Helix-mRNA: A Hybrid Foundation Model For Full Sequence mRNA Therapeutics (2502.13785v2)

Abstract: mRNA-based vaccines have become a major focus in the pharmaceutical industry. The coding sequence as well as the Untranslated Regions (UTRs) of an mRNA can strongly influence translation efficiency, stability, degradation, and other factors that collectively determine a vaccine's effectiveness. However, optimizing mRNA sequences for those properties remains a complex challenge. Existing deep learning models often focus solely on coding region optimization, overlooking the UTRs. We present Helix-mRNA, a structured state-space-based and attention hybrid model to address these challenges. In addition to a first pre-training, a second pre-training stage allows us to specialise the model with high-quality data. We employ single nucleotide tokenization of mRNA sequences with codon separation, ensuring prior biological and structural information from the original mRNA sequence is not lost. Our model, Helix-mRNA, outperforms existing methods in analysing both UTRs and coding region properties. It can process sequences 6x longer than current approaches while using only 10% of the parameters of existing foundation models. Its predictive capabilities extend to all mRNA regions. We open-source the model (https://github.com/helicalAI/helical) and model weights (https://huggingface.co/helical-ai/helix-mRNA).

• The paper introduces Helix-mRNA, a novel hybrid foundation model integrating state-space and attention methods to optimize full mRNA sequences, including untranslated regions (UTRs), crucial for therapeutic efficacy.
• Helix-mRNA employs a hybrid architecture capable of processing significantly longer sequences than previous models with fewer parameters and uses a two-stage pre-training regimen on a diverse dataset.
• Benchmarks demonstrate Helix-mRNA's superior performance over existing models in predicting key characteristics like mRNA stability, translation efficiency, and mean ribosome load.

An Overview of Helix-mRNA: A Hybrid Foundation Model for mRNA Therapeutics

The paper "Helix-mRNA: A Hybrid Foundation Model For Full Sequence mRNA Therapeutics" proposes a novel approach addressing the optimization complexities of mRNA sequences for therapeutic applications. The paper presents Helix-mRNA, a hybrid model that integrates state-space and attention-based methodologies, providing a framework optimized for comprehensive analysis of mRNA sequences, including coding regions and untranslated regions (UTRs).

Contextual Background

The development of mRNA-based therapeutics, notably marked by the advent of COVID-19 vaccines, has underscored the potential of engineered mRNAs in diverse biomedical applications. Despite this potential, achieving optimal mRNA sequences in terms of translation efficiency, stability, and degradation remains a significant challenge. Existing deep learning models predominantly focus on optimizing coding regions, often neglecting the crucial UTRs that significantly influence mRNA functionality.

Helix-mRNA Model and Methodology

Helix-mRNA aims to overcome the limitations of existing models by employing a hybrid architecture that can efficiently process long sequences and incorporate single nucleotide and codon-level tokenization. This unique approach maintains intricate biological information, particularly the structures inherent in the coding and non-coding regions of mRNA:

• Hybrid Architecture: The model integrates state-space models (SSMs) and attention-based layers. This combination enables it to process sequences significantly longer than those manageable by previous approaches, with Helix-mRNA supporting sequences up to six times the length of those handled by models like Transformer HELM using only 10% of the parameters.
• Two-Stage Pre-Training: The authors introduce a two-stage pre-training regimen with the Warmup-Stable-Decay (WSD) scheduling. This method allows for initial broad pre-training followed by a specialization phase focused on high-quality data, refining the model for specific tasks.
• Diverse Dataset: Leveraging a taxonomically diverse dataset, Helix-mRNA is trained across a wide phylum range, including eukaryotic and viral sequences. This diversity is crucial for capturing evolutionary and genomic variations.

Performance and Implications

Helix-mRNA demonstrates superior performance across a series of benchmarks compared to existing models, including CodonBERT, Transformer HELM, and their derivatives:

• In tasks such as mRNA Stability and translation efficiency prediction, Helix-mRNA achieves better results than alternative models, as shown by higher Spearman rank correlations.
• Its ability to analyze UTRs is also validated through the mean ribosome load prediction tasks, where Helix-mRNA outperforms Optimus 5-Prime across multiple cell lines.

Conclusion and Future Prospects

The development of Helix-mRNA signifies a notable advancement toward more comprehensive mRNA sequence analysis models, addressing the full spectrum of mRNA elements pivotal for therapeutic efficacy.

While the current results highlight the model's robustness and flexibility, future research could focus on expanding the dataset diversity further and exploring applications beyond therapeutics, possibly extending to industrial applications involving large-scale protein production. Moreover, as the field of mRNA therapeutics continues to evolve, models like Helix-mRNA may serve as foundational platforms facilitating rapid development and deployment of new treatments.