Summary: Protein quality assessment is a long-standing problem in bioinformatics. For more than a decade we have developed state-of-art predictors by carefully selecting and optimising inputs to a machine learning method. The correlation has increased from 0.60 in ProQ to 0.81 in ProQ2 and 0.85 in ProQ3 mainly by adding a large set of carefully tuned descriptions of a protein. Here, we show that a substantial improvement can be obtained using exactly the same inputs as in ProQ2 or ProQ3 but replacing the support vector machine by a deep neural network. This improves the Pearson correlation to 0.90 (0.85 using ProQ2 input features). Availability and Implementation: ProQ3D is freely available both as a webserver and a stand-alone program at http://proq3.bioinfo.se/ .
CITATION STYLE
Uziela, K., Hurtado, D. M., Shu, N., Wallner, B., & Elofsson, A. (2017). ProQ3D: Improved model quality assessments using deep learning. Bioinformatics, 33(10), 1578–1580. https://doi.org/10.1093/bioinformatics/btw819
Mendeley helps you to discover research relevant for your work.