High-accuracy protein structure prediction in CASP14.
Proteins
; 89(12): 1687-1699, 2021 12.
Article
in En
| MEDLINE
| ID: mdl-34218458
ABSTRACT
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Protein Conformation
/
Software
/
Proteins
/
Models, Molecular
Type of study:
Prognostic_studies
/
Qualitative_research
/
Risk_factors_studies
Language:
En
Journal:
Proteins
Journal subject:
BIOQUIMICA
Year:
2021
Document type:
Article
Affiliation country:
Germany