期刊
VACCINE
卷 25, 期 5, 页码 856-866出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.vaccine.2006.09.032
关键词
vaccine; antigen prediction
Subunit vaccine discovery is an accepted clinical priority. The empirical approach is time- and labor-consuming and can often end in failure. Rational information-driven approaches can overcome these limitations in a fast and efficient manner. However, informatics solutions require reliable algorithms for antigen identification. All known algorithms use sequence, similarity to identify antigens. However, antigenicity may be encoded subtly in a sequence and may not be directly identifiable by sequence alignment. We propose a new alignment-independent method for antigen recognition based on the principal chemical properties of protein amino acid sequences. The method is tested by cross-validation on a training set of bacterial antigens and external validation on a test set of known antigens. The prediction accuracy is 83% for the cross-validation and 80% for the external test set. Our approach is accurate and robust, and provides a potent tool for the ill silico discovery of medically relevant subunit vaccines. (c) 2006 Elsevier Ltd. All rights reserved.
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