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arXiv · 1610.05189

ProQ3D: Improved model quality assessments using Deep Learning

Karolis Uziela·David Menéndez Hurtado·Björn Wallner·Arne Elofsson
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Abstract

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: ProQ3D is freely available both as a webserver and a stand-alone program at http://proq3.bioinfo.se/

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Karolis Uziela, David Menéndez Hurtado, Björn Wallner, Arne Elofsson. 2016-10-17. ProQ3D: Improved model quality assessments using Deep Learning. https://arxiv.org/abs/1610.05189

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