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Introducing "best single template" models as reference baseline for the Continuous Automated Model Evaluation (CAMEO)

Haas, Juergen and Gumienny, Rafal and Barbato, Alessandro and Ackermann, Flavio and Tauriello, Gerardo and Bertoni, Martino and Studer, Gabriel and Smolinski, Anna and Schwede, Torsten. (2019) Introducing "best single template" models as reference baseline for the Continuous Automated Model Evaluation (CAMEO). Proteins, 87 (12). pp. 1378-1387.

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Official URL: https://edoc.unibas.ch/72365/

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Abstract

Critical blind assessment of structure prediction techniques is crucial for the scientific community to establish the state of the art, identify bottlenecks, and guide future developments. In Critical Assessment of Techniques in Structure Prediction (CASP), human experts assess the performance of participating methods in relation to the difficulty of the prediction task in a biennial experiment on approximately 100 targets. Yet, the development of automated computational modeling methods requires more frequent evaluation cycles and larger sets of data. The "Continuous Automated Model EvaluatiOn (CAMEO)" platform complements CASP by conducting fully automated blind prediction evaluations based on the weekly pre-release of sequences of those structures, which are going to be published in the next release of the Protein Data Bank (PDB). Each week, CAMEO publishes benchmarking results for predictions corresponding to a set of about 20 targets collected during a 4-day prediction window. CAMEO benchmarking data are generated consistently for all methods at the same point in time, enabling developers to cross-validate their method's performance, and referring to their results in publications. Many successful participants of CASP have used CAMEO-either by directly benchmarking their methods within the system or by comparing their own performance to CAMEO reference data. CAMEO offers a variety of scores reflecting different aspects of structure modeling, for example, binding site accuracy, homo-oligomer interface quality, or accuracy of local model confidence estimates. By introducing the "bestSingleTemplate" method based on structure superpositions as a reference for the accuracy of 3D modeling predictions, CAMEO facilitates objective comparison of techniques and fosters the development of advanced methods.
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Computational & Systems Biology > Bioinformatics (Schwede)
UniBasel Contributors:Schwede, Torsten and Haas, J├╝rgen and Gumienny, Rafal Wojciech and Barbato, Alessandro and Ackermann, Flavio and Tauriello, Gerardo and Bertoni, Martino and Studer, Gabriel and Smolinski, Anna
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Wiley-Blackwell
ISSN:1097-0134
e-ISSN:1097-0134
Note:Publication type according to Uni Basel Research Database: Journal article
Identification Number:
Last Modified:19 Aug 2020 11:51
Deposited On:19 Aug 2020 11:51

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