Protein single-model quality assessment by feature-based probability density functions

By Renzhi Cao, Jianlin Cheng. 2016

77 citations recorded by the source corpus.

Protein quality assessment (QA) has played an important role in protein structure prediction. We developed a novel single-model quality assessment method–Qprob. Qprob calculates the absolute error for each protein feature value against the true quality scores (i.e. GDT-TS scores) of protein structural models and uses them to estimate its probability density distribution for quality assessment. Qprob has been blindly tested on the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM-NOVEL server. The official CASP result shows that Qprob ranks as one of the top single-model QA methods. In addition, Qprob makes contributions to our protein tertiary structure predictor MULTICOM, which is officially ranked 3rd out of 143 predictors. The good performance shows that Qprob is good at assessing the quality of models of hard targets. These results demonstrate that this new probability density distribution based method is effective for protein single-model quality assessment and is useful for protein structure prediction. The webserver of Qprob is available at: http://calla.rnet.missouri.edu/qprob/. The software is now freely available in the web server of Qprob.

Subfields: Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

In Molecular Biology

Citations in the corpus

Learning from Protein Structure with Geometric Vector Perceptrons (2020, methodology): “Therefore, MQA methods are commonly evaluated on two metrics: a global correlation between the predicted and ground truth scores, pooled across all targets, and the average per-target correlation among only the candidate structures for a specific target [5, 11, 22].”
ProQ3D: improved model quality assessments using deep learning (2016, methodology): “, 2008) and Qprob (Cao and Cheng, 2016), as well as a reference consensus method Pcons (Lundstrom et al.”
Deep convolutional neural networks for predicting the quality of single protein structural models (2019, methodology): “…proved to be effective, such as sequence/profile alignment, predicted secondary structure and solvent accessibility of residues 8 , residue-residue contact potential 19 , torsion angle of main chain 27 , physicochemical properties 13 , and energy-based environment of residues and models 12,13,15 .”
Smooth orientation-dependent scoring function for coarse-grained protein quality assessment (2018, methodology): “These can be modeled with the widely-used SCWRL4 tool (15), as in (3), or any other method (16).”
UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling (2016, methodology): “In addition to internal UniCon3D scoring, we also use two external single model quality assessment programs (MQAPs): ProQ2 (Ray et al., 2012) and Qprob (Cao and Cheng, 2016); and two clusteringbased MQAPs: APOLLO (Wang et al., 2011) and MUFOLD-CL (Zhang and Xu, 2013) in order to rank decoys produced”