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Understanding the sequence-structure-function relationship of the large arylsulfate sulfotransferase (ASST) enzyme family for engineering novel sulfation biocatalysts

Understanding the sequence-structure-function relationship of the large arylsulfate sulfotransferase (ASST) enzyme family for engineering novel sulfation biocatalysts
了解大型芳基硫酸酯磺基转移酶 (ASST) 酶家族的序列-结构-功能关系,用于工程新型硫酸化生物催化剂
批准号:
505682627
负责人:
Dr. Mehdi Davari Dolatabadi, Ph.D.
金额:
$0.0万
依托单位国家:
德国
项目类别:
Research Grants
财政年份:
--
资助国家:
德国
项目状态:
未结题
起止时间:

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中文摘要
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英文摘要
Sulfated biomolecules are widespread in nature and play important roles in biological functions. Among the enzymes responsible for sulfation, ArylSulfate SulfoTransferases (ASSTs) are interesting biocatalysts as they use simple aromatic sulfates such as para-nitrophenyl sulfate as donors in comparison to PAPS-dependent sulfotransferases that use the complex and less stable PAPS as donor. However, very few is known about ASSTs (only one 3D-structure and its molecular mechanism described, tentative assignment into different classes according to their biochemistry or genomic context, only one natural donor and one acceptor substrate identified). According to our preliminary phylogenetic analysis on 2244 sequences of ASSTs genes, we identified 19 clades displaying reasonable boots-trap values. In analogy to CAZY or Sulfatlas databases, each of the actual 19 clades could correspond to a varying substrate specificity or/and mechanism. However, since biochemical and structural data are scarce, this hypothesis cannot be challenged by experimental data today. Moreover many of the branches (clades) coincide with taxonomy, which raises the obvious question that substrate specificity might be a trait which is linked to taxonomy.In the SulfASST project, we uses a combination of complementary approaches in bioinformatics, biochemistry, enzymology, structural biology, molecular modeling and protein engineering to obtain substantial information on the ASST enzymes. Based on the preliminary phylogenetic analysis, one representative of each of the 19 subfamilies (clades) will be expressed and screened for donor and acceptor substrates. Enzyme crystallography of 6-8 soundly selected representatives of ASSTs should provide precious details on molecular aspects of catalysis and selectivity (substrate, regiochemistry). Directed enzyme evolution (KnowVolution) and modeling will allow to obtain tailor-made biocatalysts for biotechnological purposes. Finally, this in-depth characterization of the ASSTs and rationalization of the obtained results will enable to: determine if substrate specificity is correlated to phylogeny; know if the genomic context of ASSTs genes is indicative of substrate or biological activity; decipher the structural determinants of substrate specificity/promiscuity and regioselectivity; define if enzyme mechanism is conserved throughout the different subfamilies (clades); predict substrate selectivity and regioselectivity by molecular modeling.
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Machine learning approaches for faster discovery and adaptation of enzymes for difficult chemical reactions (MacBioSyn). Part I: providing solutions for regioselective oxygenations by 2OGD oxidases
  • 批准号:
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  • 项目类别:
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  • 资助金额:
    $0.0万
  • 财政年份:
    --
  • 负责人:
    Dr. Mehdi Davari Dolatabadi, Ph.D.
  • 依托单位:
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  • 项目类别:
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  • 资助金额:
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