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PARSEbp: Pairwise Agreement-based RNA Scoring with Emphasis on Base Pairings.

Sumit Tarafder¹, Debswapna Bhattacharya¹

¹Department of Computer Science, Virginia Tech, Blacksburg, Virginia, 24061, USA.

Biorxiv : the Preprint Server for Biology

|November 24, 2025

Summary

PARSEbp is a new multi-model RNA scoring method that improves RNA 3D structure prediction by considering both 3D structural agreement and 2D base pairing consistency. It outperforms existing methods in ranking RNA conformational ensembles.

Keywords:

RNA 3D structure prediction RNA scoring function base pairing structural ensemble

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Area of Science:

Computational Biology
Structural Biology
Bioinformatics

Background:

Accurate scoring of RNA 3D structures is crucial for RNA structure prediction and conformational sampling.
Existing single-model scoring methods struggle to capture the consensus within an ensemble, hindering model selection.
There is a need for advanced multi-model scoring approaches to enhance RNA structure prediction fidelity.

Purpose of the Study:

To introduce PARSEbp, a novel multi-model RNA scoring method designed for high-fidelity RNA 3D structure evaluation.
To address the limitations of single-model scoring by integrating ensemble-level structural information.
To improve the accuracy and efficiency of RNA conformational sampling and model selection.

Main Methods:

PARSEbp integrates pairwise structural agreement across a conformational ensemble with base pairing consistency.
It leverages alignment-based global 3D structural agreement and 2D base pairing information.
A consensus similarity matrix is constructed to compute per-structure accuracy scores.

Main Results:

PARSEbp significantly outperforms existing single- and multi-model RNA scoring functions on CASP16 RNA targets.
The method demonstrates superior performance compared to traditional statistical potentials, deep learning methods, and consensus approaches.
Evaluation across diverse metrics confirms the effectiveness of PARSEbp in RNA structure scoring.

Conclusions:

PARSEbp offers a fast and effective solution for multi-model RNA scoring, enhancing RNA structure prediction.
The integration of 3D and 2D structural information provides a more comprehensive scoring mechanism.
PARSEbp represents a significant advancement in computational approaches for RNA structural analysis.