Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
CADEE: Computer-Aided Directed Evolution of Enzymes
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Structure and Molecular Biology.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Structure and Molecular Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Structure and Molecular Biology.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Structure and Molecular Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
Show others and affiliations
2017 (English)In: IUCrJ, ISSN 0972-6918, E-ISSN 2052-2525, Vol. 4, no 1, p. 50-64Article in journal (Refereed) Published
Abstract [en]

The tremendous interest in enzymes as biocatalysts has led to extensive work in enzyme engineering, as well as associated methodology development. Here, a new framework for computer-aided directed evolution of enzymes (CADEE) is presented which allows a drastic reduction in the time necessary to prepare and analyze in silico semi-automated directed evolution of enzymes. A pedagogical example of the application of CADEE to a real biological system is also presented in order to illustrate the CADEE workflow.

Place, publisher, year, edition, pages
2017. Vol. 4, no 1, p. 50-64
Keywords [en]
computational directed evolution, computational enzyme design, distributed computing, empirical valence bond, triosephosphate isomerase
National Category
Structural Biology Bioinformatics (Computational Biology) Theoretical Chemistry
Identifiers
URN: urn:nbn:se:uu:diva-314218DOI: 10.1107/S2052252516018017ISI: 000392925800007OAI: oai:DiVA.org:uu-314218DiVA, id: diva2:1070065
Funder
EU, FP7, Seventh Framework Programme, 306474Knut and Alice Wallenberg FoundationThe Royal Swedish Academy of SciencesSwedish Research Council, 2015-04928Swedish National Infrastructure for Computing (SNIC), 2015/16-12Available from: 2017-01-31 Created: 2017-01-31 Last updated: 2018-01-13Bibliographically approved
In thesis
1. Extending the Reach of Computational Approaches to Model Enzyme Catalysis
Open this publication in new window or tab >>Extending the Reach of Computational Approaches to Model Enzyme Catalysis
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Recent years have seen tremendous developments in methods for computational modeling of (bio-) molecular systems. Ever larger reactive systems are being studied with high accuracy approaches, and high-level QM/MM calculations are being routinely performed. However, applying high-accuracy methods to large biological systems is computationally expensive and becomes problematic when conformational sampling is needed. To address this challenge, classical force field based approaches such as free energy perturbation (FEP) and empirical valence bond calculations (EVB) have been employed in this work. Specifically:

  1. Force-field independent metal parameters have been developed for a range of alkaline earth and transition metal ions, which successfully reproduce experimental solvation free energies, metal-oxygen distances, and coordination numbers. These are valuable for the computational study of biological systems.

  2. Experimental studies have shown that the epoxide hydrolase from Solanum tuberosum (StEH1) is not only an enantioselective enzyme, but for smaller substrates, displays enantioconvergent behavior. For StEH1, two detailed studies, involving combined experimental and computational efforts have been performed: We first used trans-stilbene oxide to establish the basic reaction mechanism of this enzyme. Importantly, a highly conserved and earlier ignored histidine was identified to be important for catalysis. Following from this, EVB and experiment have been used to investigate the enantioconvergence of the StEH1-catalyzed hydrolysis of styrene oxide. This combined approach involved wildtype StEH1 and an engineered enzyme variant, and established a molecular understanding of enantioconvergent behavior of StEH1.

  3. A novel framework was developed for the Computer-Aided Directed Evolution of Enzymes (CADEE), in order to be able to quickly prepare, simulate, and analyze hundreds of enzyme variants. CADEE’s easy applicability is demonstrated in the form of an educational example.

In conclusion, classical approaches are a computationally economical means to achieve extensive conformational sampling. Using the EVB approach has enabled me to obtain a molecular understanding of complex enzymatic systems. I have also increased the reach of the EVB approach, through the implementation of CADEE, which enables efficient and highly parallel in silico testing of hundreds-to-thousands of individual enzyme variants.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2017. p. 67
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1484
Keywords
epoxide hydrolase, enantioselectivity, regioselectivity, enantioconvergence, biocatalysis, empirical valence bond, computational directed evolution
National Category
Theoretical Chemistry Biochemistry and Molecular Biology Structural Biology
Identifiers
urn:nbn:se:uu:diva-314686 (URN)978-91-554-9816-0 (ISBN)
Public defence
2017-03-24, A1:111a, BMC, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Funder
EU, European Research Council, 306474
Available from: 2017-03-02 Created: 2017-02-04 Last updated: 2017-03-06

Open Access in DiVA

fulltext(2020 kB)501 downloads
File information
File name FULLTEXT01.pdfFile size 2020 kBChecksum SHA-512
42aed6849fc628ef0c14e5fcbdd75af3e9d38a7b8f9ff803c0b9abe3754f3f408207ce63070a7797066812b42d15313e702ec35acd1652a343e62f8b11bb90e3
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Search in DiVA

By author/editor
Amrein, Beat AntonSteffen-Munsberg, FabianPurg, MihaKulkarni, YashrajKamerlin, Shina Caroline Lynn
By organisation
Science for Life Laboratory, SciLifeLabStructure and Molecular Biology
In the same journal
IUCrJ
Structural BiologyBioinformatics (Computational Biology)Theoretical Chemistry

Search outside of DiVA

GoogleGoogle Scholar
Total: 501 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 991 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf