ProQ3: Improved model quality assessments using Rosetta energy terms

TL;DR

ProQ3 enhances protein model quality assessment by integrating Rosetta energy terms with machine learning, outperforming previous methods like ProQ2 in accuracy and reliability.

Contribution

This work introduces ProQ3, a novel protein quality assessment method combining Rosetta energy features with machine learning, achieving superior performance over prior approaches.

Findings

ProQ3 matches ProQ2's accuracy using Rosetta energy terms.

ProQRosCen performs nearly as well as ProQRosFA.

Combining multiple methods yields the best assessment performance.

Abstract

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