Title

An SVM method using evolutionary information for the identification of allergenic proteins

Authors

Affiliation

¹Insilico Consulting, 402, Citi Centre, 39/2 Erandwane, Karve Road, Pune-411004, Maharashtra, India

Email

biotechkk@gmail.com; * Corresponding author

Article Type

Prediction Model

Date

received December 28, 2007; revised January 17, 2008; accepted January 19, 2008; published January 27, 2008

Abstract

This study presents an allergenic protein prediction system that appears to be capable of producing high sensitivity and specificity. The proposed system is based on support vector machine (SVM) using evolutionary information in the form of an amino acid position specific scoring matrix (PSSM). The performance of this system is assessed by a 10-fold cross-validation experiment using a dataset consisting of 693 allergens and 1041 non-allergens obtained from Swiss-Prot and Structural Database of Allergenic Proteins (SDAP). The PSSM method produced an accuracy of 90.1% in comparison to the methods based on SVM using amino acid, dipeptide composition, pseudo (5-tier) amino acid composition that achieved an accuracy of 86.3, 86.5 and 82.1% respectively. The results show that evolutionary information can be useful to build more effective and efficient allergen prediction systems

Keywords

allergenic proteins; evolutionary information; PSSM; amino-acid composition; dipeptide composition; SVM

Citation

Kumar & Shelokar, Bioinformation 2(6): 253-256 (2008)

Edited by

P. Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.