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Khan S.,Structural and Computational Biology Group | Garg A.,Structural and Computational Biology Group | Camacho N.,Barcelona Institute for Research in Biomedicine | Van Rooyen J.,European Molecular Biology Laboratory EMBL | And 6 more authors.
Acta Crystallographica Section D: Biological Crystallography | Year: 2013

Aminoacyl-tRNA synthetases are essential enzymes that transmit information from the genetic code to proteins in cells and are targets for antipathogen drug development. Elucidation of the crystal structure of cytoplasmic lysyl-tRNA synthetase from the malaria parasite Plasmodium falciparum (PfLysRS) has allowed direct comparison with human LysRS. The authors' data suggest that PfLysRS is dimeric in solution, whereas the human counterpart can also adopt tetrameric forms. It is shown for the first time that PfLysRS is capable of synthesizing the signalling molecule Ap4a (diadenosine tetraphosphate) using ATP as a substrate. The PfLysRS crystal structure is in the apo form, such that binding to ATP will require rotameric changes in four conserved residues. Differences in the active-site regions of parasite and human LysRSs suggest the possibility of exploiting PfLysRS for selective inhibition. These investigations on PfLysRS further validate malarial LysRSs as attractive antimalarial targets and provide new structural space for the development of inhibitors that target pathogen LysRSs selectively. © 2013 International Union of Crystallography Printed in Singapore - all rights reserved.

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 http://bioinfo.icgeb.res.in/ 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.

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

Progression through the cell cycle involves the coordinated activities of a suite of cyclin/cyclin-dependent kinase (CDK) complexes. The activities of the complexes are regulated by CDK inhibitors (CDKIs). Apart from its role as cell cycle regulators, CDKIs are involved in apoptosis, transcriptional regulation, cell fate determination, cell migration and cytoskeletal dynamics. As the complexes perform crucial and diverse functions, these are important drug targets for tumour and stem cell therapeutic interventions. However, CDKIs are represented by proteins with considerable sequence heterogeneity and may fail to be identified by simple similarity search methods. In this work we have evaluated and developed machine learning methods for identification of CDKIs. We used different compositional features and evolutionary information in the form of PSSMs, from CDKIs and non-CDKIs for generating SVM and ANN classifiers. In the first stage, both the ANN and SVM models were evaluated using Leave-One-Out Cross-Validation and in the second stage these were tested on independent data sets. The PSSM-based SVM model emerged as the best classifier in both the stages and is publicly available through a user-friendly web interface at http://bioinfo.icgeb.res.in/cdkipred. © 2010 Ramana, Gupta.

Sharma A.,Structural and Computational Biology Group | Khan S.,Structural and Computational Biology Group | Belrhali H.,European Molecular Biology Laboratory | Yogavel M.,Structural and Computational Biology Group
Journal of Structural and Functional Genomics | Year: 2014

Malaria parasites inevitably develop drug resistance to anti-malarials over time. Hence the immediacy for discovering new chemical scaffolds to include in combination malaria drug therapy. The desirable attributes of new chemotherapeutic agents currently include activity against both liver and blood stage malaria parasites. One such recently discovered compound called cladosporin abrogates parasite growth via inhibition of Plasmodium falciparum lysyl-tRNA synthetase (PfKRS), an enzyme central to protein translation. Here, we present crystal structure of ternary PfKRS-lysine-cladosporin (PfKRS-K-C) complex that reveals cladosporin's remarkable ability to mimic the natural substrate adenosine and thereby colonize PfKRS active site. The isocoumarin fragment of cladosporin sandwiches between critical adenine-recognizing residues while its pyran ring fits snugly in the ribose-recognizing cavity. PfKRS-K-C structure highlights ample space within PfKRS active site for further chemical derivatization of cladosporin. Such derivatives may be useful against additional human pathogens that retain high conservation in cladosporin chelating residues within their lysyl-tRNA synthetase. © 2014 Springer Science+Business Media Dordrecht.

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 http://bioinfo.icgeb.res.in/pvsup/. © 2014 Jagga, Gupta.

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