Literature DB >> 18712749

Constructing and analyzing the fitness landscape of an experimental evolutionary process.

Manfred T Reetz1, Joaquin Sanchis.   

Abstract

Iterative saturation mutagenesis (ISM) is a promising approach to more efficient directed evolution, especially for enhancing the enantioselectivity and/or thermostability of enzymes. This was demonstrated previously for an epoxide hydrolase (EH), after five sets of mutations led to a stepwise increase in enantioselectivity. This study utilizes these results to illuminate the nature of ISM, and identify the reasons for its operational efficacy. By applying a deconvolution strategy to the five sets of mutations and measuring the enantioselectivity factors (E) of the EH variants, DeltaDeltaG( not equal) values become accessible. With these values, the construction of the complete fitness-pathway landscape is possible. The free energy profiles of the 5!=120 evolutionary pathways leading from the wild-type to the best mutant show that 55 trajectories are energetically favored, one of which is the originally observed route. This particular pathway was analyzed in terms of epistatic effects operating between the sets of mutations at all evolutionary stages. The degree of synergism increases as the stepwise evolutionary process proceeds. When encountering a local minimum in a disfavored pathway, that is, in the case of a dead end, choosing another set of mutations at a previous stage puts the evolutionary process back on an energetically favored trajectory. The type of analysis presented here might be useful when evaluating other mutagenesis methods and strategies in directed evolution.

Mesh:

Year:  2008        PMID: 18712749     DOI: 10.1002/cbic.200800371

Source DB:  PubMed          Journal:  Chembiochem        ISSN: 1439-4227            Impact factor:   3.164


  18 in total

1.  Combinatorial reshaping of the Candida antarctica lipase A substrate pocket for enantioselectivity using an extremely condensed library.

Authors:  Anders G Sandström; Ylva Wikmark; Karin Engström; Jonas Nyhlén; Jan-E Bäckvall
Journal:  Proc Natl Acad Sci U S A       Date:  2011-12-16       Impact factor: 11.205

2.  Environmental change exposes beneficial epistatic interactions in a catalytic RNA.

Authors:  Eric J Hayden; Andreas Wagner
Journal:  Proc Biol Sci       Date:  2012-06-20       Impact factor: 5.349

3.  How mutational epistasis impairs predictability in protein evolution and design.

Authors:  Charlotte M Miton; Nobuhiko Tokuriki
Journal:  Protein Sci       Date:  2016-01-22       Impact factor: 6.725

Review 4.  Engineering the third wave of biocatalysis.

Authors:  U T Bornscheuer; G W Huisman; R J Kazlauskas; S Lutz; J C Moore; K Robins
Journal:  Nature       Date:  2012-05-09       Impact factor: 49.962

5.  Finding better protein engineering strategies.

Authors:  Romas J Kazlauskas; Uwe T Bornscheuer
Journal:  Nat Chem Biol       Date:  2009-08       Impact factor: 15.040

6.  Inferring fitness landscapes by regression produces biased estimates of epistasis.

Authors:  Jakub Otwinowski; Joshua B Plotkin
Journal:  Proc Natl Acad Sci U S A       Date:  2014-05-19       Impact factor: 11.205

7.  Learning epistatic interactions from sequence-activity data to predict enantioselectivity.

Authors:  Julian Zaugg; Yosephine Gumulya; Alpeshkumar K Malde; Mikael Bodén
Journal:  J Comput Aided Mol Des       Date:  2017-12-12       Impact factor: 3.686

Review 8.  Learning Strategies in Protein Directed Evolution.

Authors:  Xavier F Cadet; Jean Christophe Gelly; Aster van Noord; Frédéric Cadet; Carlos G Acevedo-Rocha
Journal:  Methods Mol Biol       Date:  2022

Review 9.  Exploring protein fitness landscapes by directed evolution.

Authors:  Philip A Romero; Frances H Arnold
Journal:  Nat Rev Mol Cell Biol       Date:  2009-12       Impact factor: 94.444

10.  Replaying the tape of life: quantification of the predictability of evolution.

Authors:  Alexander E Lobkovsky; Eugene V Koonin
Journal:  Front Genet       Date:  2012-11-26       Impact factor: 4.599

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