Experimental methods used for characterizing epitopes that play a vital role in the development of peptide vaccines, in diagnosis of diseases, and also for allergy research are time consuming and need There are many online epitope prediction tools that can help experimenters in short listing the candidate peptides. To predict B epitopes in an antigenic sequence, Jordan recurrent neural network (JRNN) are found to be more successful. To train and test neural networks, 262.583 B epitopes are retrieved from IEDB database. 99.9% of these epitopes have lengths in the interval 6-25 amino acids. For each of these lengths, committees of 11 expert recurrent neural networks are trained. To train these experts alongside epitopes, non needed. Non-epitopes are created as random sequences of amino acids of the same length followed by a filtering process. To distinguish epitopes and non-epitopes, the votes of eleven experts are aggrega vote. An overall accuracy of 97.23% is achieved. Then these experts are used to predict the linear b epitopes of antigen, ESAT6 (Tuberculosis).

Chou, and Fasman developed the first empirical prediction predict secondary structure of proteins from their amino acid Subsequently, a more sophisticated GOR method has been developed Although it became very popular among biologists, their accuracy was only slightly better than random. A significant improvement in prediction accuracy >70% has been achieved by ‘second generation’ methods such as PHD, SAM-T98, and PSIPRED, which utilized information c sequence conservation. Only recently F. B. Akcesme dev similarity based method to obtain an accuracy > structure prediction of any new protein. In this article we possibility of sequence similarity based secondary structure prediction of proteins. To deal with this issue, all proteins of PDB dataset for identical subsequences in the other larger proteins o is seen that around 17% of proteins in the PDB dataset subsequences in other larger proteins of PDB dataset. secondary structures of proteins are assigned as the corresponding secondary structures of identical parts in other larger proteins, t prediction accuracy is found to be 90.39 %. Therefore, an unknown protein has a chance of 17 % to have a subsequence in a larger protein in Protein Data Bank (PDB), possibility that its secondary structure be predicted with around accuracy with this method.