Analysis of cosmological bias within spherical collapse model

The goal of our research work is to analyze cosmological bias parameter. Parametric equations of spherical collapse model are used to calculate the values of spherical collapse over density and mass variance, which is further used in bias formulae to find the values of cosmological bias. Spherical collapse over density has been calculated in the range of redshift 0 to 1. Also, it is compared with the value according to the spherical collapse model. Bias is one of the parameters which are utilized to infer cosmological parameters. Extracting the cosmological parameters is very much useful to know and understand about the birth and evolution of our universe. As there is no direct probe to get the idea about the existence of dark matter. Bias factor helps to analyze about dark matter. The bias coefficient of higher order terms in Taylor series expansion are found to be in ascending order. Increasing values of bias indicate the large-scale structure formation at current epoch is more and more clustered. Values of bias are discussed in result. Also, bias values have been analyzed for redshift in the range 2 to 0. The graph has been plotted bias versus redshift. Let’s found bias decreases with decrease of redshift. That means bias evolves with redshift. Bias value less than one and negative value of bias implies that structure formation is in linear region and higher values of bias indicates the structure formation occurs in nonlinear region. Negative value of bias is also called as antibias. That means the structure formation has not started yet. It is still in linear region. The bias value nearly equal to one indicates that the structure formation has been transformed from linear region to nonlinear region. So, the result showing bias values greater than one indicates that evolution of structure formation occurs in nonlinear region

SEARCHGTr: a program for analysis of glycosyltransferases involved in glycosylation of secondary metabolites

SEARCHGTr is a web-based software for the analysis of glycosyltransferases (GTrs) involved in the biosynthesis of a variety of pharmaceutically important compounds like adriamycin, erythromycin, vancomycin etc. This software has been developed based on a comprehensive analysis of sequence/structural features of 102 GTrs of known specificity from 52 natural product biosynthetic gene clusters. SEARCHGTr is a powerful tool that correlates sequences of GTrs to the chemical structures of their corresponding substrates. This software indicates the donor/acceptor specificity and also identifies putative substrate binding residues. In addition, it provides interfaces to other public databases like GENBANK, SWISS-PROT, CAZY, PDB, PDBSum and PUBMED for extracting various information on GTrs homologous to the query sequence. SEARCHGTr would provide new dimension to our previously developed bioinformatics tool NRPS-PKS. Together, these tools facilitate comprehensive computational analysis of proteins involved in biosynthesis of aglycone core and its downstream glycosylations. Apart from presenting opportunities for rational design of novel natural products, these tools would assist in the identification of biosynthetic products of secondary metabolite gene clusters found in newly sequenced genomes. SEARCHGTr can be accessed at

Towards Prediction of Metabolic Products of Polyketide Synthases: An In Silico Analysis

Sequence data arising from an increasing number of partial and complete genome projects is revealing the presence of the polyketide synthase (PKS) family of genes not only in microbes and fungi but also in plants and other eukaryotes. PKSs are huge multifunctional megasynthases that use a variety of biosynthetic paradigms to generate enormously diverse arrays of polyketide products that posses several pharmaceutically important properties. The remarkable conservation of these gene clusters across organisms offers abundant scope for obtaining novel insights into PKS biosynthetic code by computational analysis. We have carried out a comprehensive in silico analysis of modular and iterative gene clusters to test whether chemical structures of the secondary metabolites can be predicted from PKS protein sequences. Here, we report the success of our method and demonstrate the feasibility of deciphering the putative metabolic products of uncharacterized PKS clusters found in newly sequenced genomes. Profile Hidden Markov Model analysis has revealed distinct sequence features that can distinguish modular PKS proteins from their iterative counterparts. For iterative PKS proteins, structural models of iterative ketosynthase (KS) domains have revealed novel correlations between the size of the polyketide products and volume of the active site pocket. Furthermore, we have identified key residues in the substrate binding pocket that control the number of chain extensions in iterative PKSs. For modular PKS proteins, we describe for the first time an automated method based on crucial intermolecular contacts that can distinguish the correct biosynthetic order of substrate channeling from a large number of non-cognate combinatorial possibilities. Taken together, our in silico analysis provides valuable clues for formulating rules for predicting polyketide products of iterative as well as modular PKS clusters. These results have promising potential for discovery of novel natural products by genome mining and rational design of novel natural products

Genome scale prediction of substrate specificity for acyl adenylate superfamily of enzymes based on active site residue profiles

<p>Abstract</p> <p>Background</p> <p>Enzymes belonging to acyl:CoA synthetase (ACS) superfamily activate wide variety of substrates and play major role in increasing the structural and functional diversity of various secondary metabolites in microbes and plants. However, due to the large sequence divergence within the superfamily, it is difficult to predict their substrate preference by annotation transfer from the closest homolog. Therefore, a large number of ACS sequences present in public databases lack any functional annotation at the level of substrate specificity. Recently, several examples have been reported where the enzymes showing high sequence similarity to luciferases or coumarate:CoA ligases have been surprisingly found to activate fatty acyl substrates in experimental studies. In this work, we have investigated the relationship between the substrate specificity of ACS and their sequence/structural features, and developed a novel computational protocol for <it>in silico </it>assignment of substrate preference.</p> <p>Results</p> <p>We have used a knowledge-based approach which involves compilation of substrate specificity information for various experimentally characterized ACS and derivation of profile HMMs for each subfamily. These HMM profiles can accurately differentiate probable cognate substrates from non-cognate possibilities with high specificity (Sp) and sensitivity (Sn) (Sn = 0.91-1.0, Sp = 0.96-1.0) values. Using homologous crystal structures, we identified a limited number of contact residues crucial for substrate recognition i.e. specificity determining residues (SDRs). Patterns of SDRs from different subfamilies have been used to derive predictive rules for correlating them to substrate preference. The power of the SDR approach has been demonstrated by correct prediction of substrates for enzymes which show apparently anomalous substrate preference. Furthermore, molecular modeling of the substrates in the active site has been carried out to understand the structural basis of substrate selection. A web based prediction tool <url>http://www.nii.res.in/pred_acs_substr.html</url> has been developed for automated functional classification of ACS enzymes.</p> <p>Conclusions</p> <p>We have developed a novel computational protocol for predicting substrate preference for ACS superfamily of enzymes using a limited number of SDRs. Using this approach substrate preference can be assigned to a large number of ACS enzymes present in various genomes. It can potentially help in rational design of novel proteins with altered substrate specificities.</p

A new family of type III polyketide synthases in Mycobacterium tuberculosis

The Mycobacterium tuberculosis genome has revealed a remarkable array of polyketide synthases (PKSs); however, no polyketide product has been isolated thus far. Most of the PKS genes have been implicated in the biosynthesis of complex lipids. We report here the characterization of two novel type III PKSs from M. tuberculosis that are involved in the biosynthesis of long-chain &#945;-pyrones. Measurement of steady-state kinetic parameters demonstrated that the catalytic efficiency of PKS18 protein was severalfold higher for long-chain acyl-coenzyme A substrates as compared with the small-chain precursors. The specificity of PKS18 and PKS11 proteins toward long-chain aliphatic acyl-coenzyme A (C12 to C20) substrates is unprecedented in the chalcone synthase (CHS) family of condensing enzymes. Based on comparative modeling studies, we propose that these proteins might have evolved by fusing the catalytic machinery of CHS and &#946;-ketoacyl synthases, the two evolutionarily related members with conserved thiolase fold. The mechanistic and structural importance of several active site residues, as predicted by our structural model, was investigated by performing site-directed mutagenesis. The functional identification of diverse catalytic activity in mycobacterial type III PKSs provide a fascinating example of metabolite divergence in CHS-like proteins

ASSESSMENT OF USAGE OF ANTIBIOTIC AND THEIR PATTERN OF ANTIBIOTIC SENSITIVITY TEST AMONG CHILDHOOD FEVER

Objective: The present study evaluated the pattern of antibiotic usage and sensitivity pattern among children with fever. Methods: Questionnaires was specifically designed factoring patients' demographical profile, illness history, prescription regimen, antibiotic sensitivity report. Results: A total 157 prescriptions (80% OPD and 20% IPD) of children who visited Pediatric department complaining of fever were analyzed. Maximum children were of the age group between 2 – 3 yr (41%) with male/ female ratio of 1.54. Of total 157 patients, etiology of fever was diagnosed as Viral fever (60.15%), Fever with diarrhea(5%), Fever with seizure(3%) and Bacterial fever(31.8%). Average number of drugs per prescription was 3.27. Most common antibiotic used were Cefixime (42%), Cefotaxime (38%), Ceftriaxone (8%) and Amoxicillin (12%) among total antibiotic prescribed. Most commonly encountered drugs other than antibiotic prescribed were antipyretic: paracetamol syp (95%), Nasal decongestant: phenylephrine (70%), antihistaminic: Levocetrizine (65%), Multivitamin (60%), Zinc (20%) and ORS (20%) of prescription. Most widely prescribed antibiotic was Cefixime followed by cefotaxime. All the drugs were prescribed by brand names. positive Antibiotic sensitivity report was available for only 50 patients. Gram positive microbes like Staphylococcus species was isolated in 26 % cases and Streptococcus species in 6 % cases. These gram positive microbes were 100% sensitive to Cefotaxime, tetracycline, linezolid, Ampicillin etc. The gram negative microbes isolated were E. coli (5 %), Acenatobacter species (30 %), salmonella typhi (12%), and Klebsiella sp( 21%). All of them were sensitive to Cefotaxime, Pefloxacin, Ofloxacin, Cefuroxime, etc. Conclusion: Antibiotic sensitivity of blood culture studies demonstrated that both gram positive and gram negative bacteria showed maximum sensitivity to Cefotaxime. The most commonly prescribed antibiotic encountered in the present study was Cefixime followed by Cefotaxime

In silico analysis of methyltransferase domains involved in biosynthesis of secondary metabolites

Background: Secondary metabolites biosynthesized by polyketide synthase (PKS) and nonribosomal peptide synthetase (NRPS) family of enzymes constitute several classes of therapeutically important natural products like erythromycin, rapamycin, cyclosporine etc. In view of their relevance for natural product based drug discovery, identification of novel secondary metabolite natural products by genome mining has been an area of active research. A number of different tailoring enzymes catalyze a variety of chemical modifications to the polyketide or nonribosomal peptide backbone of these secondary metabolites to enhance their structural diversity. Therefore, development of powerful bioinformatics methods for identification of these tailoring enzymes and assignment of their substrate specificity is crucial for deciphering novel secondary metabolites by genome mining. Results: In this work, we have carried out a comprehensive bioinformatics analysis of methyltransferase (MT) domains present in multi functional type I PKS and NRPS proteins encoded by PKS/NRPS gene clusters having known secondary metabolite products. Based on the results of this analysis, we have developed a novel knowledge based computational approach for detecting MT domains present in PKS and NRPS megasynthases, delineating their correct boundaries and classifying them as N-MT, C-MT and O-MT using profile HMMs. Analysis of proteins in nr database of NCBI using these class specific profiles has revealed several interesting examples, namely, C-MT domains in NRPS modules, N-MT domains with significant homology to C-MT proteins, and presence of NRPS/PKS MTs in association with other catalytic domains. Our analysis of the chemical structures of the secondary metabolites and their site of methylation suggested that a possible evolutionary basis for the presence of a novel class of N-MT domains with significant homology to C-MT proteins could be the close resemblance of the chemical structures of the acceptor substrates, as in the case of pyochelin and yersiniabactin. These two classes of MTs recognize similar acceptor substrates, but transfer methyl groups to N and C positions on these substrates. Conclusion: We have developed a novel knowledge based computational approach for identifying MT domains present in type I PKS and NRPS multifunctional enzymes and predicting their site of methylation. Analysis of nr database using this approach has revealed presence of several novel MT domains. Our analysis has also given interesting insight into the evolutionary basis of the novel substrate specificities of these MT proteins

DIPEPTIDYL PEPTIDASE-IV INHIBITORY ACTIVITIES OF MEDICINAL PLANTS: TERMINALIA ARJUNA, COMMIPHORA MUKUL, GYMNEMA SYLVESTRE, MORINDA CITRIFOLIA, EMBLICA OFFICINALIS

Objective: The present study was designed to screen the dipeptidyl peptidase-IV (DPP-IV) inhibitory ability of hydroalcoholic extracts of Terminaliaarjuna, Commiphora mukul, Gymnema sylvestre, Morinda citrifolia, and Emblica officinalis and compare their inhibitory activity with the syntheticDPP-IV inhibitors (Sitagliptin and Vildagliptin). The aim of the study was to identifying indigenous sources of DPP-IV inhibitors for the managementof type II diabetes mellitus as alternatives to their synthetic counterparts.Methods: The hydroalcoholic extract of T. arjuna, C. mukul, G. sylvestre, M. citrifolia, E. officinalis and synthetic DPP-IV inhibitors (Sitagliptin andVildagliptin) were tested in vitro for DPP-IV inhibitory activity.Results: The DPP-IV inhibitory activity of synthetic drugs Vildagliptin was found to be 90.42±7.84% and Sitagliptin 84.67±8.21%. The DPP-IVInhibitory activity of T. arjuna was found to be 83.39±7.58%, C. mukul: 92.97± 8.45%, G. sylvestre: 16.98±1.69%, M. citrifolia: 24.64±2.24%, andE. officinalis: 85.95±7.16%. C. mukul extract showed superior inhibitory activity than reference standard drugs (Sitagliptin and Vildagliptin).Conclusion: C. mukul, T. arjuna, and E. officinalis extracts possess significant DPP-IV Inhibitory activity while G. sylvestre and M. citrifolia failed tomarkedly inhibit DPP-IV enzyme.Keywords: Type II diabetes mellitus, Dipeptidyl peptidase-IV inhibitors, Plant extracts, In vitro assay

Myocardial salvaging effects and mechanisms of metformin in experimental diabetes

Background: Several epidemiological studies have found that in type II diabetic patients, Metformin improves vascular function and reduces cardiovascular events and mortality by mechanisms that are not entirely attributed to its anti- hyperglycemic effects; So far the effect of Metformin on experimentally induced myocardial infarction in setting of type II diabetic rats has not been studied. The aim of the present study was to investigate potential cardioprotective effects and mechanisms of Metformin subsequent to isoproterenol induced myocardial infarction in the setting of diabetes.Methods: Diabetes was induced with single dose of Streptozotocin (STZ): 45mg/kg ip and myocardial infarction was produced by administering isoproterenol (ISP): (85mg/kg, sc) to rats 24 and 48 h prior to sacrification (5th week). After the confirmation of diabetes on 7th day (Glucose>200mg/dl), Metformin (100 mg/kg) was administered and various parameters like anti-diabetic (Glucose, HbA1c), cardioprotective (CPK-MB, hs-CRP), metabolic (lipid profile, artherogenic potential), antioxidant (MDA) safety {pancreatic function (lipase), liver function (SGPT), kidney function (Creatinine) and histopathological indices of injury were evaluated in experimental groups.Results: Administration of STZ-ISP resulted in a significant decrease in body weight (p<0.001), diabetic changes (increase in blood glucose, HbA1c), cardiac injury (leakage of myocardial CPK-MB), altered lipid profile, anti-inflammatory, antioxidant, lipase, SGPT, creatinine levels (p<0.01) in the Diabetic- ISP Control group rats as compared to Normal Control. Metformin (100 mg/kg) treatment demonstrated significant antidiabetic as well as myocardial salvaging effects as indicated by restoration of blood glucose, HbA1c and CPK-MB levels (p<0.001) compared to Diabetic- ISP Control group. In addition, Metformin favorably modulated the lipid parameters (total cholesterol, triglycerides, HDL, LDL), artherogenic index; antioxidant (MDA) potential, Subsequent to ISP challenge, histopathological assessment of heart, pancreas and biochemical indices of injury confirmed the cardioprotective effects of Metformin (100 mg/kg) in setting of diabetes.Conclusions: The present study concluded that Metformin at 100 mg/kg demonstrated myocardial salvaging effects in type II diabetic rats challenged with experimental Myocardial infarction. The antioxidant, hypoglycemic, hypolipidemic and anti-inflammatory effects of Metformin may contribute to its beneficial effects.

Mitigating effects of vildagliptin in experimental diabetes with metabolic syndrome

Background: Vildagliptin has multiple beneficial effects reported in isolated studies like anti-diabetic, cardio protective, anti-inflammatory and antioxidant. However, there is no experimental evidence presently available with regard to the possible beneficial effects of vildagliptin on attenuating changes observed in metabolic syndrome co-existing with diabetes in experimental rats. Thus, the present study was designed to evaluate potential effects of vildagliptin on various components of metabolic syndrome. Also to elucidate the underlying mechanisms: DPP-IV, anti-inflammatory, antioxidant pathways were studied.Methods: A combination of high fat diet (HFD) and low dose of streptozotocin (STZ) 40 mg/kg was used to induce metabolic syndrome co-existing with diabetes mellitus in wistar rats. The HFD were fed to rats for 10 weeks to induce metabolic syndrome. At the end of 3 weeks, diabetes was induced by a single STZ injection (40 mg/kg body weight). Vildagliptin (10 mg/kg) was administered to rat from 5th to 10th weeks daily and various parameters of Diabetes and metabolic syndrome were studied. Also to understand the mechanisms; DPP-IV pathway, anti-inflammatory, antioxidant parameters were studied. Biochemical indices of injury (pancreatic, liver and renal function) and histopathological assessment of injury was evaluated in experimental groups. Immunohistochemistry of pancreas was done to assess beta cell mass.Results: The vildagliptin treatment ameliorated the deleterious effects associated with metabolic syndrome and diabetes. The beneficial effects demonstrated by vildagliptin on various parameters include: anti-diabetic (reduced blood glucose, HbA1c, HOMA-IR, increased serum insulin, HOMA-β and restoration of pancreatic function), central obesity (reduced body weight, abdominal circumference (AC), thoracic circumference (TC), AC/TC ratio) and hypolipidemic (favourable lipid profile, artherogenic index) activity. A significant restoration of cardiac injury as indicated by CPK-MB levels was observed. In addition, DPP-IV pathway (reduced serum DPP-IV), anti-inflammatory (reduced hs-CRP levels), and antioxidant (reduced MDA) contributed its beneficial effects in diabetes with metabolic syndrome model. The protective effects on heart, pancreas, liver and kidney were confirmed by histopathological report. The immunohistochemical report of pancreas showed preservation of beta cell mass in vildagliptin treated rats.Conclusions: Vildagliptin treatment ameliorates deleterious changes of diabetes with metabolic syndrome. Beneficial effects of vildagliptin can be attributed to hypoglycemic, hypolipidemic, antioxidant, cardioprotective and anti-inflammatory effects