ProQ3D: improved model quality assessments using deep learning.

Uziela K, Menéndez Hurtado D, Shu N, Wallner B, Elofsson A

Bioinformatics 33 (10) 1578-1580 [2017-05-15; online 2017-01-06]

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). ProQ3D is freely available both as a webserver and a stand-alone program at Supplementary data are available at Bioinformatics online.

Affiliated researcher

PubMed 28052925

DOI 10.1093/bioinformatics/btw819

Crossref 10.1093/bioinformatics/btw819

pii: 2801464

Publications 9.5.0