Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

This article is part of the supplement: Proceedings of the Tenth Annual MCBIOS Conference

Open Access Proceedings

A study and benchmark of DNcon: a method for protein residue-residue contact prediction using deep networks

Jesse Eickholt1 and Jianlin Cheng234*

Author Affiliations

1 Department of Computer Science, Central Michigan University, Mt. Pleasant, MI 48859, USA

2 Department of Computer Science, University of Missouri, Columbia, MO 65211, USA

3 Informatics Institute, University of Missouri, Columbia, MO 65211, USA

4 C. Bond Life Science Center, University of Missouri, Columbia, MO 65211, USA

For all author emails, please log on.

BMC Bioinformatics 2013, 14(Suppl 14):S12  doi:10.1186/1471-2105-14-S14-S12

Published: 9 October 2013

Abstract

Background

In recent years, the use and importance of predicted protein residue-residue contacts has grown considerably with demonstrated applications such as drug design, protein tertiary structure prediction and model quality assessment. Nevertheless, reported accuracies in the range of 25-35% stubbornly remain the norm for sequence based, long range contact predictions on hard targets. This is in spite of a prolonged effort on behalf of the community to improve the performance of residue-residue contact prediction. A thorough study of the quality of current residue-residue contact predictions and the evaluation metrics used as well as an analysis of current methods is needed to stimulate further advancement in contact prediction and its application. Such a study will better explain the quality and nature of residue-residue contact predictions generated by current methods and as a result lead to better use of this contact information.

Results

We evaluated several sequence based residue-residue contact predictors that participated in the tenth Critical Assessment of protein Structure Prediction (CASP) experiment. The evaluation was performed using standard assessment techniques such as those used by the official CASP assessors as well as two novel evaluation metrics (i.e., cluster accuracy and cluster count). An in-depth analysis revealed that while most residue-residue contact predictions generated are not accurate at the residue level, there is quite a strong contact signal present when allowing for less than residue level precision. Our residue-residue contact predictor, DNcon, performed particularly well achieving an accuracy of 66% for the top L/10 long range contacts when evaluated in a neighbourhood of size 2. The coverage of residue-residue contact areas was also greater with DNcon when compared to other methods. We also provide an analysis of DNcon with respect to its underlying architecture and features used for classification.

Conclusions

Our novel evaluation metrics demonstrate that current residue-residue contact predictions do contain a strong contact signal and are of better quality than standard evaluation metrics indicate. Our method, DNcon, is a robust, state-of-the-art residue-residue sequence based contact predictor and excelled under a number of evaluation schemes. It is available as a web service at http://iris.rnet.missouri.edu/dncon/ webcite.

Keywords:
protein residue-residue contact; contact prediction; deep learning; deep networks