Coding-Sequence Determinants of Gene Expressionin Escherichia coli
Grzegorz Kudla, et al.
Science 324, 255 (2009);
DOI: 10.1126/science.1170160
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and the magnitude of FCT. Because active torqueis proportional to n2 and passive torque to n, theratio of active to passive torque increases as nincreases (Eq. 5), even while both quantities increase individually
% t%Þ
ẇ a
Fn ðdϕ=d¼ − ðg − 1Þẇ FCT
8w

ð5Þ

The increase in the ratio indicates an enhanced
capability for active maneuvers and active stabilization, whereas the increase in FCT adds to passive stability. Thus, increasing wingbeat frequency
enhances both maneuverability and stability. Hummingbirds provide an interesting example; males
typically have greater wingbeat frequencies (21)and smaller body sizes as compared to females ofthe same species, potentially conferring a benefit
in maneuverability and therefore an advantage
in display flights (22) as well as greater stability
when experiencing an external perturbation. Thesebenefits are not without cost, because increasing
wingbeat frequency increases the inertial and profile power requirements of flapping flight.
Finally, the success of our FCT model in predicting yaw deceleration dynamics implies that
passive damping may be important to flight control in flying animals across a wide range of bodysizes. For example, if a steadily flapping animal
experiences a brief perturbation in midstroke, by
the time it is prepared to execute a corrective
wingbeat, FCT will have eroded much of the effect of the perturbation, regardless of the wingbeat
frequency employed by the animal. Thus, FCT
provides open loop stability for some aspectsof animal flight, reducing its neuromuscular andneurosensory requirements. These are not eliminated, because FCT results in asymmetric forcesfrom symmetric flapping, implying that the animal’s muscles must generate asymmetric forces
and suggesting neural regulation to enforce symmetry. Furthermore, FCT does not address all thestability problems faced by flying animals. This
study is limited to yaw dynamics in hovering or
slow-speed flight; FCT is likely to be influential
in fast forward flight, but no data are available totest such predictions. More important, a full description of body dynamics involves many factors
beyond FCT and includes modes such as pitching
and longitudinal dynamics known to be inherently
unstable in open loop conditions (23, 24) andsubject to active control (25, 26). Finally, yaw
damping due to FCT is a feature of flapping flightthat is not found in human-made fixed-wing or
rotary-wing flyers and may lead to improvementsin the stability and maneuverability of biomimetic
micro–air vehicles.
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Coding-Sequence Determinants ofGene Expression in Escherichia coli
Grzegorz Kudla,1* Andrew W. Murray,2 David Tollervey,3 Joshua B. Plotkin1†
Synonymous mutations do not alter the encoded protein, but they can influence gene expression.
To investigate how, we engineered a synthetic library of 154 genes that varied randomly at synonymoussites, but all encoded the same green fluorescent protein (GFP). When expressed in Escherichia coli,
GFP protein levels varied 250-fold across the library. GFP messenger RNA (mRNA) levels, mRNAdegradation patterns, and bacterial growth rates also varied, but codon bias did not correlate with gene
expression. Rather, the stability of mRNA folding near the ribosomal binding site explained morethan half the variation in protein levels. In our analysis, mRNA folding and associated rates oftranslation initiation play a predominant role in shaping expression levels of individual genes,
whereas codon bias influences global translation efficiency and cellular fitness.
he theory of codon bias posits that preferredcodons correlate with the abundances of
iso-accepting tRNAs (1, 2) and thereby
increase translational efficiency (3) and accuracy(4). Recent experiments have revealed other effectsof silent mutations (5–7). We synthesized a libraryof green fluorescent protein (GFP) genes that varied
randomly in their codon usage, but encoded thesame amino acid sequence (8). By placing theseT

constructs in identical regulatory contexts and measuring their expression, we isolated the effects ofsynonymous variation on gene expression.
The GFP gene consists of 240 codons. For
226 of these codons, we introduced random silent
mutations in the third base position, while keeping the first and second positions constant (Fig.
1A). The resulting synthetic GFP constructs differed by up to 180 silent substitutions, with anwww.sciencemag.org

SCIENCEVOL 324

11. S. P. Sane, J. Exp. Biol. 206, 4191 (2003).
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41 (1984).
14. See supporting material on Science Online.
15. C. H. Greenewalt, Smithson. Misc. Collect. 144, 1 (1962).
16. R. Dudley, The Biomechanics of Insect Flight. Form,
Function, Evolution (Princeton Univ. Press, Princeton, NJ,
2000).
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M. Heisenberg, J. Comp. Physiol. A 163, 389 (1988).
18. H. H. Bülthoff, T. Poggio, C. Wehrhahn, Z. Naturforsch. C
35, 811 (1980).
19. G. K. Taylor, H. G. Krapp, in Insect Mechanics andControl, J. Casas, S. J. Simpson, Eds. (Academic Press,
London, 2007), vol. 34, pp. 231–316.
20. G. Ribak, J. Swallow, J. Comp. Physiol. A 193, 1065 (2007).
21. T. A. Hunter, J. Picman, Condor 107, 570 (2005).
22. F. G. Stiles, D. L. Altshuler, R. Dudley, Auk 122, 872 (2005).
23. G. K. Taylor, A. L. R. Thomas, J. Theor. Biol. 214, 351(2002).
24. M. Sun, J. K. Wang, J. Exp. Biol. 210, 2714 (2007).
25. G. K. Taylor, A. L. R. Thomas, J. Exp. Biol. 206, 2803
(2003).
26. X. Deng, L. Schenato, S. S. Sastry, IEEE Trans. Robotics
22, 789 (2006).
27. We thank A. Biewener of Harvard University and
S. Swartz and K. Breuer of Brown University for providing
the hummingbird and fruit bat data. This work was
funded in part by NSF grant 0732267 (Division ofIntegrative Organismal Systems) to T.L.H. and NSF grant0545931 (Division of Information and Intelligent
Systems) to X.D.

Supporting Online Materialwww.sciencemag.org/cgi/content/full/324/5924/252/DC1
SOM Text
Figs. S1 to S3Table S1
References
Appendices A to D
12 November 2008; accepted 20 February 200910.1126/science.1168431

average of 114 substitutions between pairs ofconstructs (Fig. 1B and figs. S1 and S2). The
range of third-position GC content (GC3) acrossthe library of constructs encompassed virtuallyall (99%) of the GC3 values among endogenous
Escherichia coli genes, and the variation in thecodon adaptation index (CAI) (9) contained most
(96%) of the CAI values of E. coli genes (Fig. 1).
We expressed the GFP genes in E. coli using
a T7-promoter vector, and we quantified expression by spectrofluorometry. Fluorescence levels
varied 250-fold across the library, and they werehighly reproducible for each GFP construct (Spearmanr = 0.98 between biological replicates) (fig. S3).
Fluorescence variation was consistent across a
broad range of experimental conditions (fig. S4).
1
Department of Biology and Program in Applied Mathematics
and Computational Science, The University of Pennsylvania,
Philadelphia, PA 19104, USA. 2Department of Molecular andCellular Biology, Harvard University, Cambridge, MA 02138,
USA. 3Wellcome Trust Centre for Cell Biology and Centre for
Systems Biology, University of Edinburgh, Edinburgh EH93JR, UK.

*Present address: Wellcome Trust Centre for Cell Biology,
University of Edinburgh, Edinburgh EH9 3JR, UK.
†To whom correspondence should be addressed. E-mail:jplotkin@sas.upenn.edu

10 APRIL 2009255

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REPORTSAn alternative plasmid with bacterial promoter reduced overall expression levels, but the correlation
between the two expression systems remained high(r = 0.9) (fig. S4). A similar pattern of fluorescencevariation was observed in fluorescence-activatedcell sorting measurements (fig. S5). Because theencoded protein sequence was identical for all
genes, we attributed fluorescence variation to differences in protein levels. This was confirmed bystrong correlations between fluorescence and total GFP levels in Western blots (fig. S5) andCoomassie staining (r = 0.9, P < 10–15).
To test the theory that E. coli translation rates
and eventual protein levels depend on the concordance between codon usage and cellular tRNAabundances (10–12), we compared codon usage
to fluorescence among the 154 synonymous GFPvariants. Notably, neither of the two most common measures of codon bias, the CAI or the frequency of optimal codons (3), was significantly
correlated with fluorescence levels (r = 0.14, P =0.09, and r = 0.11, P = 0.16, respectively) (Fig.
2A). Moreover, some of the most highly expressed genes featured low CAI and vice versa.
Although codon adaptation near the 5′ terminus is considered particularly important for expression (12, 13), the CAI value of the first 42
bases in a GFP gene was not significantly corre-

lated with the gene’s fluorescence intensity (r =0.1, P = 0.2). Similarly, the number of rare codons(sites with CAI < 0.1) in a sequence was not
significantly correlated with fluorescence (r =–0.02, P = 0.7), and neither was the number ofpairs of consecutive rare codons (r = –0.14, P =0.09). Although specific consecutive codon pairs
have been proposed to influence translation(14, 15), the frequency of such rare pairs in agene was not significantly correlated with its fluorescence (r = 0.07, P = 0.35) (8).
Statistical analyses of which nucleotide positions influenced gene expression (fig. S6) indicated the importance of local sequence patterns,
as opposed to global codon bias. This pattern is
consistent with studies of base content (16, 17),
which suggest that mRNA structure may shapeexpression levels (18–21). Therefore, for each
GFP construct, we computed the predicted minimum free energy associated with the secondary
structure of its entire mRNA or specific regionsof its mRNA. The folding energy of the entire
mRNA was not significantly correlated with fluorescence (r = 0.16, P = 0.051), but the folding
energy of the first third of the mRNA was strongly
correlated: mRNAs with stronger structure produced lower fluorescence (r = 0.60, P < 10–15).
A moving window analysis identified a region,

Fig. 1. Synthetic library of GFP genes with randomized codon usage. (A)Degenerate oligonucleotides were mixed and assembled by polymerase chain
reaction. Fragments were then cloned, sequenced, and assembled into
complete GFP genes. Red indicates third-codon positions. Degenerate symbols
are as follows: D (A or G or T); H (A or C or T); N (A or C or G or T); R (A or G);

256

10 APRIL 2009

VOL 324from nucleotide (nt) –4 to +37 relative to start, forwhich predicted folding energy explained 44%of the variation in fluorescence levels across the
GFP library (r = 0.66, P < 10–15) (Fig. 2B). The
same folding energies explained 59% of fluorescence variation when constructs were expressedusing a bacterial promoter (r = 0.77, P < 4 × 10–16)(fig. S7). mRNA folding also correlated with fluorescence in a separate analysis of GFP constructs differing by single mutations (8).
The strong correlation between mRNA folding and fluorescence suggests the simple mechanistic explanation that tightly folded messages
obstruct translation initiation and thereby reduce
protein synthesis (22). Predicted mRNA structures for highly expressed GFPs characteristicallycontained many unpaired nucleotides near thestart codon, whereas constructs expressed at low
levels featured long hairpin loops (Fig. 2B andfig. S8), consistent with known obstructions toinitiation (22). The region of strongest correlation
between folding energy and expression did not
overlap with the Shine-Dalgarno (SD) sequence,
which suggested that SD occlusion by secondary
structure (22, 23) did not play a major role ininhibiting expression, probably because our constructs contained no noncoding mutations. By
contrast, the region of strongest effect overlapped

and Y (C or T). (B) Example alignment illustrating sequence diversity among
15 synthetic genes. Shaded boxes indicate first and second codon positions,which are conserved across the library. (C and D) The distribution of GC3 andCAI among the 154 synthetic GFP genes (C) is representative of the diversity
among the 4288 endogenous E. coli genes (D).

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REPORTSREPORTS
ber of predicted transcription termination signals,
the propensity for conformation changes into ZDNA, and the number of predicted ribonuclease(RNase) E cleavage sites (8). RNase E cleavage
sites tended to reduce expression, as expected (24),and explained 4.7% of fluorescence variation.
Although global GC content was not significantly correlated with fluorescence (r = –0.031,
P = 0.7), GC content near the start codon wasFig. 2. The determinants of gene expression.
(A) Codon adaptation was not significantly
correlated with fluorescence among the 154
GFP constructs (r = 0.14, P = 0.09). (B)Predicted 5′ mRNA folding energy wasstrongly correlated with fluorescence (r =0.66, P < 10–15). For each construct, folding
energy was calculated in a window spanning
positions –4 to +37 relative to translation
start; two sample structures are shown. (C)Sliding window analysis of mRNA folding
and fluorescence. Local mRNA folding energies were calculated in a sliding window oflength 42 nt. The significance of the correlation between local folding energy and fluorescence (negative log10 P value) is plotted
as a function of window position along thesequence. Note the overlapping locations of
the 30-nt ribosome–binding site (blue bar)and the window of strongest correlation between folding energy and fluorescence (partially overlapping red bar, nt –4 throughnt +37).

Fig. 3. Expression levels of alternative GFP constructs. The distribution of log2 normalized fluorescencelevels for (top) pGK8 (T7 promoter, no leader sequence), (middle) pGK14 (PBAD bacterial promoter, noleader sequence) and (bottom) pGK16 (trp-lac bacterial promoter, 28-codon leader sequence) expression
vectors. Fluorescence varied substantially when expressed using T7 or bacterial promoter. The addition of
a 28-codon leader sequence with low secondary structure produced uniformly high expression levels.
www.sciencemag.org

SCIENCEVOL 324strongly correlated. But this was likely mediatedby mRNA secondary structure; GC content wasitself correlated with folding energy, and folding
energy explained 10 times as much variation influorescence as was explained by GC content (8).
GFP mRNA levels, as quantified by Northernblotting, varied across the library, but the extent
of mRNA variation was three times smaller thanthat of corresponding fluorescence variation. We
also observed 3′-truncated mRNA species that differed among GFP variants, which likely reflecteddifferent stabilities of mRNA degradation intermediates (fig. S9). mRNA levels were highly correlated with fluorescence (r = 0.53) and also withfolding energy near the start codon (r = 0.33).
These relations are consistent with the hypothesisthat secondary structure influences both mRNAand protein levels through occlusion of ribosome
subunit binding. Reduced ribosome binding increases mRNA exposure to nuclease digestion,
which in turn decreases stability (25).
Bacterial growth rates were strongly influenced
by the codon usage of the expressed GFP construct (8). Elevated CAI was correlated with faster
growth (r = 0.54, P < 9 × 10–13), whereas 5′mRNA folding energy showed no significant correlation with growth (r = 0.12, P = 0.15). These
results support the hypothesis that low codon adaptation in an overexpressed gene decreases cellular
fitness (16), probably because retarded elongation
sequesters ribosomes on the GFP mRNA andthereby hinders translation of essential mRNAs. The
growth rate data could alternatively be explained by
the hypothesis that high codon adaptation reduces
the rate of deleterious protein misfolding (6, 26, 27).
Although we do not rule out this possibility, in our
experiments CAI was not correlated with the degreeof misfolding, whether it was quantified by the ratioof Coomassie to fluorescence or by the ratio ofmRNA to fluorescence (8).
Our findings lead to the following prediction:
Adding a stretch of codons with weak mRNA
structure to the 5′ end of a gene with originallystrong structure should increase expression, even
if the additional codons have low CAI. To test
this prediction, we fused a 28-codon tag to the 5′
terminus of 72 GFP constructs. The tagged constructs, which featured weak mRNA secondary
structure and low CAI (8), produced consistently
high expression, including those GFPs poorly expressed in nontagged form (Fig. 3). These results
suggest that endogenous E. coli genes may haveundergone selection for weak 5′ secondary structure. Consistent with this hypothesis, we foundthat the predicted secondary structures for the4294 E. coli genes are significantly weaker near
their start codons (nt –4 to +37) than immediately
downstream (nt +38 to +79; Wilcoxon P < 10–15).
Here, we have systematically quantified theeffects of synonymous nucleotide variation on
gene expression in E. coli, on the basis of unbiased sequences that control for regulatory context. The data reveal a predominant role for mRNA
structure around the ribosomal binding site inshaping mRNA and protein levels. By contrast,

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significantly with the 30-nt ribosome–binding site
centered around the start codon (Fig. 2C).
In a multiple regression, mRNA folding energy near the start codon (nt –4 through +37)
explained nearly 10 times as much variation in
expression levels as any other predictor variable,
including the global GC content, CAI, the number of rare-codon sites or consecutive pairs, thelength of the longest rare-codon stretch, the num-257

neither local nor global codon bias had significant effects on mRNA or protein levels. Thisfinding is consistent with the view that translation
initiation, not elongation, is rate-limiting for gene
expression (28), but it seems to contradict thewell-known correspondence between codon bias
and expression level for endogenous genes (11, 29).
There is a simple explanation to this apparent
contradiction, which reverses the arrow of causality between codon adaptation and gene expression. In one view, high CAI induces strong
protein expression (10–12), whereas we argue
that strong expression induces selection for highCAI. Unlike genome-wide correlations between
CAI and expression levels [e.g. (11)], our analyses control for noncoding regulation and, thus,
can distinguish between these two alternatives.
We propose that the correspondence between
codon adaptation and expression level among
endogenous E. coli genes arises from selection to
make translation efficient at a global level, rather
than at the level of individual genes. High CAI
increases the elongation rate, but because initiation is rate-limiting in translation, elongation rate
does not significantly affect expression. On theother hand, rapid elongation sequesters fewer
ribosomes on the message, thereby increasing thetotal rate of protein synthesis and accelerating cell
growth. A similar model for codon preference has
been proposed by Andersson and Kurland (16).

Well-adapted codons could also confer a metabolic
advantage by reducing the load of misfolded proteins (26, 27). In either case, increasing a gene’s
codon adaptation should not increase its expression. High codon adaptation in a gene should,
however, improve cellular fitness to an extent
that depends on its expression level.
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Leucine-Rich Repeat Protein Complex
Activates Mosquito Complement in
Defense Against Plasmodium Parasites
Michael Povelones, Robert M. Waterhouse, Fotis C. Kafatos, George K. Christophides*
Leucine-rich repeat–containing proteins are central to host defense in plants and animals. We showthat in the mosquito Anopheles gambiae, two such proteins that antagonize malaria parasite
infections, LRIM1 and APL1C, circulate in the hemolymph as a high-molecular-weight complex
held together by disulfide bridges. The complex interacts with the complement C3-like protein,
TEP1, promoting its cleavage or stabilization and its subsequent localization on the surface of
midgut-invading Plasmodium berghei parasites, targeting them for destruction. LRIM1 and APL1C
are members of a protein family with orthologs in other disease vector mosquitoes and appear to
be important effectors in innate mosquito defenses against human pathogens.
nopheline mosquitoes are the vectors ofmalaria that is caused by protozoan
Plasmodium parasites and claims the livesof 1 to 3 million people annually (1). Parasites
enter female mosquitoes during blood feeding anddevelop into ookinetes that on traversing the midgut
epithelium and encountering the hemolymph areattacked by the mosquito immune system (2). A

A

Division of Cell and Molecular Biology, Department of Life
Sciences, Imperial College London, Exhibition Road, London
SW7 2AZ, UK.
*To whom correspondence should be addressed. E-mail:g.christophides@imperial.ac.uk

258

few survive to transform into oocysts, which generate sporozoites capable of reinfecting humans.
The Anopheles gambiae leucine-rich repeat(LRR)–containing protein LRIM1 is a potent
Plasmodium berghei antagonist that is also involved in phagocytosis of bacteria and melanization of parasites and Sephadex beads (3–5).
Since the discovery of LRIM1, two other LRR
proteins, APL1 (6) and LRRD7 (7), have beenshown to limit Plasmodium infection. LRIM1
and APL1 (also called LRIM2) also mediate
Plasmodium lysis and melanization in Anopheles
quadriannulatus species A, contributing to thenatural refractory phenotype of these mosquitoes

10 APRIL 2009

VOL 324SCIENCE

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Arnold, Proc. Natl. Acad. Sci. U.S.A. 102, 14338 (2005).
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28. N. Jacques, M. Dreyfus, Mol. Microbiol. 4, 1063 (1990).
29. S. Ghaemmaghami et al., Nature 425, 737 (2003).
30. We thank A. Helwak, J. Lucks, P. Sharp, L. Hurst, andmembers of the Plotkin and Tollervey laboratories forconceptual input; and A. Heath (Sigma), A. Aitken,
J. Skerker, T. Shimizu, I. Iost, and J. Han for reagents,
protocols, and equipment. Support provided by the
Burroughs Wellcome Fund, James S. McDonnell
Foundation, Penn Genome Frontiers Institute, andDefense Advanced Research Projects Agency “FunBio
Program” HR0011-05-1-0057 (J.B.P.); Foundation for
Polish Science and European Molecular Biology
Organization (G.K.); Wellcome Trust/BBSRC grant
BB/DO19621/1 (D.T.).

Supporting Online Materialwww.sciencemag.org/cgi/content/full/324/5924/255/DC1
Materials and Methods
Figs. S1 to S9References
List of Oligonucleotides
23 December 2008; accepted 17 February 200910.1126/science.1170160

to parasites (8). The APL1 locus encompasses
three distinct yet highly similar genes, which
originate from recent duplications. Of these,
APL1C is the sole P. berghei antagonist; APL1A
and B do not influence infection intensities (9).
We used single and double gene knockdowns (KDs) to compare the quantitative effectsof LRIM1 and APL1 on P. berghei infections insusceptible A. gambiae and obtained an increaseby a factor of ~50 in parasite infection intensities
and no significant difference between double
and single KDs (Fig. 1A). Quantitative real-timepolymerase chain reaction confirmed equal andefficient silencing of LRIM1 and APL1 (using
primers amplifying all three APL1 genes) and nodetectable cross-silencing (fig. S1). The knownrole of LRIM1 in melanization (3, 4) prompted
us to examine whether APL1 is also required inthis immune reaction. We silenced LRIM1 andAPL1 in A. gambiae L3-5 mosquitoes, which melanize virtually all invading P. berghei ookinetes(10). Indeed, both KDs produced identical phenotypes: no melanization and an increase by afactor of ~80 in live oocyst numbers (Fig. 1, B
and C). Thus, in both Plasmodium-susceptible and-refractory mosquitoes, the effects of LRIM1 andAPL1 are qualitatively and quantitatively indistinguishable, suggesting that the two genes function
in a single genetic pathway that is disrupted bysilencing either gene alone.
Sequencing multiple LRIM1 and APL1C
cDNA clones from adult female mosquitoes verified their strong similarities in gene architecture.

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REPORTSwww.sciencemag.org/cgi/content/full/324/5924/255/DC1

Supporting Online Material for
Coding-Sequence Determinants of Gene Expression in Escherichia coli
Grzegorz Kudla, Andrew W. Murray, David Tollervey, Joshua B. Plotkin**To whom correspondence should be addressed. E-mail: jplotkin@sas.upenn.edu
Published 10 April 2009, Science 324, 255 (2009)
DOI: 10.1126/science.1170160

This PDF file includes
Materials and Methods
Figs. S1 to S9References
Other Supporting Online Material for this manuscript includes the following:(available at www.sciencemag.org/cgi/content/full/324/5924/255/DC1)
List of Oligonucleotides

SUPPORTING ONLINE MATERIALSMETHODS

Gene synthesis
In order to synthesize a library of genes with randomized codon usage, we reversetranslated the EGFP protein sequence (gi: 1543069) into a degenerate nucleotide sequence.
Four reverse-translation methods were used to obtain degenerate sequences with varying
GC content bias and E. coli codon adaptation index. The degenerate nucleotide sequenceswere split into three “thirds” of length approximately 240 nt each, and each third wasfurther split into a set of 6 or 8 overlapping degenerate oligonucleotides (see Fig. 1). A
text file containing these oligonucleotide sequences is included in the online supplement(1170160_SOM_oligos.txt). Desalted degenerate oligonucleotides were obtained from
Sigma Genosys and assembled into thirds by two rounds of PCR using Phusion
polymerase (Finnzymes) as described (1). Standard PCR conditions were: denaturation, 10
seconds, 98°C; annealing, 30 seconds, 45°C; elongation, 20 seconds, 68°C. PCR products
were digested at both ends with BamHI and XbaI (1st third), XbaI and HpaI (2nd third) or
HpaI and EcoRI (3rd third), sequentially cloned into a Gateway entry vector (pGK3, see
below), and sequenced after each cloning stage. In addition, some GFP fragments wereobtained by PCR from the pEGFP-N2 and pGFP-N2 constructs (2).
We cloned and sequenced 122 1st-third fragments, 159 2nd-third fragments, and 65
3rd-third fragments, for a total of 84,774 sequenced base pairs. 56% of the synthetic
fragments encoded the correct protein sequence. The average error rate of 1 per 458 basepairs was similar to error rates obtained in standard, non-degenerate gene synthesis (3).
We obtained 154 synthetic GFP genes encoding the wildtype protein sequence, along withseveral frame-shift mutants later used as controls. Some genes shared one or two synthetic
fragments in common.

Supplement Page 1

Bacterial strains and plasmids
The E.coli strains used for library expression were BL21(DE3) [F– ompT hsdS(rB–mB–) dcm+ gal λ(DE3)] and DH5α [F'/endA1 hsdR17(rK-mK+) supE44 thi-1 recA1 gyrA
(Nalr) relA1 D(laclZYA-argF)U169 deoR (F80dlacD(lacZ)M15)]. The DB3.1 strain(Invitrogen) was used for cloning and amplification of Gateway expression plasmids, andthe DH5α strain was used for cloning and amplification of all other plasmids. pGK3 is a
Gateway entry vector. It was obtained by partially digesting pENTR-2B (Invitrogen) withXbaI and NotI, and ligating the 2701-nt fragment with annealed 5’-phosphorylated oligos:
ggccctgcacattcagactcgagc and ctaggctcgagtctgaatgtgcag. pGK8 is an IPTG-inducible
Gateway expression vector with a T7 polymerase promoter. It was generated by inserting
the Gateway cassette RfA (Invitrogen) into the pET15b vector (Novagen) digested withXbaI and XhoI. pGK14 and pGK16 are Gateway expression vectors with bacterial
polymerase promoters. pGK14 is arabinose-inducible and was constructed by inserting the
RfA cassette into pBAD33 (4) digested with SmaI, while pGK16 is IPTG-inducible andwas generated by inserting the RfA cassette into pTRC99a (5) digested with EcoRI.
To clone the GFP genes from the pGK3 entry vector to pGK8, pGK14 or pGK16
expression vectors, Clonase II (Invitrogen) was used according to manufacturer’s
instruction. GFP genes cloned into pGK8 or pGK14 retained their original start codons,
but recombination into pGK16 resulted in an in-frame fusion of 28 codons to the 5’ end of
the GFP genes. The tagged constructs featured weak mRNA structure near the start codon(folding energy ΔG = -6.1 kcal/mol, as compared to an average energy -9.7 kcal/mol
among non-tagged genes) and low codon adaptation (CAI 0.2, as compared to an average
CAI 0.3 for codons 1-28 of non-tagged genes).
RNA and protein measurements
Bacteria were transformed using Gateway recombination reactions. Four replicatecolonies for each GFP were inoculated into LB medium with 50 ug/mL ampicillin andgrown overnight to saturation in roller drums. For fluorescence measurements, cells werediluted 1:15 into 150 uL fresh medium in a 96-well plate, grown on a vibrating platformshaker (Heidolph) for 1 hour at 37°C, and induced with 1 mM IPTG (pGK8 or pGK16
plasmids) or 2% L-arabinose (pGK14 plasmids). GFP fluorescence and OD wereSupplement Page 2

measured in a Spectramax microplate reader (Molecular Devices). Fluorescence variation
was consistent across a broad range of experimental conditions (Fig. S3).
To quantify GFP independently of its fluorescence activity, we used Coomassie
staining and western-blotting. Nine GFP constructs were assayed by western blot, and 123
constructs by Coomassie. 50 uL samples of BL21(DE3) cells expressing GFP from thepGK8 plasmid were taken 3 hours after 1 mM after IPTG induction. The cells were
centrifuged, re-suspended in 50 uL distilled water, mixed with 50 uL SDS-PAGE loading
buffer with SDS and 2-mercaptoethanol, and boiled for 5 minutes. The samples were thenseparated on 4-12% acrylamide NU-PAGE Novex Bis-Tris gels. R-250 Coomassie stainedgels were scanned and quantified with the Aida image analysis software. Selected bands
from noninduced cells were used to normalize GFP staining intensity between gels.
Western blotting was performed using a polyclonal anti-GFP antibody (#2555 Cell
Signaling) and the AlexaFluor 647-labelled donkey anti-rabbit antibody (A31573,
Invitrogen). Blots were scanned using a Fuji imager and analyzed with the Aida software.
We analyzed mRNA expression by northern blotting 79 GFP constructs. For RNA
measurements, overnight cultures were diluted 1:15 into 3 mL fresh medium, rotated for 1
hour at 37°C, induced with IPTG, rotated for 1.5 more hours, and 2 mL samples were
centrifuged and frozen in liquid nitrogen. Cells were then lysed on ice in 0.5 mL lysis
buffer (20 mM sodium acetate, 0.1% SDS, 1 mM EDTA, pH5.5), extracted twice withphenol (at 65°C) and once with chloroform (at room temperature). RNA was thenprecipitated with ethanol/sodium acetate, glyoxylated, separated on 1.2% agarose/BPTE
gels and transferred to positively charged nylon membranes (Amersham Biotech) as
described (6). 4 ug total RNA was loaded in each lane, and equal loading was confirmedby ethidium bromide staining and probing against 23S rRNA as described (7). For GFP
mRNA quantification, fragments of the 5’ and 3’ UTRs were amplified from pGK8GFP008 using the following primers: 5’UTR_U, ggggaattgtgagcggataa; 5’UTR_L,
gtcgactgaattggttccgg; 3’UTR_U, agtggtgatatcaagcttat; 3’UTR_L, tatgctagttattgctcagc.
Random-primed Klenow probes were then prepared and hybridized as described (6). Blots
were analyzed using a Fuji imager and Aida software.

Supplement Page 3

Bacterial growth rates
We assessed growth rates by optical densities of cells subsequent to induction. The
addition of 1 mM IPTG retarded growth in all clones, with effects ranging from a 2-folddecrease in growth rate to complete growth arrest for different GFP variants. At threehours post induction, cell densities varied 2-fold across the library.

The correlation

between CAI and fluorescence is even weaker after normalizing by optical density, i.e.
fluorescence per cell; and the correlation between 5’ folding energy and fluorescence iseven stronger after normalizing by optical density.
Quantifying rates of protein mis-folding
We used Coomassie measurements to analyze the possible influence of codon
adaptation on rates of protein mis-folding in our experiments.

For this purpose, wequantified the rate of mis-folding, per GFP protein, as one minus the ratio of fluorescence(i.e. functional protein) to Coomassie (i.e. total protein). The Coomassie staining method
is insensitive to changes in native protein conformation (the protein is denatured prior toelectrophoresis) or to minor sequence changes caused by occasional mis-translation. We
first converted Coomassie and fluorescence intensities into the same scale, mg GFP per
ml, based on standardization curves obtained from serial dilutions. Both Coomassie levels
and fluorescence levels exhibited linear responses to protein concentration across the range
of intensities observed in our experiments. The resulting correlation between CAI and one
minus fluorescence/coommassie was not significant (r = -0.073, p = 0.42), indicating thatcodon adaptation did not correlate with mis-folding rate in our experiments.
We alternatively quantified mis-folding rate as the ratio of mRNA (i.e. all message)
to fluorescence (i.e. functional protein). CAI was not significantly correlated with thisratio either (r = 0.09, p = 0.42).

Fluorescence normalization
We normalized fluorescence levels across experiments using a set of 12 control
GFP constructs (including 2 non-fluorescent frame-shift mutants), grown in quadruplicate
on every 96-well plate. A reference experiment was chosen arbitrarily, and fluorescencevalues from each subsequent experiment were compared to the reference. Least square

Supplement Page 4

linear fits were calculated, and the resulting slope and intercept values were used fornormalization.
Fluorescence values were highly reproducible across biological replicates.
Comparing the 10 non-mutant control GFPs across 18 plates resulted in an averagecorrelation coefficient of 0.979. Similarly, a non-parametric ANOVA indicated asignificantly larger fluorescence variance across GFP constructs than across replicates (p <1E-15).

Single-mutation GFP constructs
We performed a separate silent mutagenesis experiment on the first five codons of
one of the genes, GFP_020. We identified five cases in which a single silent mutation
altered 5’ folding energy, by at least 3.9 kcal/mol. Among the mutants, the gene withweaker predicted 5’ structure had higher expression in four cases (by 18%, 35%, 49% and83%), and lower expression in one case (by 9%). We also obtained a pair of genes thatdiffered by two silent sites (nt 6 and 15). Again, these mutations had a substantial effect on
the predicted 5’ folding energies (dG = -11.7 and dG = -7.7, respectively), and theconstruct with stronger structure exhibited a much lower protein level (fl = 2473 and fl =8051, respectively). These results confirm that the relationship between folding energy andexpression extends to constructs that differ by only a few mutations in their 5’ region.
Pairs of GFP genes differing by a single mutation:
nucleotides 1-15
(seq1)ATGGTGAGCAAGGGG
ATGGTGAGCAAGGGAATGGTGAGCAAGGGG
ATGGTGAGCAAGGGG
ATGGTGAGCAAGGGC

nucleotide substitution(seq1 → seq2)
G6 -> T
G6 -> T
C9 -> T
G6 -> CG6 -> T

energy change(seq2 - seq1)
6.2 kcal/mol
6.2 kcal/mol
3.9 kcal/mol
6.1 kcal/mol
4 kcal/mol

fl change(seq2-seq1)/seq1
35%
18%
49%
-9%
83%

Statistical analyses
All statistical analyses were performed in the R software package (8). Correlations
reported in the main text are quantified by the Spearman rank correlation coefficient andits associated p-value.
Supplement Page 5

We calculated the frequency of rare codon pairs using the CPB metric of Coleman
et al. (14). The frequency of rare pairs was not significantly correlated with fluorescence (r= 0.07, p = 0.35). Similarly, the palindromic sequence CTAG is strongly underrepresented
in the E. coli genome (16) , but the frequency of this motif in a GFP gene did not correlatewith its fluorescence (r = -0.12, p = 0.16).
For the multiple regressions, sequence-derived covariates associated with each
GFP construct included: the codon adaptation index (9) calculated for the entire or partial
coding sequence; the number of rare codons (sites with CAI < 0.1) in the coding sequence;
the number of pairs of consecutive rare codons; the length of the longest stretch ofconsecutive rare codons; the GC3 content of the coding sequence; the number of predictedrho-independent transcription termination signals, calculated using the RNAMotif program(10); the propensity for conformation changes into Z-DNA, calculated using the Zhunt
program (11); the number of potential RNAse E cleavage sites, calculated as in (12); theminimum free energy of predicted mRNA secondary structure, for the entire genesequence or portions of the sequence, calculated using the hybrid-ss-min program (13)(version 3.4, NA = RNA, t = 37, [Na+] = 1, [Mg++] = 0, maxloop = 30, prefilter = 2/2); a
categorical variable indicating whether or not the first third of the GFP was synthesizedusing the “optimized” EGFP sequence (gi: 1543069).
We performed a multiple regression in order to quantify the relative importance ofthe various predictor variables in determining rank fluorescence levels. The output of thisregression, shown below, highlights the predominant influence of mRNA folding energy
near the start of the GFP sequence (nt -4 to +37):
Multiple Regression
Coefficients
FreeEnergy-4to+37
CAI
GC3
nsites_lowCAI
npairs_lowCAI
nstretches_lowCAI
longeststretch_lowCAIFirstThirdEGFP
Terminator.Predicted

Estimate
7.462
152.911-56.326
2.575-5.3242.746
5.551-17.627
0.729

Std Error T
Pr(>|t|)1.181
6.318 3.20E-09
136.360
1.121 0.26402
51.445-1.095 0.27543
1.155
2.230 0.02731
1.798-2.962 0.00358
4.070
0.675 0.501
4.143
1.340 0.18235
9.998-1.763 0.08002
3.362
0.217 0.82868

Supplement Page 6

zDNA.Predicted
N_RNAaseE

1.269-0.836

8.023
0.320

0.158 0.87453-2.614 0.00991

Residual standard error: 29.42 on 142 degrees of freedom
Multiple R-Squared: 0.5961, Adjusted R-squared: 0.5649
F-statistic: 19.05 on 11 and 142 DF, p-value: < 2.2e-16
In order to control for possible confounding effects arising from collinearity ofpredictor variables we also performed an analogous principal component regression. The
four principal components that explained the most variance in rank fluorescence areshown below. The tables below indicate the loadings of the predictor variables on each of
the four principal components, the amount of variance in fluorescence levels explained byeach component, and the total amount of variance in fluorescence explained by eachpredictor variable, summed across all components (as in (15)):
Principal Component Regression
Loadings
Comp1
FreeEnergy-4to+37
0.956
CAI
0
GC3
0
nsites_lowCAI
0.001
npairs_lowCAI
0.027
nstretches_lowCAI
0.008
longeststretch_lowCAI 0.001
FirstThirdEGFP
0.005
Terminator.Predicted 0.002
zDNA.Predicted0
N_RNAaseE0% Variance
Component Explained:
Comp1
47.23
Comp2
4.99
Comp3
4.15
Comp4
1.55
Comp5
0.55
Comp6
0.37
Comp7
0.24

Comp2
0
0
0
0.249
0.07
0.003
0.001
0
0
0
0.676

Comp3
0
0
0
0.64
0.047
0.001
0
0
0
0
0.312

Comp4
0.005
0.001
0
0
0
0
0.014
0.977
0
0.002
0

Predictor Variable
FreeEnergy-4to+37
CAI
GC3
nsites_lowCAI
npairs_lowCAI
nstretches_lowCAI
longeststretch_lowCAISupplement Page 7

Total%
Variance
Explained
45.175
0.372
0.22
3.972
1.958
0.545
0.542

Comp8Comp9Comp10
Comp11

0.22
0.11
0.11
0.02

FirstThirdEGFP
Termintor.Predicted
zDNA.Predicted
N_RNAaseE

1.755
0.282
0.114
4.68

We also performed multiple regressions of rank fluorescence against GC3, CAI,
and free energy associated with mRNA folding, each calculated in a window from nt -4
through nt 37. (The “A” in ATG is assigned nt 0.) Both the standard and principal
component versions of this regression indicate that mRNA folding energy is the dominant
determinant of expression levels:
Multiple Regression
Coefficients

Estimate

Std Err T

Pr(>|t|)

FreeEnergy-4to+37
GC3-4to+37
CAI-4to+37

0.45472-0.29785
0.10780.09016 5.043
0.09296 -3.204
0.06272 1.719

1.30E-06
0.001655
0.087684

Residual standard error: 31.48 on 150 degrees of freedom
Multiple R-Squared: 0.5115,
Adjusted R-squared: 0.5017
F-statistic: 52.35 on 3 and 150 DF, p-value: < 2.2e-16
Principal Component Regression
LOADINGSFreeEnergy-4to+37
GC3-4to+37
CAI-4to+37

Component
Comp1
Comp2
Comp3

%
Variance
Explained:
42.24
6.12
0.6

Comp1
0.997
0.003
0.000

Comp2
0.001
0.471
0.527

Comp3
0.002
0.526
0.473

Predictor Variable
FreeEnergy-4to+37
GC3-4to+37
CAI-4to+37

Total
Variance
Explained
42.1183.322
3.508

We also performed multiple regressions of fluorescence levels against mRNA levels,
folding energy, and CAI. Folding energy explained significantly more variation influorescence levels than mRNA levels explained:
Supplement Page 8%

Multiple Regression
Coefficients

Estimate

Std Err t

Pr(>|t|)

FreeEnergy-4to+37
Northern
CAI

0.47425
0.37767
0.14690.08785 5.399
0.08723 4.33
0.08293 1.771

7.58E-07
4.56E-05
0.0806

Residual standard error: 16.68 on 75 degrees of freedom
Multiple R-Squared: 0.4918,
Adjusted R-squared: 0.4715
F-statistic: 24.19 on 3 and 75 DF, p-value: 4.693e-11
Principal Component Regression
Loadings
FreeEnergy-4to+37
Northern
CAI

Comp1
0.54
0.401
0.059

Comp2
0.017
0.233
0.75

Comp3
0.443
0.366
0.191

Component%
Variance
Explained

Predictor
Variable

Total%
Variance
Explained

Comp1

38.46

FreeEnergy-4to+37 22.636

Comp2

11.23

Northern

19.41

Comp3

3.75

CAI

11.394

Sliding window analysis of local folding energy and fluorescence
We analyzed the relationship between fluorescence intensity and folding energy insliding windows across the GFP sequence. We chose a window of size 42 bases, and we
slid the location of the window in increments of three bases across the gene sequence,
including 5’ UTR. We calculated the Spearman correlation coefficient between predictedfolding energy within each window and fluorescence intensity, across the library of GFP
genes. We identified many regions of highly significant positive correlations, including theregion from nt -4 to nt 37 for which folding energy explains nearly half of the variation influorescence intensity (r = 0.665, p < 1E-15, Fig 2b).

Supplement Page 9

SUPPLEMENTARY FIGURE CAPTIONSSUPPLEMENTARY FIGURE 1. Distance tree of synthetic GFP genes. An un-rooted
tree generated by neighbor-joining, based on the pairwise hamming distances among 168
synthetic GFP genes.
SUPPLEMENTARY FIGURE 2. GC content. GC content in a moving window acrossthree example GFP constructs.
SUPPLEMENTARY FIGURE 3. Reproducibility of fluorescence measurements instandard experimental conditions. pGK8-GFP constructs were induced with 1 mM
IPTG and grown for 3h at 37°C. (A) 24 different pGK8-GFP constructs were grown in a
96-well plate, each in 4 replicates. Groups of neighboring bars of same color represent
independent replicate clones with the same GFP sequence. (B) Average fluorescence of aset of 12 pGK8-GFP constructs, including two non-fluorescent mutants (bars 11 and 12).
Error bars represent one standard error, n = 14 to 18 experiments.
SUPPLEMENTARY FIGURE 4. Reproducibility of fluorescence measurements
across experimental conditions. (A) Fluorescence 3h after induction (diamonds) or 6hafter induction (triangles) as a function of fluorescence 1.5h after induction. pGK8-GFP
clones were induced with 1 mM IPTG and grown at 37°C. (B) Fluorescence in M9(minimal) medium and LB (rich) medium. pGK8-GFP clones were induced with 1 mM
IPTG and grown for 3h at 37°C. (C) Fluorescence from T7 promoter (pGK8) and bacterial
promoter (pGK14). pGK14-GFP constructs were transformed into DH5a cells, inducedwith 2% L-Arabinose and grown for 3h at 37°C., while pGK8-GFP constructs weretransformed into BL21-DE3 cells, induced with 1 mM IPTG and grown for 3h at 37°C.
(D) Fluorescence as a function of inducer concentration. pGK8-GFP constructs were
induced with 1 mM or 0.02 mM IPTG and grown for 3h at 37°C.
SUPPLEMENTARY FIGURE 5. Western and FACS measurements. (A) Correlation
between GFP fluorescence measured by FACS and by spectrofluorometer. (B) Western
blot analysis of GFP protein levels. (C) Correlation between GFP protein levels andfluorescence. Protein levels were assessed from the western blot analysis shown in panel(B). pGK8-GFP constructs and standard experiment conditions were used in this figure.
Supplement Page 10

SUPPLEMENTARY FIGURE 6. The site-specific influence of silent nucleotide
variation on protein levels, across the GFP sequence. At each silent position we plot
the statistical significance (negative log p-value) of a regression comparing nucleotide
identity and fluorescence levels among the 154 GFP constructs. At the codon positions
above the dashed line, the choice of G, C, A, or T in the third base-pair was significantly
correlated with fluorescence, after Bonferroni correction for multiple hypotheses.
SUPPLEMENTARY FIGURE 7. Free energy and gene expression under a bacterial
promoter. The relationship between rank 5’ mRNA folding energy and rank fluorescenceamong pGK14-GFP constructs (Spearman r = 0.77 p < 4E-16).
SUPPLEMENTARY FIGURE 8. Predicted mRNA secondary structures forrepresentative examples of GFP constructs. The figure shows three GFP constructs
with high fluorescence (top row), and three GFP constructs with low fluorescence (bottomrow). Along with the secondary structure of region nt -4 through nt +37, the figure also
indicates the free energy of each secondary structure (kcal/mol) and the fluorescence of thecorresponding GFP construct. Note that GFP constructs expressed at low levels featuremRNA structures with large hairpin loops, whereas GFP constructs expressed at highlevels feature structures with many unpaired nucleotides.
SUPPLEMENTARY FIGURE 9. Northern blot analysis of GFP mRNA levels. mRNA
from duplicate pGK8-GFP clones was isolated and analyzed by northern blotting, usingprobes against GFP 5'UTR and 3'UTR sequences common to all clones. Bottom panel,
ethidium bromide staining of total cellular RNA. Note the variable degradation patterns ofmRNAs across different GFP constructs. NI denotes no inducer.

Supplement Page 11

Supplementary References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.

J. S. Han, J. D. Boeke, Nature 429, 314 (May 20, 2004).
G. Kudla, L. Lipinski, F. Caffin, A. Helwak, M. Zylicz, PLoS Biol 4, e180 (Jun,
2006).
S. J. Kodumal et al., Proc Natl Acad Sci U S A 101, 15573 (Nov 2, 2004).
L. M. Guzman, D. Belin, M. J. Carson, J. Beckwith, J Bacteriol 177, 4121 (Jul,1995).
E. Amann, B. Ochs, K. J. Abel, Gene 69, 301 (Sep 30, 1988).
J. Sambrook, D. Russell, Molecular cloning (Cold Spring Harbor Laboratory Press,
Cold Spring Harbor, New York, 2001), pp.
A. El Hage, J. H. Alix, Mol Microbiol 51, 189 (Jan, 2004).
R Development Core Team, R: A language and environment for statisticalcomputing (R Foundation for Statistical Computing, Vienna, Austria, 2005), pp.
P. M. Sharp, W. H. Li, Nucleic Acids Res 15, 1281 (Feb 11, 1987).
E. A. Lesnik et al., Nucleic Acids Res 29, 3583 (Sep 1, 2001).
P. S. Ho, M. J. Ellison, G. J. Quigley, A. Rich, Embo J 5, 2737 (Oct, 1986).
J. A. Bernstein, A. B. Khodursky, P. H. Lin, S. Lin-Chao, S. N. Cohen, Proc Natl
Acad Sci U S A 99, 9697 (Jul 23, 2002).
N. R. Markham, M. Zuker, Nucleic Acids Res 33, W577 (Jul 1, 2005).
J. R. Coleman et al., Science 320, 1784 (Jun 27, 2008).
J. B. Plotkin, H. B. Fraser, Mol Biol Evol 24, 1113 (May, 2007).
C. Burge, A. M. Campbell, S. Karlin, Proc Natl Acad Sci U S A 89, 1358 (Feb,1992

Supplement Page 12

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.... ....
.... .......GFP
.....
...
....
05. ........
2
P 4
GF P22 0........
0.....GF.... ... ...GFP2.....
....... ......
.
8
0
1. .
FP2
G
.
.
P16 ..
.
.
.
...
..
..GF
...... FP181....
G........ P182........
F........G..GFP185........
......
84........
..GFP1FP189........
...........
...G........GFP183........
........GFP18
8.
........GF .......
P186....
....
..
.............GFP171........
.GFP...... ....... 174.......
.
..GF .GFP
1
P17
5.... 73........
.
....
............GF...G P17... FP1 2......
..
.... ......G ...........G 87.... ..
...
F ..G FP ....
...... .GF P17 FP1 178.
..
...... P1 9..
860... 0... .....
..G.G
....
FFPP1 ...... ...
.
16 59. ..
2. .....
... . .
...
.

.
...
...
4 . ..
16 ......
FP 76
.G P1
... GF.......
.....

FP22
7......
..

....
.

...
... .....
.
...G ... ....G GF
FP ..... F P4
1 G P 1.... 2..... FP2 51.. .....
....G ... 5 .. ..
.....
.... .... .
...GF........ FP40.P22.. ....
.
.
GFP ..... ....
2. .
........GF 1........
P26......
..
........GFP23........

..
... ..
... ......
0
2 0.
FPFP6 ..... .
. . .....
..G
.G
.
.
4
.
.
. .
1 8..
.......
FPP1 .....
...GGF 4...
.
.
3
....
... . .GFP
..
.
.
.
..

. . ..

.. ........G.......... ..GFPFP36........
..... ........GFP 134.......
..............GF ..GFP73........ .
. . P............GGFP 55... 114...
.....
... ..G FP 44 .....
..G F 58 .....
FP P1 ..... ...
45 26. ...
... ....
... ...
..

........GF
P135.....
........GFP42...........
....
...
.G
F
P
89........
........GFP
84...
.....
.....
........GFP83...
85............
........GFP..
FP941.............. .
9 .. .
........GGFP 86.. .....
..........GFPP81.......
...... ....GF 87.. .......... ...
.... .GFP P32...............
F 7 0 ..
...
.... .....G FP2 P3 9..
... G F 2
. . . . ..G F P.... ...... ...G
..
...

..
... .....
... .....G.GF.
.... ....G FPP31
2.... ....G FP1 8........
.... FP 31 ... ...
G.
..... ...... FP 129 .... ..
...G .G 1 .. ..
...... FP11FP6430... ........
..GF 5.. ... ...
P5 ... ... ..
..
........G ......GFP64........... ..
6....
FP142
........GF ........ ....
P68........
........GFP79........
........GFP65........
........
........GFP137
P78........ ..
........GF FP69.......
........G141...............
GFP 70 ...
........ ....GFP74......... .
.
.
.
. GFP 3......... .....
1 .. .. .
...
..... GFP1P37.P71 ........
F F .. ..
...
.. ... ....G ...G P618...
.... ..... .GF P3.
... F... .....G
.
..

.
38
... FP2.
.
... ..G
94 ....
P1 ..

...

...

.

GC3

1

0.5

0
150

300

450

Position (bp)Supplementary Figure 2

600

Supplementary Figure 3

6

53

GF
P

P7

GF

42

GF
P

38

GF
P

7

6

P3

GF

GF
P3

27

GF
P

18

GF
P

3

P1

2

GF
P1

8

GF
P

GF

0

P3

P3
GF
P8
GF
P1
2
GF
P1
3
GF
P1
8
GF
P2
7
GF
P3
6
GF
P3
7
GF
P3
8
GF
P4
2
GF
P7
6
GF
P5
3
GF
P2
0
GF
P2
1
GF
P2
2
GF
P2
5
GF
P2
6
GF
P2
8
GF
P2
9
GF
P3
0
GF
P3
1
GF
P3
4
GF
P4
1

GF

0

GF

A
10000

8000

6000

4000

2000

B
10000

9000

8000

7000

6000

5000

4000

3000

2000

1000

A

B
1500
R2 = 0.97

R2 = 0.86

15000

10000

R2 = 0.99

5000

fluorescence (M9)fluorescence (3h or 6h)

20000

0

1000

500

0
0

1000

2000

3000

0

fluorescence (1.5h)

5000

10000

fluorescence (LB)

DC150000fluorescence (1 mM IPTG)

4000
2

fluorescence (DH5a)

R = 0.67
3000

2000

1000

R2 = 0.83
100000

50000

0

0
0

5000

10000

0

50000

100000fluorescence (0.02 mM IPTG)

fluorescence (BL21)

Supplementary Figure 4

A

B
30

fluorescence (FACS)

1500
R2 = 0.95

Western, anti-GFP

15

1000

500

0
0

2000

4000

6000

8000

10000

fluorescence (fluorometer)C
protein amount (western blot)

12000
10000

R2 = 0.97

8000
6000
4000
2000
0
0

5000

10000

fluorescence (fluorometer)

Supplementary Figure 5

Significance (-log10 p)

10

8

6

4

2

0
0

200

400

Position (nt)Supplementary Figure 6600

Rank fluorescencer2 = 0.59
60

40

20

0
20

40

Rank free energy

Supplementary Figure 7

60

a
5’

a

c

c

g

g
g

10

a

g

c

a

c

t

t

g

t

g

g

g

g

a

g
g

30

40

g

c

g

GFP 61
Fl = 8740
dG = -4.3

a
a

t

g
c

a

t

g

g

g

c

c
t

g

c

g

a

t

g

20

a

a

a

a
5’

t

t

20

10

a

g

a

g
g

g

a
t

t

c

a
t

g

a
a

t

t
c

a

g

GFP 145
Fl = 278
dG = -15.4

a

a

a

a

30

g

g

t

g

t

t
40

g

20

g
a

t

30

a

a

g
g

g

c

t

a

c

t

5’

g
t
g
g

t
c

g

a

3’

a
a

a

g

t

g

a

t

g

a

10

c

c

a
g

g

c
g

t

c

c
t

g

a
g

a
g

a

c

c

t

g

g

g

GFP 205
Fl = 1513
dG = -14.7

c
g

a

10

t

5’

a

3’

3’

40

20

g

a

a

GFP 51
Fl = 8565
dG = -5.5

g

c

c
t

Supplementary Figure 8

30

g

a

t

t

c

t

t

t

t

g

a

g

g

a

a

c

t

a
40

20

g

a
g

g

GFP 40
Fl = 8632
dG = -6.1

t

c

a

g

c

a

g

a

t

a

g

5’

g

g

3’

a

g

a

t

g

a

g

g

c

20

g

t

t

a

t

c

aa

a

t

t

a

c

g

g

a

t

g

g

g

a

g

t

a

t

g

a

a

c

g

t

g

c

c

a

t

t

g

g

t

g

t

3’

t

g

t

a

g

GFP 78
Fl=874dG = -12.6

t

a

g

10

c

c

g

30

c

t

a

a

t

a

10

a

5’

40

40

3’

GFP_3’UTR

GFP_5’UTR

EtBr

2

3

8

12

13

25

27

34

36

Supplementary Figure 9

36
N.I.

51 54 GFP_ID