A new member of Tau-class glutathione S-transferase from barley leaves

Glutathione S-transferase is a family of multifunctional detoxification enzymes which are mainly cytosolic that detoxify natural and exogenous toxic compounds by conjugation with glutathione. Glutathione, an endogenous tripeptide, is important as either a reducing agent or a nucleophilic scavenger. This molecule alleviates the chemical toxicity in plants by reaction of glutathione S-transferase, and its conjugates can be transported to vacuole or apoplast. The plant soluble glutathione S-transferases grouped today into seven distinct Phi, Tau, Zeta, Theta, lambda, dehydroascorbate reductase, and tetrachlorohydroquinone dehalogenase classes. In this study, bioinformatics analysis of glutathione S-transferase gene in barley was carried out using Tau-class of barley glutathione S-transferase sequences in NCBI GenBank and isolated sequence. DNA extraction, primer design, PCR, electrophoresis, column purification, DNA sequencing and analysis by some software led to identify new sequences of Tauclass of glutathione S-transferase from barley, which is similar to Tau GST of the diploid wheat. Comparison of the deduced amino acid sequences of the three barley GST genes showed that they have 99 % identity with each other but only 45 % identity with the new GST. This sequence was submitted to NCBI GenBank with FI131240 accession number

Application of bioinformatics in diagnosis of white spot syndrome virus

White spot syndrome is one of the major problems in shrimp culture worldwide. There are different techniques like Dot blotting, PCR and using monoclonal antibodies for diagnosis of White Spot Syndrome Virus (WSSV). in the latter method, by using laboratory animals, monoclonal antibodies against different antigenic domains of proteins of the virus are developed. Then the reactivity of these antibodies with all proteins of shrimp can be tested by ELISA. As it is not known at the start of the test which parts of a protein are strong epitopes and so there is a need to test many peptides, this method is expensive and time consuming. One of the solutions for this problem is prediction of epitopes, synthesis of few peptides, and testing these peptides. Since VP28 is the most important protein of WSSV capsid, the sequences of amino acids of VP28 of four isolates of WSSV from different parts of the world were collected for this study. By using bioinformatic methods, after aligning of sequences the consensus sequence was identified. For prediction of antigenic domains of V28, seven different programs were used. The analysis through the computer programme resulted in prediction of five epitopes in V28. These parts of the protein can now be synthesized and tested for identification of the virus

Antibacterial and antibiofilm activities of Prangos acaulis Bornm. extract against Streptococcus mutans: an in silico and in vitro study

Introduction: Streptococcus mutans is a principal pathogenic agent in biofilm formation on the teeth surfaces and subsequently development of dental caries and plaque. Therefore, currently introducing novel anti-bacterial and anti-biofilm agents, especially plant based materials are highly regarded. This study was planned to investigate in silico and in vitro antibacterial activities of Prangos acaulis extracts against S. mutans in single and biofilm forms and their mutagenicity in Ames test. Methods: The anti-bacterial and anti-biofilm effects of methanol extracts from various parts of P. acaulis were evaluated using disk diffusion and microtiter assay. Moreover, the potential mutagenicity of the extracts was investigated using Ames test. In addition, dominant constitutes of P. acaulis that reported in previous studies were subjected to an in silico analysis. The ability of selected phytochemicals to inhibit the glucosyltransferase was evaluated using molecular docking method. Results: All tested extracts especially root extract had significant antibacterial activity against the single form of S. mutans and inhibited biofilm formation without any mutagenic activity. The results also confirmed that three compounds consisting of ar-curcumene, d-limonene and alpha-pinene had strong and appropriate interactions to glucosyltransferase. Conclusion: This study indicated that P. acaulis has potent antibacterial and biofilm inhibition activity against S. mutans and can be good candidate for in vitro and in vivo studies with the aim of introducing novel inhibitors of dental caries developmen

Metabolic engineering of Deinococcus radiodurans for pinene production from glycerol

Background: The objective of this work was to engineer Deinococcus radiodurans R1 as a microbial cell factory for the production of pinene, a monoterpene molecule prominently used for the production of fragrances, pharmaceutical products, and jet engine biofuels. Our objective was to produce pinene from glycerol, an abundant by-product of various industries. Results: To enable pinene production in D. radiodurans, we expressed the pinene synthase from Abies grandis, the geranyl pyrophosphate (GPP) synthase from Escherichia coli, and overexpressed the native 1-deoxy-d-xylulose 5-phosphate synthase. Further, we disrupted the deinoxanthin pathway competing for the substrate GPP by either inactivating the gene dr0862, encoding phytoene synthase, or substituting the native GPP synthase with that of E. coli. These manipulations resulted in a D. radiodurans strain capable of producing 3.2 \ub1 0.2\ua0mg/L pinene in a minimal medium supplemented with glycerol, with a yield of 0.13 \ub1 0.04\ua0mg/g glycerol in shake flask cultures. Additionally, our results indicated a higher tolerance of D. radiodurans towards pinene as compared to E. coli. Conclusions: In this study, we successfully engineered the extremophile bacterium D. radiodurans to produce pinene. This is the first study demonstrating the use of D. radiodurans as a cell factory for the production of terpenoid molecules. Besides, its high resistance to pinene makes D. radiodurans a suitable host for further engineering efforts to increase pinene titer as well as a candidate for the production of the other terpenoid molecules

Plant glutathione S-transferase classification, structure and evolution

Glutathione S-transferases are multifunctional proteins involved in diverse intracellular events such as primary and secondary metabolisms, stress metabolism, herbicide detoxification and plant protection against ozone damages, heavy metals and xenobiotics. The plant glutathione S-transferase superfamily have been subdivided into eight classes. Phi, tau, zeta, theta, lambda, dehydroascorbate reductase and tetrachlorohydroquinone dehalogenase classes are soluble and one class is microsomal. Glutathione S-transferases are mostly soluble cytoplasmic enzymes. To date, the crystal structures of over 200 soluble glutathione S-transferases, present in plants, animals and bacteria have been resolved. The structures of glutathione S-transferase influence its function. Phylogenetic analysis suggests that all soluble glutathione S-transferases have arisen from an ancient progenitor gene, through both convergent and divergent pathways.Key words: Glutathione S-transferases (GST), classification, structure, evolution, phylogenetic analysis, xenobiotics

Using Support Vector Machine and Evolutionary Profiles to Predict Antifreeze Protein Sequences

Antifreeze proteins (AFPs) are ice-binding proteins. Accurate identification of new AFPs is important in understanding ice-protein interactions and creating novel ice-binding domains in other proteins. In this paper, an accurate method, called AFP_PSSM, has been developed for predicting antifreeze proteins using a support vector machine (SVM) and position specific scoring matrix (PSSM) profiles. This is the first study in which evolutionary information in the form of PSSM profiles has been successfully used for predicting antifreeze proteins. Tested by 10-fold cross validation and independent test, the accuracy of the proposed method reaches 82.67% for the training dataset and 93.01% for the testing dataset, respectively. These results indicate that our predictor is a useful tool for predicting antifreeze proteins. A web server (AFP_PSSM) that implements the proposed predictor is freely available

Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities

The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2nd-level, 3rd-level, 4th-level, and 5th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels

iDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model

DNA-binding proteins play crucial roles in various cellular processes. Developing high throughput tools for rapidly and effectively identifying DNA-binding proteins is one of the major challenges in the field of genome annotation. Although many efforts have been made in this regard, further effort is needed to enhance the prediction power

Computer-aided design of nano-filter construction using DNA self-assembly

Computer-aided design plays a fundamental role in both top-down and bottom-up nano-system fabrication. This paper presents a bottom-up nano-filter patterning process based on DNA self-assembly. In this study we designed a new method to construct fully designed nano-filters with the pores between 5 nm and 9 nm in diameter. Our calculations illustrated that by constructing such a nano-filter we would be able to separate many molecules

Imbalanced Multi-Modal Multi-Label Learning for Subcellular Localization Prediction of Human Proteins with Both Single and Multiple Sites

It is well known that an important step toward understanding the functions of a protein is to determine its subcellular location. Although numerous prediction algorithms have been developed, most of them typically focused on the proteins with only one location. In recent years, researchers have begun to pay attention to the subcellular localization prediction of the proteins with multiple sites. However, almost all the existing approaches have failed to take into account the correlations among the locations caused by the proteins with multiple sites, which may be the important information for improving the prediction accuracy of the proteins with multiple sites. In this paper, a new algorithm which can effectively exploit the correlations among the locations is proposed by using Gaussian process model. Besides, the algorithm also can realize optimal linear combination of various feature extraction technologies and could be robust to the imbalanced data set. Experimental results on a human protein data set show that the proposed algorithm is valid and can achieve better performance than the existing approaches