Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatics method for the prediction of peptide binding to T-cell molecules. The major T-cell contributors are selected for the dataset preparation due to its availability and originality. We used a profile hidden Markov Model (HMM) for the prediction. Sensitivity (96%) and Specificity (~100%) are evaluated for the T cells epitope and nonepitopes from the test data set. The method promises 98 % accuracy and useful for vaccine development.
Conveying information secretly and establishing a hidden relationship between the message and its counterpart has been of great interest since long time. This paper presents a new scheme for coding Telugu alphabet set, its diacritics and compounds which can be used in the Linguistic Steganography as well as cryptic transmission of Telugu text. This encoding scheme helps its users to have better support in implementing the hiding techniques. This scheme is also useful to other similar Indian Languages.
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