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

IF 2.8 4区生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Proteins-Structure Function and Bioinformatics Pub Date : 2025-06-09 DOI:10.1002/prot.26856

Sicheng Zhang, Jun Li, Yuanzhe Zhou, Shi-Jie Chen

引用次数: 0

Abstract

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.

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在CASP16中增强RNA 3D结构预测：将基于物理的建模与机器学习集成以改进预测。

在第16届结构预测关键评估（CASP16）期间，Vfold团队参与了RNA单体和RNA多聚体这两个RNA类别的测试。Vfold RNA结构预测方法是分层混合的，结合基于物理模型（Vfold2D和VfoldMCPX）进行二维结构预测，基于模板和基于分子动力学模拟模型（Vfold- pipeline、IsRNA和RNAJP）进行三维结构预测。此外，Vfold将模板中的知识和最先进的机器学习模型AlphaFold3集成到我们基于物理的模型中。这种集成提高了预测的准确性。在这里，我们使用选定的靶点描述了CASP16中的Vfold方法，并展示了传统结构预测方法与机器学习模型的集成如何提高RNA结构预测的准确性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Proteins-Structure Function and Bioinformatics 生物-生化与分子生物学

CiteScore

5.90

自引率

3.40%

发文量

172

审稿时长

3 months

期刊介绍： PROTEINS : Structure, Function, and Bioinformatics publishes original reports of significant experimental and analytic research in all areas of protein research: structure, function, computation, genetics, and design. The journal encourages reports that present new experimental or computational approaches for interpreting and understanding data from biophysical chemistry, structural studies of proteins and macromolecular assemblies, alterations of protein structure and function engineered through techniques of molecular biology and genetics, functional analyses under physiologic conditions, as well as the interactions of proteins with receptors, nucleic acids, or other specific ligands or substrates. Research in protein and peptide biochemistry directed toward synthesizing or characterizing molecules that simulate aspects of the activity of proteins, or that act as inhibitors of protein function, is also within the scope of PROTEINS. In addition to full-length reports, short communications (usually not more than 4 printed pages) and prediction reports are welcome. Reviews are typically by invitation; authors are encouraged to submit proposed topics for consideration.