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A Link Between Integral Membrane Protein Expression and
Simulated Integration Efficiency
Stephen S. Marshall
#
,
Michiel J. M. Niesen
#
,
Axel Müller
,
Katrin Tiemann
,
Shyam M. Saladi
,
Rachel P. Galimidi
,
Bin Zhang
,
William M. Clemons Jr
1,2
, and
Thomas F. Miller III
1
Department of Chemistry and Chemical Engineering, California Institute of Technology,
Pasadena, CA 91125, USA
#
These authors contributed equally to this work.
Abstract
Integral membrane proteins (IMP) control the flow of information and nutrients across cell
membranes, yet IMP mechanistic studies are hindered by difficulties in expression. We investigate
this issue by addressing the connection between IMP sequence and observed expression levels. For
homologs of the IMP TatC, observed expression levels widely vary and are affected by small
changes in protein sequence. The effect of sequence changes on experimentally observed
expression levels strongly correlates with the simulated integration efficiency obtained from
coarse-grained modeling, which is directly confirmed using an
in vivo
assay. Furthermore,
mutations that improve the simulated integration efficiency likewise increase the experimentally
observed expression levels. Demonstration of these trends in both
Escherichia coli
and
Mycobacterium smegmatis
suggests that the results are general to other expression systems. This
work suggests that IMP integration is a determinant for successful expression, raising the
possibility of controlling IMP expression via rational design.
Introduction
The central role of IMPs in many biological functions motivates structural and biophysical
studies that require large amounts of purified protein, often at considerable costs in terms of
both materials and labor. A key obstacle is that only a small percentage of IMPs can be
overexpressed (
i.e.
heterologously produced at levels conducive to further study) (
Lewinson
et al., 2008
). While extensive efforts have shown promising results for individual IMPs,
including those focusing on expression conditions, host modification, and directed evolution
(Reviewed in (
Schlegel et al., 2010
,
Wagner et al., 2006
,
Scott et al., 2013
)), none of these
1
Correspondence: tfm@caltech.edu (TFM).
2
Lead Contact: clemons@caltech.edu (WMC)
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Author contributions
S.S.M., M.J.M.N., A.M., W.M.C. and T.F.M. designed research; S.S.M. and M.J.M.N. performed research; S.S.M., M.J.M.N., A.M,
K.T., S.M.S., R.P.G., and B.Z. contributed new reagents/analytic tools; S.S.M., M.J.M.N., A.M., W.M.C. and T.F.M. analyzed data;
and S.S.M., M.J.M.N., W.M.C. and T.F.M. wrote the paper.
HHS Public Access
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Published in final edited form as:
Cell Rep
. 2016 August 23; 16(8): 2169–2177. doi:10.1016/j.celrep.2016.07.042.
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has proven broadly applicable, even among homologs of a given IMP. In general, the
determinants for IMP expression are poorly understood, leading to the prevailing opinion
that problems in membrane protein expression must be addressed on a case-by-case basis.
Closely related IMP homologs can vary dramatically in the amount of protein available after
expression (
Lewinson et al., 2008
), which raises a fundamental question: What differentiates
the expression of IMP homologs? The hypothesis raised here is that the efficiency with
which an IMP is integrated into the membrane is a key determinant in the degree of
observed IMP expression.
A fundamental step in the biosynthesis of most IMPs involves their targeting to and
integration into the membrane via the Sec protein translocation channel (
Rapoport, 2007
).
Integration of IMP transmembrane domains (TMDs) into the membrane is facilitated
primarily through interaction between the nascent chain and SecY, which forms the core of
the protein translocation complex, or translocon. Following the co-translational or post-
translational insertion of nascent-protein sequences into the translocon channel, hydrophobic
segments pass through the lateral gate of SecY into the membrane to form TMDs. Factors
such as TMD hydrophobicity (
Harley et al., 1998
,
Hessa et al., 2005
) and loop charge (
von
Heijne, 1986
,
Goder and Spiess, 2003
) have been shown to affect the efficiency of TMD
integration and topogenesis. For example, TMD hydrophobicity is directly related to the
probability with which TMDs partition into the lipid bilayer, while positively charged
residues in the loop alter TMD orientation by preferentially occupying the cytosol (
Goder
and Spiess, 2003
,
Hessa et al., 2005
,
von Heijne, 1986
).
In this study, we investigate the connection between observed IMP expression levels and
Sec-facilitated IMP integration efficiency (
i.e.
, the probability of membrane integration with
the correct multi-spanning topology). Systematic investigation of chimeras within an IMP
family leads to the identification of sequence elements that modulate expression levels.
In
silico
modeling of IMP integration at the Sec translocation channel finds that the sequence
modifications that increase the calculated IMP integration efficiency correlates with
in vivo
overexpression improvements, suggesting that IMP integration efficiency is a determinant
for successful expression. The result is found to be general across distinct expression
systems (
E. coli
and
M. smegmatis
). Furthermore, an
in vivo
assay based on antibiotic
resistance in
E. coli
experimentally confirmed the model that the integration efficiency of an
individual TMD correlates with the observed IMP expression levels. The strong link
between the effect of sequence modifications on simulated integration efficiency and
experimentally measured expression levels offers future promise for the rational design of
IMP systems with increased expression levels.
Results
As a detailed case study, the TatC IMP family is employed for all experimental and
computational results reported here. A component of the bacterial twin-arginine
translocation pathway, TatC plays a key role in the transport of folded proteins across the
cytoplasmic membrane (
Bogsch et al., 1998
). The employment of TatC is well-suited for the
current study as it is reasonably sized (only six TMDs (Figure 1A)), non-essential, and
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found broadly throughout bacteria; furthermore, TatC homologs have previously been
observed to exhibit widely varying expression levels in
E. coli
(
Ramasamy et al., 2013
),
suggesting the importance of sequence-level details in the expression of this IMP.
Wild-type and chimeric TatC expression in
E. coli
It is first demonstrated that homologs of the IMP TatC exhibit large variance in observed
expression levels in
E. coli
. For a quantitative measure of IMP expression, we employ a C-
terminal fusion-tag of a green fluorescent protein (GFP) variant (
Waldo et al., 1999
) (Figure
1A) and measure whole-cell fluorescence by flow cytometry. Whole-cell fluorescence
intensity of this fusion-tag has been validated in numerous previous studies to correlate
strongly with the amount of folded IMP, rather than the total level of IMP translated (
Fluman
et al., 2014
,
Wang et al., 2011
,
Guglielmi et al., 2011
,
Geertsma et al., 2008
,
Drew et al.,
2005
); we further validate the expression levels measured from whole-cell fluorescence
(Figure 1B) using in-gel fluorescence (Figure 1C and Figure S1, Pearson correlation
coefficient,
r
= 0.914) and western blot analysis (Figure S1). With this approach, expression
levels in
E. coli
are experimentally measured for TatC homologs from a variety of bacteria,
including
Aquifex aeolicus
(
Aa
),
Bordetella parapertussis
(
Bp
),
Campylobacter jejuni
(
Cj
),
Deinococcus radiodurans
(
Dr
),
Escherichia coli
(
Ec
),
Hydrogenivirga
sp. 128-5-R1 (
Hy
),
Mycobacterium tuberculosis
(
Mt
),
Staphylococcus aureus
(
Sa
),
Vibrio cholera
(
Vc
), and
Wolinella succinogenes
(
Ws
) (sequences in Figure S2).
Figure 1B demonstrates the wide range of expression levels that are exhibited by the TatC
homologs in
E. coli
. Previous expression trials of TatC homologs identified that
Aa
TatC is
readily produced at high levels in
E. coli
, which enabled the solution of its structure
(
Ramasamy et al., 2013
,
Rollauer et al., 2012
). In contrast, low expression is found for both
the
Mycobacterium tuberculosis
TatC (hereafter referred to as
Mt
TatC(Wt-tail) and a
modified sequence truncating the un-conserved 38-residue sequence of the C-terminal loop
(hereafter referred to as
Mt
TatC) (
Ramasamy et al., 2013
).
To examine the parts of the protein sequence that affect expression, ‘swap chimeras’ were
generated by exchanging entire loops and TMDs between
Aa
TatC and
Mt
TatC (sequences in
Table S1). The TMDs and loops were defined by comparing sequence alignments and
membrane topology predictions (Figure 2B) (
Sievers et al., 2011
,
Tsirigos et al., 2015
). The
swap chimeras exhibited a wide range of expression results (Figure 2A). The C-terminal
loop sequence, referred to as the C-tail and labeled as loop 7 in Figure 1A, was found to
have a significant effect on expression levels (shaded bars in Figure 2A). Removal of the
Mt
TatC C-tail improves expression. Removal of the C-tail from the
Aa
TatC sequence leads
to a corresponding decrease in expression. Strikingly, swapping the
Aa
TatC C-tail (
Aa
-tail)
into the
Mt
TatC sequence leads to a significant improvement in expression.
The positive effect of the
Aa
-tail on
Mt
TatC expression raises the question of whether
expression can be similarly improved in other TatC homologs by substituting the
corresponding C-tail sequence (Figure 2E) with that of
Aa
TatC. Swapping the C-tail of the
various TatC homologs with the
Aa
-tail improved expression in seven out of nine of cases
(Figure 2D). Taken together, the results in Figure 2 indicate that the C-tail is a significant
factor in determining TatC expression across homologs.
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In silico
modeling of TatC integration
To investigate the mechanistic basis for the experimentally observed effect of the C-tail on
expression, we employ a recently developed
in silico
coarse-grained approach that models
cotranslational translocation on unbiased biological timescales (
Zhang and Miller, 2012b
).
The coarse-grained model, which is derived from over 16 μs of molecular dynamics
simulations of the Sec translocation channel, the membrane bilayer, and protein substrates
(
Zhang and Miller, 2010
,
Zhang and Miller, 2012a
), has been validated for the description of
Sec-facilitated membrane integration, including experimentally observed effects of amino-
acid sequence on the membrane topology of single-spanning IMPs (
Zhang and Miller,
2012b
) and multi-spanning dual-topology proteins (
Van Lehn et al., 2015
). IMP sequences
are mapped onto a Brownian dynamics model of the ribosome/translocation-channel/
nascent-protein system, and the Sec translocon-facilitated integration of the IMP into the
lipid bilayer is directly simulated in 1,200 independent minute-timescale trajectories for
each TatC (Figure 3A). The current implementation of the coarse-grained model does not
distinguish between expression systems.
Using the results of the coarse-grained model, Figure 3B presents the simulated integration
efficiency (
i.e.
, the simulated integration efficiency is defined to be the fraction of
trajectories that lead to the correct membrane topology) for several TatC sequences. Unless
otherwise specified, we define membrane topology in terms of the final orientation of the C-
tail; Figure S3 confirms that analyzing the trajectories in terms of this single-loop definition
for membrane topology correlates with defining topology in terms of all loops, while
reducing the statistical noise. The
Aa
TatC homolog exhibits significantly higher simulated
integration efficiency than the
Mt
TatC homolog, which is consistent with the relative
experimental expression levels for the two homologs in Figure 3C. Figure 3B further shows
that the
Mt
(
Aa
-tail) chimera recovers the high levels of simulated integration efficiency seen
for the
Aa
TatC homolog, further mirroring the experimental trends in IMP expression
(Figure 3C). Figure 3D presents an analysis of the orientation of each loop, indicating that
only loop 7 is significantly affected swapping the C-tail in the simulations. As is shown
schematically in Figure 3E, the simulations find that
Mt
TatC exhibits a large fraction of
trajectories in which the C-tail resides in the periplasm, such that the C-terminal TMD
(TMD 6) fails to correctly integrate into the membrane.
Additional simulations were performed for the full set of the experimentally characterized
TatC homologs (Figure S4), allowing comparison of the computationally predicted shifts in
IMP integration with those observed experimentally for IMP expression. For each homolog,
Figure 3F compares the effect of swapping the wild-type C-tail with the
Aa
-tail on both the
experimental expression level and the simulated integration efficiency. With the exception of
Vc
TatC and
Ec
TatC, Figure 3F shows consistent agreement between the computational and
experimental results in
E. coli
upon introducing the
Aa
-tail.
Confirmation of the predicted mechanism
The comparison between simulation and experiment in the previous sections suggests a
mechanism in which translocation of the C-tail of TatC into the periplasm leads to a
reduction in the observed expression level. To validate this, an experimental
in vivo
assay
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based on antibiotic resistance in
E. coli
is employed. The C-terminal GFP tag was replaced
by
β
-lactamase, such that an incorrectly oriented C-tail would confer increased resistance to
β
-lactam antibiotics (Figure 4A); an inverse correlation between antibiotic resistance and
GFP fluorescence is thus expected.
Aa
TatC,
Mt
, and
Mt
(
Aa
-tail) constructs containing the
β
-lactamase tag were expressed using the same protocol as before. Following expression, the
cells were diluted to an OD
600
of 0.1 in fresh media without inducing agent and then grown
to an OD
600
of approximately 0.5 at which point ampicillin was added. 1.5 hours after
ampicillin treatment, equal amounts of the media were plated on LB agar plates without
ampicillin (Figure 4B). The number of observed colonies is used to quantify the relative cell
survival (Figure 4C, bottom). The survival rate of
Mt
(
Aa
-tail),
Mt
, and
Aa
TatC inversely
correlates with the simulated integration efficiency of the C-tail (Figure 4C), validating the
proposed mechanism.
Tail-charge as an expression determinant: Experimental tests of computational predictions
To further establish the connection between the simulated integration efficiencies and the
experimentally observed expression levels, we examine the effect of C-tail mutations. We
focus on modifications of the C-tail amino-acid sequences that involve the introduction or
removal of charged residues, which are known to affect IMP topology and stop-transfer
efficiency (
Goder and Spiess, 2003
,
Seppälä et al., 2010
,
Zhang and Miller, 2012b
).
We begin by investigating the generic effect of the C-tail charge magnitude on TatC
simulated integration efficiency. Figure 5A presents the results of coarse-grained simulations
in which the magnitude of the charges on the C-tail of the
Mt(Aa
-tail) sequence were scaled
by a multiplicative factor,
χ
, keeping all other aspects of the protein sequence unchanged.
The simulations reveal that reducing the charge magnitude on the C-tail leads to lower
simulated integration efficiency.
To examine the corresponding effect of C-tail charge magnitude on expression levels, Figure
5B plots the ratio of experimentally observed expression for each wild-type homolog relative
to its corresponding
Aa
-tail swap chimera versus the total charge magnitude on the wild-
type C-tail. Without exception in these data, the expression of wild-type homologs with
weakly charged C-tails (relative to the
Aa
-tail) is improved upon swapping with the
Aa
-tail,
whereas the expression of homologs with strongly charged C-tails is reduced upon swapping
with the
Aa
-tail (
i.e.
all data points in Figure 5B fall into the unshaded quadrants).
Figure 5C further illustrates the effect of charge magnitude on expression by presenting the
experimentally observed expression levels for
Aa-
tail(−) swap chimeras, in which the
introduced C-tail sequence preserves the charge magnitude of the
Aa-
tail sequence while
reversing the net charge (see Figure 2E for the C-tail sequences). Despite the complete
reversal of the C-tail charge, the observed correlation between expression and C-tail charge
magnitude for these two sets of chimeras is strikingly similar (compare Figures 5B and C).
Finally, we considered a series of mutants of the
Mt
(
Aa
-tail) chimera, in which the charge
magnitude of the
Aa
-tail is reduced by mutating positively charged residues to alanine
residues (see Figure 2E for the C-tail sequences). For this series of mutants, Figure 5D
(black) shows that the simulated integration efficiency decreases with the charge of the C-
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tail, which predicts a corresponding decrease in the experimental expression levels; indeed,
the subsequent experimental measurements confirm the predicted trend (Figure 5D, blue).
Again using the antibiotic resistance assay to validate the connection between simulated
integration efficiency and observed expression, Figure 5E confirms that the simulation
results correlate with the relative survival of the
Mt
(
Aa
-tail) alanine mutants with a
β
-
lactamase tag (Figure 5E, red). In addition to providing evidence for the connection between
simulated integration efficiency and observed expression levels, the results in Figure 5
suggest that this link can be used to control IMP expression.
Transferability to another expression system
Beyond the
E. coli
overexpression host, we now examine the transferability of the relation
between simulated integration efficiency and experimental expression levels. We employ
Mycobacterium smegmatis
, a genetically tractable model organism that is phylogenetically
distinct from
E. coli
. All coding sequences were transferred into an inducible
M. smegmatis
vector, including the linker and C-terminal GFP, and expressed; expression levels were then
measured by flow cytometry and validated by western blot.
Figure 6A demonstrates that, as in
E. coli,
the experimentally observed expression levels
vary widely among the wild-type TatC homologs in
M. smegmatis
. However, comparison of
Figure 6A with Figure 1B reveals that the total expression levels for the homologs in
M.
smegmatis
are different from those seen in
E. coli
, although for both systems, the
Aa
TatC
homolog expresses strongly and
Mt
TatC expresses poorly (which is perhaps surprising,
given the close evolutionary link between
M. smegmatis
and
M. tuberculosis
). Figure 3F
also shows that replacing the wild-type C-tail with the
Aa
-tail in
M.
smegmatis
generally
increases the experimentally observed expression levels, in general agreement (six out of
nine homologs) with the previously discussed simulated integration efficiency results.
Figure 3F further shows that the subset of homologs for which the
Aa
-tail swap chimeras
lead to increased levels of expression in
M. smegmatis
is overlapping but different from the
subset associated with the
E. coli
results. This emphasizes that although the computed levels
of simulated integration efficiency agree with the observed changes in expression levels in
both expression systems, the observed expression levels depend upon the expression system,
while the simulated integration efficiencies calculated using the current implementation of
the coarse-grained model are independent of the expression system. In short, simulated
integration efficiency is a predictor of the expression levels in both systems, but it is not the
only factor contributing to the observed expression levels.
Continuing with the
M. smegmatis
expression system Figure 6B repeats the comparison
between the simulated integration efficiency and the observed expression levels for the series
of mutants of the
Mt
(
Aa
-tail) chimera, in which the positive charge of the
Aa
-tail is reduced
by mutating positively charged residues to alanine residues. The simulated integration
efficiencies, identical to those in Figure 5D, are predicted to decrease as charges are
removed. The experimental expression levels for
M. smegmatis
in Figure 6B likewise show
a decrease. Taken together, the results obtained for the
M. smegmatis
expression system
suggest that the connection between simulated integration efficiency and observed
expression levels may be generalizable beyond
E. coli.
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Transferability beyond the C-tail: Analysis of loop 5 swap chimera
As seen in Figure 3D, the coarse-grained simulations predict poor integration efficiency for
loop 5, suggesting an additional location (beyond the C-tail, loop 7) in the
Mt
TatC sequence
that can be optimized for expression. Figure 7A presents the simulated integration efficiency
for loop 5 in each of the TatC homologs, revealing a significant range of efficiencies.
Selecting the four homologs with the highest predicted simulated integration efficiency for
loop 5 (
Sa
,
Hy
,
Cj
, and
Vc
), chimera proteins were derived from the
Mt
TatC sequence by
swapping loop 5 of
Mt
TatC with the corresponding loop 5 sequence from each of these
homologs (Figure 7B). Figure 7C compares the simulated integration efficiency and
experimentally observed expression level for each chimera, revealing agreement for three
out of four cases. Comparing the simulation results in Figure 7, note that the degree of
improvement for the simulated integration efficiency obtained from the coarse-grained
simulations of the chimeras (Figure 7C) is different from that anticipated by naïve
comparison of the individual loops in the wild-type sequences (Figure 7A); this emphasizes
that the simulated integration efficiency is sensitive to elements of the IMP sequence beyond
the local segment that is being swapped. The results in Figure 7 suggest the simulated
integration efficiency can be used to identify regions beyond the TatC C-tail for modification
to improve experimental expression; more generally, it suggests the potential for identifying
local segments of an IMP amino-acid sequence that may be modified to yield increased
experimental expression.
Discussion
The mechanistic picture that emerges from the experimental and theoretical analysis of the
TatC IMP family is that the efficiency of Sec-facilitated membrane integration, which is
impacted by the IMP amino-acid sequence, is a key determinant in the degree of observed
protein expression. We observed that TatC homologs had varying levels of expression
(Figures 1B and 6A) Swap chimeras between
Aa
TatC and
Mt
TatC revealed a significant
effect of the C-tail in determining expression yields (Figure 2A), with the
Aa
-tail having a
largely positive effect that was transferrable to other homologs (Figure 3F). Coarse-grained
modeling predicted a large, sequence-dependent variation of the simulated integration
efficiency for the C-tail (Figure 3), suggesting the underlying mechanism by which the
Aa
-
tail enhances the expression of other TatC homologs. Validation of this mechanism was
experimentally demonstrated using an antibiotic-resistance assay (Figure 4). Additional
point-charge mutations in the C-tail were shown to change the simulated integration
efficiency, which in turn predicted changes in the IMP expression levels according to the
proposed mechanism; these predictions were experimentally confirmed in both
E. coli
(Figure 5) and
M. smegmatis
(Figure 6). Finally, the link between simulated integration
efficiency and experimental expression was exploited to design
Mt
TatC chimeras with
improved expression based on the loop 5 simulated integration efficiency (Figure 7).
The observed correlation between IMP integration efficiency and observed expression levels
presented here is consistent with earlier observations that expression can be modulated by
mutations of the sequence (
Sarkar et al., 2008
,
Grisshammer et al., 1993
,
Warne et al.,
2008
), as well as recent work in which mis-integrated dual-topology IMPs are shown to be
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degraded by FtsH (
Woodall et al., 2015
). However, these earlier studies did not provide a
clear mechanistic basis for the relation between IMP sequence modifications and observed
expression levels. In the current work, we demonstrate the relation between integration
efficiency and observed expression levels, and we demonstrate a tractable coarse-grained
approach for computing the simulated integration efficiency and its changes upon sequence
modifications. This work also raises the possibility of using simulated integration
efficiencies to optimize experimental expression levels, which has been demonstrated here
via the computational prediction and subsequent experimental validation of individual
charge mutations in the C-tail and of loop-5 swap chimeras.
A few comments are worthwhile with regard to the scope of the conclusions drawn here.
Firstly, the current work focused on comparing protein expression levels among IMP
sequences that involve relatively localized changes, such as single mutations or loop swap
chimeras, as opposed to predicting relative expression levels among dramatically different
IMP sequences. Secondly, the current work examines experimental conditions for the
overexpression of IMPs using the same plasmids, which may be expected to isolate the role
of membrane integration in determining the relative expression levels of closely related IMP
sequences. The prediction of expression levels among IMPs that involve more dramatic
differences in sequence may well require the consideration of other factors, beyond just the
simulated integration efficiency. Moving forward, we expect that a useful strategy will be to
systematically combine the simulated IMP integration efficiency with other sequence-based
properties to predict IMP expression levels (
Daley et al., 2005
).
The experimental and computational tools used here are readily applicable to many systems,
potentially aiding the understanding and enhancement of IMP expression in many other
systems, as well as providing fundamental tools for the investigation of co-translational IMP
folding. By demonstrating inexpensive
in silico
methods for predicting protein expression,
we note the potential for computationally guided protein expression strategies to
significantly impact the isolation and characterization of many IMPs.
Experimental Procedures
Cloning, expression and flow cytometry
Briefly, for
E. coli
all expression plasmids were derived from pET28(a+)-GFP-ccdB, with
the final expressed sequences containing a Met-Gly N-terminus followed by the IMP
sequence, a tobacco etch virus (TEV) protease site, a GFP variant (
Waldo et al., 1999
), and
an eight His tag. For
β
-lactamase constructs, the GFP sequence was replaced by a
β
-
lactamase sequence. For
M. smegmatis
expression plasmids, the entire coding region of the
TatC homologs were sub-cloned and transferred into pMyNT vector (
Noens et al., 2011
).
E.
coli
constructs were grown in BL21 Gold (DE3) cells (Agilent Technologies) at 16°C and
induced with 1 mM IPTG at an OD
600
of 0.3 then analyzed after 16 hours.
M.
smegmatis
constructs were grown in mc
2
155 cells (ATCC) at 37°C and induced at an OD
600
of 0.5 with
0.2% acetamide then analyzed after six hours. A 200 μL sample of each expression culture
was pelleted and resuspended in 1 mL of PBS. Whole-cell GFP fluorescence was measured
using a MACSQuant10 Analyzer (Miltenyi). For the ampicillin survival assay, the cells were
diluted to an OD
600
of 0.1 in fresh media following expression without inducing agent and
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then grown to an OD
600
of approximately 0.5 at which point ampicillin was added. 1.5 hours
after ampicillin treatment, equal amounts of the media were plated on LB agar plates
without ampicillin. The number of observed colonies is used to quantify the relative cell
survival. Full experimental protocols are provided in the Supplemental Experimental
Procedures.
Description of the CG model
Modeling of IMP integration in the current study was performed using a previously
developed coarse-grained (CG) method for the direct simulation of co-translational protein
translocation and membrane integration (
Zhang and Miller, 2012b
). Ribosomal translation
and membrane integration of nascent proteins are thus simulated on the minute timescale,
enabling direct comparison between theory and experiment. The CG model was previously
parameterized using extensive molecular dynamics simulations of the translocon and nascent
protein in explicit lipid and water environment (
Zhang and Miller, 2012a
,
Zhang and Miller,
2010
). The CG model has been validated against available experimental data and shown to
correctly capture effects related to nascent protein charge, hydrophobicity, length, and
translation rate in both IMP integration and protein translocation studies (
Zhang and Miller,
2012b
,
Van Lehn et al., 2015
). Details of the implementation of the CG model and the
analysis of the simulated trajectories are given in the Supplemental Experimental Procedures
and Table S2.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Acknowledgements
The authors thank R.C. Van Lehn and D.C. Rees for comments on the manuscript and D. Daley for helpful
discussion of (
Daley et al., 2005
). Work in the Clemons lab is supported by an NIH Pioneer Award to WMC
(5DP1GM105385) and an NIH training grant to SSM (NIH/NRSA training grant 5T32GM07616). Work in the
Miller group is supported in part by the Office of Naval Research (N00014-10-1-0884) and computational resources
were provided by the National Energy Research Scientific Computing Center (NERSC) and a DOE Office of
Science User Facility (DE-AC02-05CH11231).
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