Computer prediction of allergen proteins from sequence-derived protein structural and physicochemical properties.
BACKGROUND
Computational methods have been developed for predicting allergen proteins from sequence segments that show identity, homology, or motif match to a known allergen. These methods achieve good prediction accuracies, but are less effective for novel proteins with no similarity to any known allergen.


METHODS
This work tests the feasibility of using a statistical learning method, support vector machines, as such a method. The prediction system is trained and tested by using 1005 allergen proteins from the Allergome database and 22,469 non-allergen proteins from 7871 Pfam families.


RESULTS
Testing results by an independent set of 229 allergen and 6717 non-allergen proteins from 7871 Pfam families show that 93.0% and 99.9% of these are correctly predicted, which are comparable to the best results of other methods. Of the 18 novel allergen proteins non-homologous to any other proteins in the Swissprot database, 88.9% is correctly predicted. A further screening of 168,128 proteins in the Swissprot database finds that 2.9% of the proteins are predicted as allergen proteins, which is consistent with the estimated numbers from motif-based methods.


CONCLUSIONS
Our study suggests that SVM is a potentially useful method for predicting allergen proteins and it has certain capability for predicting novel allergen proteins. Our software can be accessed at .
Bioinformatics and Drug Design Group, Department of Pharmacy and Computational Science, National University of Singapore, Blk SoC 1, Level 7, 3 Science Drive 2, Singapore 117543, Singapore.
Juan
J
Cui
Lian Yi
LY
Han
Hu
H
Li
Choong Yong
CY
Ung
Zhi Qun
ZQ
Tang
Chan Juan
CJ
Zheng
Zhi Wei
ZW
Cao
Yu Zong
YZ
Chen
D000485:Allergens / Q000737:chemistry / Q000235:genetics; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008958:Models, Molecular; D015233:Models, Statistical; D008969:Molecular Sequence Data; D011237:Predictive Value of Tests; D011487:Protein Conformation; D017434:Protein Structure, Tertiary; D020539:Sequence Analysis, Protein
D000485:Allergens