Dobson Richard J B, Munroe Patricia B, Caulfield Mark J, Saqi Mansoor A S
The William Harvey Research Institute, Bart's and the London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, UK.
J Integr Bioinform. 2009 Aug 23;6(1):109. doi: 10.2390/biecoll-jib-2009-109.
Functional annotation of a protein sequence in the absence of experimental data or clear similarity to a sequence of known function is difficult. In this study, a simple set of sequence attributes based on physicochemical and predicted structural characteristics were used as input to machine learning methods. In order to improve performance through increasing the data available for training, a technique of sequence enrichment was explored. These methods were used to predict membership to 24 and 49 large and diverse protein superfamiles from the SCOP database. We found the best performance was obtained using an enriched training dataset. Accuracies of 66.3% and 55.6% were achieved on datasets comprising 24 and 49 superfamilies with LibSVM and AdaBoostM1 respectively. The methods used here confirm that domains within superfamilies share global sequence properties. We show machine learning models used to predict categories within the SCOP database can be significantly improved via a simple sequence enrichment step. These approaches can be used to complement profile methods for detecting distant relationships where function is difficult to infer.
在缺乏实验数据或与已知功能序列无明显相似性的情况下，对蛋白质序列进行功能注释是困难的。在本研究中，基于物理化学和预测结构特征的一组简单序列属性被用作机器学习方法的输入。为了通过增加可用于训练的数据来提高性能，探索了一种序列富集技术。这些方法被用于预测来自SCOP数据库的24个和49个大型且多样的蛋白质超家族的成员资格。我们发现使用富集训练数据集可获得最佳性能。使用LibSVM和AdaBoostM1分别在包含24个和49个超家族的数据集上实现了66.3%和55.6%的准确率。这里使用的方法证实超家族内的结构域共享全局序列属性。我们表明，用于预测SCOP数据库内类别的机器学习模型可通过简单的序列富集步骤得到显著改进。这些方法可用于补充轮廓方法，以检测难以推断功能的远距离关系。
