bioRxiv : the preprint server for biologySep 2, 2025

Detailed structural data improves models predicting mRNA breakdown in vials

mRNA Technology Weekly Brief ↗PubMed ↗DOI ↗OA ↗

Updated Jan 7, 2026

Abstract

A four-feature regression model, STRAND, improves mRNA stability predictions significantly.

Understanding RNA secondary structure is essential for protecting mRNA from degradation during storage.
Current global metrics like minimum free energy and average unpaired probability do not fully account for local structural variations that affect stability.
Base-pairing probability, measured in log odds, offers valuable insights that enhance stability modeling.
Combining local and global structural features in the STRAND model leads to better predictive performance for mRNA stability.

Simplified

The success of COVID-19 mRNA vaccines has made optimizing mRNAs for in-vial stability a key objective. However, we still lack a complete understanding of the sequence metrics that influence mRNA stability in solution. RNA secondary structure plays a central role in protecting against hydrolysis, the primary degradation pathway under storage conditions. Yet, the structural metrics that best guide stability-focused mRNA design remain unclear. Global metrics like minimum free energy and average unpaired probability have improved mRNA stability but fail to capture local structural variation relevant to degradation. We show that base-pairing probability, in terms of log odds, provide fine-scale, orthogonal insight that complements global metrics and improves stability modeling. By combining local and global features into a four-feature regression model, dubbed STRAND (Stability Regression Analysis using Nucleotide-Derived features), we achieve substantial gains in predictive performance over current methods. This compact and interpretable model provides a practical framework for designing mRNAs with enhanced in-solution stability.

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Detailed structural data improves models predicting mRNA breakdown in vials

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