Approaching boiling point stability of an alcohol dehydrogenase through computationally-guided enzyme engineering

  1. Friso S Aalbers
  2. Maximilian JLJ Fürst
  3. Stefano Rovida
  4. Milos Trajkovic
  5. J Rubén Gómez Castellanos
  6. Sebastian Bartsch
  7. Andreas Vogel
  8. Andrea Mattevi
  9. Marco W Fraaije  Is a corresponding author
  1. University of Groningen, Netherlands
  2. Cambridge Biomedical Campus, United Kingdom
  3. University of Pavia, Italy
  4. c-LEcta GmbH, Germany

Abstract

Enzyme instability is an important limitation for the investigation and application of enzymes. Therefore, methods to rapidly and effectively improve enzyme stability are highly appealing. In this study we applied a computational method (FRESCO) to guide the engineering of an alcohol dehydrogenase. Of the 177 selected mutations, 25 mutations brought about a significant increase in apparent melting temperature (ΔTm ≥ +3 °C). By combining mutations, a 10-fold mutant was generated with a Tm of 94 °C (+51 °C relative to wildtype), almost reaching water's boiling point, and the highest increase with FRESCO to date. The 10-fold mutant's structure was elucidated, which enabled the identification of an activity-impairing mutation. After reverting this mutation, the enzyme showed no loss in activity compared to wildtype, while displaying a Tm of 88 °C (+45 °C relative to wildtype). This work demonstrates the value of enzyme stabilization through computational library design.

Data availability

Diffraction data have been deposited in PDB under the accession codes 6TQ3, 6TQ5, and 6TQ8.Details on the structures, enzyme kinetic data and statistical analyses are included in the supplementary information.

The following data sets were generated

Article and author information

Author details

  1. Friso S Aalbers

    Molecular Enzymology Group, University of Groningen, Groningen, Netherlands
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2142-9661
  2. Maximilian JLJ Fürst

    MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge, United Kingdom
    Competing interests
    No competing interests declared.
  3. Stefano Rovida

    Department of Biology and Biotechnology L. Spallanzani"", University of Pavia, Pavia, Italy
    Competing interests
    No competing interests declared.
  4. Milos Trajkovic

    Molecular Enzymology group, University of Groningen, Groningen, Netherlands
    Competing interests
    No competing interests declared.
  5. J Rubén Gómez Castellanos

    Department of Biology and Biotechnology, University of Pavia, Pavia, Italy
    Competing interests
    No competing interests declared.
  6. Sebastian Bartsch

    R&D, c-LEcta GmbH, Leipzig, Germany
    Competing interests
    Sebastian Bartsch, A patent application on the original ADH was filed by c-LEcta (WO 2019/012095).
  7. Andreas Vogel

    R&D, c-LEcta GmbH, Leipzig, Germany
    Competing interests
    No competing interests declared.
  8. Andrea Mattevi

    Dept. Biology and Biotechnology, University of Pavia, Pavia, Italy
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9523-7128
  9. Marco W Fraaije

    Molecular Enzymology group, University of Groningen, Groningen, Netherlands
    For correspondence
    m.w.fraaije@rug.nl
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6346-5014

Funding

European Commission (EU-H2020-ROBOX grant agreement nr. 635734)

  • Friso S Aalbers
  • Maximilian JLJ Fürst
  • Stefano Rovida
  • J Rubén Gómez Castellanos
  • Sebastian Bartsch
  • Andreas Vogel
  • Andrea Mattevi
  • Marco W Fraaije

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Philip A Cole, Harvard Medical School, United States

Version history

  1. Received: December 20, 2019
  2. Accepted: March 30, 2020
  3. Accepted Manuscript published: March 31, 2020 (version 1)
  4. Version of Record published: April 17, 2020 (version 2)

Copyright

© 2020, Aalbers et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 4,495
    views
  • 634
    downloads
  • 35
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Friso S Aalbers
  2. Maximilian JLJ Fürst
  3. Stefano Rovida
  4. Milos Trajkovic
  5. J Rubén Gómez Castellanos
  6. Sebastian Bartsch
  7. Andreas Vogel
  8. Andrea Mattevi
  9. Marco W Fraaije
(2020)
Approaching boiling point stability of an alcohol dehydrogenase through computationally-guided enzyme engineering
eLife 9:e54639.
https://doi.org/10.7554/eLife.54639

Share this article

https://doi.org/10.7554/eLife.54639

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Weichen Song, Yongyong Shi, Guan Ning Lin
    Tools and Resources

    We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS–trait associations with a significance of p < 5 × 10−8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway–trait associations and 153 tissue–trait associations with strong biological interpretability, including ‘circadian pathway-chronotype’ and ‘arachidonic acid-intelligence’. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1–39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.

    1. Computational and Systems Biology
    Qianmu Yuan, Chong Tian, Yuedong Yang
    Tools and Resources

    Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven’t fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at https://bio-web1.nscc-gz.cn/app/GPSite.