Skip to main content
SpringerLink
Log in

Computational Tools for Designing Smart Libraries

  • Protocol
  • First Online:
Directed Evolution Library Creation

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

Abstract

Traditional directed evolution experiments are often time-, labor- and cost-intensive because they involve repeated rounds of random mutagenesis and the selection or screening of large mutant libraries. The efficiency of directed evolution experiments can be significantly improved by targeting mutagenesis to a limited number of hot-spot positions and/or selecting a limited set of substitutions. The design of such “smart” libraries can be greatly facilitated by in silico analyses and predictions. Here we provide an overview of computational tools applicable for (a) the identification of hot-spots for engineering enzyme properties, and (b) the evaluation of predicted hot-spots and selection of suitable amino acids for substitutions. The selected tools do not require any specific expertise and can easily be implemented by the wider scientific community.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bornscheuer UT, Huisman GW, Kazlauskas RJ et al (2012) Engineering the third wave of biocatalysis. Nature 485:185–194

    Article  CAS  PubMed  Google Scholar 

  2. Chica RA, Doucet N, Pelletier JN (2005) Semi-rational approaches to engineering enzyme activity: combining the benefits of directed evolution and rational design. Curr Opin Biotechnol 16:378–384

    Article  CAS  PubMed  Google Scholar 

  3. Lutz S (2010) Beyond directed evolution–semi-rational protein engineering and design. Curr Opin Biotechnol 21:734–743

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  4. Bommarius AS, Blum JK, Abrahamson MJ (2011) Status of protein engineering for biocatalysts: how to design an industrially useful biocatalyst. Curr Opin Chem Biol 15:194–200

    Article  CAS  PubMed  Google Scholar 

  5. Goldsmith M, Tawfik DS (2012) Directed enzyme evolution: beyond the low-hanging fruit. Curr Opin Struct Biol 22:406–412

    Article  CAS  PubMed  Google Scholar 

  6. Reetz MT, Kahakeaw D, Lohmer R (2008) Addressing the numbers problem in directed evolution. Chembiochem 9:1797–1804

    Article  CAS  PubMed  Google Scholar 

  7. Reetz MT, Wu S (2008) Greatly reduced amino acid alphabets in directed evolution: making the right choice for saturation mutagenesis at homologous enzyme positions. Chem Commun 43:5499–5501. http://www.ncbi.nlm.nih.gov/pubmed/18997931

  8. Barrozo A, Borstnar R, Marloie G et al (2012) Computational protein engineering: bridging the gap between rational design and laboratory evolution. Int J Mol Sci 13:12428–12460

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  9. Damborsky J, Brezovsky J (2014) Computational tools for designing and engineering enzymes. Curr Opin Chem Biol 19C:8–16

    Google Scholar 

  10. Chaparro-Riggers JF, Polizzi KM, Bommarius AS (2007) Better library design: data-driven protein engineering. Biotechnol J 2:180–191

    Article  PubMed  Google Scholar 

  11. Moore GL, Maranas CD (2004) Computational challenges in combinatorial library design for protein engineering. AIChE J 50:262–272

    Article  CAS  Google Scholar 

  12. Wong TS, Roccatano D, Schwaneberg U (2007) Steering directed protein evolution: strategies to manage combinatorial complexity of mutant libraries. Environ Microbiol 9:2645–2659

    Article  CAS  PubMed  Google Scholar 

  13. Fox RJ, Huisman GW (2008) Enzyme optimization: moving from blind evolution to statistical exploration of sequence-function space. Trends Biotechnol 26:132–138

    Article  CAS  PubMed  Google Scholar 

  14. 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 2:e201209008

    Article  PubMed Central  PubMed  Google Scholar 

  15. Dalby PA (2003) Optimising enzyme function by directed evolution. Curr Opin Struct Biol 13:500–505

    Article  CAS  PubMed  Google Scholar 

  16. Hibbert EG, Dalby PA (2005) Directed evolution strategies for improved enzymatic performance. Microb Cell Fact 4:29

    Article  PubMed Central  PubMed  Google Scholar 

  17. Morley KL, Kazlauskas RJ (2005) Improving enzyme properties: when are closer mutations better? Trends Biotechnol 23:231–237

    Article  CAS  PubMed  Google Scholar 

  18. Paramesvaran J, Hibbert EG, Russell AJ et al (2009) Distributions of enzyme residues yielding mutants with improved substrate specificities from two different directed evolution strategies. Protein Eng Des Sel 22:401–411

    Article  CAS  PubMed  Google Scholar 

  19. Laskowski RA, Swindells MB (2011) LigPlot+: multiple ligand-protein interaction diagrams for drug discovery. J Chem Inf Model 51:2778–2786

    Article  CAS  PubMed  Google Scholar 

  20. Stierand K, Rarey M (2010) Drawing the PDB: protein-ligand complexes in two dimensions. ACS Med Chem Lett 1:540–545

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  21. Sobolev V, Sorokine A, Prilusky J et al (1999) Automated analysis of interatomic contacts in proteins. Bioinformatics 15:327–332

    Article  CAS  PubMed  Google Scholar 

  22. Krissinel E, Henrick K (2007) Inference of macromolecular assemblies from crystalline state. J Mol Biol 372:774–797

    Article  CAS  PubMed  Google Scholar 

  23. Biesiada J, Porollo A, Velayutham P et al (2011) Survey of public domain software for docking simulations and virtual screening. Hum Genomics 5:497–505

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  24. Li X, Li Y, Cheng T et al (2010) Evaluation of the performance of four molecular docking programs on a diverse set of protein-ligand complexes. J Comput Chem 31:2109–2125

    Article  PubMed  Google Scholar 

  25. Morris GM, Lim-Wilby M (2008) Molecular docking. In: Kukol A (ed) Molecular modeling of proteins, Methods in molecular biology. Humana Press, Totowa, NJ, pp 3–23

    Google Scholar 

  26. McDonald IK, Thornton JM (1994) Satisfying hydrogen bonding potential in proteins. J Mol Biol 238:777–793

    Article  CAS  PubMed  Google Scholar 

  27. Sobolev V, Wade RC, Vriend G et al (1996) Molecular docking using surface complementarity. Proteins 25:120–129

    Article  CAS  PubMed  Google Scholar 

  28. Henrich S, Salo-Ahen OMH, Huang B et al (2010) Computational approaches to identifying and characterizing protein binding sites for ligand design. J Mol Recognit 23:209–219

    CAS  PubMed  Google Scholar 

  29. Laurie ATR, Jackson RM (2006) Methods for the prediction of protein-ligand binding sites for structure-based drug design and virtual ligand screening. Curr Protein Pept Sci 7:395–406

    Article  CAS  PubMed  Google Scholar 

  30. Pérot S, Sperandio O, Miteva MA et al (2010) Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery. Drug Discov Today 15:656–667

    Article  PubMed  Google Scholar 

  31. Dundas J, Ouyang Z, Tseng J et al (2006) CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Res 34:W116–W118

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  32. Zhang Z, Li Y, Lin B et al (2011) Identification of cavities on protein surface using multiple computational approaches for drug binding site prediction. Bioinformatics 27:2083–2088

    Article  CAS  PubMed  Google Scholar 

  33. Hendlich M, Rippmann F, Barnickel G (1997) LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins. J Mol Graph Model 15:359–363

    Article  CAS  PubMed  Google Scholar 

  34. Laurie ATR, Jackson RM (2005) Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites. Bioinformatics 21:1908–1916

    Article  CAS  PubMed  Google Scholar 

  35. Hernandez M, Ghersi D, Sanchez R (2009) SITEHOUND-web: a server for ligand binding site identification in protein structures. Nucleic Acids Res 37:W413–W416

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  36. Schmidtke P, Le Guilloux V, Maupetit J et al (2010) fpocket: online tools for protein ensemble pocket detection and tracking. Nucleic Acids Res 38:W582–W589

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  37. La D, Esquivel-Rodríguez J, Venkatraman V et al (2009) 3D-SURFER: software for high-throughput protein surface comparison and analysis. Bioinformatics 25:2843–2844

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  38. Binkowski TA, Naghibzadeh S, Liang J (2003) CASTp: Computed Atlas of Surface Topography of proteins. Nucleic Acids Res 31:3352–3355

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  39. Pavelka A, Chovancova E, Damborsky J (2009) HotSpot Wizard: a web server for identification of hot spots in protein engineering. Nucleic Acids Res 37:W376–W383

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  40. Porter CT, Bartlett GJ, Thornton JM (2004) The Catalytic Site Atlas: a resource of catalytic sites and residues identified in enzymes using structural data. Nucleic Acids Res 32:D129–D133

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  41. Magrane M, Uniprot Consortium (2011) UniProt Knowledgebase: a hub of integrated protein data. Database (Oxford) 2011:bar009

    Article  Google Scholar 

  42. Chovancova E, Pavelka A, Benes P et al (2012) CAVER 3.0: a tool for the analysis of transport pathways in dynamic protein structures. PLoS Comput Biol 8:e1002708

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  43. Ashkenazy H, Erez E, Martz E et al (2010) ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids. Nucleic Acids Res 38:W529–W533

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  44. Huang B (2009) MetaPocket: a meta approach to improve protein ligand binding site prediction. OMICS 13:325–330

    Article  CAS  PubMed  Google Scholar 

  45. Prokop Z, Gora A, Brezovsky J et al (2012) Engineering of protein tunnels: keyhole-lock-key model for catalysis by the enzymes with buried active sites. In: Lutz S, Bornscheuer UT (eds) Protein engineering handbook, vol 3. Wiley-VCH, Weinheim, pp 421–464

    Google Scholar 

  46. Brezovsky J, Chovancova E, Gora A et al (2013) Software tools for identification, visualization and analysis of protein tunnels and channels. Biotechnol Adv. 31:38–49

    Google Scholar 

  47. Berka K, Hanák O, Sehnal D et al (2012) MOLEonline 2.0: interactive web-based analysis of biomacromolecular channels. Nucleic Acids Res 40:W222–W227

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  48. Yaffe E, Fishelovitch D, Wolfson HJ et al (2008) MolAxis: a server for identification of channels in macromolecules. Nucleic Acids Res 36:W210–W215

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  49. Becker OM, Karplus M (2005) Guide to biomolecular simulations. Springer, New York

    Google Scholar 

  50. Lindahl ER (2008) Molecular dynamics simulations. In: Kukol A (ed) Molecular modeling of proteins, Methods in molecular biology. Humana Press, Totowa, NJ, pp 3–23

    Chapter  Google Scholar 

  51. Polizzi KM, Bommarius AS, Broering JM et al (2007) Stability of biocatalysts. Curr Opin Chem Biol 11:220–225

    Article  CAS  PubMed  Google Scholar 

  52. Reetz MT, Carballeira JD (2007) Iterative saturation mutagenesis (ISM) for rapid directed evolution of functional enzymes. Nat Protoc 2:891–903

    Article  CAS  PubMed  Google Scholar 

  53. Reetz MT, Carballeira JD, Vogel A (2006) Iterative saturation mutagenesis on the basis of B factors as a strategy for increasing protein thermostability. Angew Chem Int Ed Engl 45:7745–7751

    Article  CAS  PubMed  Google Scholar 

  54. Koudelakova T, Chaloupkova R, Brezovsky J et al (2013) Engineering enzyme stability and resistance to an organic cosolvent by modification of residues in the access tunnel. Angew Chem Int Ed Engl 52(7):1959–1963. doi:10.1002/anie.201206708

    Article  CAS  PubMed  Google Scholar 

  55. Potapov V, Cohen M, Schreiber G (2009) Assessing computational methods for predicting protein stability upon mutation: good on average but not in the details. Protein Eng Des Sel 22:553–560

    Article  CAS  PubMed  Google Scholar 

  56. Khan S, Vihinen M (2010) Performance of protein stability predictors. Hum Mutat 31:675–684

    Article  CAS  PubMed  Google Scholar 

  57. Thiltgen G, Goldstein RA (2012) Assessing predictors of changes in protein stability upon mutation using self-consistency. PLoS One 7:e46084

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  58. Dehouck Y, Kwasigroch JM, Gilis D et al (2011) PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality. BMC Bioinformatics 12:151

    Article  PubMed Central  PubMed  Google Scholar 

  59. Dehouck Y, Grosfils A, Folch B et al (2009) Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0. Bioinformatics 25:2537–2543

    Article  CAS  PubMed  Google Scholar 

  60. Guerois R, Nielsen JE, Serrano L (2002) Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. J Mol Biol 320:369–387

    Article  CAS  PubMed  Google Scholar 

  61. Schymkowitz J, Borg J, Stricher F et al (2005) The FoldX web server: an online force field. Nucleic Acids Res 33:W382–W388

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  62. Van Durme J, Delgado J, Stricher F et al (2011) A graphical interface for the FoldX forcefield. Bioinformatics 27:1711–1712

    Article  PubMed  Google Scholar 

  63. Valdar WSJ (2002) Scoring residue conservation. Proteins 48:227–241

    Article  CAS  PubMed  Google Scholar 

  64. Johansson F, Toh H (2010) A comparative study of conservation and variation scores. BMC Bioinformatics 11:388

    Article  PubMed Central  PubMed  Google Scholar 

  65. Mihalek I, Res I, Lichtarge O (2004) A family of evolution-entropy hybrid methods for ranking protein residues by importance. J Mol Biol 336:1265–1282

    Article  CAS  PubMed  Google Scholar 

  66. Morgan DH, Kristensen DM, Mittelman D et al (2006) ET viewer: an application for predicting and visualizing functional sites in protein structures. Bioinformatics 22:2049–2050

    Article  CAS  PubMed  Google Scholar 

  67. Ma B-G, Berezovsky IN (2010) The MBLOSUM: a server for deriving mutation targets and position-specific substitution rates. J Biomol Struct Dyn 28:415–419

    Article  CAS  PubMed  Google Scholar 

  68. Crooks GE, Hon G, Chandonia JM et al (2004) WebLogo: a sequence logo generator. Genome Res 14:1188–1190

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  69. Jochens H, Bornscheuer UT (2010) Natural diversity to guide focused directed evolution. Chembiochem 11:1861–1866

    Article  CAS  PubMed  Google Scholar 

  70. Mayrose I, Graur D, Ben-Tal N et al (2004) Comparison of site-specific rate-inference methods for protein sequences: empirical Bayesian methods are superior. Mol Biol Evol 21:1781–1791

    Article  CAS  PubMed  Google Scholar 

  71. Pupko T, Bell RE, Mayrose I et al (2002) Rate4Site: an algorithmic tool for the identification of functional regions in proteins by surface mapping of evolutionary determinants within their homologues. Bioinformatics 18:S71–S77

    Article  PubMed  Google Scholar 

  72. Goldenberg O, Erez E, Nimrod G et al (2009) The ConSurf-DB: pre-calculated evolutionary conservation profiles of protein structures. Nucleic Acids Res 37:D323–D327

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  73. Joosten RP, Te Beek TAH, Krieger E et al (2011) A series of PDB related databases for everyday needs. Nucleic Acids Res 39:D411–D419

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  74. Kuipers RK, Joosten H-J, Van Berkel WJH et al (2010) 3DM: systematic analysis of heterogeneous superfamily data to discover protein functionalities. Proteins 78:2101–2113

    CAS  PubMed  Google Scholar 

  75. Kuipers R, Van den Bergh T, Joosten H-J et al (2010) Novel tools for extraction and validation of disease-related mutations applied to Fabry disease. Hum Mutat 31:1026–1032

    Article  CAS  PubMed  Google Scholar 

  76. Zhou H, Zhou Y (2002) Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Sci 11:2714–2726

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  77. Parthiban V, Gromiha MM, Schomburg D (2006) CUPSAT: prediction of protein stability upon point mutations. Nucleic Acids Res 34:W239–W242

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  78. Capriotti E, Fariselli P, Casadio R (2005) I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res 33:W306–W310

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  79. Ng PC, Henikoff S (2006) Predicting the effects of amino acid substitutions on protein function. Annu Rev Genomics Hum Genet 7:61–80

    Article  CAS  PubMed  Google Scholar 

  80. Thusberg J, Olatubosun A, Vihinen M (2011) Performance of mutation pathogenicity prediction methods on missense variants. Hum Mutat 32:358–368

    Article  PubMed  Google Scholar 

  81. Thusberg J, Vihinen M (2009) Pathogenic or not? And if so, then how? Studying the effects of missense mutations using bioinformatics methods. Hum Mutat 30:703–714

    Article  CAS  PubMed  Google Scholar 

  82. Ng PC, Henikoff S (2003) SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res 31:3812–3814

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  83. Choi Y, Sims GE, Murphy S et al (2012) Predicting the functional effect of amino acid substitutions and indels. PLoS One 7:e46688

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  84. Thomas PD, Campbell MJ, Kejariwal A et al (2003) PANTHER: a library of protein families and subfamilies indexed by function. Genome Res 13:2129–2141

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  85. Kumar P, Henikoff S, Ng PC (2009) Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc 4:1073–1081

    Article  CAS  PubMed  Google Scholar 

  86. Nov Y (2012) When second best is good enough: another probabilistic look at saturation mutagenesis. Appl Environ Microbiol 78:258–262

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  87. Firth AE, Patrick WM (2008) GLUE-IT and PEDEL-AA: new programmes for analyzing protein diversity in randomized libraries. Nucleic Acids Res 36:W281–W285

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  88. Firth AE, Patrick WM (2005) Statistics of protein library construction. Bioinformatics 21:3314–3315

    Article  CAS  PubMed  Google Scholar 

  89. Bohác M, Nagata Y, Prokop Z et al (2002) Halide-stabilizing residues of haloalkane dehalogenases studied by quantum mechanic calculations and site-directed mutagenesis. Biochemistry 41:14272–14280

    Article  PubMed  Google Scholar 

  90. Kleywegt GJ, Harris MR, Zou JY et al (2004) The Uppsala Electron-Density Server. Acta Crystallogr D Biol Crystallogr 60:2240–2249

    Article  PubMed  Google Scholar 

  91. Leis S, Schneider S, Zacharias M (2010) In silico prediction of binding sites on proteins. Curr Med Chem 17:1550–1562

    Article  CAS  PubMed  Google Scholar 

  92. Xin F, Radivojac P (2011) Computational methods for identification of functional residues in protein structures. Curr Protein Pept Sci 12:456–469

    Article  CAS  PubMed  Google Scholar 

  93. Do CB, Katoh K (2008) Protein multiple sequence alignment. In: Thompson JD, Ueffing M, Schaeffer-Reiss C (eds) Functional proteomics, Methods in molecular biology. Humana Press, Totowa, NJ, pp 379–413

    Chapter  Google Scholar 

  94. Pirovano W, Heringa J (2008) Multiple sequence alignment. In: Keith JM (ed) Bioinformatics, Methods in molecular biology. Humana Press, Totowa, NJ, pp 143–161

    Chapter  Google Scholar 

  95. Pei J (2008) Multiple protein sequence alignment. Curr Opin Struct Biol 18:382–386

    Article  CAS  PubMed  Google Scholar 

  96. Finn RD, Tate J, Mistry J et al (2008) The Pfam protein families database. Nucleic Acids Res 36:D281–D288

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  97. Sirim D, Wagner F, Wang L et al (2011) The Laccase Engineering Database: a classification and analysis system for laccases and related multicopper oxidases. Database (Oxford) 2011:bar006

    Article  Google Scholar 

  98. Fischer M, Pleiss J (2003) The Lipase Engineering Database: a navigation and analysis tool for protein families. Nucleic Acids Res 31:319–321

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  99. Fischer M, Knoll M, Sirim D et al (2007) The Cytochrome P450 Engineering Database: a navigation and prediction tool for the cytochrome P450 protein family. Bioinformatics 23:2015–2017

    Article  CAS  PubMed  Google Scholar 

  100. Chaloupková R, Sýkorová J, Prokop Z et al (2003) Modification of activity and specificity of haloalkane dehalogenase from Sphingomonas paucimobilis UT26 by engineering of its entrance tunnel. J Biol Chem 278:52622–52628

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The research work of the authors is supported by the Grant Agency of the Czech Republic (P207/12/0775 and P503/12/0572), the Czech Ministry of Education (LO1214, LH14027, CZ.1.07/2.3.00/30.0037), and the European Regional Development Fund (CZ.1.05/2.1.00/01.0001). MetaCentrum is acknowledged for providing access to computing facilities, supported by the Czech Ministry of Education of the Czech Republic (LM2010005).

Author information

Authors and Affiliations

  1. Loschmidt Laboratories, Masaryk University, Kamenice 5/A13, 625 00, Brno, Czech Republic

    Eva Sebestova, Jaroslav Bendl & Jan Brezovsky

  2. Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00, Brno, Czech Republic

    Jiri Damborsky

Authors
  1. Eva Sebestova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jaroslav Bendl
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jan Brezovsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiri Damborsky
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiri Damborsky .

Editor information

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

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this protocol

Cite this protocol

Sebestova, E., Bendl, J., Brezovsky, J., Damborsky, J. (2014). Computational Tools for Designing Smart Libraries. 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_20

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-1053-3_20

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4939-1052-6

  • Online ISBN: 978-1-4939-1053-3

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation