Dinesh Gupta

Identifying Bacterial Virulent Proteins by Fusing a Set of Classifiers Based on Variants of Chou's Pseudo Amino Acid Composition and on Evolutionary Information

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.

VirulentPred: a SVM based prediction method for virulent proteins in bacterial pathogens

BackgroundPrediction of bacterial virulent protein sequences has implications for identification and characterization of novel virulence-associated factors, finding novel drug/vaccine targets against proteins indispensable to pathogenicity, and understanding the complex virulence mechanism in pathogens.ResultsIn the present study we propose a bacterial virulent protein prediction method based on bi-layer cascade Support Vector Machine (SVM). The first layer SVM classifiers were trained and optimized with different individual protein sequence features like amino acid composition, dipeptide composition (occurrences of the possible pairs of ith and i+1th amino acid residues), higher order dipeptide composition (pairs of ith and i+2nd residues) and Position Specific Iterated BLAST (PSI-BLAST) generated Position Specific Scoring Matrices (PSSM). In addition, a similarity-search based module was also developed using a dataset of virulent and non-virulent proteins as BLAST database. A five-fold cross-validation technique was used for the evaluation of various prediction strategies in this study. The results from the first layer (SVM scores and PSI-BLAST result) were cascaded to the second layer SVM classifier to train and generate the final classifier. The cascade SVM classifier was able to accomplish an accuracy of 81.8%, covering 86% area in the Receiver Operator Characteristic (ROC) plot, better than that of either of the layer one SVM classifiers based on single or multiple sequence features.ConclusionVirulentPred is a SVM based method to predict bacterial virulent proteins sequences, which can be used to screen virulent proteins in proteomes. Together with experimentally verified virulent proteins, several putative, non annotated and hypothetical protein sequences have been predicted to be high scoring virulent proteins by the prediction method. VirulentPred is available as a freely accessible World Wide Web server – VirulentPred, at http://bioinfo.icgeb.res.in/virulent/.

Chaperones and foldases in endoplasmic reticulum stress signaling in plants.

Molecular chaperones and foldases are a diverse group of proteins that in vivo bind to misfolded or unfolded proteins (non-native or unstable proteins) and play important role in their proper folding. Stress conditions compel altered and heightened chaperone and foldase expression activity in the endoplasmic reticulum (ER), which highlights the role of these proteins, due to which several of the proteins under these classes were identified as heat shock proteins. Different chaperones and foldases are active in different cellular compartment performing specific tasks. The review will discuss the role of the ER chaperones and foldases under stress conditions to maintain proper protein folding dynamics in the plant cells and recent advances in the field. The ER chaperones and foldases, which are described in article, are binding protein (BiP), glucose regulated protein (GRP94), protein-disulfide isomerase (PDI), peptidyl-prolyl isomerases (PPI), immunophilins, calnexin and calreticulin.

The evolutionary dynamics of variant antigen genes in Babesia reveal a history of genomic innovation underlying host-parasite interaction

Babesia spp. are tick-borne, intraerythrocytic hemoparasites that use antigenic variation to resist host immunity, through sequential modification of the parasite-derived variant erythrocyte surface antigen (VESA) expressed on the infected red blood cell surface. We identified the genomic processes driving antigenic diversity in genes encoding VESA (ves1) through comparative analysis within and between three Babesia species, (B. bigemina, B. divergens and B. bovis). Ves1 structure diverges rapidly after speciation, notably through the evolution of shortened forms (ves2) from 5′ ends of canonical ves1 genes. Phylogenetic analyses show that ves1 genes are transposed between loci routinely, whereas ves2 genes are not. Similarly, analysis of sequence mosaicism shows that recombination drives variation in ves1 sequences, but less so for ves2, indicating the adoption of different mechanisms for variation of the two families. Proteomic analysis of the B. bigemina PR isolate shows that two dominant VESA1 proteins are expressed in the population, whereas numerous VESA2 proteins are co-expressed, consistent with differential transcriptional regulation of each family. Hence, VESA2 proteins are abundant and previously unrecognized elements of Babesia biology, with evolutionary dynamics consistently different to those of VESA1, suggesting that their functions are distinct.

A cyanobacterial serine protease of Plasmodium falciparum is targeted to the apicoplast and plays an important role in its growth and development

The prokaryotic ATP‐dependent protease machineries such as ClpQY and ClpAP in the malaria parasite may represent potential drug targets. In the present study, we show that the orthologue of cyanobacterial ClpP protease in Plasmodium falciparum (PfClpP) is expressed in the asexual blood stages and possesses serine protease activity. The PfClpP was localized in the apicoplast using a GFP‐targeting approach, immunoelectron microscopy and by immunofluorescence assays. A set of cell permeable β‐lactones, which specifically bind with the active site of prokaryotic ClpP, were screened using an in vitro protease assay of PfClpP. A PfClpP‐specific protease inhibitor was identified in the screen, labelled as U1‐lactone. In vitro growth of the asexual stage parasites was significantly inhibited by U1‐lactone treatment. The U1‐treated parasites showed developmental arrest at the late‐schizont stage. We further show that the U1‐lactone treatment resulted in formation of abnormal apicoplasts which were not able to grow and segregate in the parasite progeny; these effects were also evident by blockage in the replication of the apicoplast genome. Overall, our data show that the PfClpP protease has confirmed localization in the apicoplast and it plays important role in development of functional apicoplasts.

ProtRepeatsDB: a database of amino acid repeats in genomes

BackgroundGenome wide and cross species comparisons of amino acid repeats is an intriguing problem in biology mainly due to the highly polymorphic nature and diverse functions of amino acid repeats. Innate protein repeats constitute vital functional and structural regions in proteins. Repeats are of great consequence in evolution of proteins, as evident from analysis of repeats in different organisms. In the post genomic era, availability of protein sequences encoded in different genomes provides a unique opportunity to perform large scale comparative studies of amino acid repeats. ProtRepeatsDB http://bioinfo.icgeb.res.in/repeats/ is a relational database of perfect and mismatch repeats, access to which is designed as a resource and collection of tools for detection and cross species comparisons of different types of amino acid repeats.DescriptionProtRepeatsDB (v1.2) consists of perfect as well as mismatch amino acid repeats in the protein sequences of 141 organisms, the genomes of which are now available. The web interface of ProtRepeatsDB consists of different tools to perform repeat s; based on protein IDs, organism name, repeat sequences, and keywords as in FASTA headers, size, frequency, gene ontology (GO) annotation IDs and regular expressions (REGEXP) describing repeats. These tools also allow formulation of a variety of simple, complex and logical queries to facilitate mining and large-scale cross-species comparisons of amino acid repeats. In addition to this, the database also contains sequence analysis tools to determine repeats in user input sequences.ConclusionProtRepeatsDB is a multi-organism database of different types of amino acid repeats present in proteins. It integrates useful tools to perform genome wide queries for rapid screening and identification of amino acid repeats and facilitates comparative and evolutionary studies of the repeats. The database is useful for identification of species or organism specific repeat markers, interspecies variations and polymorphism.

Molecular modeling studies of the interaction between Plasmodium falciparum HslU and HslV subunits.

Abstract The PfHslUV, a Plasmodium falciparum homolog of prokaryotic HslUV systems, is a newly identified drug target. The HslUV complex is an assembly of Heat Shock Locus gene products U and V. The formation of complete complex is essential for the proteasome to carry out its biochemical and physiological role in the parasite, namely to degrade specific target proteins in an ATP-dependent chaperone assisted manner. PfHslV subunit, a protease, exhibits increased proteolytic activity in the presence of PfHslU, the subunit believed to be responsible for allosteric activation of PfHslV. In the present work, we have employed computational methods to simulate the interaction of PfHslU and PfHslV subunits. We have used three methods—namely homology modeling, molecular docking and computational alanine scanning to model the complex, to predict the binding mode of PfHslU-V interaction and to predict the binding-energy hot-spots in protein-protein interface, respectively. The three dimensional models of PfHslV and PfHslU have been generated using MODELLER, based on the crystal structures of prokaryotic HslUV complex as templates. The modeled structures were docked using PatchDock, a geometry-based molecular docking algorithm. Finally, a three-dimensional PfHslUV complex model was generated that helped in comparing protein-protein interface characteristics with that of crystal structures of prokaryotic HslUV. Further, computational alanine scanning analysis of the generated complex was performed to calculate the binding free energy changes (ΔΔGbind), which helped in identifying residues crucial for PfHslU and PfHslV interactions.

CyclinPred: A SVM-Based Method for Predicting Cyclin Protein Sequences

Functional annotation of protein sequences with low similarity to well characterized protein sequences is a major challenge of computational biology in the post genomic era. The cyclin protein family is once such important family of proteins which consists of sequences with low sequence similarity making discovery of novel cyclins and establishing orthologous relationships amongst the cyclins, a difficult task. The currently identified cyclin motifs and cyclin associated domains do not represent all of the identified and characterized cyclin sequences. We describe a Support Vector Machine (SVM) based classifier, CyclinPred, which can predict cyclin sequences with high efficiency. The SVM classifier was trained with features of selected cyclin and non cyclin protein sequences. The training features of the protein sequences include amino acid composition, dipeptide composition, secondary structure composition and PSI-BLAST generated Position Specific Scoring Matrix (PSSM) profiles. Results obtained from Leave-One-Out cross validation or jackknife test, self consistency and holdout tests prove that the SVM classifier trained with features of PSSM profile was more accurate than the classifiers based on either of the other features alone or hybrids of these features. A cyclin prediction server- CyclinPred has been setup based on SVM model trained with PSSM profiles. CyclinPred prediction results prove that the method may be used as a cyclin prediction tool, complementing conventional cyclin prediction methods.

FaaPred: A SVM-Based Prediction Method for Fungal Adhesins and Adhesin-Like Proteins

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.

Identification of mirtrons in rice using MirtronPred: a tool for predicting plant mirtrons.

Studies from flies and insects have reported the existence of a special class of miRNA, called mirtrons that are produced from spliced-out introns in a DROSHA-independent manner. The spliced-out lariat is debranched and refolded into a stem-loop structure resembling the pre-miRNA, which can then be processed by DICER into mature ~21 nt species. The mirtrons have not been reported from plants. In this study, we present MirtronPred, a web based server to predict mirtrons from intronic sequences. We have used the server to predict 70 mirtrons in rice introns that were put through a stringent selection filter to shortlist 16 best sequences. The prediction accuracy was subsequently validated by northern analysis and RT-PCR of a predicted Os-mirtron-109. The target sequences for this mirtron were also found in the rice degradome database. The possible role of the mirtron in rice regulon is discussed. The MirtronPred web server is available at http://bioinfo.icgeb.res.in/mirtronPred.