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Enhancing RNA 3D Structure Prediction: A Hybrid Approach Combining Expert Knowledge and Computational Tools in CASP16
Bowen Xiao1,2, Yaohuang Shi1,3, Lin Huang4
1Guangzhou National Laboratory, Guangzhou, China.
Proteins
|August 8, 2025
Summary
Predicting RNA 3D structures is challenging. Our hybrid computational and expert approach achieved second place in CASP16, improving accuracy for gene expression and regulation.
Area of Science:
- Computational biology
- Structural biology
- Bioinformatics
Background:
- RNA 3D structures are vital for gene expression and regulation.
- Predicting RNA structures is difficult due to complex interactions, ion needs, flexibility, and limited data.
Purpose of the Study:
- To present a hybrid computational and expert strategy for RNA 3D structure prediction.
- To evaluate the method's performance in the CASP16 RNA structure prediction challenge.
Main Methods:
- Combined computational tools (template-based modeling, deep learning like AlphaFold3) with expert refinement.
- Integrated experimental constraints and iterative optimization.
- Developed modular workflows for diverse RNA targets.
Main Results:
- Achieved second place in CASP16 based on the sum Z-score.
- For short RNAs (<200 nt) with templates, 75% of predictions had RMSD < 5 Å, all < 10 Å.
- Showed promising results for novel sequences without templates, like R1209.
Conclusions:
- The hybrid approach significantly enhances RNA 3D structure prediction accuracy.
- Expert knowledge integration is crucial for future improvements, especially for complex structures like pseudoknots.
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