General Information
| Description | Improved contact predictions improve protein models |
| Homepage | https://www.github.com/ElofssonLab/pcons-fold |
Publications
PconsFold: improved contact predictions improve protein models
Motivation:
Recently it has been shown that the quality of protein contact prediction from evolutionary information can be improved significantly if direct and indirect information is separated. Given sufficiently large protein families, the contact predictions contain sufficient information to predict the structure of many protein families. However, since the first studies contact prediction methods have improved. Here, we ask how much the final models are improved if improved contact predictions are used.
Results:
In a small benchmark of 15 proteins, we show that the TM-scores of top-ranked models are improved by on average 33% using PconsFold compared with the original version of EVfold. In a larger benchmark, we find that the quality is improved with 15–30% when using PconsC in comparison with earlier contact prediction methods. Further, using Rosetta instead of CNS does not significantly improve global model accuracy, but the chemistry of models generated with Rosetta is improved.
Availability:
PconsFold is a fully automated pipeline for ab initio protein structure prediction based on evolutionary information. PconsFold is based on PconsC contact prediction and uses the Rosetta folding protocol. Due to its modularity, the contact prediction tool can be easily exchanged. The source code of PconsFold is available on GitHub at https://www.github.com/ElofssonLab/pcons-fold under the MIT license. PconsC is available from http://c.pcons.net/.
Contact:
arne@bioinfo.se
Supplementary information:
Supplementary data are available at Bioinformatics online.
Related Tools
PconsC
Published June 2013
Combination of direct information methods and alignments improves contact prediction
GDFuzz3D
Published June 2015
A method for protein 3D structure reconstruction from contact maps, based on a non-Euclidean distance function
NeBcon
Published March 2017
Protein contact map prediction using neural network training coupled with naïve Bayes classifiers
QAcon
Published November 2016
Single model quality assessment using protein structural and contact information with machine learning techniques
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