Doytchinova Irini A, Flower Darren R
Faculty of Pharmacy, Medical University of Sofia, Dunav st. 2, 1000 Sofia, Bulgaria.
Vaccine. 2007 Jan 15;25(5):856-66. doi: 10.1016/j.vaccine.2006.09.032. Epub 2006 Sep 28.
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 in silico discovery of medically relevant subunit vaccines.
亚单位疫苗研发是公认的临床重点。经验性方法耗时费力，且常常以失败告终。基于合理信息驱动的方法能够快速高效地克服这些局限性。然而，信息学解决方案需要可靠的算法来识别抗原。所有已知算法都利用序列相似性来识别抗原。然而，抗原性可能在序列中被微妙地编码，并且可能无法通过序列比对直接识别。我们提出了一种基于蛋白质氨基酸序列主要化学性质的、与比对无关的新抗原识别方法。该方法在一组细菌抗原训练集上进行交叉验证，并在一组已知抗原测试集上进行外部验证。交叉验证的预测准确率为83%，外部测试集的预测准确率为80%。我们的方法准确且稳健，为在计算机上发现医学相关亚单位疫苗提供了有力工具。
