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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.


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
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.

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