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<p>Channelopathies represent a group of diseases often caused by missense variants in ion channels affecting the functioning of tissues like the nervous system, heart, and muscle. The gold standard for functionally characterizing a variant is to measure the electrophysiological changes in channel properties using cell-based heterologous expression systems. As this method is time-consuming and generally unavailable, clinical practice often relies on in-silico models to predict the functional consequences of ion channel variants. We constructed a Missense ION (MissION) channel variant classifier based on a protein language model and trained it on 1996 gain- or loss-of-function variants, the largest set collected to date, in order to predict the functional effects of variants across a broad range of ion channels. MissION achieves a significant increase in predictive performance (Area Under the Receiver Operating Characteristic Curve (ROC-AUC): 0.918, compared to 0.884 and 0.779 for the current leading models). Moreover, the model generalizes well to ion channel genes for which little or no electrophysiological recordings are available. MissION provides functional predictions for over 600,000 ion channel variants, made available through an online interface that allows variant interpretation for a wide range of channelopathies.</p>

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Functional effect predictions for ion channel missense variants using a protein language model

  • 期刊论文

  • 发表日期：

    2026年6月22日

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  • Seán Gies,
  • Artoghrul Alishbayli,
  • Paul H. E. Tiesinga,
  • Marijn B. Martens

摘要

Channelopathies represent a group of diseases often caused by missense variants in ion channels affecting the functioning of tissues like the nervous system, heart, and muscle. The gold standard for functionally characterizing a variant is to measure the electrophysiological changes in channel properties using cell-based heterologous expression systems. As this method is time-consuming and generally unavailable, clinical practice often relies on in-silico models to predict the functional consequences of ion channel variants. We constructed a Missense ION (MissION) channel variant classifier based on a protein language model and trained it on 1996 gain- or loss-of-function variants, the largest set collected to date, in order to predict the functional effects of variants across a broad range of ion channels. MissION achieves a significant increase in predictive performance (Area Under the Receiver Operating Characteristic Curve (ROC-AUC): 0.918, compared to 0.884 and 0.779 for the current leading models). Moreover, the model generalizes well to ion channel genes for which little or no electrophysiological recordings are available. MissION provides functional predictions for over 600,000 ion channel variants, made available through an online interface that allows variant interpretation for a wide range of channelopathies.