Improved prediction of allergenicity by combination of multiple sequence motifs.
The identification and validation of protein allergens have become more important nowadays as more and more transgenic proteins are introduced into our food chains. Current allergen prediction algorithms focus on the identification of single motif or single allergen peptide for allergen detection. However, an analysis of the 575 allergen dataset shows that most allergens contain multiple motifs. Here, we present a novel algorithm that detects allergen by making use of combinations of motifs. Sensitivity of 0.772 and specificity of 0.904 were achieved by the proposed algorithm to predict allergen. The specificity of the proposed approach is found to be significantly higher than traditional single motif approaches. The high specificity of the proposed algorithm is useful in filtering out false positives, especially when laboratory resources are limited.
Kong
Waiming
W
Bioinformatics Group, Nanyang Polytechnic, Singapore. KONG_Wai_Ming@nyp.gov.sg
Tan
Tsu Soo
TS
Tham
Lawrence
L
Choo
Keng Wah
KW
D000465:Algorithms; D000485:Allergens / Q000737:chemistry*; D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D017343:Genes, Plant; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated; D010940:Plant Proteins / Q000737:chemistry; D030821:Plants, Genetically Modified; D017434:Protein Structure, Tertiary; D012680:Sensitivity and Specificity; D020539:Sequence Analysis, Protein; D012984:Software
D000485:Allergens; D010940:Plant Proteins