Format

Send to

Choose Destination
See comment in PubMed Commons below
Bioinformatics. 2016 Sep 27. pii: btw618. [Epub ahead of print]

Comparing co-evolution methods and their application to template-free protein structure prediction.

Author information

  • 1Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.
  • 2Department of Informatics, UCB Pharma, Slough SL1 3WE, UK.
  • 3Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China.

Abstract

MOTIVATION:

Co-evolution methods have been used as contact predictors to identify pairs of residues that share spatial proximity. Such contact predictors have been compared in terms of the precision of their predictions, but there is no study that compares their usefulness to model generation.

RESULTS:

We compared eight different co-evolution methods for a set of ∼3500 proteins and found that metaPSICOV stage 2 produces, on average, the most precise predictions. Precision of all the methods is dependent on SCOP class, with most methods predicting contacts in all α and membrane proteins poorly. The contact predictions were then used to assist in de novo model generation. We found that it was not the method with the highest average precision, but rather metaPSICOV stage 1 predictions that consistently led to the best models being produced. Our modelling results show a correlation between the proportion of predicted long range contacts that are satisfied on a model and its quality. We used this proportion to effectively classify models as correct/incorrect; discarding decoys classified as incorrect led to an enrichment in the proportion of good decoys in our final ensemble by a factor of seven. For 17 out of the 18 cases where correct answers were generated, the best models were not discarded by this approach. We were also able to identify eight cases where no correct decoy had been generated.

AVAILABILITY AND IMPLEMENTATION:

Data is available for download from: http://opig.stats.ox.ac.uk/resources CONTACT: saulo.deoliveira@dtc.ox.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

PMID:
27682254
DOI:
10.1093/bioinformatics/btw618
[PubMed - as supplied by publisher]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Silverchair Information Systems
    Loading ...
    Support Center