RNAbpFlow：用于条件RNA三维结构生成的碱基对增强SE(3)流匹配

RNAbpFlow: Base pair-augmented SE(3)-flow matching for conditional RNA 3D structure generation.

作者信息

Tarafder Sumit, Bhattacharya Debswapna

机构信息

Department of Computer Science, Virginia Tech, Blacksburg, Virginia, 24061, USA.

出版信息

bioRxiv. 2025 Jan 26:2025.01.24.634669. doi: 10.1101/2025.01.24.634669.

DOI:10.1101/2025.01.24.634669

PMID:39896539

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11785242/

Abstract

MOTIVATION

Despite the groundbreaking advances in deep learning-enabled methods for bimolecular modeling, predicting accurate three-dimensional (3D) structures of RNA remains challenging due to the highly flexible nature of RNA molecules combined with the limited availability of evolutionary sequences or structural homology.

RESULTS

We introduce RNAbpFlow, a novel sequence- and base-pair-conditioned SE(3)-equivariant flow matching model for generating RNA 3D structural ensemble. Leveraging a nucleobase center representation, RNAbpFlow enables end-to-end generation of all-atom RNA structures without the explicit or implicit use of evolutionary information or homologous structural templates. Experimental results show that base pairing conditioning leads to broadly generalizable performance improvements over current approaches for RNA topology sampling and predictive modeling in large-scale benchmarking.

AVAILABILITY

RNAbpFlow is freely available at https://github.com/Bhattacharya-Lab/RNAbpFlow.

摘要

动机

尽管在基于深度学习的双分子建模方法方面取得了突破性进展，但由于RNA分子具有高度灵活性，且进化序列或结构同源性的可用性有限，预测RNA准确的三维（3D）结构仍然具有挑战性。

结果

我们引入了RNAbpFlow，这是一种新颖的基于序列和碱基对条件的SE(3)等变流匹配模型，用于生成RNA 3D结构集合。利用核碱基中心表示，RNAbpFlow能够在不明确或隐含使用进化信息或同源结构模板的情况下，端到端地生成全原子RNA结构。实验结果表明，在大规模基准测试中，碱基配对条件相比于当前的RNA拓扑采样和预测建模方法，能够带来广泛可推广的性能提升。