Automated server predictions in CASP7

@article{Battey2007AutomatedSP,
  title={Automated server predictions in CASP7},
  author={James N D Battey and J{\"u}rgen Kopp and Lorenza Bordoli and Randy J. Read and Neil D. Clarke and Torsten Schwede},
  journal={Proteins: Structure},
  year={2007},
  volume={69}
}
With each round of CASP (Critical Assessment of Techniques for Protein Structure Prediction), automated prediction servers have played an increasingly important role. Today, most protein structure prediction approaches in some way depend on automated methods for fold recognition or model building. The accuracy of server predictions has significantly increased over the last years, and, in CASP7, we observed a continuation of this trend. In the template‐based modeling category, the best… Expand
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References

SHOWING 1-10 OF 83 REFERENCES
EVA: evaluation of protein structure prediction servers
TLDR
EVA (http://cubic.columbia.edu/eva/) is a web server for evaluation of the accuracy of automated protein structure prediction methods, and provides useful information to developers as well as users of prediction methods. Expand
Assessment of disorder predictions in CASP7
TLDR
Significant differences between different prediction methods were identified with regard to their sensitivity and specificity in correctly predicting ordered and disordered residues based on a protein target sequence, which is of relevance for practical applications of these computational tools. Expand
Assessment of predictions submitted for the CASP7 domain prediction category
TLDR
The results of the analysis clearly demonstrate that the best methods are able to make consistently reliable predictions when the target has a structural template, although they are less good when the domain break occurs in a region not covered by a template. Expand
CAFASP3: The third critical assessment of fully automated structure prediction methods
TLDR
The results of the fully automated CAFASP3 experiment, which was carried out in parallel with CASP5, show that significant progress has been achieved in automatic structure prediction and has important implications to the prospects of automated structure modeling in the context of structural genomics. Expand
CAFASP‐1: Critical assessment of fully automated structure prediction methods
TLDR
The results showed that current fully automatic fold recognition servers can often identify remote similarities when pairwise sequence search methods fail, but it is clear that current automated fold recognition methods can not yet compete with “human‐expert plus machine” predictions. Expand
Pcons.net: protein structure prediction meta server
TLDR
The Pcons.net Meta Server provides improved automated tools for protein structure prediction and analysis using consensus, and implements all the steps necessary to produce a high quality model of a protein. Expand
Assessment of predictions submitted for the CASP7 function prediction category.
TLDR
A full overview of the Critical Assessment of Protein Structure Prediction (CASP7) function prediction category is presented, with a focus on binding site prediction as the only category that can be truly assessed in the CASP spirit. Expand
A decade of CASP: progress, bottlenecks and prognosis in protein structure prediction.
  • J. Moult
  • Computer Science, Medicine
  • Current opinion in structural biology
  • 2005
TLDR
Current major challenges are refining comparative models so that they match experimental accuracy, obtaining accurate sequence alignments for models based on remote evolutionary relationships, and extending template-free modeling methods so thatthey produce more accurate models, handle parts of comparative models not available from a template and deal with larger structures. Expand
Protein structure prediction using a variety of profile libraries and 3D verification
This study is intended to construct a useful method for fold recognition, regardless of whether the proteins to be compared are evolutionarily related. We developed several descendants of ourExpand
Assessment of CASP7 predictions for template‐based modeling targets
TLDR
The accuracy of predicted protein models for 108 target domains was assessed based on a detailed comparison between the experimental and predicted structures and it showed that the best groups produced models closer to the target structure than the best single template for a significant number of targets. Expand
...
1
2
3
4
5
...