Varun Jaiswal

Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions

BackgroundSubunit vaccines based on recombinant proteins have been effective in preventing infectious diseases and are expected to meet the demands of future vaccine development. Computational approach, especially reverse vaccinology (RV) method has enormous potential for identification of protein vaccine candidates (PVCs) from a proteome. The existing protective antigen prediction software and web servers have low prediction accuracy leading to limited applications for vaccine development. Besides machine learning techniques, those software and web servers have considered only protein’s adhesin-likeliness as criterion for identification of PVCs. Several non-adhesin functional classes of proteins involved in host-pathogen interactions and pathogenesis are known to provide protection against bacterial infections. Therefore, knowledge of bacterial pathogenesis has potential to identify PVCs.ResultsA web server, Jenner-Predict, has been developed for prediction of PVCs from proteomes of bacterial pathogens. The web server targets host-pathogen interactions and pathogenesis by considering known functional domains from protein classes such as adhesin, virulence, invasin, porin, flagellin, colonization, toxin, choline-binding, penicillin-binding, transferring-binding, fibronectin-binding and solute-binding. It predicts non-cytosolic proteins containing above domains as PVCs. It also provides vaccine potential of PVCs in terms of their possible immunogenicity by comparing with experimentally known IEDB epitopes, absence of autoimmunity and conservation in different strains. Predicted PVCs are prioritized so that only few prospective PVCs could be validated experimentally. The performance of web server was evaluated against known protective antigens from diverse classes of bacteria reported in Protegen database and datasets used for VaxiJen server development. The web server efficiently predicted known vaccine candidates reported from Streptococcus pneumoniae and Escherichia coli proteomes. The Jenner-Predict server outperformed NERVE, Vaxign and VaxiJen methods. It has sensitivity of 0.774 and 0.711 for Protegen and VaxiJen dataset, respectively while specificity of 0.940 has been obtained for the latter dataset.ConclusionsBetter prediction accuracy of Jenner-Predict web server signifies that domains involved in host-pathogen interactions and pathogenesis are better criteria for prediction of PVCs. The web server has successfully predicted maximum known PVCs belonging to different functional classes. Jenner-Predict server is freely accessible at http://117.211.115.67/vaccine/home.html

Quantitative structure-activity relationship (QSAR) studies as strategic approach in drug discovery

Drug design is a process which is driven by technological breakthroughs implying advanced experimental and computational methods. Nowadays, the techniques or the drug design methods are of paramount importance for prediction of biological profile, identification of hits, generation of leads, and moreover to accelerate the optimization of leads into drug candidates. Quantitative structure–activity relationship (QSAR) has served as a valuable predictive tool in the design of pharmaceuticals and agrochemicals. From decades to recent research, QSAR methods have been applied in the development of relationship between properties of chemical substances and their biological activities to obtain a reliable statistical model for prediction of the activities of new chemical entities. Classical QSAR studies include ligands with their binding sites, inhibition constants, rate constants, and other biological end points, in addition molecular to properties such as lipophilicity, polarizability, electronic, and steric properties or with certain structural features. 3D-QSAR has emerged as a natural extension to the classical Hansch and Free–Wilson approaches, which exploit the three-dimensional properties of the ligands to predict their biological activities using robust chemometric techniques such as PLS, G/PLS, and ANN. This paper provides an overview of 1-6 dimension-based developed QSAR methods and their approaches. In particular, we present various dimensional QSAR approaches, such as comparative molecular field analysis (CoMFA), comparative molecular similarity analysis, Topomer CoMFA, self-organizing molecular field analysis, comparative molecule/pseudo receptor interaction analysis, comparative molecular active site analysis, and FLUFF-BALL, 4D-QSAR, and G-QSAR approaches.

Sex-Biased Temporal Gene Expression in Male and Female Floral Buds of Seabuckthorn (Hippophae rhamnoides).

Seabuckthorn is an economically important dioecious plant in which mechanism of sex determination is unknown. The study was conducted to identify seabuckthorn homologous genes involved in floral development which may have role in sex determination. Forty four putative Genes involved in sex determination (GISD) reported in model plants were shortlisted from literature survey, and twenty nine seabuckthorn homologous sequences were identified from available seabuckthorn genomic resources. Of these, 21 genes were found to differentially express in either male or female flower bud stages. HrCRY2 was significantly expressed in female flower buds only while HrCO had significant expression in male flowers only. Among the three male and female floral development stages (FDS), male stage II had significant expression of most of the GISD. Information on these sex-specific expressed genes will help in elucidating sex determination mechanism in seabuckthorn.

Mining whole genomes and transcriptomes of Jatropha (Jatropha curcas) and Castor bean (Ricinus communis) for NBS-LRR genes and defense response associated transcription factors

Jatropha (Jatropha curcas L.) and Castor bean (Ricinus communis) are oilseed crops of family Euphorbiaceae with the potential of producing high quality biodiesel and having industrial value. Both the bioenergy plants are becoming susceptible to various biotic stresses directly affecting the oil quality and content. No report exists as of today on analysis of Nucleotide Binding Site-Leucine Rich Repeat (NBS-LRR) gene repertoire and defense response transcription factors in both the plant species. In silico analysis of whole genomes and transcriptomes identified 47 new NBS-LRR genes in both the species and 122 and 318 defense response related transcription factors in Jatropha and Castor bean, respectively. The identified NBS-LRR genes and defense response transcription factors were mapped onto the respective genomes. Common and unique NBS-LRR genes and defense related transcription factors were identified in both the plant species. All NBS-LRR genes in both the species were characterized into Toll/interleukin-1 receptor NBS-LRRs (TNLs) and coiled-coil NBS-LRRs (CNLs), position on contigs, gene clusters and motifs and domains distribution. Transcript abundance or expression values were measured for all NBS-LRR genes and defense response transcription factors, suggesting their functional role. The current study provides a repertoire of NBS-LRR genes and transcription factors which can be used in not only dissecting the molecular basis of disease resistance phenotype but also in developing disease resistant genotypes in Jatropha and Castor bean through transgenic or molecular breeding approaches.

Prediction and analysis of promiscuous T cell-epitopes derived from the vaccine candidate antigens of Leishmania donovani binding to MHC class-II alleles using in silico approach

Visceral leishmaniasis is a dreadful infectious disease and caused by the intracellular protozoan parasites, Leishmania donovani and Leishmania infantum. Despite extensive efforts for developing effective prophylactic vaccine, still no vaccine is available against leishmaniasis. However, advancement in immunoinformatics methods generated new dimension in peptide based vaccine development. The present study was aimed to identify T-cell epitopes from the vaccine candidate antigens like Lipophosphogylcan-3(LPG-3) and Nucleoside hydrolase (NH) from the L. donovani using in silico methods. Available best tools were used for the identification of promiscuous peptides for MHC class-II alleles. A total of 34 promiscuous peptides from LPG-3, 3 from NH were identified on the basis of their 100% binding affinity towards all six HLA alleles, taken in this study. These peptides were further checked computationally to know their IFN-γ and IL4 inducing potential and nine peptides were identified. Peptide binding interactions with predominant HLA alleles were done by docking. Out of nine docked promiscuous peptides, only two peptides (QESRILRVIKKKLVR, RILRVIKKKLVRKTL), from LPG-3 and one peptide (FDKFWCLVIDALKRI) from NH showed lowest binding energy with all six alleles. These promiscuous T-cell epitopes were predicted on the basis of their antigenicity, hydrophobicity, potential immune response and docking scores. The immunogenicity of predicted promiscuous peptides might be used for subunit vaccine development with immune-modulating adjuvants.

DRPPP: A machine learning based tool for prediction of disease resistance proteins in plants

Plant disease outbreak is increasing rapidly around the globe and is a major cause for crop loss worldwide. Plants, in turn, have developed diverse defense mechanisms to identify and evade different pathogenic microorganisms. Early identification of plant disease resistance genes (R genes) can be exploited for crop improvement programs. The present prediction methods are either based on sequence similarity/domain-based methods or electronically annotated sequences, which might miss existing unrecognized proteins or low similarity proteins. Therefore, there is an urgent need to devise a novel machine learning technique to address this problem. In the current study, a SVM-based tool was developed for prediction of disease resistance proteins in plants. All known disease resistance (R) proteins (112) were taken as a positive set, whereas manually curated negative dataset consisted of 119 non-R proteins. Feature extraction generated 10,270 features using 16 different methods. The ten-fold cross validation was performed to optimize SVM parameters using radial basis function. The model was derived using libSVM and achieved an overall accuracy of 91.11% on the test dataset. The tool was found to be robust and can be used for high-throughput datasets. The current study provides instant identification of R proteins using machine learning approach, in addition to the similarity or domain prediction methods.

Design, synthesis, and biological evaluation of oxindole derivatives as antidepressive agents.

The 3-substituted oxindole derivatives were designed, synthesized, and evaluated for antidepressant activity by employing forced swimming test, tail suspension test, and MAO-A inhibition assay. Results of biological studies revealed that the majority of compounds exhibited potent to moderately potent activity and among them, 12 displayed potency comparable to that of the imipramine with %DID of 37.95 and 44.84 in the FST and TST, respectively. At the same time, imipramine showed %DID of 43.62 and 50.64 in the FST and TST, correspondingly. In the MAO-A inhibition assay, 12 showed an IC50 of 18.27 μmol, whereas the reference drug moclobemide displayed an IC50 of 13.1 μmol. The SAR study disclosed that the presence of bromo atom at the phenyl/furanyl or thienyl moiety in the oxindole derivatives was critical for the antidepressant activity.

Identification, validation, and expression of ABC transporters in Podophyllum hexandrum and their role in podophyllotoxin biosynthesis

Podophyllum hexandrum Royle is an important medicinal herb of North-Western Himalayas, and podophyllotoxin, being its major metabolite, has been used extensively in the preparation of several anticancer drugs. Podophyllotoxin accumulates in rhizomes; however, no information exists on the role of ATP-binding cassette (ABC) transporters vis-à-vis podophyllotoxin content. The present study reports identification, validation, and expression analysis of ABC transporter genes from P. hexandrum. Total 252 ABC transporter genes were identified as unigenes out of which 22 were further validated using real time qPCR in different tissues of varying podophyllotoxin content. Differential expression analysis and Pearson’s correlation coefficient revealed two candidate genes PhABC6 and PhABCIII having a positive correlation with the podophyllotoxin content. PhABCIV showed the highest expression in rhizomes (20.53-folds compared to shoots) suggesting its possible role in transport and accumulation of podophyllotoxin.

Homology modelling of frequent HLA class-II alleles: A perspective to improve prediction of HLA binding peptide and understand the HLA associated disease susceptibility

Human leukocyte antigen (HLA) plays significant role via the regulation of immune system and contribute in the progression and protection of many diseases. HLA molecules bind and present peptides to T- cell receptors which generate the immune response. HLA peptide interaction and molecular function of HLA molecule is the key to predict peptide binding and understanding its role in different diseases. The availability of accurate three dimensional (3D) structures is the initial step towards this direction. In the present work, homology modelling of important and frequent HLA-DRB1 alleles (07:01, 11:01 and 09:01) was done and acceptable models were generated. These modelled alleles were further refined and cross validated by using several methods including Ramachandran plot, Z-score, ERRAT analysis and root mean square deviation (RMSD) calculations. It is known that numbers of allelic variants are related to the susceptibility or protection of various infectious diseases. Difference in amino acid sequences and structures of alleles were also studied to understand the association of HLA with disease susceptibility and protection. Susceptible alleles showed more amino acid variations than protective alleles in three selected diseases caused by different pathogens. Amino acid variations at binding site were found to be more than other part of alleles. RMSD values were also higher at variable positions within binding site. Higher RMSD values indicate that mutations occurring at peptide binding site alter protein structure more than rest of the protein. Hence, these findings and modelled structures can be used to design HLA-DRB1 binding peptides to overcome low prediction accuracy of HLA class II binding peptides. Furthermore, it may help to understand the allele specific molecular mechanisms involved in susceptibility/resistance against pathogenic diseases.

Common antigens prediction in bacterial bioweapons: A perspective for vaccine design

Bioweapons (BWs) are a serious threat to mankind and the lack of efficient vaccines against bacterial bioweapons (BBWs) further worsens the situation in face of BW attack. Experts believe that difficulties in detection and ease in dissemination of deadly pathogens make BW a better option for attack compared to nuclear weapons. Molecular biology techniques facilitate the use of genetically modified BBWs thus creating uncertainty on which bacteria will be used for BW attack. In the present work, available resources such as proteomic sequences of BBWs, protective antigenic proteins (PAPs) reported in Protegen database and VaxiJen dataset, and immunogenic epitopes in immune epitope database (IEDB) were used to predict potential broad-specific vaccine candidates against BBWs. Comparison of proteomes sequences of BBWs and their analyses using in-house PERL scripts identified 44 conserved proteins and many of them were known to be immunogenic. Comparison of conserved proteins against PAPs identified six either as PAPs or their homologues with a potential of providing protection against multiple pathogens. Similarly, mapping of conserved proteins against experimentally known IEDB epitopes identified six epitopes which had exact epitope match in four proteins including three from earlier predicted six PAPs. These epitopes were also reported to provide protection against several pathogens. In the backdrop of conserved heat shock GroEL protein from Salmonella enterica providing protection against five diverse bacterial pathogens involved in different diseases, and synthetic proteins produced by combination of epitopes from Mycobacterium tuberculosis and 4 viruses providing protection against both bacterium and viruses, the identified putative immunogenic conserved proteins and immune-protective epitopes can further be explored for their potential as broad-specific vaccine candidates against BBWs.