1. Biochemistry and Chemical Biology
  2. Computational and Systems Biology

Experimental verification of the error minimization theory using non-standard genetic codes constructed in vitro

  1. Ryota Miyachi
  2. Norikazu Ichihashi  Is a corresponding author
  1. Department of Life Science, Graduate School of Arts and Sciences, The University of Tokyo, Japan
  2. Komaba Institute for Science, The University of Tokyo, Japan
  3. Research Center for Complex Systems Biology, Universal Biology Institute, The University of Tokyo, Japan
https://doi.org/10.7554/eLife.111164.3
Share this article
Cite this article
  1. Ryota Miyachi
  2. Norikazu Ichihashi
(2026)
Experimental verification of the error minimization theory using non-standard genetic codes constructed in vitro
eLife 15:RP111164.
https://doi.org/10.7554/eLife.111164.3
  2. Download BibTeX
  3. Download .RIS
5 figures, 11 tables and 2 additional files

Figures

Figure 1 with 2 supplements
Download asset Open asset
In vitro construction of minimal, near-standard, and standard genetic codes (SGCs).

(A) Anticodons of tRNAs and their corresponding codon assignments in the minimal genetic code (MGC), the near-SGC, and the SGC. Each codon is colored according to the physicochemical properties of the assigned amino acid: hydrophobic (green), aromatic (yellow), polar uncharged (orange), basic (blue), and acidic (pink). In each box, the anticodons of tRNAs (left) and the corresponding codons (right) are shown. (B) Codon sets used for reporter genes. The 21 codons contain only codons that are usable for MGC. Reporter genes composed of these codons were used in D and the subsequent experiments using non-SGCs. The 32 and 46 codons contain codons that are usable for near-SGC and SGC, respectively. Reporter genes composed of these codons were used in D. (C) Schematic of the translation assay. Reporter genes (NanoLuc, 1 nM) consisting of the 21, 32, or 46-codon were translated in a customized reconstituted translation system lacking endogenous tRNAs (tRNA-free PURE system [tfPURE]) supplemented with in vitro-synthesized tRNAs corresponding to MGC, near-SGC, or SGC (IPEN tRNA at 100 ng/µL; all other tRNAs at 12 ng/µL), and T7 RNA polymerase (0.42 U/µL) at 30 °C for 16 hr. (D) NanoLuc activity after incubation. In near-SGC (RV), two tRNAs (tRNAValCAC and tRNAArgCCU) were increased to 100 ng/µL. Each dot represents an independent experiment (n=3). Bars indicate mean values, and error bars represent SDs. Statistical comparisons in (D) were performed using one-way ANOVA followed by Tukey’s post hoc test on NanoLuc activity; major comparisons are summarized in Appendix 1—table 8.

Figure 1—figure supplement 1
Download asset Open asset
Effect of tRNAValCAC and tRNAArgAGG concentrations on NanoLuc translation using near-SGC.

The concentrations of both tRNAs were increased simultaneously at equal ratios. Each dot represents the results of three technical replicates, and error bars represent SDs.

Figure 1—figure supplement 2
Download asset Open asset
Translation of each NanoLuc templates using Native Escherichia coli tRNAs.

Translation of NanoLuc templates encoded with 21, 32, or 46 codons using native E. coli tRNAs (600 ng/µL) in the tfPURE system.

Figure 2 with 1 supplement
Download asset Open asset
Reassignment experiments to test the availability of 10 vacant codons for Ala, Ser, and Leu.

(A) Schematic illustration of reassignment experiments. Translation with the original MGC and NanoLuc template is shown at the top for comparison. An example of Ala reassignment to the UUG codon is shown at the bottom. In this example, three Ala codons in the NanoLuc sequence were replaced with one type of vacant codon (e.g. UUG), generating a 21+1 (UUG-Ala) codon set. Similar reassignment experiments were performed for three amino acids (Ala, Ser, and Leu) and nine vacant codons. Specifically, two Ala codons (Ala16 and Ala120), three Ser codons (Ser31, Ser49, and Ser150), or four Leu codons (Leu32, Leu67, Leu144, and Leu170) were replaced. (B) NanoLuc translation results for each codon reassignment experiment. Translation reactions were performed in tfPURE supplemented with a 21-tRNA mixture (600 ng/µL), one tRNA variant (12 ng/µL each), and each NanoLuc template (1 nM) that contains 2–4 of a corresponding codon to be tested (21+1 NNN-Ala/Ser/Leu codons). Reactions were incubated at 30 °C for 16 h, after which NanoLuc activity was measured. As a control, translation reactions lacking the additional tRNA variant were conducted (21 code, gray bars) and compared to the data with the additional tRNA (21+1 code, pink bars). Additional controls included translation without any tRNA (no tRNA) and translation using MGC with NanoLuc templates encoded by the original 21 codons (21 codons), both shown for comparison. Each dot represents three technical replicates, and error bars represent SDs. For each template, NanoLuc activity in the 21-code and corresponding 21+1 code conditions was compared using Welch’s t-test on luminescence. Statistical results are summarized in Appendix 1—table 9.

Figure 2—figure supplement 1
Download asset Open asset
Optimization of the concentration of each anticodon variant tRNA.

For 15 of the 25 anticodon variant tRNAs that exhibited relatively low translational activity, the effect of increasing tRNA concentration on translation efficiency was examined. Translation assays were performed under the same conditions as described in Figure 2. The original concentration of each tRNA variant was 12 ng/µL. The concentrations selected for use in subsequent experiments are indicated by red circles.

Figure 3 with 3 supplements
Download asset Open asset
Distribution of mutational costs of reassigned genetic codes.

(A) Calculation method of mutational costs for each genetic code based on three physicochemical properties of amino acids. The average change in each of the three physicochemical properties of amino acids upon single-nucleotide substitutions from the 21 codons was calculated (see Methods for details). In the reassigned genetic codes analyzed here, one of three amino acids (Ala, Ser, or Leu) was assigned to each of the nine vacant codons shown in gray, and the costs were calculated for all possible reassignment combinations. (B) Representative comparison of the near-SGC and PRmax code. Codon assignment schemes are shown on the left, and heatmap representations of the assigned amino acid values for polar requirement (PR), molecular volume (MV), and hydropathy index (HI) are shown on the right. The corresponding CostPR, CostMV, and CostHI values are indicated above each heatmap. (C) Distributions of mutational costs for each physicochemical property of amino acids. Dashed lines indicate the cost values of 10 genetic codes selected for experimental construction. Red dashed lines indicate the minimum and maximum cost values for each cost definition, and orange lines indicate the cost values of near-SGC. (D, E, F) Genetic codes exhibiting the minimum and maximum mutational costs based on PR (D), MV (E), and HI (F). The physicochemical values of amino acids assigned to each codon are shown as heatmaps.

Figure 3—figure supplement 1
Download asset Open asset
Polar requirement (PR) values of amino acids assigned in the constructed non-standard genetic codes (non-SGCs).

The experimentally constructed non-SGCs and their corresponding mutational costs are shown. For each genetic code, the PR values of amino acids assigned to individual codons are displayed as heatmaps. Near-SGC and MGC are shown for comparison.

Figure 3—figure supplement 2
Download asset Open asset
Molecular volume (MR) values of amino acids assigned in the constructed non-standard genetic codes (non-SGCs).

The experimentally constructed non-SGCs and their corresponding mutational costs are shown. For each genetic code, the MR values of amino acids assigned to individual codons are displayed as heatmaps. Near-SGC and MGC are shown for comparison.

Figure 3—figure supplement 3
Download asset Open asset
Hydropathy index (HI) values of amino acids assigned in the constructed non-standard genetic codes (non-SGCs).

The experimentally constructed non-SGCs and their corresponding mutational costs are shown. For each genetic code, the HI values of amino acids assigned to individual codons are displayed as heatmaps. Near-SGC and MGC are shown for comparison.

Figure 4 with 2 supplements
Download asset Open asset
Translation of random mutagenesis libraries with near-SGC.

(A) Schematic overview of the protein activity assay using a random mutation library. Reporter genes composed of the 21 codons were subjected to random mutagenesis by error-prone PCR at different Mn2+ concentrations to generate DNA libraries, as shown in Figure 4—figure supplement 1. These libraries (5 nM) were translated using near-SGC, consisting of a 32-tRNA mixture (tRNAIPEN, tRNAValCAC, and tRNAArgCCU at 100 ng/µL; all other tRNAs at 12 ng/µL) in tfPURE, including T7 RNA polymerase (1.7 U/µL) at 30 °C for 16 h, and each protein activity was measured. (B, C, D) Dependence of β-galactosidase (GAL) (B), firefly luciferase (Luc) (C), and mStayGold (mSG) (D) activity on mutation rate. Note that the vertical axis of panel C (Luc) is on a log scale. Each dot represents the results of three technical replicates, and error bars represent SDs. The lower x-axis indicates the estimated number of mutations per gene, calculated by multiplying the mutation rate per base by the coding sequence length of each reporter gene. Spearman’s rank correlation coefficients were ρ = −0.90 for GAL, ρ = −1.00 for Luc, and ρ = −1.00 for mSG.

Figure 4—figure supplement 1
Download asset Open asset
Construction of random libraries by error-prone PCR and analysis of mutation patterns.

(A) Schematic of the method for preparing DNA libraries. Reporter gene templates composed of the 21 codons (Figure 1B) were amplified by a first PCR using either a high-fidelity polymerase (KOD Plus Neo) or Taq DNA polymerase under error-prone conditions in the presence of Mn2+. A second PCR was then performed using the high-fidelity polymerase to append a C-terminal HiBiT tag for protein quantification; these products were used for subsequent translation assays under each genetic code. For sequencing, a third PCR was performed using the high-fidelity polymerase to add Illumina adapters and unique barcode sequences identifying each reporter gene and PCR condition. (B) Mean per-base error rates under each 1st PCR condition. The mutation probability was calculated for each position of the template, and the simple average across all positions was used as the mean per-base error rate (see Methods). (C) Mutation spectra under each 1st PCR condition. The fractions of each substitution type were computed across all observed substitutions (e.g. ‘A>C’ denotes substitution from A to C). (D) Position-wise distribution of error rate. The mutation probability at each nucleotide position is shown.

Figure 4—figure supplement 2
Download asset Open asset
Position-wise distribution of non-reference rates in random mutagenesis libraries.

For each reporter gene, the non-reference rate at each position was calculated from amplicon sequencing data and plotted along the analyzed region. Low- and high-mutation libraries are shown separately. These profiles show the positional distribution of mutations introduced by error-prone PCR.

Figure 5 with 7 supplements
Download asset Open asset
Translation of mutagenized DNA libraries with non-standard genetic codes (non-SGCs).

(A) Schematic of the experiment for comparing protein activities translated with different genetic codes. Random libraries prepared at low and high mutation rates were translated using either the 10 non-SGCs or the near-SGC (RV). Translation conditions were identical to those described in Figure 4. (B, D, F) Protein activities of products translated with each genetic code using low- and high-mutation DNA libraries. Activities are shown for β-galactosidase (GAL; B, mutation rate = 2.6 × 10–3 per base), firefly luciferase (Luc; D, mutation rate = 2.7 × 10–3 per base), and mStayGold (mSG; F, mutation rate = 4.8 × 10–3 per base). Quantification of protein synthesis levels for GAL is shown in Figure 5—figure supplement 1. (C, E, G) Ratios of protein activity of high-mutation libraries to those of low-mutation libraries, plotted against the corresponding theoretical mutational costs. Data are shown for GAL (C), Luc (E), and mSG (G). Mean values of three technical replicates are shown with SDs for GAL. For GAL activity in (B), two-way ANOVA was performed using genetic code and mutation level as factors. Significant main effects of genetic code and mutation level were detected (both P<0.0001), whereas their interaction was not significant. For (C), (E), and (G), Spearman’s rank correlation analysis was performed between each mutational cost metric and the high-/low-mutation activity ratio. Statistical details are summarized in Appendix 1—table 10.

Figure 5—figure supplement 1
Download asset Open asset
Quantification of translated GAL protein concentrations across different genetic codes.

GAL protein synthesized using each low- or high-mutation library under each genetic code was quantified by the HiBiT assay. A HiBiT tag was fused to the C-terminus of the GAL gene. After translation, the HiBiT tag formed an active NanoLuc luciferase upon addition of LgBiT, and the resulting luminescence was measured. A standard curve generated using known concentrations of a HiBiT control protein was measured in parallel and used to quantify the amount of synthesized GAL protein.

Figure 5—figure supplement 2
Download asset Open asset
Translation of GAL random library with non-standard genetic codes (non-SGCs).

(A) Schematic of the experimental procedure. Random DNA libraries prepared at low and high mutation rates were translated using 10 non-SGCs or near-SGC with the GAL random DNA library (5 nM). After incubation at 30 °C for 16 h, GAL activity was quantified. (B, C) Distribution of GAL protein activity plotted against theoretical mutational cost for the low-mutation library (B) and the high-mutation library (C).

Figure 5—figure supplement 3
Download asset Open asset
Translation of Luc random library with non-standard genetic codes (non-SGCs).

(A) Schematic of the experimental procedure. Random DNA libraries prepared at low and high mutation rates were translated using 10 non-SGCs or near-SGC with the Luc random DNA library (5 nM). After incubation at 30 °C for 16 h, luciferase activity was quantified. (B, C) Distribution of Luc protein activity plotted against theoretical mutational cost for the low-mutation library (B) and high-mutation library (C).

Figure 5—figure supplement 4
Download asset Open asset
Translation of mSG random library with non-standard genetic codes (non-SGCs).

(A) Schematic of the experimental procedure. Random DNA libraries prepared at low and high mutation rates were translated using 10 non-SGCs or near-SGC with the mSG random DNA library (5 nM). During incubation at 30 °C for 16 h, mSG fluorescence was quantified. (B, C) Distribution of mSG protein activity plotted against theoretical mutational cost for the low-mutation library (B) and high-mutation library (C).

Figure 5—figure supplement 5
Download asset Open asset
Distributions of the mutational costs when assigning all 20 amino acids to the vacant codons (orange).

(A) Calculation method of mutational costs for each genetic code based on three physicochemical properties of amino acids. For each genetic code, the average magnitude of change in amino acid physicochemical properties resulting from single-nucleotide substitutions from the 21 codons was calculated, taking mutation weighting into account (see Methods). Three physicochemical metrics were used: polar requirement (PR), molecular volume (MV), and hydropathy index (HI). In this analysis, all 20 amino acids were randomly assigned to each of the nine vacant codon boxes (gray), in contrast to the analysis in Figure 3B, in which only three amino acids (Ala, Leu, and Ser) were assigned. Mutational costs were calculated for 1,000,000 randomly sampled genetic codes. (B, C, D) Distributions of mutational costs for each physicochemical property of amino acids when assigning 20 amino acids (orange). The distributions obtained when assigning only three amino acids (Ala, Ser, and Leu), identical to the data shown in Figure 3B, are shown for comparison (blue). Dashed lines indicate the maximum and minimum values of the blue distribution, the cost ranges of the experimentally constructed non-standard genetic codes (non-SGCs) in this study.

Figure 5—figure supplement 6
Download asset Open asset
Distributions of the mutational costs when assigning all 20 amino acids to all sense codons (orange).

(A) Calculation method of mutational costs for each genetic code based on three physicochemical properties of amino acids. For each genetic code, the average magnitude of change in amino acid physicochemical properties resulting from single-nucleotide substitutions from all sense codons was calculated, taking mutation weighting into account (see Methods). Three physicochemical metrics were used: polar requirement (PR), molecular volume (MV), and hydropathy index (HI). In this analysis, all 20 amino acids were randomly assigned to each of the 20 codon boxes (gray) while preserving the degeneracy pattern of the standard genetic code (SGC). Mutational costs were calculated for 1,000,000 randomly sampled genetic codes. (B, C, D) Distributions of mutational costs for each physicochemical property of amino acids when assigning all 20 amino acids into all sense codons (orange). The distributions obtained when assigning only three amino acids (Ala, Ser, and Leu), identical to the data shown in Figure 3B, are shown for comparison (blue). Dashed lines indicate the maximum and minimum values of the blue distribution, the cost ranges of the experimentally constructed non-standard genetic codes (non-SGCs) in this study.

Figure 5—figure supplement 7
Download asset Open asset
Integrated mutational cost analysis combining PR, MV, and HI.

(A) Distribution of integrated min–max costs among 19,683 candidate non-standard genetic codes (non-SGCs). For each cost metric, CostPR, CostMV, and CostHI were min–max normalized across the candidate non-SGCs and averaged with equal weights. The orange dashed line indicates the near-SGC reference, and the red dashed line indicates the candidate non-SGC with the lowest and highest integrated cost. The green dashed lines indicate the cost values of 10 genetic codes selected for experimental construction. (B) Distribution of integrated z-score costs among candidate non-SGCs. For each metric, costs were z-score normalized using the mean and SD of the candidate non-SGC distribution and averaged with equal weights.

Tables

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Recombinant DNA reagentNanoLuc reporter gene (21 codons)Twist Bioscience (fragment synthesis); this papern/aDesigned to use only 21 codons compatible with MGC; sequences listed in Appendix 1—table 1
Recombinant DNA reagentNanoLuc reporter gene (32 codons)Twist Bioscience (fragment synthesis); this papern/aDesigned to use 32 codons compatible with near-SGC; sequences listed in Appendix 1—table 1
Recombinant DNA reagentNanoLuc reporter gene (46 codons)Twist Bioscience (fragment synthesis); this papern/aDesigned to use 46 codons compatible with SGC; sequences listed in Appendix 1—table 1
Recombinant DNA reagentNanoLuc reporter genes for codon reassignment experiments (21+1 variants; 25 constructs total)Twist Bioscience (fragment synthesis); this papern/aBased on 21-codon NanoLuc; 2 Ala, 3 Ser, or 4 Leu codons replaced with target vacant codons; sequences listed in Appendix 1—table 1
Recombinant DNA reagentlacZ (β-galactosidase; GAL) reporter geneTwist Bioscience (fragment synthesis); this papern/aDesigned using 21-codon set; sequences listed in Appendix 1—table 1
Recombinant DNA reagentFirefly luciferase (Luc) reporter geneTwist Bioscience (fragment synthesis); this papern/aDesigned using 21-codon set; sequences listed in Appendix 1—table 1
Recombinant DNA reagentmStayGold (mSG) reporter geneTwist Bioscience (fragment synthesis); this papern/aDesigned using 21-codon set; sequences listed in Appendix 1—table 1
Recombinant DNA reagentPlasmid templates for tRNA transcription (21-tRNA set for MGC)Miyachi et al., 2025; Miyachi et al., 2022PMID:40858540; PMID:35848947Used as templates for site-directed mutagenesis (inverse PCR) to generate anticodon variants
Recombinant DNA reagentPlasmid templates for tRNA transcription (11 additional tRNAs for near-SGC; 14 for SGC; anticodon variants for Ala, Ser, Leu)This papern/aAnticodon regions replaced by site-directed mutagenesis (inverse PCR); primer sequences in Appendix 1—table 2; tRNA sequences in Appendix 1—table 3
Sequence-based reagent21-tRNA set for MGC (in vitro-transcribed or chemically synthesized tRNAs)Miyachi et al., 2025; Miyachi et al., 2022; Agilent (chemical synthesis)PMID:40858540; PMID:35848947tRNA sequences in Appendix 1—table 3; chemically synthesized tRNAs: tRNAAsn(GUU), tRNAGln(CUC), tRNAfMet(CAU), tRNAIle(GAU), tRNATrp(CCA), tRNAPro(GGG); remainder transcribed in vitro
Sequence-based reagent11 additional tRNAs for near-SGC: tRNALeu(CAA), tRNASer(CGA), tRNALeu(CAG), tRNAPro(CGG), tRNAArg(CCG), tRNASer(GCU), tRNAThr(CGU), tRNAArg(CCU), tRNAVal(CAC), tRNAAla(CGC), tRNAGly(CCC)This papern/aAnticodon region replaced from 21-tRNA set templates; tRNA sequences in Appendix 1—table 3; (tRNAVal(CAC) and tRNAArg(CCU) used at 100 ng/µL; others at 12 ng/µL)
Sequence-based reagent14 additional tRNAs with UNN anticodons for SGC (NNA codon decoding)This papern/aAnticodon region replaced from 21-tRNA set templates; tRNA sequences in Appendix 1—table 3
Sequence-based reagentAnticodon variant tRNAs for Ala, Ser, and Leu (reassignment to 9 vacant codons: UUG, UCG, CUG, CCG, CGG, ACG, AGC, GUG, GCG)This papern/aUsed to construct 10 non-SGCs; tRNA concentrations: tRNAAla(CAA) 40 ng/µL, tRNAAla(CGA) 60 ng/µL, tRNAAla(CGU) 60 ng/µL, tRNALeu(CGU) 80 ng/µL, tRNALeu(CGA) 80 ng/µL, tRNALeu(GCU) 80 ng/µL; others 12 ng/µL
Sequence-based reagentPCR primers (for DNA template preparation, tRNA transcription template preparation, library preparation, and Illumina sequencing)This papern/aSequences listed in Appendix 1—table 2; primers include T7 promoter-containing forward primers and 2'-O-methyl-modified reverse primers for run-off tRNA transcription
Peptide, recombinant proteinT7 RNA polymeraseTakara Bion/aUsed for in vitro tRNA transcription (1 U/µL) and cell-free translation (0.42–1.7 U/µL)
Peptide, recombinant proteinKOD Plus Neo DNA polymeraseToyobon/aUsed for low-mutation PCR, tRNA template preparation, and library preparation
Peptide, recombinant proteinTaq DNA polymeraseNew England Biolabsn/aUsed for error-prone PCR; supplemented with MgCl2 (3.0 mM final) and MnCl2 (10–350 µM)
Peptide, recombinant proteinDpnINew England Biolabsn/aUsed to remove template plasmid after PCR amplification of tRNA transcription templates; incubated at 37 °C for 2 h
Peptide, recombinant proteinInorganic pyrophosphataseNew England Biolabsn/aUsed in in vitro tRNA transcription reactions (2 U/µL)
Peptide, recombinant proteinRNasin (ribonuclease inhibitor)Promegan/aUsed in in vitro tRNA transcription reactions (0.8 U/µL)
Peptide, recombinant proteinPURE system components (ribosomes, translation factors, aminoacyl-tRNA synthetases, energy-regeneration components)Miyachi et al., 2025; Miyachi et al., 2022PMID:40858540; PMID:35848947Laboratory-made tRNA-free PURE system (tfPURE); individual factors expressed with His-tags and purified by affinity +gel-filtration chromatography; EF-Tu subjected to two rounds of affinity purification; ribosomes purified by butyl column +sucrose cushion +ultrafiltration; complete composition in Appendix 1—table 6
Commercial assay or kitLuciferase Assay ReagentPromegan/aUsed for firefly luciferase activity measurement (1 µL translation reaction +30 µL reagent)
Commercial assay or kitNanoLuc Assay ReagentPromegan/aUsed for NanoLuc luciferase activity measurement (1 µL translation reaction +50 µL reagent)
Commercial assay or kitHiBiT tag / HiBiT assay systemPromegan/aUsed for quantification of C-terminally tagged translation products; HiBiT tag attached by overlap extension PCR (2nd PCR)
Commercial assay or kitFastGene Gel/PCR Extraction KitNippon Geneticsn/aUsed for purification of PCR products and DNA fragments
Commercial assay or kitPureLink RNA Mini KitInvitrogenn/aUsed for purification of in vitro-transcribed tRNAs
Chemical compound, drugMnCl2Othern/aUsed in error-prone PCR at 10, 50, 100, 250, or 350 µM to modulate mutation rates; promotes Taq polymerase infidelity
Chemical compound, drugTokyoGreen-βGalSekisui Medicaln/aFluorescent substrate for β-galactosidase (GAL) activity assay (5 µM final); GAL activity determined from slope of fluorescence time course
Chemical compound, drugNTPs (ATP, GTP, CTP, UTP); GMPOthern/aUsed in in vitro tRNA transcription (2 mM each NTP; 3 mM GMP)
Chemical compound, drugSpermidineOthern/aUsed in in vitro tRNA transcription buffer (2 mM)
Chemical compound, drugDTTOthern/aUsed in tRNA transcription (5 mM) and GAL assay buffer (5 mM)
Software, algorithmBWA-MEMOtherRRID:SCR_010910Used for paired-end read alignment to amplicon reference sequences
Software, algorithmSAMtoolsOtherRRID:SCR_002105Used for BAM processing (sorting, indexing) and position-wise base extraction (mpileup; MAPQ ≥20, Phred Q≥30)
Software, algorithmcutadaptOtherRRID:SCR_011841Used for demultiplexing of sequencing reads by barcode sequence (perfect full-length match required)
Software, algorithmMutational cost calculation scriptThis papern/aCustom script to calculate CostPR, CostMV, and CostHI for 19,683 candidate genetic codes; based on formulations in Freeland and Hurst, 1998 and Haig and Hurst, 1991; substitution weights in Appendix 1—table 4; amino acid physicochemical values in Appendix 1—table 5.
The code is included in Source Code file (Source code 1).
OtherGloMax luminometerPromegaRRID:SCR_018613Used to measure firefly luciferase and NanoLuc luminescence
OtherMx3005P real-time PCR systemAgilent Technologiesn/aUsed for continuous fluorescence monitoring of mStayGold (mSG) during translation (up to 16 h); also used for GAL fluorescence assay (FAM detection settings, 1 min intervals for 60 min)
OtherNanoDrop spectrophotometerThermo Fisher ScientificRRID:SCR_016517Used for DNA and RNA concentration determination by absorbance at A260
OtherIllumina MiSeqIlluminaRRID:SCR_016379Used for paired-end sequencing of random mutation libraries (~500 bp amplicons)
Appendix 1—table 1
DNA sequences used in this study.
TemplateSequence
Nanoluc_32 C
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAaAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTGGAGGACTTTGTGGGTGACTGGCGGCAGACGGCTGGGTACAACTTGGACCAGGTTCTGGAGCAGGGTGGGGTGTCTTCGCTCTTTCAGAACCTGGGTGTTAGCGTGACTCCTATTCAGCGCATTGTTTTGTCTGGGGAGAACGGTCTGAAGATTGACATTCACGTGATTATTCCGTACGAGGGGCTCTCGGGTGACCAGATGGGGCAGATTGAGAAGATTTTTAAGGTTGTGTACCCTGTTGACGACCACCACTTTAAGGTGATTCTGCACTACGGTACGTTGGTTATTGACGGGGTGACTCCGAACATGATTGACTACTTTGGTAGGCCTTACGAGGGGATTGCGGTTTTTGACGGTAAGAAGATTACGGTGACTGGGACGCTGTGGAACGGTAACAAGATTATTGACGAGCGGCTCATTAACCCGGACGGGAGCCTGTTGTTTCGCGTTACTATTAACGGTGTGACGGGGTGGAGGCTGTGTGAGCGGATTCTCGCGTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_46 C
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTGGAGGACTTTGTGGGTGACTGGCGGCAGACGGCTGGGTACAACTTGGACCAAGTATTAGAACAGGGAGGGGTTTCTTCGCTCTTTCAAAACCTAGGTGTGAGCGTAACACCTATTCAGCGCATAGTTTTATCAGGAGAGAACGGTCTGAAGATTGACATACACGTGATTATACCGTACGAAGGGCTCTCGGGAGACCAAATGGGGCAGATTGAGAAAATATTTAAGGTAGTTTACCCAGTGGACGACCACCACTTTAAAGTAATTCTACACTACGGTACGTTGGTTATAGACGGAGTGACTCCGAACATGATTGACTACTTTGGTAGGCCTTACGAAGGGATAGCAGTATTTGACGGAAAGAAAATTACAGTTACTGGGACGCTGTGGAACGGTAACAAGATAATTGACGAGCGATTAATAAACCCAGACGGATCACTGTTGTTTAGAGTGACTATTAACGGTGTAACAGGGTGGCGACTATGTGAAAGAATACTCGCGTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21 C
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTGGAGGACTTTGTTGGTGACTGGCGGCAGACTGCTGGTTACAACCTGGACCAGGTTCTGGAGCAGGGTGGTGTTTCTTCTCTGTTTCAGAACCTGGGTGTTTCTGTTACTCCTATTCAGCGGATTGTTCTGTCTGGTGAGAACGGTCTGAAGATTGACATTCACGTTATTATTCCTTACGAGGGTCTGTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTGCACTACGGTACTCTGGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGGCCTTACGAGGGTATTGCTGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTGTGGAACGGTAACAAGATTATTGACGAGCGGCTGATTAACCCTGACGGTTCTCTGCTGTTTCGGGTTACTATTAACGGTGTTACTGGTTGGCGGCTGTGTGAGCGGATTCTGGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21+Ala_CAA
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTCGAGGACTTTGTTGGTGACTGGCGCCAGACTTTGGGTTACAACCTCGACCAGGTTCTCGAGCAGGGTGGTGTTTCTTCTCTCTTTCAGAACCTCGGTGTTTCTGTTACTCCTATTCAGCGCATTGTTCTCTCTGGTGAGAACGGTCTCAAGATTGACATTCACGTTATTATTCCTTACGAGGGTCTCTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTCCACTACGGTACTCTCGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGCCCTTACGAGGGTATTTTGGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTCTGGAACGGTAACAAGATTATTGACGAGCGCCTCATTAACCCTGACGGTTCTCTCCTCTTTCGCGTTACTATTAACGGTGTTACTGGTTGGCGCCTCTGTGAGCGCATTCTCGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21+Ala_CGA
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTCGAGGACTTTGTTGGTGACTGGCGCCAGACTTCGGGTTACAACCTCGACCAGGTTCTCGAGCAGGGTGGTGTTTCTTCTCTCTTTCAGAACCTCGGTGTTTCTGTTACTCCTATTCAGCGCATTGTTCTCTCTGGTGAGAACGGTCTCAAGATTGACATTCACGTTATTATTCCTTACGAGGGTCTCTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTCCACTACGGTACTCTCGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGCCCTTACGAGGGTATTTCGGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTCTGGAACGGTAACAAGATTATTGACGAGCGCCTCATTAACCCTGACGGTTCTCTCCTCTTTCGCGTTACTATTAACGGTGTTACTGGTTGGCGCCTCTGTGAGCGCATTCTCGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21+Ala_CAG
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTCGAGGACTTTGTTGGTGACTGGCGCCAGACTCTGGGTTACAACCTCGACCAGGTTCTCGAGCAGGGTGGTGTTTCTTCTCTCTTTCAGAACCTCGGTGTTTCTGTTACTCCTATTCAGCGCATTGTTCTCTCTGGTGAGAACGGTCTCAAGATTGACATTCACGTTATTATTCCTTACGAGGGTCTCTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTCCACTACGGTACTCTCGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGCCCTTACGAGGGTATTCTGGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTCTGGAACGGTAACAAGATTATTGACGAGCGCCTCATTAACCCTGACGGTTCTCTCCTCTTTCGCGTTACTATTAACGGTGTTACTGGTTGGCGCCTCTGTGAGCGCATTCTCGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21+Ala_CGG
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTCGAGGACTTTGTTGGTGACTGGCGCCAGACTCCGGGTTACAACCTCGACCAGGTTCTCGAGCAGGGTGGTGTTTCTTCTCTCTTTCAGAACCTCGGTGTTTCTGTTACTCCTATTCAGCGCATTGTTCTCTCTGGTGAGAACGGTCTCAAGATTGACATTCACGTTATTATTCCTTACGAGGGTCTCTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTCCACTACGGTACTCTCGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGCCCTTACGAGGGTATTCCGGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTCTGGAACGGTAACAAGATTATTGACGAGCGCCTCATTAACCCTGACGGTTCTCTCCTCTTTCGCGTTACTATTAACGGTGTTACTGGTTGGCGCCTCTGTGAGCGCATTCTCGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21+Ala_CCG
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTCGAGGACTTTGTTGGTGACTGGCGCCAGACTCGGGGTTACAACCTCGACCAGGTTCTCGAGCAGGGTGGTGTTTCTTCTCTCTTTCAGAACCTCGGTGTTTCTGTTACTCCTATTCAGCGCATTGTTCTCTCTGGTGAGAACGGTCTCAAGATTGACATTCACGTTATTATTCCTTACGAGGGTCTCTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTCCACTACGGTACTCTCGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGCCCTTACGAGGGTATTCGGGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTCTGGAACGGTAACAAGATTATTGACGAGCGCCTCATTAACCCTGACGGTTCTCTCCTCTTTCGCGTTACTATTAACGGTGTTACTGGTTGGCGCCTCTGTGAGCGCATTCTCGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21+Ala_CGU
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTCGAGGACTTTGTTGGTGACTGGCGCCAGACTACGGGTTACAACCTCGACCAGGTTCTCGAGCAGGGTGGTGTTTCTTCTCTCTTTCAGAACCTCGGTGTTTCTGTTACTCCTATTCAGCGCATTGTTCTCTCTGGTGAGAACGGTCTCAAGATTGACATTCACGTTATTATTCCTTACGAGGGTCTCTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTCCACTACGGTACTCTCGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGCCCTTACGAGGGTATTACGGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTCTGGAACGGTAACAAGATTATTGACGAGCGCCTCATTAACCCTGACGGTTCTCTCCTCTTTCGCGTTACTATTAACGGTGTTACTGGTTGGCGCCTCTGTGAGCGCATTCTCGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21+Ala_GCU
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTCGAGGACTTTGTTGGTGACTGGCGCCAGACTAGCGGTTACAACCTCGACCAGGTTCTCGAGCAGGGTGGTGTTTCTTCTCTCTTTCAGAACCTCGGTGTTTCTGTTACTCCTATTCAGCGCATTGTTCTCTCTGGTGAGAACGGTCTCAAGATTGACATTCACGTTATTATTCCTTACGAGGGTCTCTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTCCACTACGGTACTCTCGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGCCCTTACGAGGGTATTAGCGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTCTGGAACGGTAACAAGATTATTGACGAGCGCCTCATTAACCCTGACGGTTCTCTCCTCTTTCGCGTTACTATTAACGGTGTTACTGGTTGGCGCCTCTGTGAGCGCATTCTCGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21+Ala_CAC
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTCGAGGACTTTGTTGGTGACTGGCGCCAGACTGTGGGTTACAACCTCGACCAGGTTCTCGAGCAGGGTGGTGTTTCTTCTCTCTTTCAGAACCTCGGTGTTTCTGTTACTCCTATTCAGCGCATTGTTCTCTCTGGTGAGAACGGTCTCAAGATTGACATTCACGTTATTATTCCTTACGAGGGTCTCTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTCCACTACGGTACTCTCGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGCCCTTACGAGGGTATTGTGGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTCTGGAACGGTAACAAGATTATTGACGAGCGCCTCATTAACCCTGACGGTTCTCTCCTCTTTCGCGTTACTATTAACGGTGTTACTGGTTGGCGCCTCTGTGAGCGCATTCTCGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21+Ser_CAA
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTCGAGGACTTTGTTGGTGACTGGCGCCAGACTGCTGGTTACAACCTCGACCAGGTTCTCGAGCAGGGTGGTGTTTCTTTGCTCTTTCAGAACCTCGGTGTTTCTGTTACTCCTATTCAGCGCATTGTTCTCTTGGGTGAGAACGGTCTCAAGATTGACATTCACGTTATTATTCCTTACGAGGGTCTCTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTCCACTACGGTACTCTCGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGCCCTTACGAGGGTATTGCTGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTCTGGAACGGTAACAAGATTATTGACGAGCGCCTCATTAACCCTGACGGTTTGCTCCTCTTTCGCGTTACTATTAACGGTGTTACTGGTTGGCGCCTCTGTGAGCGCATTCTCGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21+Ser_CAG
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTCGAGGACTTTGTTGGTGACTGGCGCCAGACTGCTGGTTACAACCTCGACCAGGTTCTCGAGCAGGGTGGTGTTTCTCTGCTCTTTCAGAACCTCGGTGTTTCTGTTACTCCTATTCAGCGCATTGTTCTCCTGGGTGAGAACGGTCTCAAGATTGACATTCACGTTATTATTCCTTACGAGGGTCTCTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTCCACTACGGTACTCTCGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGCCCTTACGAGGGTATTGCTGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTCTGGAACGGTAACAAGATTATTGACGAGCGCCTCATTAACCCTGACGGTCTGCTCCTCTTTCGCGTTACTATTAACGGTGTTACTGGTTGGCGCCTCTGTGAGCGCATTCTCGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21+Ser_CGG
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTCGAGGACTTTGTTGGTGACTGGCGCCAGACTGCTGGTTACAACCTCGACCAGGTTCTCGAGCAGGGTGGTGTTTCTCCGCTCTTTCAGAACCTCGGTGTTTCTGTTACTCCTATTCAGCGCATTGTTCTCCCGGGTGAGAACGGTCTCAAGATTGACATTCACGTTATTATTCCTTACGAGGGTCTCTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTCCACTACGGTACTCTCGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGCCCTTACGAGGGTATTGCTGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTCTGGAACGGTAACAAGATTATTGACGAGCGCCTCATTAACCCTGACGGTCCGCTCCTCTTTCGCGTTACTATTAACGGTGTTACTGGTTGGCGCCTCTGTGAGCGCATTCTCGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21+Ser_CCG
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTCGAGGACTTTGTTGGTGACTGGCGCCAGACTGCTGGTTACAACCTCGACCAGGTTCTCGAGCAGGGTGGTGTTTCTCGGCTCTTTCAGAACCTCGGTGTTTCTGTTACTCCTATTCAGCGCATTGTTCTCCGGGGTGAGAACGGTCTCAAGATTGACATTCACGTTATTATTCCTTACGAGGGTCTCTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTCCACTACGGTACTCTCGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGCCCTTACGAGGGTATTGCTGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTCTGGAACGGTAACAAGATTATTGACGAGCGCCTCATTAACCCTGACGGTCGGCTCCTCTTTCGCGTTACTATTAACGGTGTTACTGGTTGGCGCCTCTGTGAGCGCATTCTCGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21+Ser_CGU
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTCGAGGACTTTGTTGGTGACTGGCGCCAGACTGCTGGTTACAACCTCGACCAGGTTCTCGAGCAGGGTGGTGTTTCTACGCTCTTTCAGAACCTCGGTGTTTCTGTTACTCCTATTCAGCGCATTGTTCTCACGGGTGAGAACGGTCTCAAGATTGACATTCACGTTATTATTCCTTACGAGGGTCTCTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTCCACTACGGTACTCTCGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGCCCTTACGAGGGTATTGCTGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTCTGGAACGGTAACAAGATTATTGACGAGCGCCTCATTAACCCTGACGGTACGCTCCTCTTTCGCGTTACTATTAACGGTGTTACTGGTTGGCGCCTCTGTGAGCGCATTCTCGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21+Ser_CAC
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTCGAGGACTTTGTTGGTGACTGGCGCCAGACTGCTGGTTACAACCTCGACCAGGTTCTCGAGCAGGGTGGTGTTTCTGTGCTCTTTCAGAACCTCGGTGTTTCTGTTACTCCTATTCAGCGCATTGTTCTCGTGGGTGAGAACGGTCTCAAGATTGACATTCACGTTATTATTCCTTACGAGGGTCTCTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTCCACTACGGTACTCTCGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGCCCTTACGAGGGTATTGCTGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTCTGGAACGGTAACAAGATTATTGACGAGCGCCTCATTAACCCTGACGGTGTGCTCCTCTTTCGCGTTACTATTAACGGTGTTACTGGTTGGCGCCTCTGTGAGCGCATTCTCGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21+Ser_CGC
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTCGAGGACTTTGTTGGTGACTGGCGCCAGACTGCTGGTTACAACCTCGACCAGGTTCTCGAGCAGGGTGGTGTTTCTGCGCTCTTTCAGAACCTCGGTGTTTCTGTTACTCCTATTCAGCGCATTGTTCTCGCGGGTGAGAACGGTCTCAAGATTGACATTCACGTTATTATTCCTTACGAGGGTCTCTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTCCACTACGGTACTCTCGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGCCCTTACGAGGGTATTGCTGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTCTGGAACGGTAACAAGATTATTGACGAGCGCCTCATTAACCCTGACGGTGCGCTCCTCTTTCGCGTTACTATTAACGGTGTTACTGGTTGGCGCCTCTGTGAGCGCATTCTCGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21+Leu_CGA
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTCGAGGACTTTGTTGGTGACTGGCGCCAGACTGCTGGTTACAACCTCGACCAGGTTCTCGAGCAGGGTGGTGTTTCTTCTTCGTTTCAGAACCTCGGTGTTTCTGTTACTCCTATTCAGCGCATTGTTCTCTCTGGTGAGAACGGTCTCAAGATTGACATTCACGTTATTATTCCTTACGAGGGTTCGTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTCCACTACGGTACTCTCGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGCCCTTACGAGGGTATTGCTGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTCTGGAACGGTAACAAGATTATTGACGAGCGCTCGATTAACCCTGACGGTTCTCTCCTCTTTCGCGTTACTATTAACGGTGTTACTGGTTGGCGCCTCTGTGAGCGCATTTCGGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21+Leu_CGG
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTCGAGGACTTTGTTGGTGACTGGCGCCAGACTGCTGGTTACAACCTCGACCAGGTTCTCGAGCAGGGTGGTGTTTCTTCTCCGTTTCAGAACCTCGGTGTTTCTGTTACTCCTATTCAGCGCATTGTTCTCTCTGGTGAGAACGGTCTCAAGATTGACATTCACGTTATTATTCCTTACGAGGGTCCGTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTCCACTACGGTACTCTCGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGCCCTTACGAGGGTATTGCTGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTCTGGAACGGTAACAAGATTATTGACGAGCGCCCGATTAACCCTGACGGTTCTCTCCTCTTTCGCGTTACTATTAACGGTGTTACTGGTTGGCGCCTCTGTGAGCGCATTCCGGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21+Leu_CCG
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTCGAGGACTTTGTTGGTGACTGGCGCCAGACTGCTGGTTACAACCTCGACCAGGTTCTCGAGCAGGGTGGTGTTTCTTCTCGGTTTCAGAACCTCGGTGTTTCTGTTACTCCTATTCAGCGCATTGTTCTCTCTGGTGAGAACGGTCTCAAGATTGACATTCACGTTATTATTCCTTACGAGGGTCGGTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTCCACTACGGTACTCTCGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGCCCTTACGAGGGTATTGCTGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTCTGGAACGGTAACAAGATTATTGACGAGCGCCGGATTAACCCTGACGGTTCTCTCCTCTTTCGCGTTACTATTAACGGTGTTACTGGTTGGCGCCTCTGTGAGCGCATTCGGGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21+Leu_GCU
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTCGAGGACTTTGTTGGTGACTGGCGCCAGACTGCTGGTTACAACCTCGACCAGGTTCTCGAGCAGGGTGGTGTTTCTTCTAGCTTTCAGAACCTCGGTGTTTCTGTTACTCCTATTCAGCGCATTGTTCTCTCTGGTGAGAACGGTCTCAAGATTGACATTCACGTTATTATTCCTTACGAGGGTAGCTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTCCACTACGGTACTCTCGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGCCCTTACGAGGGTATTGCTGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTCTGGAACGGTAACAAGATTATTGACGAGCGCAGCATTAACCCTGACGGTTCTCTCCTCTTTCGCGTTACTATTAACGGTGTTACTGGTTGGCGCCTCTGTGAGCGCATTAGCGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21+Leu_CGU
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTCGAGGACTTTGTTGGTGACTGGCGCCAGACTGCTGGTTACAACCTCGACCAGGTTCTCGAGCAGGGTGGTGTTTCTTCTACGTTTCAGAACCTCGGTGTTTCTGTTACTCCTATTCAGCGCATTGTTCTCTCTGGTGAGAACGGTCTCAAGATTGACATTCACGTTATTATTCCTTACGAGGGTACGTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTCCACTACGGTACTCTCGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGCCCTTACGAGGGTATTGCTGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTCTGGAACGGTAACAAGATTATTGACGAGCGCACGATTAACCCTGACGGTTCTCTCCTCTTTCGCGTTACTATTAACGGTGTTACTGGTTGGCGCCTCTGTGAGCGCATTACGGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21+Leu_CAC
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTCGAGGACTTTGTTGGTGACTGGCGCCAGACTGCTGGTTACAACCTCGACCAGGTTCTCGAGCAGGGTGGTGTTTCTTCTGTGTTTCAGAACCTCGGTGTTTCTGTTACTCCTATTCAGCGCATTGTTCTCTCTGGTGAGAACGGTCTCAAGATTGACATTCACGTTATTATTCCTTACGAGGGTGTGTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTCCACTACGGTACTCTCGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGCCCTTACGAGGGTATTGCTGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTCTGGAACGGTAACAAGATTATTGACGAGCGCGTGATTAACCCTGACGGTTCTCTCCTCTTTCGCGTTACTATTAACGGTGTTACTGGTTGGCGCCTCTGTGAGCGCATTGTGGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Nanoluc_21+Leu_CGC
*Nannoluc gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTTTACTCTCGAGGACTTTGTTGGTGACTGGCGCCAGACTGCTGGTTACAACCTCGACCAGGTTCTCGAGCAGGGTGGTGTTTCTTCTGCGTTTCAGAACCTCGGTGTTTCTGTTACTCCTATTCAGCGCATTGTTCTCTCTGGTGAGAACGGTCTCAAGATTGACATTCACGTTATTATTCCTTACGAGGGTGCGTCTGGTGACCAGATGGGTCAGATTGAGAAGATTTTTAAGGTTGTTTACCCTGTTGACGACCACCACTTTAAGGTTATTCTCCACTACGGTACTCTCGTTATTGACGGTGTTACTCCTAACATGATTGACTACTTTGGTCGCCCTTACGAGGGTATTGCTGTTTTTGACGGTAAGAAGATTACTGTTACTGGTACTCTCTGGAACGGTAACAAGATTATTGACGAGCGCGCGATTAACCCTGACGGTTCTCTCCTCTTTCGCGTTACTATTAACGGTGTTACTGGTTGGCGCCTCTGTGAGCGCATTGCGGCTTAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
GAL_21 C
*GAL gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGACTATGATTACTGACTCTCTCGCTGTTGTTCTCCAGCGCCGCGACTGGGAGAACCCTGGTGTTACTCAGCTCAACCGCCTCGCTGCTCACCCTCCTTTTGCTTCTTGGCGCAACTCTGAGGAGGCTCGCACTGACCGCCCTTCTCAGCAGCTCCGCTCTCTCAACGGTGAGTGGCGCTTTGCTTGGTTTCCTGCTCCTGAGGCTGTTCCTGAGTCTTGGCTCGAGTGTGACCTCCCTGAGGCTGACACTGTTGTTGTTCCTTCTAACTGGCAGATGCACGGTTACGACGCTCCTATTTACACTAACGTTACTTACCCTATTACTGTTAACCCTCCTTTTGTTCCTACTGAGAACCCTACTGGTTGTTACTCTCTCACTTTTAACGTTGACGAGTCTTGGCTCCAGGAGGGTCAGACTCGCATTATTTTTGACGGTGTTAACTCTGCTTTTCACCTCTGGTGTAACGGTCGCTGGGTTGGTTACGGTCAGGACTCTCGCCTCCCTTCTGAGTTTGACCTCTCTGCTTTTCTCCGCGCTGGTGAGAACCGCCTCGCTGTTATGGTTCTCCGCTGGTCTGACGGTTCTTACCTCGAGGACCAGGACATGTGGCGCATGTCTGGTATTTTTCGCGACGTTTCTCTCCTCCACAAGCCTACTACTCAGATTTCTGACTTTCACGTTGCTACTCGCTTTAACGACGACTTTTCTCGCGCTGTTCTCGAGGCTGAGGTTCAGATGTGTGGTGAGCTCCGCGACTACCTCCGCGTTACTGTTTCTCTCTGGCAGGGTGAGACTCAGGTTGCTTCTGGTACTGCTCCTTTTGGTGGTGAGATTATTGACGAGCGCGGTGGTTACGCTGACCGCGTTACTCTCCGCCTCAACGTTGAGAACCCTAAGCTCTGGTCTGCTGAGATTCCTAACCTCTACCGCGCTGTTGTTGAGCTCCACACTGCTGACGGTACTCTCATTGAGGCTGAGGCTTGTGACGTTGGTTTTCGCGAGGTTCGCATTGAGAACGGTCTCCTCCTCCTCAACGGTAAGCCTCTCCTCATTCGCGGTGTTAACCGCCACGAGCACCACCCTCTCCACGGTCAGGTTATGGACGAGCAGACTATGGTTCAGGACATTCTCCTCATGAAGCAGAACAACTTTAACGCTGTTCGCTGTTCTCACTACCCTAACCACCCTCTCTGGTACACTCTCTGTGACCGCTACGGTCTCTACGTTGTTGACGAGGCTAACATTGAGACTCACGGTATGGTTCCTATGAACCGCCTCACTGACGACCCTCGCTGGCTCCCTGCTATGTCTGAGCGCGTTACTCGCATGGTTCAGCGCGACCGCAACCACCCTTCTGTTATTATTTGGTCTCTCGGTAACGAGTCTGGTCACGGTGCTAACCACGACGCTCTCTACCGCTGGATTAAGTCTGTTGACCCTTCTCGCCCTGTTCAGTACGAGGGTGGTGGTGCTGACACTACTGCTACTGACATTATTTGTCCTATGTACGCTCGCGTTGACGAGGACCAGCCTTTTCCTGCTGTTCCTAAGTGGTCTATTAAGAAGTGGCTCTCTCTCCCTGGTGAGACTCGCCCTCTCATTCTCTGTGAGTACGCTCACGCTATGGGTAACTCTCTCGGTGGTTTTGCTAAGTACTGGCAGGCTTTTCGCCAGTACCCTCGCCTCCAGGGTGGTTTTGTTTGGGACTGGGTTGACCAGTCTCTCATTAAGTACGACGAGAACGGTAACCCTTGGTCTGCTTACGGTGGTGACTTTGGTGACACTCCTAACGACCGCCAGTTTTGTATGAACGGTCTCGTTTTTGCTGACCGCACTCCTCACCCTGCTCTCACTGAGGCTAAGCACCAGCAGCAGTTTTTTCAGTTTCGCCTCTCTGGTCAGACTATTGAGGTTACTTCTGAGTACCTCTTTCGCCACTCTGACAACGAGCTCCTCCACTGGATGGTTGCTCTCGACGGTAAGCCTCTCGCTTCTGGTGAGGTTCCTCTCGACGTTGCTCCTCAGGGTAAGCAGCTCATTGAGCTCCCTGAGCTCCCTCAGCCTGAGTCTGCTGGTCAGCTCTGGCTCACTGTTCGCGTTGTTCAGCCTAACGCTACTGCTTGGTCTGAGGCTGGTCACATTTCTGCTTGGCAGCAGTGGCGCCTCGCTGAGAACCTCTCTGTTACTCTCCCTGCTGCTTCTCACGCTATTCCTCACCTCACTACTTCTGAGATGGACTTTTGTATTGAGCTCGGTAACAAGCGCTGGCAGTTTAACCGCCAGTCTGGTTTTCTCTCTCAGATGTGGATTGGTGACAAGAAGCAGCTCCTCACTCCTCTCCGCGACCAGTTTACTCGCGCTCCTCTCGACAACGACATTGGTGTTTCTGAGGCTACTCGCATTGACCCTAACGCTTGGGTTGAGCGCTGGAAGGCTGCTGGTCACTACCAGGCTGAGGCTGCTCTCCTCCAGTGTACTGCTGACACTCTCGCTGACGCTGTTCTCATTACTACTGCTCACGCTTGGCAGCACCAGGGTAAGACTCTCTTTATTTCTCGCAAGACTTACCGCATTGACGGTTCTGGTCAGATGGCTATTACTGTTGACGTTGAGGTTGCTTCTGACACTCCTCACCCTGCTCGCATTGGTCTCAACTGTCAGCTCGCTCAGGTTGCTGAGCGCGTTAACTGGCTCGGTCTCGGTCCTCAGGAGAACTACCCTGACCGCCTCACTGCTGCTTGTTTTGACCGCTGGGACCTCCCTCTCTCTGACATGTACACTCCTTACGTTTTTCCTTCTGAGAACGGTCTCCGCTGTGGTACTCGCGAGCTCAACTACGGTCCTCACCAGTGGCGCGGTGACTTTCAGTTTAACATTTCTCGCTACTCTCAGCAGCAGCTCATGGAGACTTCTCACCGCCACCTCCTCCACGCTGAGGAGGGTACTTGGCTCAACATTGACGGTTTTCACATGGGTATTGGTGGTGACGACTCTTGGTCTCCTTCTGTTTCTGCTGAGTTTCAGCTCTCTGCTGGTCGCTACCACTACCAGCTCGTTTGGTGTCAGAAGTAAAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Luciferase_21 C
*Luciferase gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTTGTTTAACTTTAAGAAGGAGATATACATATGGAGGACGCTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGAGGACGCTAAGAACATTAAGAAGGGTCCTGCTCCTTTTTACCCTCTCGAGGACGGTACTGCTGGTGAGCAGCTCCACAAGGCTATGAAGCGCTACGCTCTCGTTCCTGGTACTATTGCTTTTACTGACGCTCACATTGAGGTTAACATTACTTACGCTGAGTACTTTGAGATGTCTGTTCGCCTCGCTGAGGCTATGAAGCGCTACGGTCTCAACACTAACCACCGCATTGTTGTTTGTTCTGAGAACTCTCTCCAGTTTTTTATGCCTGTTCTCGGTGCTCTCTTTATTGGTGTTGCTGTTGCTCCTGCTAACGACATTTACAACGAGCGCGAGCTCCTCAACTCTATGAACATTTCTCAGCCTACTGTTGTTTTTGTTTCTAAGAAGGGTCTCCAGAAGATTCTCAACGTTCAGAAGAAGCTCCCTATTATTCAGAAGATTATTATTATGGACTCTAAGACTGACTACCAGGGTTTTCAGTCTATGTACACTTTTGTTACTTCTCACCTCCCTCCTGGTTTTAACGAGTACGACTTTGTTCCTGAGTCTTTTGACCGCGACAAGACTATTGCTCTCATTATGAACTCTTCTGGTTCTACTGGTCTCCCTAAGGGTGTTGCTCTCCCTCACCGCACTGCTTGTGTTCGCTTTTCTCACGCTCGCGACCCTATTTTTGGTAACCAGATTATTCCTGACACTGCTATTCTCTCTGTTGTTCCTTTTCACCACGGTTTTGGTATGTTTACTACTCTCGGTTACCTCATTTGTGGTTTTCGCGTTGTTCTCATGTACCGCTTTGAGGAGGAGCTCTTTCTCCGCTCTCTCCAGGACTACAAGATTCAGTCTGCTCTCCTCGTTCCTACTCTCTTTTCTTTTTTTGCTAAGTCTACTCTCATTGACAAGTACGACCTCTCTAACCTCCACGAGATTGCTTCTGGTGGTGCTCCTCTCTCTAAGGAGGTTGGTGAGGCTGTTGCTAAGCGCTTTCACCTCCCTGGTATTCGCCAGGGTTACGGTCTCACTGAGACTACTTCTGCTATTCTCATTACTCCTGAGGGTGACGACAAGCCTGGTGCTGTTGGTAAGGTTGTTCCTTTTTTTGAGGCTAAGGTTGTTGACCTCGACACTGGTAAGACTCTCGGTGTTAACCAGCGCGGTGAGCTCTGTGTTCGCGGTCCTATGATTATGTCTGGTTACGTTAACAACCCTGAGGCTACTAACGCTCTCATTGACAAGGACGGTTGGCTCCACTCTGGTGACATTGCTTACTGGGACGAGGACGAGCACTTTTTTATTGTTGACCGCCTCAAGTCTCTCATTAAGTACAAGGGTTACCAGGTTGCTCCTGCTGAGCTCGAGTCTATTCTCCTCCAGCACCCTAACATTTTTGACGCTGGTGTTGCTGGTCTCCCTGACGACGACGCTGGTGAGCTCCCTGCTGCTGTTGTTGTTCTCGAGCACGGTAAGACTATGACTGAGAAGGAGATTGTTGACTACGTTGCTTCTCAGGTTACTACTGCTAAGAAGCTCCGCGGTGGTGTTGTTTTTGTTGACGAGGTTCCTAAGGGTCTCACTGGTAAGCTCGACGCTCGCAAGATTCGCGAGATTCTCATTAAGGCTAAGAAGGGTGGTAAGTCTAAGCTCTAAAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
mStayGold_21 C
*mStayGold gene		GGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACATATGGTTTCTACTGGTGAGGAGCTCTTTACTGGTGTTGTTCCTTTTAAGTTTCAGCTCAAGGGTACTATTAACGGTAAGTCTTTTACTGTTGAGGGTGAGGGTGAGGGTAACTCTCACGAGGGTTCTCACAAGGGTAAGTACGTTTGTACTTCTGGTAAGCTCCCTATGTCTTGGGCTGCTCTCGGTACTTCTTTTGGTTACGGTATGAAGTACTACACTAAGTACCCTTCTGGTCTCAAGAACTGGTTTCACGAGGTTATGCCTGAGGGTTTTACTTACGACCGCCACATTCAGTACAAGGGTGACGGTTCTATTCACGCTAAGCACCAGCACTTTATGAAGAACGGTACTTACCACAACATTGTTGAGTTTACTGGTCAGGACTTTAAGGAGAACTCTCCTGTTCTCACTGGTGACATGGACGTTTCTCTCCCTAACGAGGTTCAGCACATTCCTATTGACGACGGTGTTGAGTGTACTGTTACTCTCCAGTACCCTCTCCTCTCTGACGAGTCTAAGTGTGTTGAGGCTTACCAGAACACTATTATTAAGCCTCTCCACAACCAGCCTGCTCCTGACGTTCCTTTTCACTGGATTCGCAAGCAGTACACTCAGTCTAAGGACGACACTGAGGAGCGCGACCACATTATTCAGTCTGAGACTCTCGAGGCTCACCTCTAAAAAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGG
Appendix 1—table 2
Primers used in this study.
Primer no.Sequence
1GGCGATTAAGTTGGGTAACGCCAG
2CCGGCTCGTATGTTGTGTGG
3GGTTCTGGTGGTAACTCTGGTTCTTCTGGTGGTTCTTCTGGTGTTTCTGGTTGGC
4GGCCGCAAGCCTATTAAGAAATCTTCTTAAAGAGGCGCCAACCAGAAACACCAGAAG
5GAACCAGAGTTACCACCAGAACCCTTCTGACACCAAACGAGCTGG
6GAACCAGAGTTACCACCAGAACCGAGCTTAGACTTACCACCCTTCTTAGC
7GAACCAGAGTTACCACCAGAACCGAGGTGAGCCTCGAGAGTCTC
8GGCCGCAAGCCTATTAAGAAATC
9CCGCGTAATACGACTCACTATAGGGGCTATAGCTCAGCTG
10TGGTGGAGCTAAGCGGGATCG
11ATGCAAGCGCTCTCCCAGC
12GGAGAGCGCTTGCATCGCATGCAAGAGGTCAG
13CCGCGTAATACGACTCACTATAGCGCCCGTAGCTCAG
14TGGCGCGCCCGACAGGATTCG
15AGGGCAGCGCTCTATCCAGCTGAG
16ATAGAGCGCTGCCCTGCGGAGGCAGAGG
17ATAGAGCGCTGCCCTCCTGAGGCAGAGGTC
18CCGCGTAATACGACTCACTATAGGAGCGGTAGTTCAGTCG
19TGGCGGAACGGACGGGACTCG
20CCGCGTAATACGACTCACTATAGGCGCGTTAACAAAGCG
21TGGAGGCGCGTTCCGGAGTCG
22CCGCGTAATACGACTCACTATAGCGGGAATAGCTCAGTTG
23TGGAGCGGGAAACGAGAC
24AAGGTCGTGCTCTACCAACTGAGCTATTC
25GTAGAGCACGACCTTCCCAAGGTCGGGGTC
26CCGCGTAATACGACTCACTATAGTCCCCTTCGTCTAGAGG
27TGGCGTCCCCTAGGGGATTCG
28CCGCGTAATACGACTCACTATAGGTGGCTATAGCTCAGTTG
29TGGGGTGGCTAATGGGATTCG
30CCGCGTAATACGACTCACTATAGCGAAGGTGGCGGAATTG
31TGGTGCGAGGGGGGGGA
32AAGCTAGCGCGTCTACCAATTCCGC
33TAGACGCGCTAGCTTCAAGTGTTAGTGTCCTTAC
34TAGACGCGCTAGCTTGAGGTGTTAGTGTCCTTAC
35CCGCGTAATACGACTCACTATAGGGTCGTTAGCTCAGTTGG
36TGGTGGGTCGTGCAGGATT
37CCGCGTAATACGACTCACTATAGGCTACGTAGCTCAGTTG
38TGGTGGCTACGACGGGATTCG
39CCGCGTAATACGACTCACTATAGCCCGGATAGCTCAGTC
40TGGTGCCCGGACTCGGAA
41CCGCGTAATACGACTCACTATAGGTGAGGTGTCCGAGTG
42TGGCGGTGAGGGGGGGATTCG
43AGGCGTGCTCCTTCAGCCACTCG
44TGAAGGAGCACGCCTCGAAAGTGTGTATACGGCAAC
45TGAAGGAGCACGCCTGCTAAGTGTGTATACGGCAAC
46CCGCGTAATACGACTCACTATAGCTGATATGGCTCAGTTGG
47TGGTGCTGATACCCAGAGTCG
48AAGGGTGCGCTCTACCAACTGAGC
49GTAGAGCGCACCCTTCGTAAGGGTGAGGTCCCCAG
50CCGCGTAATACGACTCACTATAGGTGGGGTTCCCGAG
51TGGTGGTGGGGGAAGGATTCG
52CCGCGTAATACGACTCACTATAGCGTCCGTAGCTCAGTTG
53TGGTGCGTCCGAGTGGACTCG
54AAGGTGGTGCTCTAACCAACTGAGCTAC
55TTAGAGCACCACCTTCACATGGTGGGGGTCGG
56GAGATTAATACGACTCACTATAGGGCACGTAGCGCAGCCTGG
57TGGTCGGCACGAGAGGATTT
58ATGACGGTGCGCTACCAGGCTGC
59GTAGCGCACCGTCATCGGGTGTCGGGGG
60GGAGAGCGCTTGCATCAAATGCAAGAGGTCAG
61GGAGAGCGCTTGCATCGAATGCAAGAGGTCAG
62GGAGAGCGCTTGCATCAGATGCAAGAGGTCAG
63GGAGAGCGCTTGCATCGGATGCAAGAGGTCAG
64GGAGAGCGCTTGCATCCGATGCAAGAGGTCAG
65GGAGAGCGCTTGCATGCTATGCAAGAGGTCAG
66GGAGAGCGCTTGCATCGTATGCAAGAGGTCAG
67GGAGAGCGCTTGCATCACATGCAAGAGGTCAG
68GGAGAGCGCTTGCATCCCATGCAAGAGGTCAG
69TGAAGGAGCACGCCTCAAAAGTGTGTATACGGCAAC
70TGAAGGAGCACGCCTCAGAAGTGTGTATACGGCAAC
71TGAAGGAGCACGCCTCGGAAGTGTGTATACGGCAAC
72TGAAGGAGCACGCCTCCGAAGTGTGTATACGGCAAC
73TGAAGGAGCACGCCTCGTAAGTGTGTATACGGCAAC
74TGAAGGAGCACGCCTCACAAGTGTGTATACGGCAAC
75TGAAGGAGCACGCCTCGCAAGTGTGTATACGGCAAC
76TGAAGGAGCACGCCTCCCAAGTGTGTATACGGCAAC
77TAGACGCGCTAGCTTCGAGTGTTAGTGTCCTTAC
78TAGACGCGCTAGCTTCGGGTGTTAGTGTCCTTAC
79TAGACGCGCTAGCTTCCGGTGTTAGTGTCCTTAC
80TAGACGCGCTAGCTTGCTGTGTTAGTGTCCTTAC
81TAGACGCGCTAGCTTCGTGTGTTAGTGTCCTTAC
82TAGACGCGCTAGCTTCACGTGTTAGTGTCCTTAC
83TAGACGCGCTAGCTTCGCGTGTTAGTGTCCTTAC
84TAGACGCGCTAGCTTCCCGTGTTAGTGTCCTTAC
85GGAGAGCGCTTGCATUGCATGCAAGAGGTCAG
86ATAGAGCGCTGCCCTTCGGAGGCAGAGG
87ATAGAGCGCTGCCCTTCTGAGGCAGAGGTC
88GAGATTAATACGACTCACTATAGGGGGTATCGCCAAGCGGTAAG
89TGGCGGGGGTACGAGGATTCG
90AATCCGGTGCCTTACCGCTTG
91GTAAGGCACCGGATTTTGATTCCGGCATTCC
92AGGGCGGTGTCCTGGGCCTC
93CCAGGACACCGCCCTTTCACGGCGGTAAC
94GTAGAGCACGACCTTTCCAAGGTCGGGGTC
95GAGATTAATACGACTCACTATAGGGCTTGTAGCTCAGGTGGTTAG
96TGGTAGGCCTGAGTGGACTTG
97AGGGGTGCGCTCTAACCACC
98TTAGAGCGCACCCCTTATAAGGGTGAGGTCG
99TAGACGCGCTAGCTTTAAGTGTTAGTGTCCTTACG
100TAGACGCGCTAGCTTTAGGTGTTAGTGTCCTTACG
101AGTCAACTGCTCTACCAACTGAGC
102GTAGAGCAGTTGACTTTTAATCAATTGGTCGC
103GTAGCGCACCGTCATTGGGTGTCGGGGG
104TGAAGGAGCACGCCTTGAAAGTGTGTATACGGCAAC
105GTAGAGCGCACCCTTTGTAAGGGTGAGGTCCCCAG
106TTAGAGCACCACCTTTACATGGTGGGGGTCGG
107ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGTAACCTGCTTCTTGGCGCAACTCTG
108ACACTCTTTCCCTACACGACGCTCTTCCGATCTGAATTGGGGCTTCTTGGCGCAACTCTG
109ACACTCTTTCCCTACACGACGCTCTTCCGATCTAACCACTCGCTTCTTGGCGCAACTCTG
110ACACTCTTTCCCTACACGACGCTCTTCCGATCTCAGGGTATGCTTCTTGGCGCAACTCTG
111ACACTCTTTCCCTACACGACGCTCTTCCGATCTTAGGAGCAGCTTCTTGGCGCAACTCTG
112GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGAGACTTCGGTCCTCGAGGTAAGAACCGTC
113ACACTCTTTCCCTACACGACGCTCTTCCGATCTATCACTGCTTACAACGAGCGCGAGCTC
114ACACTCTTTCCCTACACGACGCTCTTCCGATCTGAAGGCAATTACAACGAGCGCGAGCTC
115ACACTCTTTCCCTACACGACGCTCTTCCGATCTCTCGAGAATTACAACGAGCGCGAGCTC
116ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGTCAACATTACAACGAGCGCGAGCTC
117ACACTCTTTCCCTACACGACGCTCTTCCGATCTCAAGAGTCTTACAACGAGCGCGAGCTC
118GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAGGGTGAGCGGAGAAAGAGCTCCTCC
119ACACTCTTTCCCTACACGACGCTCTTCCGATCTGGACTAGTGTTGAGGGTGAGGGTGAGG
120ACACTCTTTCCCTACACGACGCTCTTCCGATCTCAGAGGAAGTTGAGGGTGAGGGTGAGG
121ACACTCTTTCCCTACACGACGCTCTTCCGATCTCGCTTGATGTTGAGGGTGAGGGTGAGG
122ACACTCTTTCCCTACACGACGCTCTTCCGATCTGGAAATCGGTTGAGGGTGAGGGTGAGG
123ACACTCTTTCCCTACACGACGCTCTTCCGATCTAGTGCTCTGTTGAGGGTGAGGGTGAGG
124GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTCGATCTGGCGAATCCAGTGAAAAGGAACGTC
Appendix 1—table 3
tRNAs used in this study.
tRNA(or pre-tRNA)Primer set for IVT templatePrimer set for mutationSequence
tRNAAlaGGC9, 10-GGGGCUAUAGCUCAGCUGGGAGAGCGCUUGCAUGGCAUGCAAGAGGUCAGCGGUUCGAUCCCGCUUAGCUCCACCA
tRNAAlaCGC9, 1011, 12GGGGCUAUAGCUCAGCUGGGAGAGCGCUUGCAUCGCAUGCAAGAGGUCAGCGGUUCGAUCCCGCUUAGCUCCACCA
tRNAArgCCG13, 14-GCGCCCGUAGCUCAGCUGGAUAGAGCGCUGCCCUCCGGAGGCAGAGGUCUCAGGUUCGAAUCCUGUCGGGCGCGCCA
tRNAArgGCG13, 1415, 16GCGCCCGUAGCUCAGCUGGAUAGAGCGCUGCCCUGCGGAGGCAGAGGUCUCAGGUUCGAAUCCUGUCGGGCGCGCCA
tRNAArgCCU13, 1415, 17GCGCCCGUAGCUCAGCUGGAUAGAGCGCUGCCCUCCUGAGGCAGAGGUCUCAGGUUCGAAUCCUGUCGGGCGCGCCA
tRNAAsnGUC--UCCUCUGUAGUUCAGUCGGUAGAACGGCGGACUGUUAAUCCGUAUGUCACUGGUUCGAGUCCAGUCAGAGGAGCCA
tRNAAspGUC18, 19-GGAGCGGUAGUUCAGUCGGUUAGAAUACCUGCCUGUCACGCAGGGGGUCGCGGGUUCGAGUCCCGUCCGUUCCGCCA
tRNACysGCA20, 21-GGCGCGUUAACAAAGCGGUUAUGUAGCGGAUUGCAAAUCCGUCUAGUCCGGUUCGACUCCGGAACGCGCCUCCA
tRNAGlnCUG--UGGGGUAUCGCCAAGCGGUAAGGCACCGGAUUCUGAUUCCGGCAUUCCGAGGUUCGAAUCCUCGUACCCCAGCCA
tRNAGlyGCC22, 23-GCGGGAAUAGCUCAGUUGGUAGAGCACGACCUUGCCAAGGUCGGGGUCGCGAGUUCGAGUCUCGUUUCCCGCUCCA
tRNAGlyCCC22, 2324, 25GCGGGAAUAGCUCAGUUGGUAGAGCACGACCUUCCCAAGGUCGGGGUCGCGAGUUCGAGUCUCGUUUCCCGCUCCA
tRNAGluCUC26, 27-GUCCCCUUCGUCUAGAGGCCCAGGACACCGCCCUCUCACGGCGGUAACAGGGGUUCGAAUCCCCUAGGGGACGCCA
tRNAHisGUG28, 29-GGUGGCUAUAGCUCAGUUGGUAGAGCCCUGGAUUGUGAUUCCAGUUGUCGUGGGUUCGAAUCCCAUUAGCCACCCCA
tRNAIleGAU--AGGCUUGUAGCUCAGGUGGUUAGAGCGCACCCCUGAUAAGGGUGAGGUCGGUGGUUCAAGUCCACUCAGGCCUACCA
tRNALeuCAG30, 31-GCGAAGGUGGCGGAAUUGGUAGACGCGCUAGCUUCAGGUGUUAGUGUCCUUACGGACGUGGGGGUUCAAGUCCCCCCCCUCGCACCA
tRNALeuCAA30, 3132, 33GCGAAGGUGGCGGAAUUGGUAGACGCGCUAGCUUCAAGUGUUAGUGUCCUUACGGACGUGGGGGUUCAAGUCCCCCCCCUCGCACCA
tRNALeuGAG30, 3132, 34GCGAAGGUGGCGGAAUUGGUAGACGCGCUAGCUUGAGGUGUUAGUGUCCUUACGGACGUGGGGGUUCAAGUCCCCCCCCUCGCACCA
tRNALysCUU35, 36-GGGUCGUUAGCUCAGUUGGUAGAGCAGUUGACUCUUAAUCAAUUGGUCGCAGGUUCGAAUCCUGCACGACCCACCA
tRNAfMetCAU--CGCGGGGUGGAGCAGCCUGGUAGCUCGUCGGGCUCAUAACCCGAAGAUCGUCGGUUCAAAUCCGGCCCCCGCAACCA
tRNAmMetCAU37, 38-GGCUACGUAGCUCAGUUGGUUAGAGCACAUCACUCAUAAUGAUGGGGUCACAGGUUCGAAUCCCGUCGUAGCCACCA
tRNAPheGAA39, 40-GCCCGGAUAGCUCAGUCGGUAGAGCAGGGGAUUGAAAAUCCCCGUGUCCUUGGUUCGAUUCCGAGUCCGGGCACCA
tRNASerGGA41, 42-GGUGAGGUGUCCGAGUGGCUGAAGGAGCACGCCUGGAAAGUGUGUAUACGGCAACGUAUCGGGGGUUCGAAUCCCCCCCUCACCGCCA
tRNASerCGA41, 4243, 44GGUGAGGUGUCCGAGUGGCUGAAGGAGCACGCCUCGAAAGUGUGUAUACGGCAACGUAUCGGGGGUUCGAAUCCCCCCCUCACCGCCA
tRNASerGCU41, 4243, 45GGUGAGGUGUCCGAGUGGCUGAAGGAGCACGCCUGCUAAGUGUGUAUACGGCAACGUAUCGGGGGUUCGAAUCCCCCCCUCACCGCCA
tRNAThrGGU46, 47-GCUGAUAUGGCUCAGUUGGUAGAGCGCACCCUUGGUAAGGGUGAGGUCCCCAGUUCGACUCUGGGUAUCAGCACCA
tRNAThrCGU46, 4748, 49GCUGAUAUGGCUCAGUUGGUAGAGCGCACCCUUCGUAAGGGUGAGGUCCCCAGUUCGACUCUGGGUAUCAGCACCA
tRNATrpCCA--AGGGGCGUAGUUCAAUUGGUAGAGCACCGGUCUCCAAAACCGGGUGUUGGGAGUUCGAGUCUCUCCGCCCCUGCCA
tRNATyrGUA50, 51-GGUGGGGUUCCCGAGCGGCCAAAGGGAGCAGACUGUAAAUCUGCCGUCACAGACUUCGAAGGUUCGAAUCCUUCCCCCACCACCA
tRNAValGAC52, 53-GCGUCCGUAGCUCAGUUGGUUAGAGCACCACCUUGACAUGGUGGGGGUCGGUGGUUCGAGUCCACUCGGACGCACCA
tRNAValCAC52, 5354, 55GCGUCCGUAGCUCAGUUGGUUAGAGCACCACCUUCACAUGGUGGGGGUCGGUGGUUCGAGUCCACUCGGACGCACCA
tRNAProGGG--CGGCACGUAGCGCAGCCUGGUAGCGCACCGUCAUGGGGUGUCGGGGGUCGGAGGUUCAAAUCCUCUCGUGCCGACCA
tRNAProCGG56, 5758, 59GGGCACGUAGCGCAGCCUGGUAGCGCACCGUCAUCGGGUGUCGGGGGUCGGAGGUUCAAAUCCUCUCGUGCCGACCA
tRNAAlaCAA
*anticodon		9, 1011, 60GGGGCUAUAGCUCAGCUGGGAGAGCGCUUGCAUCAAAUGCAAGAGGUCAGCGGUUCGAUCCCGCUUAGCUCCACCA
tRNAAlaCGA9, 1011, 61GGGGCUAUAGCUCAGCUGGGAGAGCGCUUGCAUCGAAUGCAAGAGGUCAGCGGUUCGAUCCCGCUUAGCUCCACCA
tRNAAlaCAG9, 1011, 62GGGGCUAUAGCUCAGCUGGGAGAGCGCUUGCAUCAGAUGCAAGAGGUCAGCGGUUCGAUCCCGCUUAGCUCCACCA
tRNAAlaCGG9, 1011, 63GGGGCUAUAGCUCAGCUGGGAGAGCGCUUGCAUCGGAUGCAAGAGGUCAGCGGUUCGAUCCCGCUUAGCUCCACCA
tRNAAlaCCG9, 1011, 64GGGGCUAUAGCUCAGCUGGGAGAGCGCUUGCAUCCGAUGCAAGAGGUCAGCGGUUCGAUCCCGCUUAGCUCCACCA
tRNAAlaGCU9, 1011, 65GGGGCUAUAGCUCAGCUGGGAGAGCGCUUGCAUGCUAUGCAAGAGGUCAGCGGUUCGAUCCCGCUUAGCUCCACCA
tRNAAlaCGU9, 1011, 66GGGGCUAUAGCUCAGCUGGGAGAGCGCUUGCAUCGUAUGCAAGAGGUCAGCGGUUCGAUCCCGCUUAGCUCCACCA
tRNAAlaCAC9, 1011, 67GGGGCUAUAGCUCAGCUGGGAGAGCGCUUGCAUCACAUGCAAGAGGUCAGCGGUUCGAUCCCGCUUAGCUCCACCA
tRNAAlaCCC9, 1011, 68GGGGCUAUAGCUCAGCUGGGAGAGCGCUUGCAUCCCAUGCAAGAGGUCAGCGGUUCGAUCCCGCUUAGCUCCACCA
tRNASerCAA
*anticodon		41, 4243, 69GGUGAGGUGUCCGAGUGGCUGAAGGAGCACGCCUCAAAAGUGUGUAUACGGCAACGUAUCGGGGGUUCGAAUCCCCCCCUCACCGCCA
tRNASerCAG41, 4243, 70GGUGAGGUGUCCGAGUGGCUGAAGGAGCACGCCUCAGAAGUGUGUAUACGGCAACGUAUCGGGGGUUCGAAUCCCCCCCUCACCGCCA
tRNASerCGG41, 4243, 71GGUGAGGUGUCCGAGUGGCUGAAGGAGCACGCCUCGGAAGUGUGUAUACGGCAACGUAUCGGGGGUUCGAAUCCCCCCCUCACCGCCA
tRNASerCCG41, 4243, 72GGUGAGGUGUCCGAGUGGCUGAAGGAGCACGCCUCCGAAGUGUGUAUACGGCAACGUAUCGGGGGUUCGAAUCCCCCCCUCACCGCCA
tRNASerCGU41, 4243, 73GGUGAGGUGUCCGAGUGGCUGAAGGAGCACGCCUCGUAAGUGUGUAUACGGCAACGUAUCGGGGGUUCGAAUCCCCCCCUCACCGCCA
tRNASerCAC41, 4243, 74GGUGAGGUGUCCGAGUGGCUGAAGGAGCACGCCUCACAAGUGUGUAUACGGCAACGUAUCGGGGGUUCGAAUCCCCCCCUCACCGCCA
tRNASerCGC41, 4243, 75GGUGAGGUGUCCGAGUGGCUGAAGGAGCACGCCUCGCAAGUGUGUAUACGGCAACGUAUCGGGGGUUCGAAUCCCCCCCUCACCGCCA
tRNASerCCC41, 4243, 76GGUGAGGUGUCCGAGUGGCUGAAGGAGCACGCCUCCCAAGUGUGUAUACGGCAACGUAUCGGGGGUUCGAAUCCCCCCCUCACCGCCA
tRNALeuCGA
*anticodon		30, 3132, 77GCGAAGGUGGCGGAAUUGGUAGACGCGCUAGCUUCGAGUGUUAGUGUCCUUACGGACGUGGGGGUUCAAGUCCCCCCCCUCGCACCA
tRNALeuCGG30, 3132, 78GCGAAGGUGGCGGAAUUGGUAGACGCGCUAGCUUCGGGUGUUAGUGUCCUUACGGACGUGGGGGUUCAAGUCCCCCCCCUCGCACCA
tRNALeuCCG30, 3132, 79GCGAAGGUGGCGGAAUUGGUAGACGCGCUAGCUUCCGGUGUUAGUGUCCUUACGGACGUGGGGGUUCAAGUCCCCCCCCUCGCACCA
tRNALeuGCU30, 3132, 80GCGAAGGUGGCGGAAUUGGUAGACGCGCUAGCUUGCUGUGUUAGUGUCCUUACGGACGUGGGGGUUCAAGUCCCCCCCCUCGCACCA
tRNALeuCGU30, 3132, 81GCGAAGGUGGCGGAAUUGGUAGACGCGCUAGCUUCGUGUGUUAGUGUCCUUACGGACGUGGGGGUUCAAGUCCCCCCCCUCGCACCA
tRNALeuCAC30, 3132, 82GCGAAGGUGGCGGAAUUGGUAGACGCGCUAGCUUCACGUGUUAGUGUCCUUACGGACGUGGGGGUUCAAGUCCCCCCCCUCGCACCA
tRNALeuCGC30, 3132, 83GCGAAGGUGGCGGAAUUGGUAGACGCGCUAGCUUCGCGUGUUAGUGUCCUUACGGACGUGGGGGUUCAAGUCCCCCCCCUCGCACCA
tRNALeuCCC30, 3132, 84GCGAAGGUGGCGGAAUUGGUAGACGCGCUAGCUUCCCGUGUUAGUGUCCUUACGGACGUGGGGGUUCAAGUCCCCCCCCUCGCACCA
tRNAAlaUGC9, 1011, 85GGGGCUAUAGCUCAGCUGGGAGAGCGCUUGCAUUGCAUGCAAGAGGUCAGCGGUUCGAUCCCGCUUAGCUCCACCA
tRNAArgUCG13, 1415, 86GCGCCCGTAGCTCAGCTGGATAGAGCGCTGCCCTUCGGAGGCAGAGGTCTCAGGTTCGAATCCTGTCGGGCGCGCCA
tRNAArgUCU13, 1415, 87GCGCCCGTAGCTCAGCTGGATAGAGCGCTGCCCTUCUGAGGCAGAGGTCTCAGGTTCGAATCCTGTCGGGCGCGCCA
tRNAGlnUUG88, 8990, 91TGGGGTATCGCCAAGCGGTAAGGCACCGGATTTTGATTCCGGCATTCCGAGGTTCGAATCCTCGTACCCCAGCCA
tRNAGluUUC26, 2792, 93GTCCCCTTCGTCTAGAGGCCCAGGACACCGCCCTTTCACGGCGGTAACAGGGGTTCGAATCCCCTAGGGGACGCCA
tRNAGlyUCC22, 2324, 94GCGGGAATAGCTCAGTTGGTAGAGCACGACCTTUCCAAGGTCGGGGTCGCGAGTTCGAGTCTCGTTTCCCGCTCCA
tRNAIleUAU95, 9697, 98AGGCTTGTAGCTCAGGTGGTTAGAGCGCACCCCTTATAAGGGTGAGGTCGGTGGTTCAAGTCCACTCAGGCCTACCA
tRNALeuUAA30, 3132, 99GCGAAGGUGGCGGAAUUGGUAGACGCGCUAGCUUUAAGUGUUAGUGUCCUUACGGACGUGGGGGUUCAAGUCCCCCCCCUCGCACCA
tRNALeuUAG30, 3132, 100GCGAAGGUGGCGGAAUUGGUAGACGCGCUAGCUUUAGGUGUUAGUGUCCUUACGGACGUGGGGGUUCAAGUCCCCCCCCUCGCACCA
tRNALysUUU35, 36101, 102GGGTCGTTAGCTCAGTTGGTAGAGCAGTTGACTTTTAATCAATTGGTCGCAGGTTCGAATCCTGCACGACCCACCA
tRNAProUGG56, 5758, 103CGGCACGTAGCGCAGCCTGGTAGCGCACCGTCATUGGGTGTCGGGGGTCGGAGGTTCAAATCCTCTCGTGCCGACCA
tRNASerUGA41, 4243, 104GGUGAGGUGUCCGAGUGGCUGAAGGAGCACGCCUUGAAAGUGUGUAUACGGCAACGUAUCGGGGGUUCGAAUCCCCCCCUCACCGCCA
tRNAThrUGU46, 4748, 105GCTGATATGGCTCAGTTGGTAGAGCGCACCCTTUGUAAGGGTGAGGTCCCCAGTTCGACTCTGGGTATCAGCACCA
tRNAValUAC52, 5354, 106GCGTCCGTAGCTCAGTTGGTTAGAGCACCACCTTUACATGGTGGGGGTCGGTGGTTCGAGTCCACTCGGACGCACCA
Appendix 1—table 4
Weights for nucleotide substitution probabilities.
First baseSecond baseThird base
For transitions10.51
For transversions0.50.11
Appendix 1—table 5
Physicochemical property values of amino acids used for cost evaluation.
Amino acidPolar requirement (PR)Molecular volume (MV)Hydropathy index (HI)
Ala7311.8
Arg9.1124–4.5
Asp1354–3.5
Asn1056–3.5
Cys4.8552.5
Glu12.583–3.5
Gln8.685–3.5
Gly7.93–0.4
His8.496–3.2
Ile4.91114.5
Leu4.91113.8
Lys10.1119–3.9
Met5.31051.9
Phe51322.8
Pro6.632.5–1.6
Ser7.532–0.8
Thr6.661–0.7
Trp5.2170–0.9
Tyr5.4136–1.3
Val5.6844.2
Appendix 1—table 6
Standard composition of the tRNA-free PURE system (tfPURE).
ComponentConcentrationComponentConcentration
Initiation Factor 125 µMTryptophanyl-tRNA Synthetase28 nM
Initiation Factor 21.0 µMTyrosyl-tRNA Synthetase0.15 µM
Initiation Factor 34.9 µMValyl-tRNA Synthetase17 nM
Elongation Factor G1.1 µMMethionyl-tRNA Formyltransferase0.59 µM
Elongation Factor Tu80 µMMyokinase1.4 µM
Elongation Factor Ts3.3 µMCreatine kinase0.25 µM
Release Factor 149 nMNucleoside diphosphate kinase16 nM
Release Factor 248 nMPyrophosphatase41 nM
Release Factor 30.17 µMTrigger Factor1.0 µM
Ribosome Recycling Factor3.9 µMcoli DEAH type RNA helicase A63 nM
Alanyl-tRNA Synthetase0.73 µMRibosome1.0 µM
Arginyl-tRNA Synthetase31 nMTyrosine0.30 mM
Asparaginyl-tRNA Synthetase0.42 µMCysteine0.30 mM
Asparagyl-tRNA Synthetase0.12 µM18 other amino acids0.36 mM
Cysteinyl-tRNA Synthetase24 nMATP0.38 mM
Glutaminyl-tRNA Synthetase60 nMGTP0.25 mM
Glutamyl-tRNA Synthetase0.23 µMCTP0.13 mM
Glycyl-tRNA Synthetase86 nMUTP0.13 mM
Histidyl-tRNA Synthetase85 nMN-2-hydroxyethylpiperazine-N'–2-ethanesulfonic acid (pH7.6)0.10 M
Isoleucyl-tRNA Synthetase0.37 µMGlutamic acid potassium salt70 mM
Leucyl-tRNA Synthetase41 nMSpermidine0.375 mM
Lysyl-tRNA Synthetase0.12 µMCreatine phosphate25 mM
Methionyl-tRNA Synthetase0.11 µMDithiothreitol6 mM
Phenylalanyl-tRNA Synthetase0.13 µM10-formyl-5,6,7,8-tetrahydro folic acid10 µg/mL
Prolyl-tRNA Synthetase0.17 µMYeast inorganic pyrophosphatase (NEB)0.2 units/mL
Seryl-tRNA Synthetase78 nMRNase Plus Inhibitor (Promega)0.1 U/µL
Threonyl-tRNA Synthetase84 nMT7 RNAP (Takara)1.7 U/µL
Appendix 1—table 7
tRNA composition in each genetic code.
tRNA
(ng/ µL)		MGCnear-SGC(RV)SGCCode1Code2Code3Code4Code5Code6Code7Code8Code9Code10
tRNAAlaGGC12121212121212121212121212
tRNAAlaCGC-121212-12-12----12
tRNAArgCCG-1212----------
tRNAArgGCG12121212121212121212121212
tRNAArgCCU-10010012121212121212121212
tRNAAspGUC12121212121212121212121212
tRNACysGCA12121212121212121212121212
tRNAGlyGCG12121212121212121212121212
tRNAGlyCCC-121212121212121212121212
tRNAGluCUC100100100100100100100100100100100100100
tRNAHisGUG12121212121212121212121212
tRNALeuCAG-1212------12-12-
tRNALeuCAA-1212--12-12-12-12-
tRNALeuGAG12121212121212121212121212
tRNALysCUU12121212121212121212121212
tRNAmMetCAU12121212121212121212121212
tRNAPheGAA12121212121212121212121212
tRNASerGGA12121212121212121212121212
tRNASerCGA-1212--------1212
tRNASerGCU-1212---12-----12
tRNAThrGGU12121212121212121212121212
tRNAThrCGU-1212----------
tRNATyrGUA12121212121212121212121212
tRNAValGAC12121212121212121212121212
tRNAValCAC-100100----------
tRNAProCGG-100100----------
tRNAProGGG100100100100100100100100100100100100100
tRNAIleGAU100100100100100100100100100100100100100
tRNAAsnGUU100100100100100100100100100100100100100
tRNAGlnCUG12121212121212121212121212
tRNATrpCCA12121212121212121212121212
tRNAfMetCAU12121212121212121212121212
tRNAAlaUGC--12----------
tRNAArgUCG--12----------
tRNAArgUCU--12----------
tRNAGlnUUG--12----------
tRNAGluUUC--100----------
tRNAGlyUCC--12----------
tRNAIleUAU--100----------
tRNALeuUAA--12----------
tRNALeuUAG--12----------
tRNALysUUU--12----------
tRNAProUGG--100----------
tRNASerUGA--12----------
tRNAThrUGU--12----------
tRNAValUAC--12----------
tRNAAlaCAA---40--40---40-40
tRNAAlaCGA----60-60--60---
tRNAAlaCAG---12-12-12--12-12
tRNAAlaCGG---12-----12---
tRNAAlaCCG------12---12--
tRNAAlaGCU-------12-1212--
tRNAAlaCGU---60---------
tRNAAlaCAC------12---12-12
tRNASerCAA----12---12----
tRNASerCAG----12-12-12----
tRNASerCGG----1212-12---1212
tRNASerCCG-------12-12--12
tRNASerCGU----80-8080-80-8080
tRNASerCAC-------1212----
tRNASerCGC---------12-12-
tRNALeuCGA---80-80-8080-80--
tRNALeuCGG------12-12-1212-
tRNALeuCCG---121212--12--12-
tRNALeuGCU---808080--80----
tRNALeuCGU-----12--12-12--
tRNALeuCAC---121212---12-12-
tRNALeuCGC----12-12-12-12--
Appendix 1—table 8
Major statistical comparisons of NanoLuc translation efficiency in Figure 1D.
Comparisonp value (Tukey’s HSD test)
MGC vs near-SGC2.45×10–6
near-SGC vs near-SGC (RV)1.04×10–6
MGC vs near-SGC (RV)0.9503
SGC vs near-SGC (RV)2.44×10–15
Appendix 1—table 9
Welch’s t-test for NanoLuc translation using variant tRNAs in Figure 2.
Ala
CodonUUGUCGCUGCCGCGGACGAGCGUGGGG
p value1.12×10–31.98×10–31.15×10–31.36×10–31.49×10–31.44×10–31.94×10–31.50×10–31.80×10–3
Ser
CodonUUGCUGCCGCGGACGGUGGCGGGG
p value1.98×10–32.26×10–31.90×10–43.37×10–25.14×10–41.11×10–29.08×10–40.778
Leu
CodonUCGCCGCGGAGCACGGUGGCGGGG
p value3.17×10–41.06×10–53.58×10–46.19×10–59.88×10–41.11×10–33.21×10–42.07×10–4
Appendix 1—table 10
Spearman correlation analysis for Figure 5.
ReporterCost metricSpearman’s ρp Value
GALCost_PR0.1090.75
GALCost_MV0.1000.770
GALCost_HI0.001.00
LucCost_PR–0.0360.915
LucCost_MV0.2450.467
LucCost_HI–0.0550.873
mSGCost_PR–0.1910.574
mSGCost_MV–0.0180.958
mSGCost_HI–0.2270.502

Additional files

MDAR checklist
https://cdn.elifesciences.org/articles/111164/elife-111164-mdarchecklist1-v1.docx
Source code 1

This code was used for the calculation of the cost of each genetic code.

https://cdn.elifesciences.org/articles/111164/elife-111164-code1-v1.zip

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Ryota Miyachi
  2. Norikazu Ichihashi
(2026)
Experimental verification of the error minimization theory using non-standard genetic codes constructed in vitro
eLife 15:RP111164.
https://doi.org/10.7554/eLife.111164.3