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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

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Progress and Bottlenecks for Deep Learning in Computational Structure Biology: CASP Round XVI.

Andriy Kryshtafovych¹, Torsten Schwede^2,3, Maya Topf^4,5

¹Genome Center, University of California, California, US.

|November 3, 2025

Summary

The latest Critical Assessment of protein Structure Prediction (CASP16) shows deep learning excels in protein and ligand-protein structures but struggles with RNA. Future improvements may combine AI with physics-based methods.

Keywords:

CASP CASP16 RNA structure prediction community wide experiment macromolecular ensembles model accuracy protein structure prediction protein‐ligand complexes

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Area of Science:

Computational structural biology
Bioinformatics
Structural modeling

Background:

Community-wide experiments like CASP rigorously assess computational structural biology methods.
Recent years have seen significant advancements, with deep learning methods, particularly AlphaFold variants, dominating many areas.
The focus has shifted from method utility to achieving experimental accuracy.

Purpose of the Study:

To evaluate the state-of-the-art in computational structure prediction at CASP16.
To assess the performance of deep learning and traditional methods across various structural biology targets.
To identify current limitations and future trends in structure prediction accuracy.

Main Methods:

Analysis of results from the Critical Assessment of protein Structure Prediction (CASP16) experiment.
Comparison of deep learning methods (e.g., AlphaFold variants) against traditional approaches.
Evaluation of structure prediction accuracy for monomer proteins, protein complexes, RNA, macromolecular ensembles, and ligand-protein interactions.

Main Results:

Deep learning methods show high accuracy for monomer proteins, approaching experimental uncertainty limits.
Significant accuracy gains were observed for protein complexes, with room for further improvement.
Deep learning methods were unsuccessful for RNA structure prediction, performing similarly to traditional methods.
Ligand-protein structure and affinity predictions improved substantially with deep learning, though not reaching experimental accuracy.
Methods for accuracy estimation proved effective in selecting high-quality models for protein complexes.

Conclusions:

Deep learning dominates protein structure prediction, but RNA remains a challenge.
Combining physics-inspired methods with deep learning and increasing training data are promising future directions.
Continued advancements are needed to bridge the gap between predicted and experimental accuracy in all categories.