ArticleProteins2011
Evaluation of model quality predictions in CASP9.
Article in Proteins, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
50 citing papers in PubMed.
- Highlights of Model Quality Assessment in CASP16.Proteins · 2026Article
- The physics-AI dialogue in drug design.RSC medicinal chemistry · 2025Review
- Challenges in bridging the gap between protein structure prediction and functional interpretation.Proteins · 2025Article
- New prediction categories in CASP15.Proteins · 2023Article
- iQDeep: an integrated web server for protein scoring using multiscale deep learning models.Journal of molecular biology · 2023Article
- Quality Estimates for 3D Protein Models.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Estimation of model accuracy by a unique set of features and tree-based regressor.Scientific reports · 2022Article
- Assessment of protein model structure accuracy estimation in CASP14: Old and new challenges.Proteins · 2021Article
- Modeling SARS-CoV-2 proteins in the CASP-commons experiment.Proteins · 2021Article
- Using physical features of protein core packing to distinguish real proteins from decoys.Protein science : a publication of the Protein Society · 2020Article
- Machine Learning Approaches for Quality Assessment of Protein Structures.Biomolecules · 2020Review
- QMEANDisCo-distance constraints applied on model quality estimation.Bioinformatics (Oxford, England) · 2020Article
- Assessment of protein model structure accuracy estimation in CASP13: Challenges in the era of deep learning.Proteins · 2019Article
- PSICA: a fast and accurate web service for protein model quality analysis.Nucleic acids research · 2019Article
- Graph-Based Community Detection for Decoy Selection in Template-Free Protein Structure Prediction.Molecules (Basel, Switzerland) · 2019Article
- Evaluation system and web infrastructure for the second cryo-EM model challenge.Journal of structural biology · 2018Article
- Article
- Assessment of model accuracy estimations in CASP12.Proteins · 2018Article
- Leishmania infantum 5'-Methylthioadenosine Phosphorylase presents relevant structural divergence to constitute a potential drug target.BMC structural biology · 2017Article
- MQAPRank: improved global protein model quality assessment by learning-to-rank.BMC bioinformatics · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
CASP has been assessing the state of the art in the a priori estimation of accuracy of protein structure prediction since 2006. The inclusion of model quality assessment category in CASP contributed to a rapid development of methods in this area. In the last experiment, 46 quality assessment groups tested their approaches to estimate the accuracy of protein models as a whole and/or on a per-residue basis. We assessed the performance of these methods predominantly on the basis of the correlation between the predicted and observed quality of the models on both global and local scales. The ability of the methods to identify the models closest to the best one, to differentiate between good and bad models, and to identify well modeled regions was also analyzed. Our evaluations demonstrate that even though global quality assessment methods seem to approach perfection point (weighted average per-target Pearson's correlation coefficients are as high as 0.97 for the best groups), there is still room for improvement. First, all top-performing methods use consensus approaches to generate quality estimates, and this strategy has its own limitations. Second, the methods that are based on the analysis of individual models lag far behind clustering techniques and need a boost in performance. The methods for estimating per-residue accuracy of models are less accurate than global quality assessment methods, with an average weighted per-model correlation coefficient in the range of 0.63-0.72 for the best 10 groups.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.