ProQ3: Improved model quality assessments using Rosetta energy terms
By Karolis Uziela, Nanjiang Shu, Björn Wallner, Arne Elofsson. 2016
101 citations recorded by the source corpus.
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 http://proq3.bioinfo.se/.
Subfields: Electrical and Electronic Engineering, Materials Chemistry, Spectroscopy
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
In Materials Chemistry
- Liam J. McGuffin
- Torsten Schwede
- Andriy Kryshtafovych
- Björn Wallner
- Arne Elofsson
- Jianlin Cheng
- David Ménendez Hurtado
- Karolis Uziela
In Spectroscopy
- Andriy Kryshtafovych
- Bohdan Monastyrskyy
- David Ménendez Hurtado
- Sergei Grudinin
- Torsten Schwede
- Georgy Derevyanko
- Yoshua Bengio
- Guillaume Lamoureux
Citations in the corpus
- ProQ3D: improved model quality assessments using deep learning (2016, methodology): “In ProQ2 profile weights were added to improve the predictions (Ray et al., 2012) and in ProQ3 (Uziela et al., 2016) we added energy terms calculated from Rosetta (Leaver-Fay et al., 2011).”
- Assessment of Protein Model Structure Accuracy Estimation in CASP13: Challenges in the Era of Deep Learning (2019, methodology): “24 FIGURE 5 Performance comparison of EMA methods relative to the reference methods over the last three CASPs.”
- Estimation of model accuracy in CASP13 (2019, background): “1,6,20 Both ProQ2 and ProQ3 use a simple linear SVM to combine the many input features when estimating the accuracy of a model.”
- GraphQA: protein model quality assessment using graph convolutional networks (2020, unknown): “The field of bioinformatics, on the other hand, has witnessed noticeable progress in QA for more than a decade: from earlier works using artificial neural networks (Wallner & Elofsson, 2006) or support vector machines (Ray et al., 2012; Uziela et al., 2016) to more recent deep learning methods based”
- Deep convolutional networks for quality assessment of protein folds (2018, methodology): “ProQ3D [11] uses the same high-level input features as the earlier ProQ3 method [12] but achieves better performance by replacing the support vector machine model by a deep neural network.”