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Design of a stable human acid-β-glucosidase: towards improved Gaucher disease therapy and mutation classification

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388955%3A_____%2F23%3A00570872" target="_blank" >RIV/61388955:_____/23:00570872 - isvavai.cz</a>

  • Result on the web

    <a href="https://hdl.handle.net/11104/0342198" target="_blank" >https://hdl.handle.net/11104/0342198</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1111/febs.16758" target="_blank" >10.1111/febs.16758</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Design of a stable human acid-β-glucosidase: towards improved Gaucher disease therapy and mutation classification

  • Original language description

    Acid-β-glucosidase (GCase, EC3.2.1.45), the lysosomal enzyme which hydrolyzes the simple glycosphingolipid, glucosylceramide (GlcCer), is encoded by the GBA1 gene. Biallelic mutations in GBA1 cause the human inherited metabolic disorder, Gaucher disease (GD), in which GlcCer accumulates, while heterozygous GBA1 mutations are the highest genetic risk factor for Parkinson's disease (PD). Recombinant GCase (e.g., Cerezyme®) is produced for use in enzyme replacement therapy for GD and is largely successful in relieving disease symptoms, except for the neurological symptoms observed in a subset of patients. As a first step toward developing an alternative to the recombinant human enzymes used to treat GD, we applied the PROSS stability-design algorithm to generate GCase variants with enhanced stability. One of the designs, containing 55 mutations compared to wild-type human GCase, exhibits improved secretion and thermal stability. Furthermore, the design has higher enzymatic activity than the clinically used human enzyme when incorporated into an AAV vector, resulting in a larger decrease in the accumulation of lipid substrates in cultured cells. Based on stability-design calculations, we also developed a machine learning-based approach to distinguish benign from deleterious (i.e., disease-causing) GBA1 mutations. This approach gave remarkably accurate predictions of the enzymatic activity of single-nucleotide polymorphisms in the GBA1 gene that are not currently associated with GD or PD. This latter approach could be applied to other diseases to determine risk factors in patients carrying rare mutations.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10403 - Physical chemistry

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    FEBS Journal

  • ISSN

    1742-464X

  • e-ISSN

    1742-4658

  • Volume of the periodical

    290

  • Issue of the periodical within the volume

    13

  • Country of publishing house

    GB - UNITED KINGDOM

  • Number of pages

    17

  • Pages from-to

    3383-3399

  • UT code for WoS article

    000955057500001

  • EID of the result in the Scopus database

    2-s2.0-85150990685