ArticleProteins2018
Assessment of model accuracy estimations in CASP12.
Article in Proteins, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Challenges in bridging the gap between protein structure prediction and functional interpretation.Proteins · 2025Article
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- iQDeep: an integrated web server for protein scoring using multiscale deep learning models.Journal of molecular biology · 2023Article
- Estimation of model accuracy by a unique set of features and tree-based regressor.Scientific reports · 2022Article
- Data Size and Quality Matter: Generating Physically-Realistic Distance Maps of Protein Tertiary Structures.Biomolecules · 2022Article
- Modeling SARS-CoV-2 proteins in the CASP-commons experiment.Proteins · 2021Article
- Assessment of protein model structure accuracy estimation in CASP14: Old and new challenges.Proteins · 2021Article
- Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14.Scientific reports · 2021Article
- SPServer: split-statistical potentials for the analysis of protein structures and protein-protein interactions.BMC bioinformatics · 2021Article
- Using physical features of protein core packing to distinguish real proteins from decoys.Protein science : a publication of the Protein Society · 2020Article
- QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks.Bioinformatics (Oxford, England) · 2020Article
- Reducing Ensembles of Protein Tertiary Structures Generated De Novo via Clustering.Molecules (Basel, Switzerland) · 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
- Introducing "best single template" models as reference baseline for the Continuous Automated Model Evaluation (CAMEO).Proteins · 2019Article
- Article
- Driven to near-experimental accuracy by refinement via molecular dynamics simulations.Proteins · 2019Article
- In silico prediction of prolactin molecules as a tool for equine genomics reproduction.Molecular diversity · 2019Article
- Unsupervised and Supervised Learning over theEnergy Landscape for Protein Decoy Selection.Biomolecules · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The record high 42 model accuracy estimation methods were tested in CASP12. The paper presents results of the assessment of these methods in the whole-model and per-residue accuracy modes. Scores from four different model evaluation packages were used as the "ground truth" for assessing accuracy of methods' estimates. They include a rigid-body score-GDT_TS, and three local-structure based scores-LDDT, CAD and SphereGrinder. The ability of methods to identify best models from among several available, predict model's absolute accuracy score, distinguish between good and bad models, predict accuracy of the coordinate error self-estimates, and discriminate between reliable and unreliable regions in the models was assessed. Single-model methods advanced to the point where they are better than clustering methods in picking the best models from decoy sets. On the other hand, consensus methods, taking advantage of the availability of large number of models for the same target protein, are still better in distinguishing between good and bad models and predicting local accuracy of models. The best accuracy estimation methods were shown to perform better with respect to the frozen in time reference clustering method and the results of the best method in the corresponding class of methods from the previous CASP. Top performing single-model methods were shown to do better than all but three CASP12 tertiary structure predictors when evaluated as model selectors.
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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.