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Computational optimization of DEK1 calpain domain solubility through integrated structural modelling and data-driven targeted mutagenesis
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  • Published: 08 February 2026

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

  • Mohammad Dabiri1,
  • Zdenko Levarski1,2,
  • Eva Struhárňanská1,
  • Viktor Demko3,4,
  • Vladimir Beneš5,
  • Jan Turňa1,2 &
  • …
  • Stanislav Stuchlík1,2 

Scientific Reports , Article number:  (2026) Cite this article

  • 1475 Accesses

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Biochemistry
  • Biophysics
  • Computational biology and bioinformatics
  • Structural biology

Abstract

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.

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Data availability

The data generated and analysed during this study, including molecular dynamics simulation outputs, structural models, and solubility feature datasets and code resources will be made available from the corresponding authors upon reasonable request. Due to the size and computational nature of the datasets, they are not hosted in a public repository. Full mutagenesis dataset and related materials are provided in supplementary information.

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Funding

This work is the result of implementation of Slovak Research and Development Agency grants APVV-21-0227, APVV-21-0215, APVV-22-0161, Comenius University grant UK/1088/2025 and by implementation of the project 101160008 “Fostering Excellence in Advanced Genomics and Proteomics Research at Comenius University in Bratislava – FORGENOM II” funded by the Horizon Europe program.

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Authors and Affiliations

  1. Department of Molecular Biology, Faculty of Natural Sciences, Comenius University in Bratislava, Bratislava, 842 15, Slovak Republic

    Mohammad Dabiri, Zdenko Levarski, Eva Struhárňanská, Jan Turňa & Stanislav Stuchlík

  2. Science Park, Comenius University in Bratislava, Bratislava, 841 04, Slovak Republic

    Zdenko Levarski, Jan Turňa & Stanislav Stuchlík

  3. Department of Plant Physiology, Faculty of Natural Sciences, Comenius University in Bratislava, Bratislava, 842 15, Slovak Republic

    Viktor Demko

  4. Plant Science and Biodiversity Centre, Slovak Academy of Sciences, Bratislava, 84523, Slovak Republic

    Viktor Demko

  5. Genomics Core Facility, EMBL Heidelberg, 69117, Heidelberg, Germany

    Vladimir Beneš

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  1. Mohammad Dabiri
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  2. Zdenko Levarski
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  3. Eva Struhárňanská
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  4. Viktor Demko
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  5. Vladimir Beneš
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  6. Jan Turňa
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  7. Stanislav Stuchlík
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Contributions

MD and ZL: conceived and designed the study, MD: developed the structural prediction and mutagenesis workflow, and performed all molecular dynamics simulations and solubility analyses. Data processing, interpretation, and manuscript writing and editing were carried out by MD and ZL. VD and ES: Provided recommendations and scientific guidance of research. All aspects of the research were conducted under the academic supervision of JT, VD, VB and SS who provided critical feedback on the study design and manuscript.

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Correspondence to Mohammad Dabiri or Zdenko Levarski.

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Dabiri, M., Levarski, Z., Struhárňanská, E. et al. Computational optimization of DEK1 calpain domain solubility through integrated structural modelling and data-driven targeted mutagenesis. Sci Rep (2026). https://doi.org/10.1038/s41598-026-38805-z

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  • Received: 08 September 2025

  • Accepted: 31 January 2026

  • Published: 08 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-38805-z

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Keywords

  • Calpain
  • Protein solubility
  • Mutagenesis
  • Molecular dynamics
  • Structure prediction
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