![]() Received: JAccepted: ApPublished: April 29, 2020Ĭopyright: © 2020 Tung et al. PLoS ONE 15(4):Įditor: Mingyang Lu, Jackson Laboratory, UNITED STATES Ĭitation: Tung C-H, Chien C-H, Chen C-W, Huang L-Y, Liu Y-N, Chu Y-W (2020) QUATgo: Protein quaternary structural attributes predicted by two-stage machine learning approaches with heterogeneous feature encoding. Finally, QUATgo is freely accessible to the public as a web server via the site. In the case study, the accuracy of QUATgo can reach 61.5% for predicting the quaternary structure of influenza virus hemagglutinin proteins. QUATgo has 49.0% cross-validation accuracy and 31.1% independent test accuracy. QUATgo solves the problem of insufficient data for a single subunit using a two-stage architecture and uses 10-fold cross-validation to test the predictive accuracy of the classifier. Meanwhile, three kinds of sequence coding were used, including dipeptide composition, which was used for the first time to predict protein quaternary structural attributes, and protein half-life characteristics, and we modified the coding method of the functional domain composition proposed by predecessors to solve the problem of large feature vectors. To this end, we proposed a tool that can predict more than 12 subunit protein oligomers, QUATgo. ![]() However, the existing methods do not consider the integration of heterogeneous coding and the accuracy of subunit categories with limited data. Predicting these attributes is an essential task in computational biology for the advancement of proteomics. ![]() Many proteins exist in natures as oligomers with various quaternary structural attributes rather than as single chains.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |