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Motivation: To assess the quality of a protein model, i.e. to estimate howclose it is to its native structure, using no other information than thestructure of the model has been shown to be useful for structure prediction.The state of the art method, ProQ2, is based on a machine learning approachthat uses a number of features calculated from a protein model. Here, weexamine if these features can be exchanged with energy terms calculated fromRosetta and if a combination of these terms can improve the quality assessment.Results: When using the full atom energy function from Rosetta in ProQRosFA theQA is on par with our previous state-of-the-art method, ProQ2. The method basedon the low-resolution centroid scoring function, ProQRosCen, performs almost aswell and the combination of all the three methods, ProQ2, ProQRosFA andProQCenFA into ProQ3 show superior performance over ProQ2. Availability: ProQ3is freely available on BitBucket at https://bitbucket.org/ElofssonLab/proq3

ProQ3: Improved model quality assessments using Rosetta energy terms