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Computational Design of Stable and Soluble Biocatalysts

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F19%3A00071086" target="_blank" >RIV/00159816:_____/19:00071086 - isvavai.cz</a>

  • Alternative codes found

    RIV/00216305:26230/18:PU130812 RIV/00216224:14310/19:00113346

  • Result on the web

    <a href="https://pubs.acs.org/doi/abs/10.1021/acscatal.8b03613" target="_blank" >https://pubs.acs.org/doi/abs/10.1021/acscatal.8b03613</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1021/acscatal.8b03613" target="_blank" >10.1021/acscatal.8b03613</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Computational Design of Stable and Soluble Biocatalysts

  • Original language description

    Natural enzymes are delicate biomolecules possessing only marginal thermodynamic stability. Poorly stable, misfolded, and aggregated proteins lead to huge economic losses in the biotechnology and biopharmaceutical industries. Consequently, there is a need to design optimized protein sequences that maximize stability, solubility, and activity over a wide range of temperatures and pH values in buffers of different composition and in the presence of organic cosolvents. This has created great interest in using computational methods to enhance biocatalysts&apos; robustness and solubility. Suitable methods include (i) energy calculations, (ii) machine learning, (iii) phylogenetic analyses, and (iv) combinations of these approaches. We have witnessed impressive progress in the design of stable enzymes over the last two decades, but predictions of protein solubility and expressibility are scarce. Stabilizing mutations can be predicted accurately using available force fields, and the number of sequences available for phylogenetic analyses is growing. In addition, complex computational workflows are being implemented in intuitive web tools, enhancing the quality of protein stability predictions. Conversely, solubility predictors are limited by the lack of robust and balanced experimental data, an inadequate understanding of fundamental principles of protein aggregation, and a dearth of structural information on folding intermediates. Here we summarize recent progress in the development of computational tools for predicting protein stability and solubility, critically assess their strengths and weaknesses, and identify apparent gaps in data and knowledge. We also present perspectives on the computational design of stable and soluble biocatalysts.

  • 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

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2019

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    ACS Catalysis

  • ISSN

    2155-5435

  • e-ISSN

  • Volume of the periodical

    9

  • Issue of the periodical within the volume

    2

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    22

  • Pages from-to

    1033-1054

  • UT code for WoS article

    000458707000028

  • EID of the result in the Scopus database