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  A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes

Cadet, F., Fontaine, N., Li, G., Sanchis, J., Chong, M. N. F., Pandjaitan, R., et al. (2018). A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes. Scientific Reports, 8: 16757. doi:10.1038/s41598-018-35033-y.

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 Creators:
Cadet, Frédéric1, Author
Fontaine, Nicolas1, Author
Li, Guangyue2, Author
Sanchis, Joaquin3, Author
Chong, Matthieu Ng Fuk1, Author
Pandjaitan, Rudy1, Author
Vetrivel, Iyanar1, Author
Offmann, Bernard4, Author
Reetz, Manfred T.2, 5, Author              
Affiliations:
1PEACCEL, Protein Engineering Accelerator, Paris, France, ou_persistent22              
2Department of Chemistry, Philipps-University, Marburg, Germany, ou_persistent22              
3Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Parkville, Australia, ou_persistent22              
4UFIP, UMR 6286 CNRS, UFR Sciences et Techniques, Université de Nantes, Nantes, France, ou_persistent22              
5Research Department Reetz, Max-Planck-Institut für Kohlenforschung, Max Planck Society, ou_1445588              

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 Abstract: Directed evolution is an important research activity in synthetic biology and biotechnology. Numerous reports describe the application of tedious mutation/screening cycles for the improvement of proteins. Recently, knowledge-based approaches have facilitated the prediction of protein properties and the identification of improved mutants. However, epistatic phenomena constitute an obstacle which can impair the predictions in protein engineering. We present an innovative sequence-activity relationship (innov’SAR) methodology based on digital signal processing combining wet-lab experimentation and computational protein design. In our machine learning approach, a predictive model is developed to find the resulting property of the protein when the n single point mutations are permuted (2n combinations). The originality of our approach is that only sequence information and the fitness of mutants measured in the wet-lab are needed to build models. We illustrate the application of the approach in the case of improving the enantioselectivity of an epoxide hydrolase from Aspergillus niger. n = 9 single point mutants of the enzyme were experimentally assessed for their enantioselectivity and used as a learning dataset to build a model. Based on combinations of the 9 single point mutations (29), the enantioselectivity of these 512 variants were predicted, and candidates were experimentally checked: better mutants with higher enantioselectivity were indeed found.

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Language(s): eng - English
 Dates: 2018-08-032018-10-262018-11-13
 Publication Status: Published online
 Pages: 15
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s41598-018-35033-y
 Degree: -

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Title: Scientific Reports
  Abbreviation : Sci. Rep.
Source Genre: Journal
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Publ. Info: London, UK : Nature Publishing Group
Pages: - Volume / Issue: 8 Sequence Number: 16757 Start / End Page: - Identifier: ISSN: 2045-2322
CoNE: https://pure.mpg.de/cone/journals/resource/2045-2322