Entity

Time filter

Source Type


Panwar B.,Chandigarh Institute of Microbial Technology
BMC genomics | Year: 2010

Aminoacyl tRNA synthetases (aaRSs) catalyse the first step of protein synthesis in all organisms. They are responsible for the precise attachment of amino acids to their cognate transfer RNAs. There are twenty different types of aaRSs, unique for each amino acid. These aaRSs have been divided into two classes, each comprising ten enzymes. It is important to predict and classify aaRSs in order to understand protein synthesis. In this study, all models were developed on a non-redundant dataset containing 117 aaRSs and an equal number of non-aaRSs, in which no two sequences have more than 30% similarity. First, we applied the similarity search technique, BLAST, and achieved a maximum accuracy of 67.52%. We observed that 62% of tRNA synthetases contain one or more domains from amongst the following four PROSITE domains: PS50862, PS00178, PS50860 and PS50861. An SVM-based model was developed to discriminate between aaRSs, and non-aaRSs, and achieved a maximum MCC of 0.68 with accuracy of 83.73%, using selective dipeptide composition. We developed a hybrid approach and achieved a maximum MCC of 0.72 with accuracy of 85.49%, where SVM model developed using selected dipeptide composition and information of four PROSITE domains. We further developed an SVM-based model for classifying the aaRSs into class-1 and class-2, using selective dipeptide composition and achieved an MCC of 0.79. We also observed that two domains (PS00178, PS50889) in class-1 and three domains (PS50862, PS50860, PS50861) in class-2 were preferred. A hybrid method was developed using these domains as descriptor, along with selected dipeptide composition, and achieved an MCC of 0.87 with a sensitivity of 94.55% and an accuracy of 93.19%. All models were evaluated using a five-fold cross-validation technique. We have analyzed protein sequences of aaRSs (class-1 and class-2) and non-aaRSs and identified interesting patterns. The high accuracy achieved by our SVM models using selected dipeptide composition demonstrates that certain types of dipeptide are preferred in aaRSs. We were able to identify PROSITE domains that are preferred in aaRSs and their classes, providing interesting insights into tRNA synthetases. The method developed in this study will be useful for researchers studying aaRS enzymes and tRNA biology. The web-server based on the above study, is available at http://www.imtech.res.in/raghava/icaars/. Source


Raghava G.P.,Chandigarh Institute of Microbial Technology
BMC bioinformatics | Year: 2014

BACKGROUND: In past number of methods have been developed for predicting post-translational modifications in proteins. In contrast, limited attempt has been made to understand post-transcriptional modifications. Recently it has been shown that tRNA modifications play direct role in the genome structure and codon usage. This study is an attempt to understand kingdom-wise tRNA modifications particularly uridine modifications (UMs), as majority of modifications are uridine-derived.RESULTS: A three-steps strategy has been applied to develop an efficient method for the prediction of UMs. In the first step, we developed a common prediction model for all the kingdoms using a dataset from MODOMICS-2008. Support Vector Machine (SVM) based prediction models were developed and evaluated by five-fold cross-validation technique. Different approaches were applied and found that a hybrid approach of binary and structural information achieved highest Area under the curve (AUC) of 0.936. In the second step, we used newly added tRNA sequences (as independent dataset) of MODOMICS-2012 for the kingdom-wise prediction performance evaluation of previously developed (in the first step) common model and achieved performances between the AUC of 0.910 to 0.949. In the third and last step, we used different datasets from MODOMICS-2012 for the kingdom-wise individual prediction models development and achieved performances between the AUC of 0.915 to 0.987.CONCLUSIONS: The hybrid approach is efficient not only to predict kingdom-wise modifications but also to classify them into two most prominent UMs: Pseudouridine (Y) and Dihydrouridine (D). A webserver called tRNAmod (http://crdd.osdd.net/raghava/trnamod/) has been developed, which predicts UMs from both tRNA sequences and whole genome. Source


Makkar R.S.,279. Sweet Alyssum Dr | Cameotra S.S.,Chandigarh Institute of Microbial Technology | Banat I.M.,University of Ulster
AMB Express | Year: 2011

Biosurfactants are amphiphilic molecules that have both hydrophilic and hydrophobic moieties which partition preferentially at the interfaces such as liquid/liquid, gas/liquid or solid/liquid interfaces. Such characteristics enable emulsifying, foaming, detergency and dispersing properties. Their low toxicity and environmental friendly nature and the wide range of potential industrial applications in bioremediation, health care, oil and food processing industries makes them a highly sought after group of chemical compounds. Interest in them has also been encouraged because of the potential advantages they offer over their synthetic counterparts in many fields spanning environmental, food, biomedical, petrochemical and other industrial applications. Their large scale production and application however are currently restricted by the high cost of production and by the limited understanding of their interactions with cells and with the abiotic environment. In this paper, we review the current knowledge and latest advances in the search for cost effective renewable agro industrial alternative substrates for their production. © 2011. Makkar; licensee Springer. Source


Arora P.K.,Chandigarh Institute of Microbial Technology
PLoS ONE | Year: 2012

A 4-Chloro-2-nitrophenol (4C2NP) decolourizing strain RKJ 700 was isolated from soil collected from a pesticide contaminated site of India and identified as Bacillus subtilis on the basis of the 16S rRNA gene sequence analysis. Bacillus subtilis RKJ 700 decolourized 4C2NP up to concentration of 1.5 mM in the presence of additional carbon source. The degradation pathway of 4C2NP was studied and 4-chloro-2-aminophenol, 4-chloro-2-acetaminophenol and 5-chloro-2-methylbenzoxazole (5C2MBZ) were identified as metabolites by high performance liquid chromatography and gas chromatography-mass spectrometry. Resting cell studies showed that Bacillus subtilis RKJ 700 depleted 4C2NP completely with stoichiometric formation of 5C2MBZ. This is the first report of (i) the degradation of 4C2NP at high concentration (1.5 mM) and, (ii) the formation of 5C2MBZ by a soil bacterium. © 2012 Pankaj Kumar Arora. Source


Khan N.,Chandigarh Institute of Microbial Technology
Journal of Innate Immunity | Year: 2015

Tuberculosis (TB) is one of the leading killer infectious diseases. TB patients are inflicted with devastating side effects and the toxicity of a lengthy drug regime, accentuating an urgent need to explore newer and safer treatment methods. Recently, an improved understanding of host-pathogen interaction has opened new avenues for TB treatment, including immunotherapy. This has emboldened us to devise a novel strategy to restrict Mycobacterium tuberculosis(Mtb) growth by activating dendritic cells (DCs) through the NOD-2 and TLR-4 molecules of innate immunity. Triggered DCs show a robust release of cytokines and nitric oxide, autophagy and improved migration towards the lymph nodes, and consequently impede the intracellular survival of Mtb. Of note, this approach enhanced the efficacy of TB drugs by reducing their dose to a 5-fold lesser concentration than recommended. In vivo administration of ligands of NOD-2 (NOD-2L) and TLR-4 (TLR-4L) substantially increased the pool of effector memory CD4 and CD8 T cells. Additionally, NOD-2L and TLR-4L, in conjunction with the reduced dose of isoniazid, substantially declined the Mtb burden in the lungs. In the future, adjunct therapy involving NOD-2L, TLR-4L and TB drugs may have enough potential to reduce the dose and duration of treatment of TB patients. © 2015 S. Karger AG, Basel Copyright © 2015, S. Karger AG. All rights reserved. Source

Discover hidden collaborations