title: In silico prediction of allergenic proteins.
abstract: Currently, the prediction of new allergens is becoming important due to use of genetically modified (GM) foods and biopharmaceuticals. In this chapter, we describe how to use four popular allergenic prediction servers: (1) Structural Database of Allergenic Proteins (SDAP), (2) Allermatch, (3) Evaller 2, and (4) AlgPred. The first two prediction servers are based on traditional approaches, whereas Evaller 2 and AlgPred use sophisticated machine learning techniques.
authors.affiliation: Division of Plant Biology, Bose Institute, 93/1 Acharya Prafulla Chandra Road, Kolkata, 700009, India.
authors.forename: Gaurab
authors.initials: G
authors.lastname: Sircar
authors.forename: Bodhisattwa
authors.initials: B
authors.lastname: Saha
authors.forename: Swati Gupta
authors.initials: SG
authors.lastname: Bhattacharya
authors.forename: Sudipto
authors.initials: S
authors.lastname: Saha
mesh_terms: D000485:Allergens / Q000737:chemistry / Q000276:immunology*; D000595:Amino Acid Sequence; D000818:Animals; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030562:Databases, Protein; D000939:Epitopes / Q000737:chemistry / Q000276:immunology; D006801:Humans; D007073:Immunoglobulin E / Q000276:immunology; D020407:Internet; D008969:Molecular Sequence Data; D011506:Proteins / Q000737:chemistry / Q000276:immunology*; D016415:Sequence Alignment; D012984:Software
chemical_list: D000485:Allergens; D000939:Epitopes; D011506:Proteins; D007073:Immunoglobulin E