ArticleProteins2016
CASP 11 target classification.
Article in Proteins, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
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- To split or not to split: CASP15 targets and their processing into tertiary structure evaluation units.Proteins · 2023Article
- Using steered molecular dynamic tension for assessing quality of computational protein structure models.Journal of computational chemistry · 2022Article
- Target classification in the 14th round of the critical assessment of protein structure prediction (CASP14).Proteins · 2021Article
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- DNCON2: improved protein contact prediction using two-level deep convolutional neural networks.Bioinformatics (Oxford, England) · 2018Article
- DeepSF: deep convolutional neural network for mapping protein sequences to folds.Bioinformatics (Oxford, England) · 2018Article
- Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12.Proteins · 2018Article
- RRCRank: a fusion method using rank strategy for residue-residue contact prediction.BMC bioinformatics · 2017Article
- KScons: a Bayesian approach for protein residue contact prediction using the knob-socket model of protein tertiary structure.Bioinformatics (Oxford, England) · 2016Article
- ProQ3: Improved model quality assessments using Rosetta energy terms.Scientific reports · 2016Article
- UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling.Bioinformatics (Oxford, England) · 2016Article
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- Assessment of CASP11 contact-assisted predictions.Proteins · 2016Article
- ORION: a web server for protein fold recognition and structure prediction using evolutionary hybrid profiles.Scientific reports · 2016Article
- Article
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Authors and funding
7 authors.
Funding
Abstract
Protein target structures for the Critical Assessment of Structure Prediction round 11 (CASP11) and CASP ROLL were split into domains and classified into categories suitable for assessment of template-based modeling (TBM) and free modeling (FM) based on their evolutionary relatedness to existing structures classified by the Evolutionary Classification of Protein Domains (ECOD) database. First, target structures were divided into domain-based evaluation units. Target splits were based on the domain organization of available templates as well as the performance of servers on whole targets compared to split target domains. Second, evaluation units were classified into TBM and FM categories using a combination of measures that evaluate prediction quality and template detectability. Generally, target domains with sequence-related templates and good server prediction performance were classified as TBM, whereas targets without sequence-identifiable templates and low server performance were classified as FM. As in previous CASP experiments, the boundaries for classification were blurred due to the presence of significant insertions and deteriorations in the targets with respect to homologous templates, as well as the presence of templates with partial coverage of new folds. The FM category included 45 target domains, which represents an unprecedented number of difficult CASP targets provided for modeling. Proteins 2016; 84(Suppl 1):20-33. © 2016 Wiley Periodicals, Inc.
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