Sequence-Informed Geometric Evaluation of RNA 3D Structures

2026-09-09

AI Summary

为解决RNA三维结构评估问题，提出SIRGE方法，结合序列信息与几何模型，通过预训练语言模型增强结构评价准确性。

Abstract

Computational RNA structure pipelines generate many candidate conformations for the same sequence. Reliable evaluation therefore requires more than recognising plausible geometry, it requires determining whether that geometry is compatible with the sequence. We introduce SIRGE, a sequence-informed geometric evaluator that conditions structural representations on nucleotide embeddings from a pretrained RNA language model. Early results show that SIRGE outperforms established evaluators in Kendall--$τ$ alignment, Top-1 selection, and Top-3 ranking. Controlled comparisons further show that sequence conditioning corrects errors made by an otherwise matched geometric model and improves target-level rank structure. These findings provide initial evidence that pretrained sequence representations supply ranking information that complements geometric reasoning.

Problem

• RNA 3D Structures
• Sequence Compatibility
• Structural Evaluation

Innovation

• sequence-informed geometric evaluation
• SIRGE
• nucleotide embeddings
• pretrained RNA language model
• Kendall-τ alignment