Indian Agricultural Statistics Research Institute

About: Indian Agricultural Statistics Research Institute is a facility organization based out in New Delhi, India. It is known for research contribution in the topics: Population & Small area estimation. The organization has 454 authors who have published 870 publications receiving 7987 citations.

nifPred: Proteome-Wide Identification and Categorization of Nitrogen-Fixation Proteins of Diaztrophs Based on Composition-Transition-Distribution Features Using Support Vector Machine.

Abstract: As inorganic nitrogen compounds are essential for basic building blocks of life (e.g., nucleotides and amino acids), the role of biological nitrogen-fixation (BNF) is indispensible. All nitrogen fixing microbes rely on the same nitrogenase enzyme for nitrogen reduction, which is in fact an enzyme complex consists of as many as 20 genes. However, the occurrence of six genes viz., nifB, nifD, nifE, nifH, nifK and nifN has been proposed to be essential for a functional nitrogenase enzyme. Therefore, identification of these genes is important to understand the mechanism of BNF as well as to explore the possibilities for improving BNF from agricultural sustainability point of view. Further, though the computational tools are available for the annotation and phylogenetic analysis of nifH gene sequences alone, to the best of our knowledge no tool is available for the computational prediction of the above mentioned six categories of nitrogen-fixation (nif) genes or proteins. Thus, we proposed an approach, which is first of its kind for the computational identification of nif proteins encoded by the six categories of nif genes. Sequence-derived features were employed to map the input sequences into vectors of numeric observations that were subsequently fed to the support vector machine as input. Two types of classifier were constructed: (i) a binary classifier for classification of nif and non-nitrogen-fixation (non-nif) proteins, and (ii) a multi-class classifier for classification of six categories of nif proteins. Higher accuracies were observed for the combination of composition-transition-distribution (CTD) feature set and radial kernel, as compared to the other feature-kernel combinations. The overall accuracies were observed >90% in both binary and multi-class classifications. The developed approach further achieved >92% accuracy, while evaluated with blind (independent) test datasets. The developed approach also produced higher accuracy in identifying nif proteins, while evaluated using proteome-wide datasets of several species. Furthermore, we established a prediction server nifPred (http://webapp.cabgrid.res.in/nifPred) to assist the scientific community for proteome-wide identification of six categories of nif proteins. Besides, the source code of nifPred is also available at https://github.com/PrabinaMeher/nifPred. The developed web server is expected to supplement the transcriptional profiling and comparative genomics studies for the identification

Draft Genome Sequence of Two Monosporidial Lines of the Karnal Bunt Fungus Tilletia indica Mitra (PSWKBGH-1 and PSWKBGH-2).

Abstract: Karnal bunt disease caused by the fungus Tilletia indica Mitra is a serious concern due to strict quarantines affecting international trade of wheat. We announce here the first draft assembly of two monosporidial lines, PSWKBGH-1 and -2, of this fungus, having approximate sizes of 37.46 and 37.21 Mbp, respectively.

The role of conserved residues in the catalytic activity of NDM-1: an approach involving site directed mutagenesis and molecular dynamics

Abstract: The rise of New Delhi metallo-beta-lactamase-1 (NDM-1) producers is a major public health concern due to carbapenem resistance. Infections caused by carbapenem-resistant enterobacteria (CRE) are classified as a serious problem. To understand the structure and function of NDM-1, an amino acid replacement approach is considered as one of the methods to get structural insight. Therefore, we have generated novel mutations (N193A, S217A, G219A and T262A) near active sites and an omega-like loop to study the role of conserved residues of NDM-1. The minimum inhibitory concentrations (MICs) of ampicillin, imipenem, meropenem, cefotaxime, cefoxitin and ceftazidime for all mutants were found to be reduced 2 to 6 fold, compared to a wild type NDM-1 producing strain. The Km values increased while Kcat and Kcat/Km values were decreased compared to wild type. The affinity as well as the catalysis properties of these mutants were reduced considerably for imipenem, meropenem, cefotaxime, cefoxitin, and ceftazidimem compared to wild type, hence the catalytic efficiencies (Kcat/Km) of all mutant enzymes were reduced owing to the poor affinity of the enzyme. The IC50 values of these mutants with respect to each drug were reduced compared to wild type NDM-1. MD simulations and docking results from the mutant protein models, along with the wild type example, showed stable and consistent RMSD, RMSF and Rg behavior. The α-helix content values of all mutant proteins were reduced by 13%, 6%, 14% and 9% compared to NDM-1. Hence, this study revealed the impact role of active sites near residues on the enzyme catalytic activity of NDM-1.

Fitting EXPAR Models Through the Extended Kalman Filter

Abstract: Exponential autoregressive (EXPAR) family of parametric nonlinear time-series models, which is a discrete-time approximation of continuous-time nonlinear stochastic dynamical system, is considered. A heartening feature of this model is that it is capable of describing those data sets that depict cyclical variations. The estimation procedure for EXPAR models is developed using extended Kalman filter (EKF). Through simulation studies, it is shown that EKF is very efficient for fitting EXPAR models. Formulae for optimal one-step and two-step ahead out-of-sample forecasts are derived analytically by recursive use of conditional expectation. Conditions for the existence of limit cycle behaviour for EXPAR models are also established. Superiority of EKF method vis-a-vis Genetic algorithms (GA) method for fitting EXPAR models is shown through simulation studies. As an illustration, EXPAR models are employed for modelling and forecasting Oil sardine, Mackerel and Bombay duck time-series landings data in India. It is shown that all the three fitted models exhibit the desirable feature of existence of limit cycle behaviour. It is concluded that the EXPAR model performs better than ARIMA methodology for both modelling and forecasting purposes for the data sets under consideration.

Development of Antimicrobial Peptide Prediction Tool for Aquaculture Industries

Abstract: Microbial diseases in fish, plant, animal and human are rising constantly; thus, discovery of their antidote is imperative. The use of antibiotic in aquaculture further compounds the problem by development of resistance and consequent consumer health risk by bio-magnification. Antimicrobial peptides (AMPs) have been highly promising as natural alternative to chemical antibiotics. Though AMPs are molecules of innate immune defense of all advance eukaryotic organisms, fish being heavily dependent on their innate immune defense has been a good source of AMPs with much wider applicability. Machine learning-based prediction method using wet laboratory-validated fish AMP can accelerate the AMP discovery using available fish genomic and proteomic data. Earlier AMP prediction servers are based on multi-phyla/species data, and we report here the world's first AMP prediction server in fishes. It is freely accessible at http://webapp.cabgrid.res.in/fishamp/ . A total of 151 AMPs related to fish collected from various databases and published literature were taken for this study. For model development and prediction, N-terminus residues, C-terminus residues and full sequences were considered. Best models were with kernels polynomial-2, linear and radial basis function with accuracy of 97, 99 and 97 %, respectively. We found that performance of support vector machine-based models is superior to artificial neural network. This in silico approach can drastically reduce the time and cost of AMP discovery. This accelerated discovery of lead AMP molecules having potential wider applications in diverse area like fish and human health as substitute of antibiotics, immunomodulator, antitumor, vaccine adjuvant and inactivator, and also for packaged food can be of much importance for industries.