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Background <p>Accurate identification of translation initiation sites is essential for the proper translation of mRNA into functional proteins. In eukaryotes, the choice of the translation initiation site is influenced by multiple factors, including its proximity to the 5<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12859_2025_6220_Article_IEq1.gif" Format="GIF" Height="9" Rendition="HTML" Resolution="72" Type="Linedraw" Width="7" /> </InlineMediaObject> <EquationSource Format="TEX">\(^\prime \)</EquationSource> </InlineEquation> end and the local start codon context. Translation initiation sites mark the transition from non-coding to coding regions. This fact motivates the expectation that the upstream sequence, if translated, would assemble a nonsensical order of amino acids, while the downstream sequence would correspond to the structured beginning of a protein. This distinction suggests potential for predicting translation initiation sites using a protein language model.</p> Results <p>We present NetStart 2.0, a deep learning-based model that integrates the ESM-2 protein language model with the local sequence context to predict translation initiation sites across a broad range of eukaryotic species. NetStart 2.0 was trained as a single model across multiple species, and despite the broad phylogenetic diversity represented in the training data, it consistently relied on features marking the transition from non-coding to coding regions.</p> Conclusion <p>By leveraging “protein-ness”, NetStart 2.0 achieves state-of-the-art performance in predicting translation initiation sites across a diverse range of eukaryotic species. This success underscores the potential of protein language models to bridge transcript- and peptide-level information in complex biological prediction tasks. The NetStart 2.0 webserver is available at: <a href="https://services.healthtech.dtu.dk/services/NetStart-2.0/">https://services.healthtech.dtu.dk/services/NetStart-2.0/</a>.</p>

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NetStart 2.0: prediction of eukaryotic translation initiation sites using a protein language model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月19日

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  • Line Sandvad Nielsen,
  • Anders Gorm Pedersen,
  • Ole Winther,
  • Henrik Nielsen

摘要

Background

Accurate identification of translation initiation sites is essential for the proper translation of mRNA into functional proteins. In eukaryotes, the choice of the translation initiation site is influenced by multiple factors, including its proximity to the 5 \(^\prime \) end and the local start codon context. Translation initiation sites mark the transition from non-coding to coding regions. This fact motivates the expectation that the upstream sequence, if translated, would assemble a nonsensical order of amino acids, while the downstream sequence would correspond to the structured beginning of a protein. This distinction suggests potential for predicting translation initiation sites using a protein language model.

Results

We present NetStart 2.0, a deep learning-based model that integrates the ESM-2 protein language model with the local sequence context to predict translation initiation sites across a broad range of eukaryotic species. NetStart 2.0 was trained as a single model across multiple species, and despite the broad phylogenetic diversity represented in the training data, it consistently relied on features marking the transition from non-coding to coding regions.

Conclusion

By leveraging “protein-ness”, NetStart 2.0 achieves state-of-the-art performance in predicting translation initiation sites across a diverse range of eukaryotic species. This success underscores the potential of protein language models to bridge transcript- and peptide-level information in complex biological prediction tasks. The NetStart 2.0 webserver is available at: https://services.healthtech.dtu.dk/services/NetStart-2.0/.