that Neo6 not only assumes more favorable conformation at the binding pocket of HA but also has stronger binding interaction with its receptor. Accordingly, Neo6 may become a promising candidate for developing new and more powerful drugs for treating influenza. Or at the very least, the findings reported here may provide useful insights to stimulate new strategy in this area.