Predicting protein residue-residue contacts using deep networks and boosting

摘要

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.