  Motivation: To assess the quality of a protein model, i.e. to estimate how
close it is to its native structure, using no other information than the
structure 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 approach
that uses a number of features calculated from a protein model. Here, we
examine if these features can be exchanged with energy terms calculated from
Rosetta and if a combination of these terms can improve the quality assessment.
Results: When using the full atom energy function from Rosetta in ProQRosFA the
QA is on par with our previous state-of-the-art method, ProQ2. The method based
on the low-resolution centroid scoring function, ProQRosCen, performs almost as
well and the combination of all the three methods, ProQ2, ProQRosFA and
ProQCenFA into ProQ3 show superior performance over ProQ2. Availability: ProQ3
is freely available on BitBucket at https://bitbucket.org/ElofssonLab/proq3
