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%2F00216305%3A26230%2F18%3APU130812" target="_blank" >RIV/00216305:26230/18:PU130812 - isvavai.cz</a>
Alternative codes found
RIV/00159816:_____/19:00071086 RIV/00216224:14310/19:00113346
Result on the web
<a href="https://pubs.acs.org/doi/10.1021/acscatal.8b03613" target="_blank" >https://pubs.acs.org/doi/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 co-solvents. This has created great interest in using computational methods to enhance biocatalysts 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, the number of sequences available for phylogenetic analyses is growing, and 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
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2018
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
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Volume of the periodical
2019
Issue of the periodical within the volume
9
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
2-s2.0-85059802317