The accuracy of online tools employed in attempts to predict B epitopes based on sequence are very poor. In order to improve the accuracy of these predictions it is essential to design algorithms from the features achieved in wet lab in vivo experimental models. T shed some light on accuracy and reliability of these online tools, we set an insilico experiment on five selected online tools using five antigens b-cell epitopes are known through wet lab experiments successes of online tools, we defined two measures, accuracy, and reliability. To the findings of this experiment, the most accurate tool is ABCpred with a score of 43.59 %. That is peptides that are predicted as b epitopes, cover in average 43.59 % of the wet lab listed b most reliable predictors are BCpred and AAPred with scores of 52.54%, and 52.60% respectively, which means that in average peptides that are predicted as a b-epitope by these predictors have a chance to be a real b-epitope. Combining several predictors predictors is not an advisable technique. From this experiment it is concluded that the accuracy and reliability of online away being satisfactory. The human body is a wonderful machine, which has the ability, to fight off attacking microorganisms and adapt to Very often, microorganisms or foreign particles enter the body and the body causing diseases. isease is a condition where an organism experiences impaired function often with detrimental symptoms. According to the World Health Organization, as of 2017 there were 7,186 classified different diseases and health-related ailments. 1 Virus is one of these organisms that can cause severe health issues. They invade living, normal cells and hijacks their normal molecular balance 1 http://www.who.int/classifications/ICD11January2017Ne wsletter.pdf?ua=1 Linear B Cell Epitope Predictors tools employed in attempts to predict B-cell In order to improve the algorithms to benefit xperimental models. To shed some light on accuracy and reliability of these online tools, we set an five selected online tools using five antigens whose experiments. To evaluate sures, accuracy, and , the most accurate tool is That is peptides that are predicted as bed b-epitopes. The most reliable predictors are BCpred and AAPred with scores of 52.54%, around half of the epitope by these predictors have a ombining several predictors to get better. From this experiment it is tools still are far 186 classified different diseases and Virus is one of these organisms that can cause severe health issues. They invade living, normal cells and hijacks their normal molecular balance

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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 huge resources. There are many online epitope prediction tools are available scientists in short listing the candidate peptides. To predict B in an antigenic sequence, Jordan recurrent neural network (found to besuccessful. 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-epitopes are needed. Non are created as random sequences of amino acids of the same length followed by a filtering process. To distinguish epitopes and non the votes of eleven experts are aggregated by majority vote. An overall accuracy of 97.23% is achieved. Then these experts are used to predict the Linear Bepitopes of five antigens, Plasmodium Falciparum, Human Polio Virus Sabin Strain, Meningitis, Plasmodium Vivax and Mycobacterium Tuberculosis. The success of BIRUNU is compared with the five prediction tools ABCPRED, BCPRED, K&T, BEPIPRED, and AAP.It is seen that BIRUNI outperforms all of them in the average. cells of the immunesystem pathogen's antigens by their membranebound immunoglobulinreceptors and, in response, produce antibodies specific to these antigens. Antigens have the capacity to bindby either a B antibody molecule. The part of an antigen that bind antibody iscalled a B-cell epitope. If an antigen is a protein, an epitope maybe either a short peptide fr protein sequence or a patch of atoms on the protein surfacein the three-dimensional structure. 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 huge resources. are available that can help in short listing the candidate peptides. To predict B-cell epitopes in an antigenic sequence, Jordan recurrent neural network (BIRUNI) is found to besuccessful. To train and test neural networks, 262.583 B topes are retrieved from IEDB database. 99.9% of these epitopes have 25 amino acids. For each of these lengths, committees of 11 expert recurrent neural networks are trained. To train are 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 aggregated by majority vote. An overall % is achieved. Then these experts are used to predict the Plasmodium Falciparum, Human Polio Virus Sabin Strain, Meningitis, Plasmodium Vivax and Mycobacterium The success of BIRUNU is compared with the five online K&T, BEPIPRED, and AAP.It is in the average. have the capacity to bindby either a B-cell receptor or an antibody molecule. The part of an antigen that binds to an cell epitope. ...

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