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Computational Tools for Directed Evolution: A Comparison of Prospective and Retrospective Strategies

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Directed Evolution Library Creation

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1179))

Abstract

Directed evolution methods have proved to be highly effective in the design of novel proteins and in the generation of large libraries of diverse sequences. However, searching through the vast number of mutants produced during such experiments in order to find the best represents a daunting and difficult task. In recent years, a number of computational tools have been developed to provide guidance during this exploratory process. It can, however, be unclear as to which tool or tools best complement the chosen library design strategy. In this review, we describe and critically evaluate some of the more notable tools in this area, discussing the rationale behind each, the requirements for their implementation, and potential issues faced when using them. Some examples of their application in an experimental setting are also provided. The tools have been classified based on contrasting strategies as to how they function: prospective tools SCHEMA and OPTCOMB use extant sequence and structural data to predict optimal locations for crossover sites, whereas retrospective tools ProSAR and ASRA use property data from the mutant library to predict beneficial mutations and features. From our evaluation, we suggest that each tool can play a role in the design process; however this is largely dictated by the data available and the desired experimental strategy for the project.

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Author information

Authors and Affiliations

  1. School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia

    Julian Zaugg, Yosephine Gumulya, Elizabeth M. J. Gillam & Mikael Bodén

Authors
  1. Julian Zaugg
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  2. Yosephine Gumulya
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  3. Elizabeth M. J. Gillam
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  4. Mikael Bodén
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Corresponding author

Correspondence to Mikael Bodén .

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Editors and Affiliations

  1. Chemistry and Molecular Biosciences, The University of Queensland, St. Lucia, Brisbane, Queensland, Australia

    Elizabeth M.J. Gillam

  2. Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada

    Janine N. Copp

  3. School of Biological Sciences, Victoria University, Wellington, New Zealand

    David Ackerley

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Zaugg, J., Gumulya, Y., Gillam, E.M.J., Bodén, M. (2014). Computational Tools for Directed Evolution: A Comparison of Prospective and Retrospective Strategies. In: Gillam, E., Copp, J., Ackerley, D. (eds) Directed Evolution Library Creation. Methods in Molecular Biology, vol 1179. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1053-3_21

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  • DOI: https://doi.org/10.1007/978-1-4939-1053-3_21

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4939-1052-6

  • Online ISBN: 978-1-4939-1053-3

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