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CASP16 protein monomer structure prediction assessment.

Rongqing Yuan1,2,3, Jing Zhang1,2, Andriy Kryshtafovych4

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Summary

The Critical Assessment of Structure Prediction (CASP16) shows single-domain protein fold prediction is nearly solved. AlphaFold3 (AF3) integration and improved methods boosted accuracy, though model ranking needs development.

Keywords:
AlphaFold2AlphaFold3CASP16monomerprotein structure predictionquality estimationstoichiometrystructure sampling

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein structure prediction

Background:

  • The Critical Assessment of Structure Prediction (CASP) benchmarks protein structure prediction methods.
  • CASP16 focused on monomer targets, assessing advancements in computational protein modeling.

Purpose of the Study:

  • To evaluate the performance of protein structure prediction methods in CASP16.
  • To assess the impact of new tools like AlphaFold3 (AF3) and improved methodologies on prediction accuracy.

Main Methods:

  • Analysis of CASP16 monomer target predictions.
  • Comparison of performance across participating groups and prediction tools, including AlphaFold2 (AF2) and AlphaFold3 (AF3).
  • Evaluation of novel CASP16 challenges (Phase 0, Phase 2, Model 6) and their experimental designs.

Main Results:

  • Single-domain protein fold prediction is largely solved, with no missed folds.
  • AlphaFold3 (AF3) demonstrated superiority over AlphaFold2 (AF2) in confidence estimation and model selection.
  • Top-performing groups utilized robust pipelines integrating AF3, improved multiple sequence alignments (MSAs), and construct design.
  • Increased expertise in optimizing AF2 and adoption of AF3 led to more groups outperforming ColabFold.

Conclusions:

  • While monomer modeling shows subtle progress, the field is advancing rapidly with tools like AF3.
  • Model ranking remains a significant challenge requiring further research and development.
  • Broader community engagement and improved experimental design are crucial for future CASP challenges.