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 of Qprob is available at: http://calla.rnet.missouri.edu/qprob/. The software is now freely available in the web server of Qprob.
Highlights
IntroductionBy combining the probability density distributions of all features, we can predict the global quality score of a model
The performance of Qprob is similar to the state-of-the-art single-model performance during the blind CASP11 experiment, which demonstrates the effectiveness of the probability density distribution based quality assessment methods
In the Result Section, we report the performance of our method in the CASP11 experiment
Summary
By combining the probability density distributions of all features, we can predict the global quality score of a model. The performance of Qprob is similar to the state-of-the-art single-model performance during the blind CASP11 experiment, which demonstrates the effectiveness of the probability density distribution based quality assessment methods. In the Methods Section, we describe each feature and the calculation of the global quality assessment score in detail. In the Result Section, we report the performance of our method in the CASP11 experiment. In the Discussion Section, we summarize the results and conclude with the direction of future works
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