FireProt: Energy- and Evolution-Based Computational Design of Thermostable Multiple-Point Mutants

David Bednar, Koen Beerens, Eva Sebestova, Jaroslav Bendl, Sagar Khare, Radka Chaloupkova, Zbynek Prokop, Jan Brezovsky, David Baker, Jiri Damborsky

Research output: Contribution to journalArticle

47 Citations (Scopus)

Abstract

There is great interest in increasing proteins’ stability to enhance their utility as biocatalysts, therapeutics, diagnostics and nanomaterials. Directed evolution is a powerful, but experimentally strenuous approach. Computational methods offer attractive alternatives. However, due to the limited reliability of predictions and potentially antagonistic effects of substitutions, only single-point mutations are usually predicted in silico, experimentally verified and then recombined in multiple-point mutants. Thus, substantial screening is still required. Here we present FireProt, a robust computational strategy for predicting highly stable multiple-point mutants that combines energy- and evolution-based approaches with smart filtering to identify additive stabilizing mutations. FireProt’s reliability and applicability was demonstrated by validating its predictions against 656 mutations from the ProTherm database. We demonstrate that thermostability of the model enzymes haloalkane dehalogenase DhaA and γ-hexachlorocyclohexane dehydrochlorinase LinA can be substantially increased (ΔTm = 24°C and 21°C) by constructing and characterizing only a handful of multiple-point mutants. FireProt can be applied to any protein for which a tertiary structure and homologous sequences are available, and will facilitate the rapid development of robust proteins for biomedical and biotechnological applications.

Original languageEnglish (US)
Article numbere1004556
JournalPLoS computational biology
Volume11
Issue number11
DOIs
StatePublished - Nov 1 2015

Fingerprint

haloalkane dehalogenase
Flax
Lindane
Mutation
Nanostructures
Protein Stability
Enzymes
Sequence Homology
Point Mutation
Computer Simulation
Proteins
Databases
Therapeutics
dehydrochlorinases

All Science Journal Classification (ASJC) codes

  • Genetics
  • Ecology, Evolution, Behavior and Systematics
  • Cellular and Molecular Neuroscience
  • Molecular Biology
  • Ecology
  • Computational Theory and Mathematics
  • Modeling and Simulation

Cite this

Bednar, David ; Beerens, Koen ; Sebestova, Eva ; Bendl, Jaroslav ; Khare, Sagar ; Chaloupkova, Radka ; Prokop, Zbynek ; Brezovsky, Jan ; Baker, David ; Damborsky, Jiri. / FireProt : Energy- and Evolution-Based Computational Design of Thermostable Multiple-Point Mutants. In: PLoS computational biology. 2015 ; Vol. 11, No. 11.
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Bednar, D, Beerens, K, Sebestova, E, Bendl, J, Khare, S, Chaloupkova, R, Prokop, Z, Brezovsky, J, Baker, D & Damborsky, J 2015, 'FireProt: Energy- and Evolution-Based Computational Design of Thermostable Multiple-Point Mutants', PLoS computational biology, vol. 11, no. 11, e1004556. https://doi.org/10.1371/journal.pcbi.1004556

FireProt : Energy- and Evolution-Based Computational Design of Thermostable Multiple-Point Mutants. / Bednar, David; Beerens, Koen; Sebestova, Eva; Bendl, Jaroslav; Khare, Sagar; Chaloupkova, Radka; Prokop, Zbynek; Brezovsky, Jan; Baker, David; Damborsky, Jiri.

In: PLoS computational biology, Vol. 11, No. 11, e1004556, 01.11.2015.

Research output: Contribution to journalArticle

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T2 - Energy- and Evolution-Based Computational Design of Thermostable Multiple-Point Mutants

AU - Bednar, David

AU - Beerens, Koen

AU - Sebestova, Eva

AU - Bendl, Jaroslav

AU - Khare, Sagar

AU - Chaloupkova, Radka

AU - Prokop, Zbynek

AU - Brezovsky, Jan

AU - Baker, David

AU - Damborsky, Jiri

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Y1 - 2015/11/1

N2 - There is great interest in increasing proteins’ stability to enhance their utility as biocatalysts, therapeutics, diagnostics and nanomaterials. Directed evolution is a powerful, but experimentally strenuous approach. Computational methods offer attractive alternatives. However, due to the limited reliability of predictions and potentially antagonistic effects of substitutions, only single-point mutations are usually predicted in silico, experimentally verified and then recombined in multiple-point mutants. Thus, substantial screening is still required. Here we present FireProt, a robust computational strategy for predicting highly stable multiple-point mutants that combines energy- and evolution-based approaches with smart filtering to identify additive stabilizing mutations. FireProt’s reliability and applicability was demonstrated by validating its predictions against 656 mutations from the ProTherm database. We demonstrate that thermostability of the model enzymes haloalkane dehalogenase DhaA and γ-hexachlorocyclohexane dehydrochlorinase LinA can be substantially increased (ΔTm = 24°C and 21°C) by constructing and characterizing only a handful of multiple-point mutants. FireProt can be applied to any protein for which a tertiary structure and homologous sequences are available, and will facilitate the rapid development of robust proteins for biomedical and biotechnological applications.

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