ArticleScientific reports2016
Protein single-model quality assessment by feature-based probability density functions.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Deeply conserved and expressed small ORFs in the model beetle Tribolium castaneum.BMC biology · 2026Article
- Protein Structural Model Selection Informed by Comparison of Predicted Ligand Binding Poses.Journal of chemical information and modeling · 2025Article
- Structural evolution of Delta lineage of SARS-CoV-2.International journal of biological macromolecules · 2023Article
- Illuminating the "Twilight Zone": Advances in Difficult Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
- ZoomQA: residue-level protein model accuracy estimation with machine learning on sequential and 3D structural features.Briefings in bioinformatics · 2022Article
- An integrated protein structure fitness scoring approach for identifying native-like model structures.Computational and structural biotechnology journal · 2022Article
- Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14.Scientific reports · 2021Article
- MASS: predict the global qualities of individual protein models using random forests and novel statistical potentials.BMC bioinformatics · 2020Review
- SAXSDom: Modeling multidomain protein structures using small-angle X-ray scattering data.Proteins · 2020Article
- Machine Learning Approaches for Quality Assessment of Protein Structures.Biomolecules · 2020Review
- RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites.Frontiers in bioengineering and biotechnology · 2020Article
- Estimation of model accuracy in CASP13.Proteins · 2019Article
- A simple neural network implementation of generalized solvation free energy for assessment of protein structural models.RSC advances · 2019Article
- Unsupervised and Supervised Learning over theEnergy Landscape for Protein Decoy Selection.Biomolecules · 2019Article
- PSICA: a fast and accurate web service for protein model quality analysis.Nucleic acids research · 2019Article
- Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation.Molecular therapy. Nucleic acids · 2019Article
- Identification and localization of Tospovirus genus-wide conserved residues in 3D models of the nucleocapsid and the silencing suppressor proteins.Virology journal · 2019Article
- Identification of Phage Viral Proteins With Hybrid Sequence Features.Frontiers in microbiology · 2019Article
- Predicting Ion Channels Genes and Their Types With Machine Learning Techniques.Frontiers in genetics · 2019Article
- Role of solvent accessibility for aggregation-prone patches in protein folding.Scientific reports · 2018Article
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2 authors.
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Abstract
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.
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