For the vast majority of membrane proteins, insertion into a membrane is not direct, but rather is catalyzed by a protein-conducting channel, the translocon. This channel provides a lateral exit into the bilayer while simultaneously offering a pathway into the aqueous lumen. The determinants of a nascent protein's choice between these two pathways are not comprehensively understood, although both energetic and kinetic factors have been observed. To elucidate the specific roles of some of these factors, we have carried out extensive all-atom molecular dynamics simulations of different nascent transmembrane segments embedded in a ribosome-bound bacterial translocon, SecY. Simulations on the μs time scale reveal a spontaneous motion of the substrate segment into the membrane or back into the channel, depending on its hydrophobicity. Potential of mean force (PMF) calculations confirm that the observed motion is the result of local free-energy differences between channel and membrane. Based on these and other PMFs, the time-dependent probability of membrane insertion is determined and is shown to mimic a two-state partition scheme with an apparent free energy that is compressed relative to the molecular-level PMFs. It is concluded that insertion kinetics underlies the experimentally observed thermodynamic partitioning process.
For the vast majority of membrane proteins, insertion into a membrane is not direct, but rather is catalyzed by a protein-conducting channel, the translocon. This channel provides a lateral exit into the bilayer while simultaneously offering a pathway into the aqueous lumen. The determinants of a nascent protein's choice between these two pathways are not comprehensively understood, although both energetic and kinetic factors have been observed. To elucidate the specific roles of some of these factors, we have carried out extensive all-atom molecular dynamics simulations of different nascent transmembrane segments embedded in a ribosome-bound bacterial translocon, SecY. Simulations on the μs time scale reveal a spontaneous motion of the substrate segment into the membrane or back into the channel, depending on its hydrophobicity. Potential of mean force (PMF) calculations confirm that the observed motion is the result of local free-energy differences between channel and membrane. Based on these and other PMFs, the time-dependent probability of membrane insertion is determined and is shown to mimic a two-state partition scheme with an apparent free energy that is compressed relative to the molecular-level PMFs. It is concluded that insertion kinetics underlies the experimentally observed thermodynamic partitioning process.
Synthesis and insertion of membrane proteins
into a lipid bilayer
is a fundamental biophysical process for which many aspects are not
yet understood. Insertion occurs co-translationally via a highly conserved
and specialized membrane channel, the so-called SecY translocon, which
possesses a lateral gate for exit of transmembrane (TM) segments into
the lipid bilayer.[1−3] This SecY channel, in addition to providing
a pathway into the membrane, also permits water-soluble proteins or
periplasmic domains of membrane proteins to be secreted across the
bilayer, thus acting as a switching point for protein localization.The energetics of the membrane-insertion process have been characterized
by the beautiful experimental work of von Heijne and colleagues.[4,5] However, their results have led to two currently unresolved issues
that present a great puzzle to researchers in the field. The first
concerns the magnitude of the apparent transfer free energy, the so-called
“biological hydrophobic scale”.[4] Surprisingly, the scale was found to span a narrow range of only
3–4 kcal/mol for all 20 amino acids, in stark contrast to considerations
based on the physical chemistry of hydration, as well as computational
predictions.[6] The second outstanding and
unresolved issue concerns the actual role played by non-equilibrium
kinetics in the membrane-insertion process. Peptide translation by
the ribosome, which is driven at a rate of ∼10–20 residues/s
through peptide synthesis,[7] is an irreversible
non-equilibrium process. However, whether peptide transfer from the
translocon to the membrane occurring in the later stages is primarily
governed by equilibrium or non-equilibrium events is unknown.[8]The striking similarity of the measured
biological hydrophobic
scale with a two-state partition scheme[4,5,9] has led many to postulate that insertion into the
membrane occurring in the later stage must reflect a purely thermodynamic
equilibrium process, making dynamics of the process largely irrelevant
to understanding it. Nevertheless, the molecular character of these
two putative states is not known, and it is unlikely that they would
correspond to fully secreted or fully membrane-inserted helix configurations.[8,10,11] An alternative proposal attributing
more importance to non-equilibrium aspects stipulates that modulation
of the channel’s gating kinetics by the nascent peptide is
the dominant factor controlling whether a peptide ends up being inserted
into the membrane or secreted into the cytoplasm, although membrane-peptide
interactions still play a role.[12] In support
of this view, simulation studies show aspects of opening of the translocon
by the signal anchor (SA), itself a TM segment, and factors controlling
its orientation.[13] However, the similarities
of scales determined for different membranes, including the bacterial
cytoplasmic membrane[14] and the mitochondrial
inner membrane,[15] as well as the temperature
dependence of insertion[16] are indicative
of additional factors that are not channel-specific. Despite the great
progress, fundamental questions remain about the respective roles
played by energetic and kinetic effects and how non-equilibrium effects
come into play during the membrane-insertion process.To answer
these questions, we relied on multiple computational
approaches, including μs atomic-scale molecular dynamics (MD)
simulations on Anton,[17] umbrella sampling
(US) potential of mean force (PMF) computations, and stochastic simulation
of a diffusion–elongation model describing the process of membrane
insertion over a time scale of seconds. The results lead to the formulation
of a novel hypothesis that connects the translation rate with insertion,
mediated via the progressive elongation of the nascent chain length,
in agreement with previous experiments. By effectively coupling two
widely disparate time scales—a very short one governing local
motion of the TM segment in the translocon and a very long one dictated
by the rates of translation and translocation—it is found that
an apparent two-state thermodynamic partition scheme consistent with
the biological hydrophobic scale arises actually from a non-equilibrium
diffusion–elongation process.
Methods
Construction of the simulated ribosome–translocon
system
began with the structure from Frauenfeld et al.,[18] which contains the full ribosome bound to SecYE with a
nascent chain and its SA present (PDB identifiers 3J00/3J01). Because only dynamics
near the channel and membrane are of interest in the current study,
the ribosome was truncated such that only atoms within 20 Å of
the channel were kept, ribosome atoms near the truncation boundary
being harmonically restrained. Additionally, the majority of the nascent
chain was removed from the system, leaving only the SA. The channel
was embedded in a 200-lipid mixed 75%/25% POPE/POPG membrane, which
mimics the bacterial membrane.[19] The resulting
system contains approximately 120 000 atoms and is shown in Figure S1 (Supporting Information). All figures
were made using VMD.[20]
NAMD Simulations
All equilibration and US simulations
were carried out using NAMD[21] along with
the CHARMM22/CMAP[22,23] force field for proteins and
CHARMM36 for lipids.[24] A multiple time-stepping
algorithm was employed with a 2-fs integration time step and short-range
and long-range non-bonded interactions (separated by a cutoff at 12
Å) evaluated every 1 and 3 time steps, respectively. Long-range
electrostatics were determined using the particle-mesh Ewald method.
After equilibration at a constant pressure of 1 atm, the volume was
held constant. Unless otherwise stated, all simulations were run at
a constant temperature of 323 K.
Long-Time Simulations
Long-time simulations on Anton
used the same system, force field, and multiple-time-stepping procedure
as those run using NAMD. Constant volume and temperature were maintained
using the Berendsen coupling scheme. Although an isotropic pressure
control, in which the membrane area can fluctuate, is preferred for
CHARMM36 lipids,[24] the repulsion between
neighboring ribosome images may unnaturally influence the unit-cell
area. Comparison of the excluded area as a function of z for the membrane between the fully open and fully closed states
of SecYE reveals that they are nearly identical (see Figure S2).Long-range electrostatics were calculated
using the k-Gaussian Split Ewald method on a 64×64×64 grid.
The cutoff was determined independently for each simulation, but typically
was around 13 Å. For simulations investigating the motion of
a substrate helix located at the lateral gate, i.e., those illustrated
in Figure 2, an elevated temperature of 353
K was employed to enhance the likelihood of observing helix movement
on the μs time scale; all other simulations on Anton were carried
out at 323 K. Temperatures of 353–490 K have previously been
validated for peptide–membrane partitioning studies and were
found to not significantly affect the systems’ thermodynamic
properties.[25] The total time for all Anton
simulations is ∼30 μs.
Figure 2
Spontaneous
motion of a helix in SecY. SecYE (gray and orange,
respectively) is shown in the membrane plane, cut perpendicularly
to reveal the pore ring in yellow (A,C,E), and from the cytoplasmic
side (B,D,F). The membrane is displayed as blue sticks with purple/yellow
spheres for the phosphorus atoms. The substrate helix is shown in
red. (A,B) Initial state (t = 0). (C,D) Final state
(t = 2.5 μs) for polyGln. (E,F) Final state
for polyLeu. (G) Plot of separation between the helix and the center
of the SecY channel for four segments: SA (black), polyLeu (red),
polyGln (green), and the S4 helix of KvAP (blue).
PMF Calculations
The PMFs shown in Figure 3B were calculated with US simulations,[26,27] using the colvars module of NAMD.[28] For
each of the three substrate helices examined, i.e., the SA, polyLeu,
and polyGln helices, 26 windows typically spaced 1 Å apart, beginning
at the center of SecY and ending in the membrane, were used. The final
PMFs were determined by unbiasing the histograms, shown in Figure S13, using the weighted histogram analysis
method (WHAM).[29] The net simulation time
for each helix is 250 ns, giving 750 ns in total.
Figure 3
Potential of mean force for helix exit from SecY into
the membrane.
(A) SecY is shown from the cytoplasmic side in gray and orange with
the membrane in blue. A substrate helix is shown in red at different
positions along its exit, although only one helix was present at any
given time. The green dotted lines are at r = 12
Å and r = 25 Å. (B) PMFs for the SA (black),
polyLeu (green), and polyGln (red) helices as a function of distance
from the channel center. The gray dashed lines show, in order of decreasing
dash size, the restraining potential used in the diffusion calculations
at times t = 1 s, 10 s, and 25 s.
Diffusion–Elongation Calculations
Calculations
of translocation probabilities were carried out in Matlab. The algorithms
developed involved integration of the Boltzmann distribution over
5000 irregular cells of a Voronoi tessellation outside of a predefined
cutoff radius from the center of the SecY channel, up to 2000 Å.
In order to verify that the system achieves equilibrium on a time
scale much smaller than that of the translocation process, a more
rigorous approach involving the solution of the Smoluchowski diffusion
equation was used, and the results were compared to those from the
Boltzmann simulation for an example case. In each simulation, the
potential used was composed of a widening harmonic potential mimicking
the effect of a lengthening polymer chain (see Figure 3B) and a linear fit of the radial PMF determined by all-atom
US calculations. Simulations were run for up to 50 s with a time step
falling in the range between 0.002 and 2 s. Details of the discretization
scheme, simulation algorithms, and validation simulations can be found
in the Supporting Information.Parameters
in the diffusion–elongation calculations were taken from multiple
sources. The growth rate of the nascent chain is tied to the translation
rate (for co-translational translocation), which is between 0.5 and
20 residues/s.[7,30,31] We estimated the lateral diffusion rate of the substrate helix from
restrained US simulations,[32] finding it
to fall in a range from 250 Å2/μs in the channel
to 1000 Å2/μs in the membrane, in agreement
with an experimental rate of 830 Å2/μs.[33] The rate of translocation of the nascent chain
through the channel has been determined in at least one case to be
1.6× the rate of translation, i.e., ∼4 s for 30 residues,[34] although this rate is sequence-dependent.[16,35] The rate of translocation affects the time available to the nascent
chain in the channel to commit to the membrane-integrated or secreted
pathways (see Figure 4). The channel radius, rcutoff, is taken to be 12 Å based on the
structure used (see Figure 3A), although a
range of 10–15 Å is considered.
Figure 4
Schematic of
TM segment insertion via the translocon. Upon entering
SecY (gray), the putative TM segment (red) can equilibrate quickly
in the immediate vicinity of the lateral gate, while still tethered
to the ribosome (not shown for clarity). The unidirectional arrows
indicate the irreversible processes (entry of nascent peptide into
SecY and final expulsion into the solution or the membrane), whereas
the double arrow indicates the local two-state kinetic process (between
bold parentheses) responsible for the apparent thermodynamic partition
coefficient. The commitment time is defined as the length of time
the states in parentheses persist before an irreversible course into
the membrane or the lumen is taken.
Results
The simulations carried out in this study cover
multiple functional
aspects of the translocon SecY and the membrane insertion process.
First, the dynamics of SecY, and its lateral gate in particular, in
the presence or absence of different substrate helices embedded within
are explored on the μs time scale. Next, the dynamics of a substrate
helix at SecY’s lateral gate are addressed. Finally, free-energy
and finite-element calculations of complete membrane integration are
presented.
Dynamics of SecY’s Lateral Gate
It has been
suggested previously that the opening and closing of SecY’s
lateral gate is controlled by the hydrophobicity of the nascent protein
within the channel, with hydrophobic polypeptide segments inducing
gate opening and hydrophilic ones gate closing.[12] To examine this suggestion, simulations ranging from 500
ns to 2 μs of a ribosome-bound SecY (see Figure S1) containing different nascent helices at its center,
as well as none, were carried out. Specifically, a native SA, polyLeu,
polySer, and polyGln were tested for different initial openings of
SecY’s lateral gate, including closed and partially or fully
open gates, with the distance between the Cα atoms
of residues Ser87 on SecY TM2b and Phe286 on TM7 monitored over time
(see Figure 1). These residues were chosen
as they were also used to monitor gate opening in cross-linking experiments
under different translocation conditions.[36]
Figure 1
Lateral
gate opening: SecYE shown in gray (SecY) and orange (SecE),
with lateral gate helices TM2b and TM7 highlighted in green and residues
Ser87 and Phe286 shown as red spheres. (A) Closed state of the gate
(Ser87–Phe286 distance of 7.3 Å).[3] (B) Open state from a membrane-protein-insertion intermediate structure.[18]
Lateral
gate opening: SecYE shown in gray (SecY) and orange (SecE),
with lateral gate helices TM2b and TM7 highlighted in green and residues
Ser87 and Phe286 shown as red spheres. (A) Closed state of the gate
(Ser87–Phe286 distance of 7.3 Å).[3] (B) Open state from a membrane-protein-insertion intermediate structure.[18]For simulations beginning with a closed lateral
gate (7–10
Å wide, Figure 1A), the gate opening did
fluctuate, but no correlation between the magnitude of gate opening
and hydrophobicity of the embedded helix was observed (see Figures S3 and S4). Similarly, no such correlation
appeared when the gate was started in a partially open state (14 Å)
nor in a fully open state (27 Å). In contrast, in all cases,
the helix’s contact with lipids was found to depend on its
hydrophobicity, with the SA and polyLeu helices increasing their contact
with lipids and the polySer and polyGln helices decreasing their contact
(see Figure S5). The change in interaction
area for hydrophobic segments results not from alterations to the
lateral gate, but rather from incursion of lipid tails into the channel,
shown in Figure S6. Thus, direct interaction
between the substrate helix and lipids controls its position with
respect to the channel center, rather than modulation of the gate
by the helix.Structures of SecY bound to different partners[37−39] displaying
a partially open lateral gate have contributed to the hypothesis that
partner binding can “pre-activate” the channel.[1] Electrophysiology experiments on ribosome–channel
complexes have demonstrated that the channel remains permeable to
ions and small molecules after removal of the nascent chain;[40−42] simulations on the 10-ns time scale have also shown that ribosome
binding can subtly bias the closed channel toward an open state.[43] To further examine the role of ribosome binding
on lateral gate opening, simulations of SecY with and without a ribosome
bound were performed for 1.25 μs for both the closed channel
and one at an intermediate gate opening (four simulations in total).
As illustrated in Figure S3D, for both
initial openings, the ribosome-bound SecY became more open laterally
than the free SecY. For the closed SecY a slight increase in gate
separation was observed with the ribosome bound; conversely, for the
initially open SecY the gate began to close without a ribosome bound,
supporting a role of channel-partner binding in inducing SecY to open
partially. Differences in ion conductance for the ribosome-bound and
free SecY could not be explicitly determined due to the limited frequency
of coordinate output on Anton, although it is expected that the former
is higher.[44]
TM Segment Behavior at the Lateral Gate
The structure
of a nascent membrane-protein-insertion intermediate localizes the
SA to the open lateral gate of SecY, at the boundary between channel
and membrane.[18] However, from this structure
alone, it cannot be concluded that a TM segment will move into the
membrane spontaneously, as predicted by a thermodynamic partitioning
model of membrane insertion.[9] Therefore,
to explore the dynamics of the SA at the lateral gate, a system consisting
of the membrane-bound SecYE along with a portion of the ribosome and
the TM segment was constructed and simulated. Equilibrium simulations
of 2.5 μs each were carried out on Anton at an elevated temperature
of T = 353 K (see Methods) for the SA, as well as polyLeu and polyGln helices, in order to
accelerate potential motions into or out of SecY. Finally, the KvAP
S4 TM segment, which, when isolated, is just above the threshold for
membrane insertion,[45] was also tested.For the two hydrophobic TM segments, the SA and polyLeu helix, a
gradual movement into the bilayer was observed. In both cases the
helix moved 4–5 Å into the membrane; furthermore, SecY’s
lateral gate closed behind it (see Figure 2E,F). Additionally, the
constrictive pore ring at the center of SecY closes, preventing return
of the helix to the channel. Simulation of the SA at T = 323 K demonstrates the same motion as at 353 K, but to a lesser
degree (see Figure S7). In contrast, the
polyGln and S4 helices move 5–7 Å from the lateral gate
region back into the center of SecY, with the pore ring opening wider
to accommodate them, shown in Figure 2C,D and
Figure S8. The interior of the channel
is predominantly hydrophilic,[46,47] making it a significantly
more favorable environment for the polyGln and S4 helices than the
lateral gate and surrounding membrane. As above, the diffusion of
the helix is found to correlate well with contact with lipid acyl
tails, which wrap around the TM segments, bringing them into the membrane,
while rejecting the hydrophilic segments. Thus, the motions of individual
lipids provide for rapid sampling of the membrane environment without
requiring full exit of the nascent helix from the channel.Spontaneous
motion of a helix in SecY. SecYE (gray and orange,
respectively) is shown in the membrane plane, cut perpendicularly
to reveal the pore ring in yellow (A,C,E), and from the cytoplasmic
side (B,D,F). The membrane is displayed as blue sticks with purple/yellow
spheres for the phosphorus atoms. The substrate helix is shown in
red. (A,B) Initial state (t = 0). (C,D) Final state
(t = 2.5 μs) for polyGln. (E,F) Final state
for polyLeu. (G) Plot of separation between the helix and the center
of the SecY channel for four segments: SA (black), polyLeu (red),
polyGln (green), and the S4 helix of KvAP (blue).
Thermodynamics of TM Segment Exit from SecY
Although
the previously described simulations of TM segment motion at the lateral
gate are suggestive of a thermodynamic partitioning process, the observed
behavior is nonetheless undersampled. To quantify this behavior, the
PMF as a function of substrate helix distance from the channel’s
center was determined for the SA, polyLeu, and polyGln helices. Each
PMF was calculated using approximately 350 ns of US simulations at
323 K. Because lipid diffusion occurs on a time scale of tens of nanoseconds,[48] in order to fully relax the membrane surrounding
the helix, initial states for every fourth window (i.e., every 4 Å)
were generated from 70-ns equilibrium simulations run on Anton.Potential of mean force for helix exit from SecY into
the membrane.
(A) SecY is shown from the cytoplasmic side in gray and orange with
the membrane in blue. A substrate helix is shown in red at different
positions along its exit, although only one helix was present at any
given time. The green dotted lines are at r = 12
Å and r = 25 Å. (B) PMFs for the SA (black),
polyLeu (green), and polyGln (red) helices as a function of distance
from the channel center. The gray dashed lines show, in order of decreasing
dash size, the restraining potential used in the diffusion calculations
at times t = 1 s, 10 s, and 25 s.The PMFs, shown in Figure 3, illustrate
the free-energy cost, or gain, for a substrate helix moving from the
lateral gate into the membrane or back into the channel. While the
SA and polyLeu helices find the membrane more favorable than the channel
by 1–2 and 4–5 kcal/mol, respectively, the polyGln helix
favors the channel by over 10 kcal/mol. The decrease in free energy
on going from SecY to membrane for the SA and polyLeu helices is likely
the origin of the force measured experimentally for helices in the
translocon.[49] Using the ΔG prediction server,[5] one obtains
an apparent free-energy difference for the polyGln helix ΔG = 19 kcal/mol and for the polyLeu helix ΔG = −7.5 kcal/mol; the SA gives ΔG = −0.75 kcal/mol. Although the agreement in the ranking of
the three tested segments is promising, it remains that the ΔG values from simulation, even when taken far from the channel,
are distinct from the predicted values; the statistical error in the
PMFs is at most ±0.5 kcal/mol (see Figure
S9), which is insufficient to explain the discrepancy. However,
the outcome of a two-state kinetic process is not expected to approach
a simple equilibrium partition scheme unless there are multiple back-and-forth
transitions between the two states. To wit, 10 transitions gives a
standard error of ±16%, while about 100 transitions are required
to come within at least 5% of the correct equilibrium probability.
It is unlikely that a nascent polypeptide could sample the two separate
environments a sufficient number of times to yield an apparent partition
coefficient between them, particularly given the prohibitive entropic
cost of returning to the narrow channel after reaching a distant point
in the membrane.
Kinetics of TM Segment Exit from SecY
If the range
of a nascent polypeptide were restricted instead of being completely
free to move, then only a finite region in the immediate vicinity
of the translocon would be sampled, with multiple possible returns
to the channel center. Such a restriction could arise from, e.g.,
tethering to the remainder of the nascent chain, or interactions with
the translocon or other chaperones in the membrane.[50] We have considered the first possibility, illustrated schematically
in Figure 4, by
solving for the 2D probability distribution of a substrate helix as
a function of its radially dependent PMF with an added time-dependent
restrictive potential arising from the elongation of the nascent chain.
When a stop-transfer sequence, i.e., one that halts translocation,
is in the channel, or during synthesis of a cytoplasmic domain, the
nascent chain can accumulate outside the channel;[31] thus, we modeled the exposed, cytoplasmic portion of the
nascent chain as a freely jointed chain, with the permitted lateral
motion of the adjoining helix in the channel being roughly proportional
to √N, where N is the number
of residues that were added to the polypeptide by the ribosome. Integrating
the 2D probability over the region outside the channel provides the
probability of being in the membrane as a function of time.Schematic of
TM segment insertion via the translocon. Upon entering
SecY (gray), the putative TM segment (red) can equilibrate quickly
in the immediate vicinity of the lateral gate, while still tethered
to the ribosome (not shown for clarity). The unidirectional arrows
indicate the irreversible processes (entry of nascent peptide into
SecY and final expulsion into the solution or the membrane), whereas
the double arrow indicates the local two-state kinetic process (between
bold parentheses) responsible for the apparent thermodynamic partition
coefficient. The commitment time is defined as the length of time
the states in parentheses persist before an irreversible course into
the membrane or the lumen is taken.Estimates for the parameters in the model were
extracted from previous
experiments or from simulations (see Methods), and their effect on insertion probability was determined. Decreasing
the rate of translation, which ranges from 0.5 to 20 residue/s,[7,30,31] causes the TM segment to be retained
near the channel longer and, thus, decreases the probability of insertion
on the same time scale (see Figure S11A). However, if the rate of translation also alters the rate of translocation,
such an effect may be muted; indeed, experimentally, when slowing
translation from 0.5 to 0.25 residue/s, no change in insertion probability
was observed.[30] Decreasing the rate of
translocation, which is equivalent to increasing the commitment time,
increases the membrane-insertion probability in our model, just as
seen experimentally.[16] The effects of lateral
gate opening and channel/membrane cutoff are also explored in Figure S11. Although the PMFs in Figure 3B are apparently quite noisy, the resulting probability
curves are smooth, insensitive to the rugged energetic landscape,
with insertion depending only on the overall slope.To connect
the time-dependent insertion probability to the biological
hydrophobicity scale, simplified, linear PMFs were assumed in our
kinetic model (see inset of Figure 5A and Figure S10), and the probability of membrane
insertion as a function of time was calculated as shown in Figure 5A. Using the center of
the channel and a location 15 Å away in the membrane as reference
points, each PMF, and, thus, each insertion probability curve, can
be assigned a value for ΔG(SecY→mem.);
this value simply reflects the change in energy for going up (or down)
the slope of each of the linear PMFs. Interestingly, the range of
probabilities to insert into the membrane is broadest around ΔG(SecY→mem.) = 0; in other words, the difference
in insertion probability between, e.g., −1.5 and 1.5 kcal/mol
is much greater than that between 3 and 6 kcal/mol. This enhanced
range explains the observed sensitivity of marginally hydrophobic
helices to a myriad of factors. For example, slowing translocation
through the channel enhances membrane integration for mildly hydrophobic
TM segments.[16] Similarly, a greater carboxy-tail
length succeeding the TM segment enhances integration, emphasizing
the importance of holding the TM segment near the channel, rather
than allowing it to translocate into the lumen.[30]
Figure 5
Membrane-insertion probability based on simplified PMFs. (A) Insertion
probability as a function of time is plotted for linear PMFs of varying
slope, shown in the inset plot and in Figure S10. The corresponding ΔG(SecY→mem.) values
using a reference point 15 Å into the membrane are given to the
right of each curve (a reference point of 25 Å is shown in Figure S12). (B) Insertion probability as a function
of ΔG(SecY→mem.) for commitment times,
from left to right, of t = 5, 10, 20, 30, 40, and
50 s. (C) Relationship between ΔGapp and ΔG(SecY→mem.) for the same commitment
times as in part (B). ΔGapp is defined
in the text.
Membrane-insertion probability based on simplified PMFs. (A) Insertion
probability as a function of time is plotted for linear PMFs of varying
slope, shown in the inset plot and in Figure S10. The corresponding ΔG(SecY→mem.) values
using a reference point 15 Å into the membrane are given to the
right of each curve (a reference point of 25 Å is shown in Figure S12). (B) Insertion probability as a function
of ΔG(SecY→mem.) for commitment times,
from left to right, of t = 5, 10, 20, 30, 40, and
50 s. (C) Relationship between ΔGapp and ΔG(SecY→mem.) for the same commitment
times as in part (B). ΔGapp is defined
in the text.From the plot in Figure 5A, the insertion
probability as a function of ΔG(SecY→mem.)
at fixed commitment times can be determined. The resulting curves
in Figure 5B are sigmoidal for all but the
shortest commitment times, similar to the experimental insertion probabilities
from which the biological hydrophobicity scale was determined.[4] Furthermore, the dependence of insertion probability
on commitment time displays an asymptotic behavior, with the limiting
case being near 50 s, which corresponds to the synthesis of 50 residues
at a translation rate of 1 residue/s as assumed in the model. Although
possibly coincidental, this number of residues agrees almost perfectly
with the limiting case of 40–50 C-terminal residues seen experimentally.[30]For each of the curves in Figure 5B, we
calculated an apparent insertion free energy, which was defined identically
to that in the biological hydrophobicity scale, i.e., ΔGapp = −kT ln[pins(t)/psec(t)], where pins(t) and psec(t) are the probabilities of being membrane inserted or secreted
at time t, respectively. ΔGapp is plotted as a function of ΔG(SecY→mem.) for the different commitment times in Figure 5C. The relationship is almost exactly linear in
all cases, with a slope of 0.65, indicating that the apparent insertion-free-energy
scale, ΔGapp, is compressed with
respect to the SecY-to-membrane transfer free energy. The latter free
energy, ΔG(SecY→mem.), is already compressed
with respect to the water-to-membrane transfer free energy,[8] suggesting that there are in fact two causes
to explain the oft-cited compression of the biological hydrophobicity
scale with respect to other scales.[6] Increasing
the commitment time does not change the slope of a given line but
does shift its intercept downward, thus decreasing the threshold for
membrane insertion, as also observed experimentally.[16]
Discussion
In this study, we have carried out a comprehensive
exploration
of a key stage of membrane protein development, the transfer of a
TM segment from the translocon, here SecY, to the membrane. Simulations
spanning nanoseconds to seconds permit a connection to be made between
rapidly varying interactions of individual lipids, SecY, and the substrate
helix on the one hand and the long-time-scale translocation and membrane-insertion
processes on the other hand. Furthermore, PMF- and diffusion-based
calculations elucidate the distinction between the actual free-energy
differences for the helix in the channel and in the membrane and the
apparent free energies measured experimentally.[5]It was found that while the degree of opening of
SecY’s
lateral gate has no apparent dependence on the hydrophobicity of the
nascent chain inside the channel, at least on the 1–2-μs
time scale simulated here, ribosome binding induces slight opening
of the gate or prevents its closure. SecA-mediated translocation requires
gate opening by at least 5 Å,[36] indicating
that all parts of the nascent chain that enter SecY are at least transiently
exposed to lipids. Our simulations of different nascent helices in
SecY also indicate that, for a so-called closed gate, lipids can breach
the gate to contact the helix, with the propensity to interact being
directly related to helix hydrophobicity. For a hydrophobic TM segment,
interaction with lipids draws it into the membrane, whereas a hydrophilic
segment is driven back to the channel center to minimize its contact
with lipids. Thus, direct lipid–protein interactions govern
the short-time and short-distance behavior of a nascent polypeptide
within the channel.Translation and translocation, which occur
on a much longer time
scale than fluctuations of the nascent chain in the channel, were
incorporated into a diffusion–elongation model for membrane
insertion by imposing a time-dependent restriction (due to tethering
of the helix in the SecY channel to the nascent chain in the ribosome
exit tunnel) on diffusion of the TM segment out of the channel (see
Figure 4). For the polyLeu and polyGln helices,
the large change in free energy between channel and membrane makes
their insertion (polyLeu) or lack thereof (polyGln) effectively absolute.
However, the local free energy surface for the native SA is much flatter,
generating a more varied time-dependent behavior than observed for
the other two segments. Over typical translocation time scales, the
SA partitions between channel and membrane-inserted states with a
probability determined primarily by the local environment near the
channel.Extrapolation from the PMFs for the three tested helices
to simplified,
linear PMFs illustrates the full range of insertion probabilities
and their dependence on ΔG between SecY and
membrane. Variability of insertion probability was found to be greatest
for values of ΔG(SecY→mem.) around 0,
elucidating why the insertion of marginally hydrophobic helices is
sensitive to multiple factors.[16,30] Derivation of the apparent
insertion free energy, ΔGapp, i.e.,
the same as actually measured in the biological hydrophobicity scale,
revealed a linear relationship between ΔGapp and the purely thermodynamic scale given by ΔG(SecY→mem.) (see Figure 5C). However, this relationship displays a compression of the biological
scale relative to the thermodynamic one that is completely independent
of the commitment time chosen, just as has been seen experimentally.[4,6] Taken together, our results suggest that the membrane-insertion
process is not solely thermodynamic, but is rather a competition between
kinetic and thermodynamic effects that mimics a two-state partitioning
scheme under typical cellular and experimental conditions.It
is interesting to note that the compression of the scale observed
here is due primarily to configurational entropy of the helical peptide
in the membrane. The total area of membrane accessible to the nascent
peptide increases with time and becomes rapidly much larger than the
cross-section of the interior of the translocon. Unavoidably, this
phenomenon shifts the apparent partition coefficient toward a membrane-inserted
state. The effect of the growing configurational entropy, which always
favors membrane insertion, can counteract unfavorable factors arising
from the local molecular-based PMF. As a consequence, membrane insertion
of slightly hydrophilic peptides, counterintuitively, arises to a
significant degree.The proposed mechanism for membrane insertion
developed above is
not intended to be taken as definitive or as complete. A particular
deficiency is that movement from the channel to the lumen and backsliding
are not explicitly accounted for in our description of the nascent
chain. Thus, only trends, but not absolute probabilities of insertion,
can be extracted from the model. Additionally, SecY was assumed to
be constitutively open to the membrane, whereas the lateral gate has
been shown to fluctuate, albeit on a time scale longer than that of
the translation process.[31,36] Recent coarse-grained
modeling of SecY function has also illustrated how membrane insertion
can be both kinetically and thermodynamically determined, although
the authors assumed, in contrast to our present finding, that lateral
gate fluctuations are controlled by the TM segment’s hydrophobicity.[12,51] Even if the lateral gate were constitutively open, membrane insertion
can still be regulated by the translocon, provided that the continuity
of the nascent chain keeps the TM segment near the channel. Neither
model accounts for the retention of helices near the translocon due
to protein–protein interactions with other channel partners,[50] which can prevent diffusion of the helix even
with an extended C-terminal nascent chain in the cytoplasm. More extensive
modeling and systematic experiments are needed to fully resolve the
balance between thermodynamic and kinetic factors during insertion
under a multitude of conditions, particularly in the case of multi-spanning
membrane proteins.[52] Experiments probing
the dependence of the biological hydrophobicity scale on kinetic factors,
e.g., translation rate, would be especially illuminating.
Authors: James C Gumbart; Martin B Ulmschneider; Anthony Hazel; Stephen H White; Jakob P Ulmschneider Journal: J Membr Biol Date: 2018-03-08 Impact factor: 1.843
Authors: Sara Capponi; Matthias Heyden; Ana-Nicoleta Bondar; Douglas J Tobias; Stephen H White Journal: Proc Natl Acad Sci U S A Date: 2015-07-02 Impact factor: 11.205
Authors: Justin T Marinko; Hui Huang; Wesley D Penn; John A Capra; Jonathan P Schlebach; Charles R Sanders Journal: Chem Rev Date: 2019-01-04 Impact factor: 60.622
Authors: Erhan Demirci; Tina Junne; Sefer Baday; Simon Bernèche; Martin Spiess Journal: Proc Natl Acad Sci U S A Date: 2013-11-04 Impact factor: 11.205