AlgPred: prediction of allergenic proteins and mapping of IgE epitopes

By Sudipto Saha, Gajendra P. Raghava. Nucleic Acids Res., 2006

711 citations recorded by the source corpus.

In this study a systematic attempt has been made to integrate various approaches in order to predict allergenic proteins with high accuracy. The dataset used for testing and training consists of 578 allergens and 700 non-allergens obtained from A. K. Bjorklund, D. Soeria-Atmadja, A. Zorzet, U. Hammerling and M. G. Gustafsson (2005) Bioinformatics, 21, 39–50. First, we developed methods based on support vector machine using amino acid and dipeptide composition and achieved an accuracy of 85.02 and 84.00%, respectively. Second, a motif-based method has been developed using MEME/MAST software that achieved sensitivity of 93.94 with 33.34% specificity. Third, a database of known IgE epitopes was searched and this predicted allergenic proteins with 17.47% sensitivity at specificity of 98.14%. Fourth, we predicted allergenic proteins by performing BLAST search against allergen representative peptides. Finally hybrid approaches have been developed, which combine two or more than two approaches. The performance of all these algorithms has been evaluated on an independent dataset of 323 allergens and on 101 725 non-allergens obtained from Swiss-Prot. A web server AlgPred has been developed for the predicting allergenic proteins and for mapping IgE epitopes on allergenic proteins (). AlgPred is available at .

Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/255781ed1691d8ee4c560619b24251cf78810c6d

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In Molecular Biology

1. Ivan Dimitrov
2. Irini A. Doytchinova
3. Darren R. Flower
4. Lyudmila Naneva
5. Ivan P. Bangov

In Radiology, Nuclear Medicine and Imaging

1. Ivan Dimitrov
2. Darren R. Flower
3. Irini A. Doytchinova
4. Lyudmila Naneva
5. Ivan P. Bangov

Citations in the corpus

• AllergenFP: allergenicity prediction by descriptor fingerprints (2014, methodology): “proteins with high similarity to allergenic proteins (Saha and Raghava, 2006).”
• AllerTOP - a server for in silico prediction of allergens (2013, methodology): “The servers accessible by the time of evaluations (December 2011) were AllerHunter [24] and AlgPred [15].”