Peptide based vaccine designing and immunodiagnosis is the most important field in the diagnosis and t herapy of various infectious and noninfectious diseases. It d oes critically require identification of regions in the pathogen native protein sequences, which are recognized by e ither B-cell or T-cell receptors. The antigenic reg ions of protein recognized by the binding sites of immunoglobulin m olecules are called B-cell epitopes. The experiment al identification of epitopes binding specifically to anti-peptide antibodies requires the binding assay of each peptide in an antigenic protein sequence which are very labori ous and time consuming . A bioinformatics approach to predict linear B cell epitope in a protein sequence can be the best alternative to reduce the number of peptid es to be synthesized for wet lab experimentation. The aim of this study is to develop a Python based software w ith graphical user interface for predicting the antigenic propert ies of protein. Hence the tool was named as Analysi s of protein sequence and antigenicity prediction (ASAP). ASAP p redicts the antigenicity of the protein sequence fr om its amino acid sequence, based on Chou Fasman turns and Antig enic index.