Structural and Computational Biology Group

Delhi, India

Structural and Computational Biology Group

Delhi, India
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Jagga Z.,Structural and Computational Biology Group | Gupta D.,Structural and Computational Biology Group
BMC Proceedings | Year: 2014

Background: Clear-cell Renal Cell Carcinoma (ccRCC) is the most- prevalent, chemotherapy resistant and lethal adult kidney cancer. There is a need for novel diagnostic and prognostic biomarkers for ccRCC, due to its heterogeneous molecular profiles and asymptomatic early stage. This study aims to develop classification models to distinguish early stage and late stage of ccRCC based on gene expression profiles. We employed supervised learning algorithms- J48, Random Forest, SMO and Naïve Bayes; with enriched model learning by fast correlation based feature selection to develop classification models trained on sequencing based gene expression data of RNAseq experiments, obtained from The Cancer Genome Atlas. Results: Different models developed in the study were evaluated on the basis of 10 fold cross validations and independent dataset testing. Random Forest based prediction model performed best amongst the models developed in the study, with a sensitivity of 89%, accuracy of 77% and area under Receivers Operating Curve of 0.8. Conclusions: We anticipate that the prioritized subset of 62 genes and prediction models developed in this study will aid experimental oncologists to expedite understanding of the molecular mechanisms of stage progression and discovery of prognostic factors for ccRCC tumors. © 2014 Jagga and Gupta; licensee BioMed Central Ltd.

Sherma A.,Structural and Computational Biology Group | Yogavel M.,Structural and Computational Biology Group | Sharma A.,Structural and Computational Biology Group
Scientific Reports | Year: 2016

Malaria symptoms are driven by periodic multiplication cycles of Plasmodium parasites in human red blood corpuscles (RBCs). Malaria infection still accounts for ∼600,000 annual deaths, and hence discovery of both new drug targets and drugs remains vital. In the present study, we have investigated the malaria parasite enzyme diadenosine tetraphosphate (Ap4A) hydrolase that regulates levels of signalling molecules like Ap4A by hydrolyzing them to ATP and AMP. We have tracked the spatial distribution of parasitic Ap4A hydrolase in infected RBCs, and reveal its unusual localization on the infected RBC membrane in subpopulation of infected cells. Interestingly, enzyme activity assays reveal an interaction between Ap4A hydrolase and the parasite growth inhibitor suramin. We also present a high resolution crystal structure of Ap4A hydrolase in apo-and sulphate-bound state, where the sulphate resides in the enzyme active site by mimicking the phosphate of substrates like Ap4A. The unexpected infected erythrocyte localization of the parasitic Ap4A hydrolase hints at a possible role of this enzyme in purinerigic signaling. In addition, atomic structure of Ap4A hydrolase provides insights for selective drug targeting.

Sharma A.,Structural and Computational Biology Group | Dixit S.,Structural and Computational Biology Group
Scientific Reports | Year: 2011

Thioredoxins are vital components of Plasmodium proteome and act as both reducing agents and protein disulfide reductases. The malaria parasite P. falciparum thioredoxin-2 (PfTrx-2) is part of the multi-protein complex embedded within the parasite parasitophorous vacuolar membrane (PVM) which purportedly directs protein secretion. We have characterized structural and enzymatic features of PfTrx-2, and we show that PfTrx-2 adopts a canonical thioredoxin fold but with significant structural differences in its N-terminus. Our confocal localization data suggest distinct PVM residency of PfTrx-2. Based on the crystal structure of PfTrx-2, we screened and tested small molecule drug-like libraries for compounds which target unique structural features of PfTrx-2. Disruption of PfTrx-2 interactions using specific inhibitors may result in a dysfunctional parasite translocon that is rendered unable to secrete pathogenic proteins into hosts. This approach therefore offers a new focus for anti-malarial drug development.

Nanni L.,University of Padua | Lumini A.,University of Bologna | Gupta D.,Structural and Computational Biology Group | Garg A.,University of Padua
IEEE/ACM Transactions on Computational Biology and Bioinformatics | Year: 2012

The availability of a reliable prediction method for prediction of bacterial virulent proteins has several important applications in research efforts targeted aimed at finding novel drug targets, vaccine candidates, and understanding virulence mechanisms in pathogens. In this work, we have studied several feature extraction approaches for representing proteins and propose a novel bacterial virulent protein prediction method, based on an ensemble of classifiers where the features are extracted directly from the amino acid sequence and from the evolutionary information of a given protein. We have evaluated and compared several ensembles obtained by combining six feature extraction methods and several classification approaches based on two general purpose classifiers (i.e., Support Vector Machine and a variant of input decimated ensemble) and their random subspace version. An extensive evaluation was performed according to a blind testing protocol, where the parameters of the system are optimized using the training set and the system is validated in three different independent data sets, allowing selection of the most performing system and demonstrating the validity of the proposed method. Based on the results obtained using the blind test protocol, it is interesting to note that even if in each independent data set the most performing stand-alone method is not always the same, the fusion of different methods enhances prediction efficiency in all the tested independent data sets. © 2006 IEEE.

Sharma A.,Structural and Computational Biology Group | Yogavel M.,Structural and Computational Biology Group
Journal of Structural and Functional Genomics | Year: 2012

We report the use of anionic (I-), cationic (Ba2+, Cd2+) and ionic mixtures (I- plus Ba2+) for derivatizing liver fatty acid binding protein (LFABP) crystals. Use of cationic and anionic salts in phasing experiments revealed distinct non-overlapping sites for these ions, suggesting exclusive binding regions on LFABP. Interestingly, cations of identical charge and valency (like Ba2+ and Cd 2+) bound to distinct pockets on the protein surface. Furthermore, a mixture of salts containing both I- and Ba2+ was very useful in phasing experiments as these oppositely charged ions bound to different regions of LFABP. Our data therefore suggest that cationic and anionic salt mixtures like BaCl2 with NH4I or salts like CdI, BaI where each ion has a significant anomalous signal for a given X-ray wavelength may be valuable reagents for phasing during structure determination. © 2012 Springer Science+Business Media B.V.

Ramana J.,Structural and Computational Biology Group | Gupta D.,Structural and Computational Biology Group
PLoS ONE | Year: 2010

Adhesion constitutes one of the initial stages of infection in microbial diseases and is mediated by adhesins. Hence, identification and comprehensive knowledge of adhesins and adhesin-like proteins is essential to understand adhesin mediated pathogenesis and how to exploit its therapeutic potential. However, the knowledge about fungal adhesins is rudimentary compared to that of bacterial adhesins. In addition to host cell attachment and mating, the fungal adhesins play a significant role in homotypic and xenotypic aggregation, foraging and biofilm formation. Experimental identification of fungal adhesins is labor- as well as time-intensive. In this work, we present a Support Vector Machine (SVM) based method for the prediction of fungal adhesins and adhesin-like proteins. The SVM models were trained with different compositional features, namely, amino acid, dipeptide, multiplet fractions, charge and hydrophobic compositions, as well as PSI-BLAST derived PSSM matrices. The best classifiers are based on compositional properties as well as PSSM and yield an overall accuracy of 86%. The prediction method based on best classifiers is freely accessible as a world wide web based server at faap. This work will aid rapid and rational identification of fungal adhesins, expedite the pace of experimental characterization of novel fungal adhesins and enhance our knowledge about role of adhesins in fungal infections. © 2010 Ramana, Gupta.

Sharma A.,Structural and Computational Biology Group
Biochemical Journal | Year: 2015

The Plasmodium falciparum protein translation enzymes aminoacyl-tRNA synthetases (aaRSs) are an emergent family of drug targets. The aaRS ensemble catalyses transfer of amino acids to cognate tRNAs, thus providing charged tRNAs for ribosomal consumption. P. falciparum proteome expression relies on a total of 36 aaRSs for the three translationally independent compartments of cytoplasm, apicoplast and mitochondria. In the present study, we show that, of this set of 36, a single genomic copy of mitochondrial phenylalanyl-tRNA synthetase (mFRS) is targeted to the parasite mitochondria, and that the mFRS gene is exclusive tomalaria parasites within the apicomplexan phyla. Our protein cellular localization studies based on immunofluorescence data show that, along with mFRS, P. falciparum harbours two more phenylalanyl-tRNA synthetase (FRS) assemblies that are localized to its apicoplast and cytoplasm. The 'extra' mFRS is found in mitochondria of all asexual blood stage parasites and is competent in aminoacylation. We show further that the parasite mitochondria import tRNAs from the cytoplasmic tRNA pool. Hence drug targeting of FRSs presents a unique opportunity to potentially stall protein production in all three parasite translational compartments. © The Authors Journal compilation © 2015 Biochemical Society.

Sharma A.,Structural and Computational Biology Group
Journal of Biological Chemistry | Year: 2011

We crystallized human liver fatty acid-binding protein (LFABP) in apo, holo, and intermediate states of palmitic acid engagement. Structural snapshots of fatty acid recognition, entry, and docking within LFABP support a heads-in mechanism for ligand entry. Apo-LFABP undergoes structural remodeling, where the first palmitate ingress creates the atomic environment for placement of the second palmitate. These new mechanistic insights will facilitate development of pharmacological agents against LFABP. © 2011 by The American Society for Biochemistry and Molecular Biology, Inc.

Datt M.,Structural and Computational Biology group | Sharma A.,Structural and Computational Biology group
BMC Genomics | Year: 2014

Background: Mutation(s) in proteins are a natural byproduct of evolution but can also cause serious diseases. Aminoacyl-tRNA synthetases (aaRSs) are indispensable components of all cellular protein translational machineries, and in humans they drive translation in both cytoplasm and mitochondria. Mutations in aaRSs have been implicated in a plethora of diseases including neurological conditions, metabolic disorders and cancer. Results: We have developed an algorithmic approach for genome-wide analyses of sequence substitutions that combines evolutionary, structural and functional information. This pipeline enabled us to super-annotate human aaRS mutations and analyze their linkage to health disorders. Our data suggest that in some but not all cases, aaRS mutations occur in functional and structural sectors where they can manifest their pathological effects by altering enzyme activity or causing structural instability. Further, mutations appear in both solvent exposed and buried regions of aaRSs indicating that these alterations could lead to dysfunctional enzymes resulting in abnormal protein translation routines by affecting inter-molecular interactions or by disruption of non-bonded interactions. Overall, the prevalence of mutations is much higher in mitochondrial aaRSs, and the two most often mutated aaRSs are mitochondrial glutamyl-tRNA synthetase and dual localized glycyl-tRNA synthetase. Out of 63 mutations annotated in this work, only 12 (~20%) were observed in regions that could directly affect aminoacylation activity via either binding to ATP/amino-acid, tRNA or by involvement in dimerization. Mutations in structural cores or at potential biomolecular interfaces account for ~55% mutations while remaining mutations (~25%) remain structurally un-annotated. Conclusion: This work provides a comprehensive structural framework within which most defective human aaRSs have been structurally analyzed. The methodology described here could be employed to annotate mutations in other protein families in a high-throughput manner. © 2014 Datt and Sharma.

Jagga Z.,Structural and Computational Biology Group | Gupta D.,Structural and Computational Biology Group
PLoS ONE | Year: 2014

Viral encoded RNA silencing suppressor proteins interfere with the host RNA silencing machinery, facilitating viral infection by evading host immunity. In plant hosts, the viral proteins have several basic science implications and biotechnology applications. However in silico identification of these proteins is limited by their high sequence diversity. In this study we developed supervised learning based classification models for plant viral RNA silencing suppressor proteins in plant viruses. We developed four classifiers based on supervised learning algorithms: J48, Random Forest, LibSVM and Naïve Bayes algorithms, with enriched model learning by correlation based feature selection. Structural and physicochemical features calculated for experimentally verified primary protein sequences were used to train the classifiers. The training features include amino acid composition; auto correlation coefficients; composition, transition, and distribution of various physicochemical properties; and pseudo amino acid composition. Performance analysis of predictive models based on 10 fold cross-validation and independent data testing revealed that the Random Forest based model was the best and achieved 86.11% overall accuracy and 86.22% balanced accuracy with a remarkably high area under the Receivers Operating Characteristic curve of 0.95 to predict viral RNA silencing suppressor proteins. The prediction models for plant viral RNA silencing suppressors can potentially aid identification of novel viral RNA silencing suppressors, which will provide valuable insights into the mechanism of RNA silencing and could be further explored as potential targets for designing novel antiviral therapeutics. Also, the key subset of identified optimal features may help in determining compositional patterns in the viral proteins which are important determinants for RNA silencing suppressor activities. The best prediction model developed in the study is available as a freely accessible web server pVsupPred at © 2014 Jagga, Gupta.

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