Literature DB >> 22522601

Many pathways in laboratory evolution can lead to improved enzymes: how to escape from local minima.

Yosephine Gumulya1, Joaquin Sanchis, Manfred T Reetz.   

Abstract

Directed evolution is a method to tune the properties of enzymes for use in organic chemistry and biotechnology, to study enzyme mechanisms, and to shed light on darwinian evolution in nature. In order to enhance its efficacy, iterative saturation mutagenesis (ISM) was implemented. This involves: 1) randomized mutation of appropriate sites of one or more residues; 2) screening of the initial mutant libraries for properties such as enzymatic rate, stereoselectivity, or thermal robustness; 3) use of the best hit in a given library as a template for saturation mutagenesis at the other sites; and 4) continuation of the process until the desired degree of enzyme improvement has been reached. Despite the success of a number of ISM-based studies, the question of the optimal choice of the many different possible pathways remains unanswered. Here we considered a complete 4-site ISM scheme. All 24 pathways were systematically explored, with the epoxide hydrolase from Aspergillus niger as the catalyst in the stereoselective hydrolytic kinetic resolution of a chiral epoxide. All 24 pathways were found to provide improved mutants with notably enhanced stereoselectivity. When a library failed to contain any hits, non-improved or even inferior mutants were used as templates in the continuation of the evolutionary pathway, thereby escaping from the local minimum. These observations have ramifications for directed evolution in general and for evolutionary biological studies in which protein engineering techniques are applied.
Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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Year:  2012        PMID: 22522601     DOI: 10.1002/cbic.201100784

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


  18 in total

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Journal:  Methods Mol Biol       Date:  2018

2.  Experimental interrogation of the path dependence and stochasticity of protein evolution using phage-assisted continuous evolution.

Authors:  Bryan C Dickinson; Aaron M Leconte; Benjamin Allen; Kevin M Esvelt; David R Liu
Journal:  Proc Natl Acad Sci U S A       Date:  2013-05-14       Impact factor: 11.205

3.  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

4.  Directed Evolution: Past, Present and Future.

Authors:  Ryan E Cobb; Ran Chao; Huimin Zhao
Journal:  AIChE J       Date:  2013-05       Impact factor: 3.993

5.  Kemp Eliminases of the AlleyCat Family Possess High Substrate Promiscuity.

Authors:  Elizabeth A Caselle; Jennifer H Yoon; Sagar Bhattacharya; Joel J L Rempillo; Zsófia Lengyel; Areetha D'Souza; Yurii S Moroz; Patricia L Tolbert; Alexander N Volkov; Marcello Forconi; Carlos A Castañeda; Olga V Makhlynets; Ivan V Korendovych
Journal:  ChemCatChem       Date:  2019-01-15       Impact factor: 5.686

6.  A single mutation in a regulatory protein produces evolvable allosterically regulated catalyst of nonnatural reaction.

Authors:  Olesia V Moroz; Yurii S Moroz; Yibing Wu; Alissa B Olsen; Hong Cheng; Korrie L Mack; Jaclyn M McLaughlin; Elizabeth A Raymond; Krystyna Zhezherya; Heinrich Roder; Ivan V Korendovych
Journal:  Angew Chem Int Ed Engl       Date:  2013-04-29       Impact factor: 15.336

7.  An automated flow for directed evolution based on detection of promiscuous scaffolds using spatial and electrostatic properties of catalytic residues.

Authors:  Sandeep Chakraborty
Journal:  PLoS One       Date:  2012-07-11       Impact factor: 3.240

8.  Combinatorial library based engineering of Candida antarctica lipase A for enantioselective transacylation of sec-alcohols in organic solvent.

Authors:  Ylva Wikmark; Maria Svedendahl Humble; Jan-E Bäckvall
Journal:  Angew Chem Int Ed Engl       Date:  2015-02-09       Impact factor: 15.336

Review 9.  Lipase improvement: goals and strategies.

Authors:  Arnau Bassegoda; Silvia Cesarini; Pilar Diaz
Journal:  Comput Struct Biotechnol J       Date:  2012-10-15       Impact factor: 7.271

10.  A population-based experimental model for protein evolution: effects of mutation rate and selection stringency on evolutionary outcomes.

Authors:  Aaron M Leconte; Bryan C Dickinson; David D Yang; Irene A Chen; Benjamin Allen; David R Liu
Journal:  Biochemistry       Date:  2013-02-14       Impact factor: 3.162

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