High‐accuracy protein structure prediction in CASP14

The application of state‐of‐the‐art deep‐learning approaches to the protein modeling problem has expanded the “high‐accuracy” category in CASP14 to encompass all targets. Building on the metrics used for high‐accuracy assessment in previous CASPs, we evaluated the performance of all groups that subm...

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Bibliographic Details
Published in:Proteins, structure, function, and bioinformatics structure, function, and bioinformatics, 2021-12, Vol.89 (12), p.1687-1699
Main Authors: Pereira, Joana, Simpkin, Adam J., Hartmann, Marcus D., Rigden, Daniel J., Keegan, Ronan M., Lupas, Andrei N.
Format: Article
Language:English
Subjects:
Accuracy
CASP14
Computational Biology - methods
Computational Biology - standards
Databases, Protein
Deep learning
high‐accuracy
Models, Molecular
Molecular modelling
molecular replacement
Protein Conformation
Protein structure
Proteins
Proteins - chemistry
Proteins - metabolism
Reproducibility of Results
Sequence Analysis, Protein
Software
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Summary:The application of state‐of‐the‐art deep‐learning approaches to the protein modeling problem has expanded the “high‐accuracy” category in CASP14 to encompass all targets. Building on the metrics used for high‐accuracy assessment in previous CASPs, we evaluated the performance of all groups that submitted models for at least 10 targets across all difficulty classes, and judged the usefulness of those produced by AlphaFold2 (AF2) as molecular replacement search models with AMPLE. Driven by the qualitative diversity of the targets submitted to CASP, we also introduce DipDiff as a new measure for the improvement in backbone geometry provided by a model versus available templates. Although a large leap in high‐accuracy is seen due to AF2, the second‐best method in CASP14 out‐performed the best in CASP13, illustrating the role of community‐based benchmarking in the development and evolution of the protein structure prediction field.
ISSN:0887-3585
1097-0134
DOI:10.1002/prot.26171