Considerations in the Use of Codon Optimization for Recombinant Protein Expression

  • Vincent P. MauroEmail author
  • Stephen A. Chappell
Part of the Methods in Molecular Biology book series (MIMB, volume 1850)


Codon optimization is a gene engineering approach that is commonly used for enhancing recombinant protein expression. This approach is possible because (1) degeneracy of the genetic code enables most amino acids to be encoded by multiple codons and (2) different mRNAs encoding the same protein can vary dramatically in the amount of protein expressed. However, because codon optimization potentially disrupts overlapping information encoded in mRNA coding regions, protein structure and function may be altered. This chapter discusses the use of codon optimization for various applications in mammalian cells as well as potential consequences, so that informed decisions can be made on the appropriateness of using this approach in each case.

Key words

Codon Optimization Synonymous mRNA Translation Wobble Recombinant Bioproduction Nucleic acid Therapeutics 


  1. 1.
    Van Der Kelen K, Beyaert R, Inzé D, De Veylder L (2009) Translational control of eukaryotic gene expression. Crit Rev Biochem Mol Biol 44(4):143–168CrossRefGoogle Scholar
  2. 2.
    Jan E, Mohr I, Walsh D (2016) A cap-to-tail guide to mRNA translation strategies in virus-infected cells. Annu Rev Virol 3(1):283–307CrossRefGoogle Scholar
  3. 3.
    Mauro VP, Chappell SA (2014) A critical analysis of codon optimization in human therapeutics. Trends Mol Med 20(11):604–613CrossRefGoogle Scholar
  4. 4.
    Chappell SA, Edelman GM, Mauro VP (2006) Ribosomal tethering and clustering as mechanisms for translation initiation. Proc Natl Acad Sci U S A 103(48):18077–18082CrossRefGoogle Scholar
  5. 5.
    Chappell SA, Dresios J, Edelman GM, Mauro VP (2006) Ribosomal shunting mediated by a translational enhancer element that base pairs to 18S rRNA. Proc Natl Acad Sci U S A 103(25):9488–9493CrossRefGoogle Scholar
  6. 6.
    Matsuda D, Mauro VP (2010) Determinants of initiation codon selection during translation in mammalian cells. PLoS One 5:e15057CrossRefGoogle Scholar
  7. 7.
    Apcher S, Prado Martins R, Fahraeus R (2016) The source of MHC class I presented peptides and its implications. Curr Opin Immunol 40:117–122CrossRefGoogle Scholar
  8. 8.
    Starck SR, Shastri N (2016) Nowhere to hide: unconventional translation yields cryptic peptides for immune surveillance. Immunol Rev 272(1):8–16CrossRefGoogle Scholar
  9. 9.
    Diaz de Arce AJ, Noderer WL, Wang CL (2018) Complete motif analysis of sequence requirements for translation initiation at non-AUG start codons. Nucleic Acids Res 46(2):985–994CrossRefGoogle Scholar
  10. 10.
    Hashem Y, des Georges A, Dhote V, Langlois R, Liao HY, Grassucci RA, Hellen CU, Pestova TV, Frank J (2013) Structure of the mammalian ribosomal 43S preinitiation complex bound to the scanning factor DHX29. Cell 153(5):1108–1119CrossRefGoogle Scholar
  11. 11.
    Ling C, Ermolenko DN (2016) Structural insights into ribosome translocation. Wiley Interdiscip Rev RNA 7(5):620–636CrossRefGoogle Scholar
  12. 12.
    Alkalaeva E, Mikhailova T (2017) Reassigning stop codons via translation termination: how a few eukaryotes broke the dogma. BioEssays 39:3. Scholar
  13. 13.
    Welch M, Villalobos A, Gustafsson C, Minshull J (2009) You're one in a googol: optimizing genes for protein expression. J R Soc Interface 6(Suppl 4):S467–S476CrossRefGoogle Scholar
  14. 14.
    Supek F (2016) The code of silence: widespread associations between synonymous codon biases and gene function. J Mol Evol 82(1):65–73CrossRefGoogle Scholar
  15. 15.
    Goldman E (2011) tRNA and the human genome. In: Encylopedia of life sciences. Wiley, Chichester. Scholar
  16. 16.
    Stadler M, Fire A (2011) Wobble base-pairing slows in vivo translation elongation in metazoans. RNA 17(12):2063–2073CrossRefGoogle Scholar
  17. 17.
    Wang H, McManus J, Kingsford C (2017) Accurate recovery of ribosome positions reveals slow translation of wobble-pairing codons in yeast. J Comput Biol 24(6):486–500CrossRefGoogle Scholar
  18. 18.
    Ayyar BV, Arora S, Ravi SS (2017) Optimizing antibody expression: the nuts and bolts. Methods 116:51–62CrossRefGoogle Scholar
  19. 19.
    Williams JA (2014) Improving DNA vaccine performance through vector design. Curr Gene Ther 14(3):170–189CrossRefGoogle Scholar
  20. 20.
    Angov E, Hillier CJ, Kincaid RL, Lyon JA (2008) Heterologous protein expression is enhanced by harmonizing the codon usage frequencies of the target gene with those of the expression host. PLoS One 3(5):e2189CrossRefGoogle Scholar
  21. 21.
    Rodriguez A, Wright G, Emrich S, Clark PL (2018) %MinMax: a versatile tool for calculating and comparing synonymous codon usage and its impact on protein folding. Protein Sci 27(1):356–362CrossRefGoogle Scholar
  22. 22.
    Richardson SM, Wheelan SJ, Yarrington RM, Boeke JD (2006) GeneDesign: rapid, automated design of multikilobase synthetic genes. Genome Res 6(4):550–556CrossRefGoogle Scholar
  23. 23.
    Villalobos A, Ness JE, Gustafsson C, Minshull J, Govindarajan S (2006) Gene designer: a synthetic biology tool for constructing artificial DNA segments. BMC Bioinformatics 7:285CrossRefGoogle Scholar
  24. 24.
    Kimchi-Sarfaty C, Schiller T, Hamasaki-Katagiri N, Khan MA, Yanover C, Sauna ZE (2013) Building better drugs: developing and regulating engineered therapeutic proteins. Trends Pharmacol Sci 34(10):534–548CrossRefGoogle Scholar
  25. 25.
    Presnyak V, Alhusaini N, Chen YH, Martin S, Morris N, Kline N, Olson S, Weinberg D, Baker KE, Graveley BR, Coller J (2015) Codon optimality is a major determinant of mRNA stability. Cell 160(6):1111–1124CrossRefGoogle Scholar
  26. 26.
    Zhou Z, Dang Y, Zhou M, Li L, Yu CH, Fu J, Chen S, Liu Y (2016) Codon usage is an important determinant of gene expression levels largely through its effects on transcription. Proc Natl Acad Sci U S A 113(41):E6117–E6125CrossRefGoogle Scholar
  27. 27.
    Newman ZR, Young JM, Ingolia NT, Barton GM (2016) Differences in codon bias and GC content contribute to the balanced expression of TLR7 and TLR9. Proc Natl Acad Sci U S A 113(10):E1362–E1371CrossRefGoogle Scholar
  28. 28.
    Bazzini AA, Del Viso F, Moreno-Mateos MA, Johnstone TG, Vejnar CE, Qin Y, Yao J, Khokha MK, Giraldez AJ (2016) Codon identity regulates mRNA stability and translation efficiency during the maternal-to-zygotic transition. EMBO J 35(19):2087–2103CrossRefGoogle Scholar
  29. 29.
    Kelsic ED, Chung H, Cohen N, Park J, Wang HH, Kishony R (2016) RNA structural determinants of optimal codons revealed by MAGE-Seq. Cell Syst 3(6):563–571.e6CrossRefGoogle Scholar
  30. 30.
    Cheong DE, Ko KC, Han Y, Jeon HG, Sung BH, Kim GJ, Choi JH, Song JJ (2015) Enhancing functional expression of heterologous proteins through random substitution of genetic codes in the 5′ coding region. Biotechnol Bioeng 112(4):822–826CrossRefGoogle Scholar
  31. 31.
    Martínez MA, Jordan-Paiz A, Franco S, Nevot M (2016) Synonymous virus genome recoding as a tool to impact viral fitness. Trends Microbiol 24(2):134–147CrossRefGoogle Scholar
  32. 32.
    de Fabritus L, Nougairède A, Aubry F, Gould EA, de Lamballerie X (2015) Attenuation of tick-borne encephalitis virus using large-scale random codon re-encoding. PLoS Pathog 11(3):e1004738CrossRefGoogle Scholar
  33. 33.
    Wang B, Yang C, Tekes G, Mueller S, Paul A, Whelan SP, Wimmer E (2015) Recoding of the vesicular stomatitis virus L gene by computer-aided design provides a live, attenuated vaccine candidate. MBio 6(2):e00237-15CrossRefGoogle Scholar
  34. 34.
    Wang E, Wang J, Chen C, Xiao Y (2015) Computational evidence that fast translation speed can increase the probability of cotranslational protein folding. Sci Rep 5:15316CrossRefGoogle Scholar
  35. 35.
    Gamble CE, Brule CE, Dean KM, Fields S, Grayhack EJ (2016) Adjacent codons act in concert to modulate translation efficiency in yeast. Cell 166(3):679–690CrossRefGoogle Scholar
  36. 36.
    Harigaya Y, Parker R (2017) The link between adjacent codon pairs and mRNA stability. BMC Genomics 18(1):364CrossRefGoogle Scholar
  37. 37.
    McCarthy C, Carrea A, Diambra L (2017) Bicodon bias can determine the role of synonymous SNPs in human diseases. BMC Genomics 18(1):227CrossRefGoogle Scholar
  38. 38.
    Gardin J, Yeasmin R, Yurovsky A, Cai Y, Skiena S, Futcher B (2014) Measurement of average decoding rates of the 61 sense codons in vivo. Elife 3.
  39. 39.
    Dana A, Tuller T (2014) The effect of tRNA levels on decoding times of mRNA codons. Nucleic Acids Res 42(14):9171–9181CrossRefGoogle Scholar
  40. 40.
    Dana A, Tuller T (2014) Mean of the typical decoding rates: a new translation efficiency index based on the analysis of ribosome profiling data. G3 (Bethesda) 5(1):73–80CrossRefGoogle Scholar
  41. 41.
    Yu CH, Dang Y, Zhou Z, Wu C, Zhao F, Sachs MS, Liu Y (2015) Codon usage influences the local rate of translation elongation to regulate co-translational protein folding. Mol Cell 59(5):744–754CrossRefGoogle Scholar
  42. 42.
    Paulet D, David A, Rivals E (2017) Ribo-seq enlightens codon usage bias. DNA Res 24(3):303–310CrossRefGoogle Scholar
  43. 43.
    Pouyet F, Mouchiroud D, Duret L, Sémon M (2017) Recombination, meiotic expression and human codon usage. elife 6:e27344. Scholar
  44. 44.
    Schmitt BM, Rudolph KL, Karagianni P, Fonseca NA, White RJ, Talianidis I, Odom DT, Marioni JC, Kutter C (2014) High-resolution mapping of transcriptional dynamics across tissue development reveals a stable mRNA-tRNA interface. Genome Res 24(11):1797–1807CrossRefGoogle Scholar
  45. 45.
    Rudolph KL, Schmitt BM, Villar D, White RJ, Marioni JC, Kutter C, Odom DT (2016) Codon-driven translational efficiency is stable across diverse mammalian cell states. PLoS Genet 12(5):e1006024CrossRefGoogle Scholar
  46. 46.
    Gingold H, Tehler D, Christoffersen NR, Nielsen MM, Asmar F, Kooistra SM, Christophersen NS, Christensen LL, Borre M, Sørensen KD, Andersen LD, Andersen CL, Hulleman E, Wurdinger T, Ralfkiær E, Helin K, Grønbæk K, Ørntoft T, Waszak SM, Dahan O, Pedersen JS, Lund AH, Pilpel Y (2014) A dual program for translation regulation in cellular proliferation and differentiation. Cell 158(6):1281–1292CrossRefGoogle Scholar
  47. 47.
    Bali V, Bebok Z (2015) Decoding mechanisms by which silent codon changes influence protein biogenesis and function. Int J Biochem Cell Biol 64:58–74CrossRefGoogle Scholar
  48. 48.
    Kirchner S, Cai Z, Rauscher R, Kastelic N, Anding M, Czech A, Kleizen B, Ostedgaard LS, Braakman I, Sheppard DN, Ignatova Z (2017) Alteration of protein function by a silent polymorphism linked to tRNA abundance. PLoS Biol 15(5):e2000779CrossRefGoogle Scholar
  49. 49.
    Simhadri VL, Hamasaki-Katagiri N, Lin BC, Hunt R, Jha S, Tseng SC, Wu A, Bentley AA, Zichel R, Lu Q, Zhu L, Freedberg DI, Monroe DM, Sauna ZE, Peters R, Komar AA, Kimchi-Sarfaty C (2017) Single synonymous mutation in factor IX alters protein properties and underlies haemophilia B. J Med Genet 54(5):338–345CrossRefGoogle Scholar
  50. 50.
    Firth AE (2014) Mapping overlapping functional elements embedded within the protein-coding regions of RNA viruses. Nucleic Acids Res 42(20):12425–11239CrossRefGoogle Scholar
  51. 51.
    Fahraeus R, Marin M, Olivares-Illana V (2016) Whisper mutations: cryptic messages within the genetic code. Oncogene 35(29):3753–3759CrossRefGoogle Scholar
  52. 52.
    Ingolia NT, Lareau LF, Weissman JS (2011) Ribosome profiling of mouse embryonic stem cells reveals the complexity and dynamics of mammalian proteomes. Cell 147(4):789–802CrossRefGoogle Scholar
  53. 53.
    Hoekema A, Kastelein RA, Vasser M, de Boer HA (1987) Codon replacement in the PGK1 gene of Saccharomyces cerevisiae: experimental approach to study the role of biased codon usage in gene expression. Mol Cell Biol 7(8):2914–2924CrossRefGoogle Scholar
  54. 54.
    Kotula L, Curtis PJ (1991) Evaluation of foreign gene codon optimization in yeast: expression of a mouse IG kappa chain. Bio/Technology 9(12):1386–1389CrossRefGoogle Scholar
  55. 55.
    Fang J, Zou L, Zhou X, Cheng B, Fan J (2014) Synonymous rare arginine codons and tRNA abundance affect protein production and quality of TEV protease variant. PLoS One 9(11):e112254CrossRefGoogle Scholar
  56. 56.
    Zhou M, Wang T, Fu J, Xiao G, Liu Y (2015) Nonoptimal codon usage influences protein structure in intrinsically disordered regions. Mol Microbiol 97(5):974–987CrossRefGoogle Scholar
  57. 57.
    Cripwell RA, Rose SH, van Zyl WH (2017) Expression and comparison of codon optimised Aspergillus tubingensis amylase variants in Saccharomyces cerevisiae. FEMS Yeast Res 17:4CrossRefGoogle Scholar
  58. 58.
    Zucchelli E, Pema M, Stornaiuolo A, Piovan C, Scavullo C, Giuliani E, Bossi S, Corna S, Asperti C, Bordignon C, Rizzardi GP, Bovolenta C, Zucchelli E, Pema M, Stornaiuolo A, Piovan C, Scavullo C, Giuliani E, Bossi S, Corna S, Asperti C, Bordignon C, Rizzardi GP, Bovolenta C (2017) Codon optimization leads to functional impairment of RD114-TR envelope glycoprotein. Mol Ther Methods Clin Dev 4:102–114CrossRefGoogle Scholar
  59. 59.
    Malarkannan S, Horng T, Shih PP, Schwab S, Shastri N (1999) Presentation of out-of-frame peptide/MHC class I complexes by a novel translation initiation mechanism. Immunity 10(6):681–690CrossRefGoogle Scholar
  60. 60.
    Lorenz FK, Wilde S, Voigt K, Kieback E, Mosetter B, Schendel DJ, Uckert W (2015) Codon optimization of the human papillomavirus E7 oncogene induces a CD8+ T cell response to a cryptic epitope not harbored by wild-type E7. PLoS One 10(3):e0121633CrossRefGoogle Scholar
  61. 61.
    Li C, Goudy K, Hirsch M, Asokan A, Fan Y, Alexander J, Sun J, Monahan P, Seiber D, Sidney J, Sette A, Tisch R, Frelinger J, Samulski RJ (2009) Cellular immune response to cryptic epitopes during therapeutic gene transfer. Proc Natl Acad Sci U S A 106(26):10770–10774CrossRefGoogle Scholar
  62. 62.
    Chacón KM, Petti LM, Scheideman EH, Pirazzoli V, Politi K, DiMaio D (2014) De novo selection of oncogenes. Proc Natl Acad Sci U S A 111(1):E6–E14CrossRefGoogle Scholar
  63. 63.
    Gehrmann M, Doss BT, Wagner M, Zettlitz KA, Kontermann RE, Foulds G, Pockley AG, Multhoff G (2011) A novel expression and purification system for the production of enzymatic and biologically active human granzyme B. J Immunol Methods 371(1–2):8–17CrossRefGoogle Scholar
  64. 64.
    Yablonovitch AL, Deng P, Jacobson D, Li JB (2017) The evolution and adaptation of A-to-I RNA editing. PLoS Genet 13(11):e1007064CrossRefGoogle Scholar
  65. 65.
    Ensterö M, Akerborg O, Lundin D, Wang B, Furey TS, Ohman M, Lagergren J (2010) A computational screen for site selective A-to-I editing detects novel sites in neuron specific Hu proteins. BMC Bioinformatics 11:6CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.The Scripps Research InstituteLa JollaUSA
  2. 2.LeidosSan DiegoUSA

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