Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12

By J. Haas, Alessandro Barbato, Dario Behringer, Gabriel Studer, Steven Roth, Martino Bertoni, Khaled Mostaguir, Rafal Gumienny, Torsten Schwede. 2018

152 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Accurate prediction of protein structures and interactions using a 3-track neural network (2021, neutral): “Fortunately, the Continuous Automated Model Evaluation (CAMEO) experiment (7) tests structure prediction servers blindly on protein structures as they are submitted to the PDB.”
Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom (2023, positive): “…blind testing of RFAA prediction performance, we enrolled an RFAA server in the blind Continuous Automated Model EvaluatiOn (CAMEO) ligand-docking evaluation, which carries out predictions on all struc- turessubmittedtothePDBeachweekwitheachenrolledserverandevaluatestheirperfor-mance( 16 – 18 ).”
Assessment of three-dimensional RNA structure prediction in CASP15 (2023, neutral): “59,60 Across all targets, lDDT values for best predictions ranged from 0.5 to between each pair of scores labeled on each row and column, colored by high correlation (dark blue), no correlation (white).”
Evolutionary-scale prediction of atomic level protein structure with a language model (2022, background): “[36] JÈ urgen Haas, Alessandro Barbato, Dario Behringer, Gabriel Studer, Steven Roth, Martino Bertoni, Khaled Mostaguir, Rafal Gumienny, and Torsten Schwede.”
Improved protein structure prediction using predicted interresidue orientations (2019, positive): “We further tested our method on 131 hard targets from the CAMEO experiments (22) over the 6 mo between 8 December 2018 and 1 June 2019.”
Introducing “best single template” models as reference baseline for the Continuous Automated Model Evaluation (CAMEO) (2019, methodology): “Recent advances have again pushed the boundaries as compared to the special issue 2 years ago, 4 which marks the summit of continuous improvements over the last 22 years (Figure 4).”
IntFOLD: an integrated web resource for high performance protein structure and function prediction (2019, methodology): “The TS predictions from the IntFOLD5 server are continuously evaluated by the CAMEO project (8).”
SWISS-MODEL: homology modelling of protein structures and complexes (2018, positive): “In order to provide objective assessments of modelling performance, SWISS-MODEL participates in the CAMEO project (https://cameo3d.org) (32).”
ProteinNet: a standardized data set for machine learning of protein structure (2019, methodology): “Unlike CASP, CAMEO is continually running and thus can be used for assessment at any time.”
Protein Data Bank: the single global archive for 3D macromolecular structure data (2018, methodology): “Release of PDB data in two phases is intended to assist the computational biology community in operating various prediction challenges, including CASP (32), CAPRI (33), CAMEO (34) and D3R (35).”
RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning (2022, methodology): “Critical Assessment of Structure Prediction (CASP (39)), Continuous Automated Model EvaluatiOn (CAMEO (40))).”
QMEANDisCo—distance constraints applied on model quality estimation (2019, methodology): “Such methods have the advantage of only requiring a single model as input, but they are often outperformed by consensus methods that base their prediction on an ensemble of models (Kryshtafovych et al. , 2016, 2018).”
Evaluation of template-based modeling in CASP13 (2019, background): “31 In these scripts, a full MR search is not carried out; rather, the model is superimposed on each copy Since CASP7, it has become much more common for predictors to submit local error estimates in the B-factor fields of submitted models.”
OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors (2021, methodology): “CAMEO (60), collected by OPUS-Rota3 [4], contains 60 hard targets (we discard one target with over 900 residues in length) released between January 2020 and July 2020 from the CAMEO website [33].”