Model-guided mechanism discovery and parameter selection for directed evolution

  • Sarah C. Stainbrook
  • Keith E. J. TyoEmail author
Methods and protocols


Directed evolution is frequently applied to identify genetic variants with improvements in a single or multiple properties. When used to improve multiple properties simultaneously, a common strategy is to apply alternating rounds of selection criteria to enrich for variants with each desirable trait. In particular, counterselection, or selection against undesired traits rather than for desired ones, has been successfully employed in many studies. Although the sequence and stringency of alternating selective pressures for different traits are known to be highly consequential for the outcome of the screen, the effects of these parameters have not been systematically evaluated. We developed a method for producing a statistical modeling framework to elucidate these effects. The model uses single-cell fluorescence intensity distributions to estimate the proportions of phenotypic populations within a library and then predicts the changes in these proportions depending on specified positive selective or counterselective pressures. We validated the approach using recently described systems for metabolite-responsive bacterial transcription factors and yeast G-protein-coupled receptors. Finally, we applied the model to identify biological sources that exert undesirable selective pressure on libraries during sorting. Notably, these pressures produce substantial artifacts that, if unaddressed, can lead to failure of the screen. This method for model generation can be applied to FACS-based directed evolution experiments to create a quantitative framework that identifies subtle population effects. Such models can guide the choice of experimental design parameters to better enrich for true positive genetic variants and improve the chance of successful directed evolution.


Counterselection Directed evolution Computational modeling Fluorescence-activated cell sorting 



We are grateful to Joshua Leonard for the gift of the library and to Joshua Leonard, Joseph Muldoon, and Peter Su for helpful discussion during the preparation of this manuscript.

Authors’ contributions

SCS collected and analyzed data and performed simulations; SCS and KT wrote the manuscript.


This work is funded by the National Science Foundation Grant DGE-1324585, the Bill and Melinda Gates Foundation Grant OPP1061177, and the National Institutes of Health (NIH) Common Fund [5R01MH103910-02]. This work was supported by the Northwestern University – Flow Cytometry Core Facility supported by Cancer Center Support Grant (NCI CA060553). Flow Cytometry Cell Sorting was performed on a BD FACSAria SORP system, purchased through the support of NIH 1S10OD011996-01.

Compliance with ethical standards

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and material

All data generated or analyzed during this study are included in this published article [and its supplementary information files]. The code used to analyze these datasets and to simulate sorting is available in the Github repository,

Competing interests

The authors declare that they have no competing interests.

Supplementary material

253_2019_10179_MOESM1_ESM.pdf (1.9 mb)
ESM 1 (PDF 1931 kb)


  1. Adeniran A, Stainbrook S, Bostick JW, Tyo KEJ (2018) Detection of a peptide biomarker by engineered yeast receptors. ACS Synth Biol 7:696–705CrossRefGoogle Scholar
  2. Boder ET, Wittrup KD (1998) Optimal screening of surface-displayed polypeptide libraries. Biotechnol Prog 14:55–62CrossRefGoogle Scholar
  3. Chao G, Lau WL, Hackel BJ, Sazinsky SL, Lippow SM, Wittrup KD (2006) Isolating and engineering human antibodies using yeast surface display. Nat Protoc 1:755–768CrossRefGoogle Scholar
  4. Chou HH, Keasling JD (2013) Programming adaptive control to evolve increased metabolite production. Nat Commun 4:1–8Google Scholar
  5. Feng J, Jester BW, Tinberg CE, Mandell DJ, Antunes MS, Chari R, Morey KJ, Rios X, Medford JI, Church JM, Stanley Fields, David Baker, (2015) A general strategy to construct small molecule biosensors in eukaryotes. eLife 4Google Scholar
  6. Li L, Liang J, Hong W, Zhao Y, Sun S, Yang X, Xu A, Hang H, Wu L, Chen S (2015) Evolved Bacterial Biosensor for Arsenite Detection in Environmental Water. Environmental Science & Technology 49(10):6149–6155Google Scholar
  7. Liu Y, Zhuang Y, Ding D, Xu Y, Sun J, Zhang D (2017) Biosensor-based evolution and elucidation of a biosynthetic pathway in Escherichia coli. ACS Synth Biol 6:837–848CrossRefGoogle Scholar
  8. Packer MS, Liu DR (2015) Methods for the directed evolution of proteins. Nat Rev Genet 16:379–394CrossRefGoogle Scholar
  9. Porter EB, Polaski JT, Morck MM, Batey RT (2017) Recurrent RNA motifs as scaffolds for genetically encodable small-molecule biosensors. Nat Chem Biol 13:295–301CrossRefGoogle Scholar
  10. Rajewsky K (1996) Clonal selection and learning in the antibody system. Nature 381:751–758CrossRefGoogle Scholar
  11. Rogers JK, Guzman CD, Taylor ND, Raman S, Anderson K, Church GM (2015) Synthetic biosensors for precise gene control and real-time monitoring of metabolites. Nucleic Acids Res 43:7648–7660CrossRefGoogle Scholar
  12. Röthlisberger D, Khersonsky O, Wollacott AM, Jiang L, DeChancie J, Betker J, Gallaher JL, Althoff EA, Zanghellini A, Dym O, Albeck S, Houk KN, Tawfik DS, Baker D (2008) Kemp elimination catalysts by computational enzyme design. Nature 453:190–195CrossRefGoogle Scholar
  13. Stainbrook SC, Yu JS, Reddick MP, Bagheri N, Tyo KEJ (2017) Modulating and evaluating receptor promiscuity through directed evolution and modeling. Protein Eng Des Sel:1–11Google Scholar
  14. Tao H, Cornish VW (2002) Milestones in directed enzyme evolution. Curr Opin Chem Biol 6:858–864CrossRefGoogle Scholar
  15. Tracewell CA, Arnold FH (2009) Directed enzyme evolution: climbing fitness peaks one amino acid at a time. Curr Opin Chem Biol 13:3–9CrossRefGoogle Scholar
  16. Verma R, Schwaneberg U, Roccatano D (2012) Computer-aided protein directed evolution: a review of web servers, databases and other computational tools for protein engineering. Comput Struct Biotechnol J 1:e201209008CrossRefGoogle Scholar
  17. Voigt CA, Mayo SL, Arnold FH, Wang ZG (2001) Computationally focusing the directed evolution of proteins. J Cell Biochem 84:58–63CrossRefGoogle Scholar
  18. Wang Q, Liu W, Xing Y, Yang X, Wang K, Jiang R, Wang P, Zhao Q (2014) Screening of DNA aptamers against myoglobin using a positive and negative selection units integrated microfluidic chip and its biosensing application. Anal Chem 86:6572–6579CrossRefGoogle Scholar
  19. Wu J, Jiang P, Chen W, Xiong D, Huang L, Jia J, Chen Y, Jin JM, Tang SY (2017) Design and application of a lactulose biosensor. Scientific Reports 7 (1)Google Scholar
  20. Younger AKD, Su PY, Shepard AJ, Udani SV, Cybulski TR, Tyo KEJ, Leonard JN (2018) Development of novel metabolite-responsive transcription factors via transposon-mediated protein fusion. Protein Eng Des Sel 31:55–63CrossRefGoogle Scholar
  21. Yu JS, Pertusi DA, Adeniran AV, Tyo KEJ, Wren J (2017) CellSort: a support vector machine tool for optimizing fluorescence-activated cell sorting and reducing experimental effort. Bioinformatics 33:909–916PubMedGoogle Scholar
  22. Yurtsev E, Friedman J & Gore J (2015) FlowCytometryTools: Version 0.4.5.Google Scholar
  23. Zahnd C, Sarkar CA, Plückthun A (2010) Computational analysis of off-rate selection experiments to optimize affinity maturation by directed evolution. Protein Eng Des Sel 23:175–184CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Interdisciplinary Biological Sciences ProgramNorthwestern UniversityEvanstonUSA
  2. 2.Chemical and Biological EngineeringNorthwestern UniversityEvanstonUSA

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