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Publication Details

Computational Design of Stable and Soluble Biocatalysts

MUSIL Miloš, KONEGGER Hannes, HON Jiří, BEDNÁŘ David and DAMBORSKÝ Jiří. Computational Design of Stable and Soluble Biocatalysts. ACS Catalysis, vol. 2019, no. 9, pp. 1033-1054. ISSN 2155-5435. Available from: https://pubs.acs.org/doi/10.1021/acscatal.8b03613
Czech title
Výpočetní návrh stabilních a solubilních biokatalyzátorů
Type
journal article
Language
english
Authors
Musil Miloš, Ing. (DIFS FIT BUT)
Konegger Hannes (LL)
Hon Jiří, Ing. (DIFS FIT BUT)
Bednář David, Mgr. (LL)
Damborský Jiří, prof. Mgr., Dr. (LL)
URL
Keywords
Aggregation,Computational Design,Force Field,Expressibility,Machine Learning,Phylogenetic Analysis,Enzyme Stability,Enzyme Solubility
Abstract
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.
Published
2018
Pages
1033-1054
Journal
ACS Catalysis, vol. 2019, no. 9, ISSN 2155-5435
Publisher
American Chemical Society
DOI
BibTeX
@ARTICLE{FITPUB11893,
   author = "Milo\v{s} Musil and Hannes Konegger and Ji\v{r}\'{i} Hon and David Bedn\'{a}\v{r} and Ji\v{r}\'{i} Damborsk\'{y}",
   title = "Computational Design of Stable and Soluble Biocatalysts",
   pages = "1033--1054",
   journal = "ACS Catalysis",
   volume = 2019,
   number = 9,
   year = 2018,
   ISSN = "2155-5435",
   doi = "10.1021/acscatal.8b03613",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/11893"
}
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