ProQ3D: Improved model quality assessments using Deep Learning

2016/10/17 by Karolis Uziela, Uziela, Karolis, David Menéndez Hurtado +5
Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #Biomolecules (q-bio.BM) #FOS: Biological sciences #Gene expression and cancer classification #Machine Learning in Bioinformatics

paper · pdf · doi:10.48550/arxiv.1610.05189

openalex publication_date 2016/10/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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