CEP server () provides a web interface to the conformational epitope prediction algorithm developed in-house. The algorithm, apart from predicting conformational epitopes, also predicts antigenic determinants and sequential epitopes. The epitopes are predicted using 3D structure data of protein antigens, which can be visualized graphically. The algorithm employs structure-based Bioinformatics approach and solvent accessibility of amino acids in an explicit manner. Accuracy of the algorithm was found to be 75% when evaluated using X-ray crystal structures of Ag–Ab complexes available in the PDB. This is the first and the only method available for the prediction of conformational epitopes, which is an attempt to map probable antibody-binding sites of protein antigens.

Interactions between different phytoplankton taxa and heterotrophic bacterial communities within aquatic environments can differentially support growth of various heterotrophic bacterial species. In this study, phytoplankton diversity was studied using traditional microscopic techniques and the bacterial communities associated with phytoplankton bloom were studied using High Throughput Sequencing (HTS) analysis of 16S rRNA gene amplicons from the V1-V3 and V3-V4 hypervariable regions. Samples were collected from Lake Akersvannet, a eutrophic lake in South Norway, during the growth season from June to August 2013. Microscopic examination revealed that the phytoplankton community was mostly represented by Cyanobacteria and the dinoflagellate Ceratium hirundinella. The HTS results revealed that Proteobacteria (Alpha, Beta, and Gamma), Bacteriodetes, Cyanobacteria, Actinobacteria and Verrucomicrobia dominated the bacterial community, with varying relative abundances throughout the sampling season. Species level identification of Cyanobacteria showed a mixed population of Aphanizomenon flos-aquae, Microcystis aeruginosa and Woronichinia naegeliana. A significant proportion of the microbial community was composed of unclassified taxa which might represent locally adapted freshwater bacterial groups. Comparison of cyanobacterial species composition from HTS and microscopy revealed quantitative discrepancies, indicating a need for cross validation of results. To our knowledge, this is the first study that uses HTS methods for studying the bacterial community associated with phytoplankton blooms in a Norwegian lake. The study demonstrates the value of considering results from multiple methods when studying bacterial communities.

Japanese encephalitis virus (JEV), a mosquito-borne flavivirus, is an important human pathogen. The envelope glycoprotein (Egp), a major structural antigen, is responsible for viral haemagglutination and eliciting neutralising antibodies. The three-dimensional structure of the Egp of JEV was predicted using the knowledge-based homology modeling approach and X-ray structure data of the Egp of tick-borne encephalitis virus as a template (Rey et al., 1995). In the initial stages of optimisation, a distance-dependent dielectric constant of 4r(ij) was used to simulate the solvent effect. The predicted structure was refined by solvating the protein in a 10-A layer of water by explicitly considering 4867 water molecules. Four independent structure evaluation methods report this structure to be acceptable stereochemically and geometrically. The Egp of JEV has an extended structure with seven beta-sheets, two alpha-helices, and three domains. The water-solvated structure was used to delineate conformational and sequential epitopes. These results document the importance of tertiary structure in understanding the antigenic properties of flaviviruses in general and JEV in particular. The conformational epitope prediction method could be used to identify conformational epitopes on any protein antigen with known three-dimensional structure. This is one of the largest proteins whose three-dimensional structure has been predicted using an homology modeling approach and water as a solvent.

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The scientific community is overwhelmed by the voluminous increase in the quantum of data on biological systems, including but not limited to the immune system. Consequently, immunoinformatics databases are continually being developed to accommodate this ever increasing data and analytical tools are continually being developed to analyze the same. Therefore, researchers are now equipped with numerous databases, analytical and prediction tools, in anticipation of better means of prevention of and therapeutic intervention in diseases of humans and other animals. Epitope is a part of an antigen, recognized either by B- or T-cells and/or molecules of the host immune system. Since only a few amino acid residues that comprise an epitope (instead of the whole protein) are sufficient to elicit an immune response, attempts are being made to identify or predict this critical stretch or patch of amino acid residues, i.e., T-cell epitopes and B-cell epitopes to be included in multiple-subunit vaccines. T-cell epitope prediction is a challenge owing to the high degree of MHC polymorphism and disparity in the volume of data on various steps encountered in the generation and presentation of T-cell epitopes in the living systems. Many algorithms/methods developed to predict T-cell epitopes and Web servers incorporating the same are available. These are based on approaches like considering amphipathicity profiles of proteins, sequence motifs, quantitative matrices (QM), artificial neural networks (ANN), support vector machines (SVM), quantitative structure activity relationship (QSAR) and molecular docking simulations, etc. This chapter aims to introduce the reader to the principle(s) underlying some of these methods/algorithms as well as procedural and practical aspects of using the same.