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Protein quality assessment (QA) has played an important role in proteinstructure prediction. We developed a novel single-model quality assessmentmethod - Qprob. Qprob calculates the absolute error for each protein featurevalue against the true quality scores (i.e. GDT-TS scores) of proteinstructural models, and uses them to estimate its probability densitydistribution for quality assessment. Qprob has been blindly tested on the 11thCritical Assessment of Techniques for Protein Structure Prediction (CASP11) asMULTICOM-NOVEL server. The official CASP result shows that Qprob ranks as oneof the top single-model QA methods. In addition, Qprob makes contributions toour protein tertiary structure predictor MULTICOM, which is officially ranked3rd out of 143 predictors. The good performance shows that Qprob is good atassessing the quality of models of hard targets. These results demonstrate thatthis new probability density distribution based method is effective for proteinsingle-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/.

Protein single-model quality assessment by feature-based probability density functions