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Bioinformatics. 2018 Dec 24. doi: 10.1093/bioinformatics/bty1036. [Epub ahead of print]

PconsC4: fast, accurate, and hassle-free contact predictions.

Author information

1
Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden.

Abstract

Motivation:

Residue contact prediction was revolutionized recently by the introduction of direct coupling analysis (DCA). Further improvements, in particular for small families, have been obtained by the combination of DCA and deep learning methods. However, existing deep learning contact prediction methods often rely on a number of external programs and are therefore computationally expensive.

Results:

Here, we introduce a novel contact predictor, PconsC4, which performs on par with state of the art methods. PconsC4 is heavily optimized, does not use any external programs and therefore is significantly faster and easier to use than other methods.

Availability:

PconsC4 is freely available under the GPL license from https://github.com/ElofssonLab/PconsC4. Installation is easy using the pip command and works on any system with Python 3.5 or later and a GCC compiler. It does not require a GPU nor special hardware.

Supplementary information:

All data used in the development is available at Bioinformatics online.

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