Journal
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
Volume 87, Issue 12, Pages 1378-1387Publisher
WILEY
DOI: 10.1002/prot.25815
Keywords
benchmarking; CAMEO; CASP; continuous evaluation; homo-oligomer interface accuracy; ligand binding-site accuracy; model quality assessment; model confidence; oligomeric assessment; protein structure modeling; protein structure prediction
Categories
Funding
- ELIXIR Excelerate (H2020) [676559]
- National Institute of General Medical Sciences [U01 GM093324-01]
- Swiss Institute of Bioinformatics
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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.
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