ProQ3: Improved model quality assessments using Rosetta energy terms.

Uziela K, Shu N, Wallner B, Elofsson A

Sci Rep 6 (-) 33509 [2016-10-04; online 2016-10-04]

Quality assessment of protein models using no other information than the structure of the model itself has been shown to be useful for structure prediction. Here, we introduce two novel methods, ProQRosFA and ProQRosCen, inspired by the state-of-art method ProQ2, but using a completely different description of a protein model. ProQ2 uses contacts and other features calculated from a model, while the new predictors are based on Rosetta energies: ProQRosFA uses the full-atom energy function that takes into account all atoms, while ProQRosCen uses the coarse-grained centroid energy function. The two new predictors also include residue conservation and terms corresponding to the agreement of a model with predicted secondary structure and surface area, as in ProQ2. We show that the performance of these predictors is on par with ProQ2 and significantly better than all other model quality assessment programs. Furthermore, we show that combining the input features from all three predictors, the resulting predictor ProQ3 performs better than any of the individual methods. ProQ3, ProQRosFA and ProQRosCen are freely available both as a webserver and stand-alone programs at

Bioinformatics Support and Infrastructure [Collaborative]

Bioinformatics Support, Infrastructure and Training [Collaborative]

PubMed 27698390

DOI 10.1038/srep33509

Crossref 10.1038/srep33509

pii: srep33509
pmc: PMC5048106

Publications 9.5.0