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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 http://proq3.bioinfo.se/. arne@bioinfo.se. Supplementary data are available at Bioinformatics online.
Bioinformatics Support and Infrastructure [Collaborative]
Bioinformatics Support, Infrastructure and Training [Collaborative]
PubMed 28052925
DOI 10.1093/bioinformatics/btw819
Crossref 10.1093/bioinformatics/btw819
pii: btw819