Protein single-model quality assessment by feature-based probability
density functions
release_dlx3smiksvh4rm6242trruosdm
by
Renzhi Cao, Jianlin Cheng
2016
Abstract
Protein quality assessment (QA) has played an important role in protein
structure prediction. We developed a novel single-model quality assessment
method - Qprob. Qprob calculates the absolute error for each protein feature
value against the true quality scores (i.e. GDT-TS scores) of protein
structural models, and uses them to estimate its probability density
distribution for quality assessment. Qprob has been blindly tested on the 11th
Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as
MULTICOM-NOVEL server. The official CASP result shows that Qprob ranks as one
of the top single-model QA methods. In addition, Qprob makes contributions to
our protein tertiary structure predictor MULTICOM, which is officially ranked
3rd out of 143 predictors. The good performance shows that Qprob is good at
assessing the quality of models of hard targets. These results demonstrate that
this new probability density distribution based method is effective for protein
single-model quality assessment and is useful for protein structure prediction.
The webserver and software packages of Qprob are available at:
http://calla.rnet.missouri.edu/qprob/.
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