Work

Predicting protein residue–residue contacts using deep networks and boosting

Year: 2012

Type: article

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: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Source: Bioinformatics

DOI: 10.1093/bioinformatics/bts598

Authors: Jesse Eickholt, Jianlin Cheng

Institution: University of Missouri

Language: English

Topic: Protein Structure and Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funder: National Institute of General Medical Sciences

Award: Distance-based ab initio protein structure prediction