Enhancing RNA 3D Structure Prediction in CASP16: Integrating Physics-Based Modeling With Machine Learning for Improved Predictions

Sicheng Zhang

doi:10.1002/prot.26856

Enhancing RNA 3D Structure Prediction in CASP16: Integrating Physics-Based Modeling With Machine Learning for Improved Predictions

2025-06-09

0

PRE

AI

S

Sicheng Zhang

李军 (Jun Li)

Y

Yuanzhe Zhou

S

Shi‐Jie Chen *

DOI：10.1002/prot.26856

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During the 16th Critical Assessment of Structure Prediction (CASP16), the Vfold team participated in the two RNA categories: RNA Monomers and RNA Multimers. The Vfold RNA structure prediction method is hierarchical and hybrid, incorporating physics-based models (Vfold2D and VfoldMCPX) for 2D structure prediction, template-based and molecular dynamics simulation-based models (Vfold-Pipeline, IsRNA and RNAJP) for 3D structure prediction. Additionally, Vfold integrates knowledge from templates and the state-of-the-art machine learning model AlphaFold3 into our physics-based models. This integration enhances the prediction accuracy. Here we describe the Vfold approach in CASP16 using selected targets and show how the integration of traditional structure prediction methods with machine learning models can improve RNA structure prediction accuracy.

Keywords:

CASP16

RNA 3D structure

RNA structure prediction

Enhancing RNA 3D Structure Prediction in CASP16: Integrating Physics-Based Modeling With Machine Learning for Improved Predictions

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