Predicting protein residue-residue contacts using deep networks and boosting

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Abstract

Motivation:

Protein residue-residue contacts continue to play a larger and larger role in protein tertiary structure modeling and evaluation. Yet, while the importance of contact information increases, the performance of sequence-based contact predictors has improved slowly. New approaches and methods are needed to spur further development and progress in the field.

Results:

Here we present DNCON, a new sequence-based residue-residue contact predictor using deep networks and boosting techniques. Making use of graphical processing units and CUDA parallel computing technology, we are able to train large boosted ensembles of residue-residue contact predictors achieving state-of-the-art performance.

Availability:

The web server of the prediction method (DNCON) is available at http://iris.rnet.missouri.edu/dncon/. Contact: chengji@missouri.edu

Supplementary information:

Supplementary data are available at Bioinformatics online.

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