Predicting protein residue-residue contacts using deep networks and boosting

Jesse Eickholt, Jianlin Cheng

Research output: Contribution to journalArticlepeer-review

122 Scopus citations

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.

Original languageEnglish
Pages (from-to)3066-3072
Number of pages7
JournalBioinformatics
Volume28
Issue number23
DOIs
StatePublished - Dec 2012

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