Modeling Alternative Conformational States in CASP16.

Area of Science:

• Structural Biology
• Computational Biology
• Biophysics

Background:

• The CASP16 Ensemble Prediction experiment evaluated computational methods for modeling molecules in various shapes.
• Accurate modeling of proteins, nucleic acids, and their complexes in multiple conformational states is crucial for understanding biological function.

Purpose of the Study:

• To assess the state-of-the-art in computational modeling for multi-state molecular systems.
• To identify progress and persistent challenges in predicting conformational ensembles.

Main Methods:

• Evaluation of computational models against experimental structures for multi-state targets.
• Analysis of prediction accuracy using TM-score for protein-ligand complexes, protein-DNA complexes, and various RNA structures.
• Comparison of different prediction strategies, including AlphaFold2 and AlphaFold3.

Main Results:

• Some methods achieved reasonable accuracy for a few multi-state protein targets, particularly when template structures were available.
• Overall prediction accuracy for multi-state targets was lower than for single-state targets in previous CASP experiments.
• AlphaFold2, with enhanced protocols, and the AlphaFold3 server showed promise, but individual groups sometimes outperformed them.
• Predictions for RNA targets and protein-DNA complexes generally fell short of desired accuracy levels.

Conclusions:

• Significant challenges remain in accurately modeling conformational ensembles, especially for RNA and large molecular assemblies.
• Multi-state modeling represents a critical frontier in structural biology, requiring further methodological development.