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.