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 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.

Automated summary

The application of state-of-the-art deep-learning approaches to protein modeling problem has expanded the high-accuracy category in CASP14, evaluating the performance of different groups and introducing new metrics. Despite the significant progress made by AlphaFold2, the second-best method in CASP14 outperformed the best method in CASP13, demonstrating the role of community-based benchmarking in the development of protein structure prediction field.