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<p>The DEFECTIVE KERNEL 1 (DEK1) protein plays essential functions throughout plant development. DEK1 is a multidomain 240&#xa0;kDa protein with yet unsolved 3D structure. To facilitate structural and functional studies of DEK1, here we investigate its calpain protease core domain (CysPc) from <i>Physcomitrium patens</i>. Using integrated structural modelling we propose targeted mutagenesis of CysPc to enhance its solubility during recombinant protein production. We created a pipeline to predict the topology of the CysPc domain with improved precision, providing a robust framework for further exploration. We evaluated the native and mutant structures by MD simulations, concentrating on several solubility-related parameters. Following these features, we implemented specific single, double, and triple amino acid mutagenesis to select variants with improved solubility. Our method preserves overall structural integrity while reducing aggregation-prone traits. We advocate for the utilization of optimized data driven method that can effectively traverse the extensive combinatorial space and prioritize mutation sets with the greatest potential for enhancing solubility. This framework provides a logical, data-driven approach to improving protein solubility, particularly beneficial in situations lacking high-resolution structural data.</p>

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Computational optimization of DEK1 calpain domain solubility through integrated structural modelling and data-driven targeted mutagenesis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月8日

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  • Mohammad Dabiri,
  • Zdenko Levarski,
  • Eva Struhárňanská,
  • Viktor Demko,
  • Vladimir Beneš,
  • Jan Turňa,
  • Stanislav Stuchlík

摘要

The DEFECTIVE KERNEL 1 (DEK1) protein plays essential functions throughout plant development. DEK1 is a multidomain 240 kDa protein with yet unsolved 3D structure. To facilitate structural and functional studies of DEK1, here we investigate its calpain protease core domain (CysPc) from Physcomitrium patens. Using integrated structural modelling we propose targeted mutagenesis of CysPc to enhance its solubility during recombinant protein production. We created a pipeline to predict the topology of the CysPc domain with improved precision, providing a robust framework for further exploration. We evaluated the native and mutant structures by MD simulations, concentrating on several solubility-related parameters. Following these features, we implemented specific single, double, and triple amino acid mutagenesis to select variants with improved solubility. Our method preserves overall structural integrity while reducing aggregation-prone traits. We advocate for the utilization of optimized data driven method that can effectively traverse the extensive combinatorial space and prioritize mutation sets with the greatest potential for enhancing solubility. This framework provides a logical, data-driven approach to improving protein solubility, particularly beneficial in situations lacking high-resolution structural data.