Michelle W Lee1, Ernest Y Lee1, Ghee Hwee Lai1, Nolan W Kennedy2, Ammon E Posey3, Wujing Xian1, Andrew L Ferguson4,4, R Blake Hill2, Gerard C L Wong1,1,1. 1. Department of Bioengineering, Department of Chemistry & Biochemistry, and California NanoSystems Institute, University of California, Los Angeles, Los Angeles, California 90095, United States. 2. Department of Biochemistry, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, United States. 3. Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States. 4. Department of Materials Science and Engineering and Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.
Abstract
Dnm1 and Fis1 are prototypical proteins that regulate yeast mitochondrial morphology by controlling fission, the dysregulation of which can result in developmental disorders and neurodegenerative diseases in humans. Loss of Dnm1 blocks the formation of fission complexes and leads to elongated mitochondria in the form of interconnected networks, while overproduction of Dnm1 results in excessive mitochondrial fragmentation. In the current model, Dnm1 is essentially a GTP hydrolysis-driven molecular motor that self-assembles into ring-like oligomeric structures that encircle and pinch the outer mitochondrial membrane at sites of fission. In this work, we use machine learning and synchrotron small-angle X-ray scattering (SAXS) to investigate whether the motor Dnm1 can synergistically facilitate mitochondrial fission by membrane remodeling. A support vector machine (SVM)-based classifier trained to detect sequences with membrane-restructuring activity identifies a helical Dnm1 domain capable of generating negative Gaussian curvature (NGC), the type of saddle-shaped local surface curvature found on scission necks during fission events. Furthermore, this domain is highly conserved in Dnm1 homologues with fission activity. Synchrotron SAXS measurements reveal that Dnm1 restructures membranes into phases rich in NGC, and is capable of inducing a fission neck with a diameter of 12.6 nm. Through in silico mutational analysis, we find that the helical Dnm1 domain is locally optimized for membrane curvature generation, and phylogenetic analysis suggests that dynamin superfamily proteins that are close relatives of human dynamin Dyn1 have evolved the capacity to restructure membranes via the induction of curvature mitochondrial fission. In addition, we observe that Fis1, an adaptor protein, is able to inhibit the pro-fission membrane activity of Dnm1, which points to the antagonistic roles of the two proteins in the regulation of mitochondrial fission.
Dnm1 and Fis1 are prototypical proteins that regulate yeast mitochondrial morphology by controlling fission, the dysregulation of which can result in developmental disorders and neurodegenerative diseases in humans. Loss of Dnm1 blocks the formation of fission complexes and leads to elongated mitochondria in the form of interconnected networks, while overproduction of Dnm1 results in excessive mitochondrial fragmentation. In the current model, Dnm1 is essentially a GTP hydrolysis-driven molecular motor that self-assembles into ring-like oligomeric structures that encircle and pinch the outer mitochondrial membrane at sites of fission. In this work, we use machine learning and synchrotron small-angle X-ray scattering (SAXS) to investigate whether the motor Dnm1 can synergistically facilitate mitochondrial fission by membrane remodeling. A support vector machine (SVM)-based classifier trained to detect sequences with membrane-restructuring activity identifies a helical Dnm1 domain capable of generating negative Gaussian curvature (NGC), the type of saddle-shaped local surface curvature found on scission necks during fission events. Furthermore, this domain is highly conserved in Dnm1 homologues with fission activity. Synchrotron SAXS measurements reveal that Dnm1 restructures membranes into phases rich in NGC, and is capable of inducing a fission neck with a diameter of 12.6 nm. Through in silico mutational analysis, we find that the helical Dnm1 domain is locally optimized for membrane curvature generation, and phylogenetic analysis suggests that dynamin superfamily proteins that are close relatives of humandynamin Dyn1 have evolved the capacity to restructure membranes via the induction of curvature mitochondrial fission. In addition, we observe that Fis1, an adaptor protein, is able to inhibit the pro-fission membrane activity of Dnm1, which points to the antagonistic roles of the two proteins in the regulation of mitochondrial fission.
The morphology
and distribution
of mitochondria, which vary among different cell types[1] and respond to different cellular conditions,[2−5] are crucial for maintaining normal cell function.[6] Mitochondrial morphology and intracellular distribution
are primarily governed by a balance between the antagonistic processes
of fusion and fission.[7−9] Excessive fusion results in elongated mitochondria
that form highly interconnected net-like structures, whereas uninhibited
fission leads to mitochondrial fragmentation.[7,10−12] Recent studies have associated perturbations in these
dynamic processes with developmental defects and neurodegenerative
diseases.[12−14] Proteins that regulate and maintain mitochondrial
morphology, therefore, play important roles in health and disease.The machinery involved in mitochondrial fission was first identified
in yeast to include Dnm1 and Fis1,[6,9,11,15] with Drp1 and hFis1
as their respective conserved human homologues.[10,16−19] A highly conserved cytosolic dynamin-related GTPase, Dnm1 (∼85
kDa) in yeastSaccharomyces cerevisiae and Drp1 (∼82
kDa) in mammals, is the major essential protein involved in eukaryotic
mitochondrial fission.[9] Loss of Dnm1/Drp1
blocks the formation of fission complexes[15,20] and results in the formation of net-like structures.[6,8,9] Likewise, mutations in Dnm1 homologues
were also shown to block fission,[17,21] while overproduction
of Dnm1 leads to increased mitochondrial fragmentation.[22] Dnm1/Drp1 is characterized by an N-terminal
GTPase, a middle domain, and a C-terminal GTPase effector domain involved
in self-assembly.[7,23] In the current model, Dnm1/Drp1
is essentially a molecular motor recruited from the cytosol and self-assembles
into spiral-like or ring-like oligomeric structures that encircle
the outer mitochondrial membrane at sites of fission. GTP hydrolysis
leads to Dnm1/Drp1 conformational changes that constrict the membranes
to drive membrane scission.[24−28]The process of budding and scission, however, requires the
generation
of significant local negative Gaussian curvature (NGC) in membranes.[29] NGC is the saddle-shaped membrane surface curvature
found in the pinched regions of mitochondria undergoing fission. Interestingly,
recent work has indicated that specific membrane lipids can facilitate
mitochondrial fission,[29−33] and that lipids can play a role in binding, recruiting, and activating
proteins that mediate fission, including Drp1.[29,34−36] For example, a cryo-EM study has shown that the active
helical conformation of Drp1 is stabilized through direct interactions
with cardiolipin.[37] Furthermore, studies
have shown that induced membrane curvature via protein crowding can
also facilitate membrane fission.[38−40] In this work, we use
machine learning and synchrotron small-angle X-ray scattering (SAXS)
to investigate whether Dnm1–lipid interactions can play a role
in facilitating mitochondrial membrane remodeling and fission. To
assess whether Dnm1 contains subdomains that can potentially induce
membrane curvature necessary for scission, we screen the protein using
a recently developed machine-learning-based classifier[41] that predicts whether a given α-helical
peptide sequence can generate NGC. (The machine learning was performed
on α-helical sequences.) Using this classifier, we find a high-scoring
N-terminal α-helical domain (LEDLIPTVNKLQDVMYD)
in the protein sequence of Dnm1, which suggests that this domain may
be able to remodel membranes by generating NGC. Interestingly, this
sequence is conserved in Dnm1 homologues with fission activity. To
test experimentally whether Dnm1 has the capacity to facilitate fission
by restructuring membranes in a manner synergistic with its molecular-motor-based
mitochondrial “pinching” activity, SAXS is used to investigate
the full spectrum of Dnm1-induced membrane deformations in vesicles
with mitochondrial-like lipid compositions. SAXS results show that
Dnm1 indeed restructures membranes into phases rich in NGC, in agreement
with the machine-learning results. The capacity to induce NGC suggests
that Dnm1 function is more complex than just simply mechanochemical
constriction activity mediated by its oligomerization. In fact, the
membrane deformations generated by Dnm1 promote fission by rendering
membranes more amenable to the restructuring necessary for scission.
We find that the observed degree of induced NGC is consistent with
a fission neck having a diameter of 12.6 nm. An in silico mutational analysis of the N-terminal helix of Dnm1 suggests that
it is locally optimized for the induction of membrane curvature. In
addition, phylogenetic analysis of dynamin superfamily members reveals
that proteins close in mutational distance to humandynamin Dyn1 have
evolved the ability to remodel membranes through curvature generation.
These results suggest that Dnm1-induced membrane curvature and mechanochemical
forces function cooperatively to efficiently catalyze mitochondrial
fission, supporting the notion that the fission reaction depends on
a combination of both protein and membrane mechanics. Remarkably,
we find that the adaptor protein Fis1 inhibits the pro-fission membrane
activity of Dnm1 by quantitatively suppressing Dnm1-induced NGC. Based
on these antagonistic qualities of Dnm1 and Fis1, we suggest the possibility
that the two proteins work in concert to modulate mitochondrial fission.
Results
and Discussion
Machine Learning Predicts Dnm1 To Contain
Membrane-Destabilizing
Sequences
NGC can be described as saddle-splay curvature
due to its shape: the surface curves upward in one direction and downward
in the orthogonal direction. This specific type of curvature is topologically
required for membrane-destabilizing processes, such as budding and
pore formation. Moreover, it is the type of local surface curvature
found on scission necks during fission events.[42−44] (The pinched
fission neck is a classic example of NGC.) A large body of work has
identified short peptides that can sense[45] or induce membrane curvature and remodeling.[45−49] Membrane active subsequences derived from viral fusion
and fission proteins have been shown to disrupt and bend lipid bilayers.[46,50,51] In fact, for peptides that function
through membrane destabilization, a strong correlation has been found
between their ability to generate NGC and their activity.[42] For example, NGC generation has been experimentally
observed using SAXS for antimicrobial peptides (AMPs),[42,52−56] cell-penetrating peptides (CPPs),[43,57−59] and viral fusion and fission proteins.[60,61] These findings collectively suggest that generation of NGC is important
in membrane-destabilizing processes.To assess whether Dnm1
contains subdomains that may induce membrane curvature important for
fission, we employed a recently developed machine-learning classifier
trained to predict the likelihood of membrane-restructuring activity
for a given α-helical peptide sequence.[41] This classifier, which was originally trained with AMP sequences,
has been shown to be a good detector of NGC-generating α-helical
domains in diverse families of proteins, including membrane-active
segments of viral fusion proteins and endocytosis/exocytosis proteins.
More notably, its effectiveness in detecting membrane-destabilizing
activity in peptide sequences allows for the recognition of previously
undetected membrane activity in existing proteins or peptides, such
as the neuropeptide hormones.[41]The
classifier is based on a linear support vector machine (SVM)
that takes as input n = 12 physicochemical descriptors
generated from the peptide sequence and outputs a score σ specifying
the distance of the peptide from an (n – 1)
= 11 dimensional hyperplane trained to optimally separate 243 known
α-helical curvature-generating peptides from 243 decoy peptides.
A large, positive σ score correlates with increased ability
to induce NGC in membranes, whereas a negative σ score indicates
a lack of membrane-disruptive activity. This score can be converted
through a monotonic function into a probability 0 < P(+1) < 1 that the peptide induces membrane curvature. Large, positive
values of σ (P(+1) > 0.95) indicate a high
likelihood of membrane activity (ability to generate NGC), and negative
values of σ (P(+1) < 0.50) indicate a low
probability of membrane activity. Testing of the classifier on a blind
balanced test set of 86 peptides demonstrated 91.9% prediction accuracy,
93.0% specificity, and 90.7% sensitivity. Experimental validation
of computational predictions was carried out using SAXS. A subset
of α-helical test peptides were incubated with model membranes,
and induced NGC was measured. A strong correlation between the ability
to generate NGC in membranes and the distance-to-hyperplane SVM metric σ
was observed. Classification of a single peptide requires ∼0.1
s of CPU time permitting high-throughput computational screening for
membrane-active peptide discovery and design. Full details of data
set curation, physicochemical descriptor selection, and model training
and validation is provided in refs (41) and (62).We use the classifier to evaluate whether Dnm1 contains
amino acid
sequences that might remodel membranes through membrane curvature
induction. To locate potential curvature-generating domains in Dnm1
(UniProtKB: P54861), we enumerated all possible 10–25 amino acid subsequences
of the entire protein sequence and performed a moving window scan
with the membrane activity prediction tool. To visualize the membrane
activity landscape along the length of Dnm1, a normalized net σ
score was calculated by aligning all windowed sequences and averaging
over σ values (Figure A). Maxima at positive σ values in this landscape correspond
to subsequences that have high likelihood of membrane activity. In
conjunction with this scan, we predicted the secondary structure of
Dnm1 using two methods: the DSSP secondary structure prediction algorithm[63] and sequence alignment with the known crystal
structure of human homologue Drp1 (PDB: 4BEJ).[64] Surprisingly,
when we compared the results of the moving window scan with the secondary
structure prediction (Figure S1), we found
a membrane-destabilizing α-helical sequence (LEDLIPTVNKLQDVMYD,
σ = 1.36, P(+1) = 0.99) with an exceedingly
high score (Figure B). This sequence lies in a highly conserved N-terminal region shared
with Dnm1 homologues that also have membrane activity (Figure C,D; Figure S5). Likewise, the corresponding aligned sequence for Drp1
was also predicted to be membrane-destabilizing (MEALIPVINKLQDVFNT,
σ = 1.51, P(+1) = 0.99). In addition to this
sequence, the algorithm predicted six other helices of Dnm1 to potentially
possess membrane activity, but with lower confidence (P(+1) < 0.95). These machine-learning results predict that Dnm1
contains multiple α-helical sequences with potential membrane-destabilizing
activity, including one particularly high scoring domain (Figure B). These findings
from machine learning suggest the testable hypothesis that, in addition
to promoting mitochondrial fission through mechanochemical constriction
activity, the GTPase-driven molecular motor Dnm1 may have the capacity
to enhance mitochondrial fission through induced NGC membrane deformations.
Figure 1
Machine-learning
screen of Dnm1 yields a putative membrane-destabilizing
sequence in the conserved N-terminal domain. (A) Normalized σ
scores of a moving-window scan of Dnm1 for membrane activity as a
function of amino acid position. Scores are shown for the first 300
amino acids of Dnm1. The green bar highlights the location of the
top-scoring helical subsequence of Dnm1. (B) Table showing the top-scoring
helical subsequences from Dnm1 with σ > 0 and their corresponding
σ and P(+1) assignments from the machine-learning
classifier. The top hit LEDLIPTVNKLQDVMYD corresponds
to a segment of Dnm1 that contains the first N-terminal helix (highlighted
in panel A). This sequence has a P(+1) score >95%,
which indicates a high likelihood of NGC generation and membrane restructuring
ability. (C) 3D homology structure model of yeast Dnm1 (UniProtKB: P54861) (right)
based on the known crystal structure of human Drp1 (PDB: 4BEJ) (left). The first
N-terminal helices in both protein sequences are depicted in blue
and indicated by the arrows. (D) Sequence alignment of the N-terminal
regions of Drp1 and Dnm1 shows significant sequence homology and conservation.
The first two conserved N-terminal helices in both sequences are boxed
in red. The sequence boxed in green (LEDLIPTVNKLQDVMYD)
corresponds to the top hit from the machine-learning screen for membrane-destabilizing
segments of Dnm1, while the corresponding aligned sequence from Drp1
is boxed in orange (MEALIPVINKLQDVFNT).
Machine-learning
screen of Dnm1 yields a putative membrane-destabilizing
sequence in the conserved N-terminal domain. (A) Normalized σ
scores of a moving-window scan of Dnm1 for membrane activity as a
function of amino acid position. Scores are shown for the first 300
amino acids of Dnm1. The green bar highlights the location of the
top-scoring helical subsequence of Dnm1. (B) Table showing the top-scoring
helical subsequences from Dnm1 with σ > 0 and their corresponding
σ and P(+1) assignments from the machine-learning
classifier. The top hit LEDLIPTVNKLQDVMYD corresponds
to a segment of Dnm1 that contains the first N-terminal helix (highlighted
in panel A). This sequence has a P(+1) score >95%,
which indicates a high likelihood of NGC generation and membrane restructuring
ability. (C) 3D homology structure model of yeastDnm1 (UniProtKB: P54861) (right)
based on the known crystal structure of humanDrp1 (PDB: 4BEJ) (left). The first
N-terminal helices in both protein sequences are depicted in blue
and indicated by the arrows. (D) Sequence alignment of the N-terminal
regions of Drp1 and Dnm1 shows significant sequence homology and conservation.
The first two conserved N-terminal helices in both sequences are boxed
in red. The sequence boxed in green (LEDLIPTVNKLQDVMYD)
corresponds to the top hit from the machine-learning screen for membrane-destabilizing
segments of Dnm1, while the corresponding aligned sequence from Drp1
is boxed in orange (MEALIPVINKLQDVFNT).
Dnm1 Remodels Membranes and Induces Negative
Gaussian Curvature
To Promote Mitochondrial Membrane Fission
To test the predictions
from the machine-learning classifier, we assessed the ability of Dnm1
to generate membrane-destabilizing curvature. Using high-resolution
synchrotron SAXS, we quantitatively characterized Dnm1-induced membrane
deformations in model mitochondrial membranes. Small unilamellar vesicles
(SUVs) were prepared from ternary phospholipid mixtures of phosphatidylethanolamine
(PE), phosphatidylcholine (PC), and cardiolipin (CL) at molar ratios
of 75/15/10 and 75/5/20 to mimic the lipid compositions of mitochondrial
membranes.[30,35,65−67] Dnm1 was incubated with SUVs at a protein-to-lipid
(P/L) molar ratio of 1/1000, and
the resulting membrane structures were characterized using SAXS. The
Dnm1 concentrations used were comparable to those used in a large
number of prior Dnm1–lipid interaction studies (0.2–10
μM).[25,27,31,64,68−70]We found that Dnm1 restructured the lipid vesicles into phases
rich in NGC, while the control samples of SUVs only showed a broad
characteristic feature consistent with the form factor of unilamellar
vesicles (Figure S2). For the model membrane
75/15/10 PE/PC/CL, we observed a coexistence of two phases: (1) one
set of correlation peaks with Q-ratios √1:√3:√4:√7:√9,
consistent with an inverted hexagonal (HII) phase with
a lattice parameter of 7.88 nm; (2) a second set of peaks with Q-ratios √2:√3:√4:√6:√8:√9:√10,
which indexed to a Pn3m “double-diamond”
cubic (QII) phase with a lattice parameter of 34.16 nm
(Figure ). Similarly,
the model membrane 75/5/20 PE/PC/CL exhibited HII and Pn3m QII phases with lattice
parameters of 7.70 and 16.15 nm, respectively. However, for this membrane
composition, an additional third set of peaks was identified, which
indexed to an Im3m “plumber’s
nightmare” QII phase with a lattice parameter of
20.65 nm. These coexisting Pn3m and Im3m QII phases for this membrane
had lattice parameters with a ratio close to the Bonnet ratio of 1.279,[71] indicating that they are near equilibrium with
the quantitative amount of membrane curvature balanced between the
two cubic phases. Additional measurements on phosphatidylserine (PS)-containing
SUVs incubated with Dnm1 having a maltose-binding protein (MBP) tag
also showed the induction of HII and Im3m QII phases, indicating that the NGC-generating
activity is robust, consistent with earlier findings that MBP, or
other N-terminal fusion tags like GFP, do not interfere with Dnm1
activity[68,72,73] (Figure S3). A bicontinuous cubic phase, such
as Pn3m and Im3m, consists of two nonintersecting aqueous regions that
are separated by a lipid bilayer. The center of this bilayer traces
out a periodic minimal surface that has NGC at every point. From our
SAXS measurements, we found that Dnm1 promotes saddle-shaped membrane
deformations to stabilize bulk cubic phases in model membranes with
lipid compositions that closely resemble those of mitochondrial membranes,
but not others (data not shown). These observations of a strong Dnm1-induced
propensity to restructure a lamellar bilayer into an NGC-rich cubic
phase mirror the required membrane-remodeling events involved in mitochondrial
fission. More specifically, NGC promotes membrane deformations that
enable the adoption of the unique structures and morphologies that
characterize mitochondrial fission. For instance, NGC manifests at
the constriction necks of mitochondrial membranes that are formed
during the fission process. Remarkably, this intrinsic membrane activity
occurs in the absence of GTP binding or hydrolysis, and indicates
that Dnm1 may help mediate membrane fission through the induction
of NGC in a manner similar to that previously shown by viral scission
proteins.[59]
Figure 2
Dnm1 generates NGC necessary
for mitochondrial membrane fission.
(A) SAXS spectra from 75/15/10 and 75/5/20 PE/PC/CL model mitochondrial
membranes incubated with Dnm1 at P/L = 1/1000. Correlation peaks corresponding to assigned reflections
are indicated for hexagonal (green) and cubic (black lines) phases.
(B) 3D reconstructions depicting Im3m (left) and Pn3m (right) cubic
phases, which have continuous surfaces with NGC at every point. (C)
Indexing of Dnm1-induced HII, and Pn3m and Im3m QII phases for 75/15/10 (red) and 75/5/20 (blue) PE/PC/CL membranes.
Plots of the measured Q positions, Qmeasured, versus the assigned reflections in terms of
Miller indices, for hexagonal
and for cubic phases. The lattice
parameters
were calculated from the slopes of the linear regressions through
the points and are provided in the legend.
Dnm1 generates NGC necessary
for mitochondrial membrane fission.
(A) SAXS spectra from 75/15/10 and 75/5/20 PE/PC/CL model mitochondrial
membranes incubated with Dnm1 at P/L = 1/1000. Correlation peaks corresponding to assigned reflections
are indicated for hexagonal (green) and cubic (black lines) phases.
(B) 3D reconstructions depicting Im3m (left) and Pn3m (right) cubic
phases, which have continuous surfaces with NGC at every point. (C)
Indexing of Dnm1-induced HII, and Pn3m and Im3m QII phases for 75/15/10 (red) and 75/5/20 (blue) PE/PC/CL membranes.
Plots of the measured Q positions, Qmeasured, versus the assigned reflections in terms of
Miller indices, for hexagonal
and for cubic phases. The lattice
parameters
were calculated from the slopes of the linear regressions through
the points and are provided in the legend.One question to ask is whether the amount of NGC induced
by Dnm1
is quantitatively close to the amount found in mitochondrial fission
events. We compare the quantitative amount of NGC generated by Dnm1
per unit area with the amount of NGC in a constricted fission membrane
neck approximated by a catenoid surface[61,74] (red-dashed
box, Figure A,B).
The average amount of Gaussian curvature, K, in a
cubic phase can be calculated using the equation ⟨K⟩ = (2πχ)/(A0a2), where a is the lattice
parameter. The Euler characteristic, χ, and surface area per
unit cell, A0, are constants specific
to each cubic phase. For Pn3m, χ
= −2 and A0 = 1.919, and for Im3m, χ = −4 and A0 = 2.345.[71] For the 75/5/20
PE/PC/CL model membrane, Dnm1 generated Pn3m and Im3m QII phases with lattice parameters of 16.15 and 20.65 nm, respectively,
which both amount to ⟨K⟩ = −2.5
× 10–2 nm–2. The shape of
a constricted scission neck is modeled using a catenoid surface (Figure B), which is a minimal
surface that has Gaussian curvature K(z) = −sech4(z/r)/r2, where the r is
the radius of the circular cross section along the neck axis, z. We find that a value of K = −2.5
× 10–2 nm–2 equates to a
constricted membrane neck that has a radius of 6.3 nm at its narrowest
point, z = 0 (denoted by *, Figure B,C). These results show that the Dnm1-induced
NGC corresponds to a fission neck of approximately 12.6 nm diameter
in the absence of nucleotide. This value is considerably smaller than
the neck diameter typically observed using electron microscopy and
super-resolution microscopy for Dnm1/Drp1 interacting with model membranes
with simplified compositions.[75,76] In fact, neither protein
has successfully demonstrated scission of lipid tubes in vitro.[24,27,77−79] Although GTP binding is known to further constrict the membrane
tubule,[70] this constriction appears insufficient
to cause membrane scission. Recent findings demonstrate that dynamin-2
is necessary for the final stages of membrane scission of human mitochondria.[79] Thus, our observation of high membrane curvatures
and small fission necks via Dnm1-induced membrane remodeling synergistic
to motor-driven membrane constriction is highly suggestive of a composite,
mutually-amplifying fission mechanism, and that membranes with a significant
portion of negative spontaneous curvature lipids, such as mitochondrial
membranes, can be remodeled by Dnm1 into narrower fission necks via
NGC generation.
Figure 3
Dnm1-mediated NGC corresponds to a fission neck with a
diameter
of 12.6 nm. (A) A scission neck (red-dashed box) is formed during
the mitochondrial fission process. This structure can be modeled as
a catenoid surface, which allows us to estimate the neck size that
can be produced by a given quantitative amount of NGC. (B) Schematic
of a catenoid surface approximating a mitochondrial fission neck (an
enlarged view of the red-boxed region in panel A) with a diameter
of 12.6 nm at the narrowest cross section, which occurs at z = 0 (indicated by *), where z describes
the distance along the catenoid axis (i.e., the length of the neck).
(C) A plot of the calculated Gaussian curvature (K) along the surface of the model catenoid as a function of z-distance (nm). Since the catenoid is rotationally symmetric
about the z-axis, this describes K everywhere along the surface. Far from the scission neck (e.g., z = ± 10 nm), the Gaussian curvature is near zero.
As z approaches the narrowest cross section along
the catenoid surface, NGC increases drastically and reaches a maximum
value at z = 0 (denoted by * in panel B). Maximal
NGC (*) corresponds to a value of K = −2.5
× 10–2 nm–2, which is the
quantitative amount of NGC observed in the bicontinuous cubic phases
generated by Dnm1 in 75/5/20 PE/PC/CL membranes (Figure ).
Dnm1-mediated NGC corresponds to a fission neck with a
diameter
of 12.6 nm. (A) A scission neck (red-dashed box) is formed during
the mitochondrial fission process. This structure can be modeled as
a catenoid surface, which allows us to estimate the neck size that
can be produced by a given quantitative amount of NGC. (B) Schematic
of a catenoid surface approximating a mitochondrial fission neck (an
enlarged view of the red-boxed region in panel A) with a diameter
of 12.6 nm at the narrowest cross section, which occurs at z = 0 (indicated by *), where z describes
the distance along the catenoid axis (i.e., the length of the neck).
(C) A plot of the calculated Gaussian curvature (K) along the surface of the model catenoid as a function of z-distance (nm). Since the catenoid is rotationally symmetric
about the z-axis, this describes K everywhere along the surface. Far from the scission neck (e.g., z = ± 10 nm), the Gaussian curvature is near zero.
As z approaches the narrowest cross section along
the catenoid surface, NGC increases drastically and reaches a maximum
value at z = 0 (denoted by * in panel B). Maximal
NGC (*) corresponds to a value of K = −2.5
× 10–2 nm–2, which is the
quantitative amount of NGC observed in the bicontinuous cubic phases
generated by Dnm1 in 75/5/20 PE/PC/CL membranes (Figure ).Coupled with evidence implicating key roles for specific
membrane
lipids in mitochondrial fission, such as the interactions between
CL and Drp1 in forming CL-enriched membrane regions[29,35,70,80,81] that may facilitate membrane remodeling[29] and activation of Drp1 GTPase activity,[30,37] the results described here further demonstrate the crucial function
of membrane dynamics in the fission process. These findings using
synchrotron SAXS, which are in agreement with the machine-learning
classifier predictions for the existence of a membrane-active domain
that is conserved between Dnm1 and its homologue Drp1, point to the
ability of Dnm1 to catalyze mitochondrial fission via its GTPase hydrolysis-driven
mitochondrial pinching activity in conjunction with synergistic membrane-remodeling
activity.The results presented here make contact with a key
aspect of mitochondrial
fission related to Fis1, a protein localized to the outer mitochondrial
membrane. Previous studies have suggested that yeastFis1 recruits
Dnm1 from the cytosol to the mitochondrial membrane.[6,73,82] However, other studies have recently
provided evidence that Fis1 is instead dispensable for mitochondrial
membrane fission, supporting the notion that Dnm1/Drp1-mediated mitochondrial
fission is not necessarily a conserved function of Fis1.[77] Furthermore, the human homologue hFis1 does
not appear to recruit Drp1 to mitochondria.[83,84] Therefore, although Fis1 is a conserved factor for fission and believed
to function with Dnm1, its specific role in mitochondrial fission
is unclear. Using the same approach used for Dnm1, we examine Fis1
for possible membrane activity.
Fis1 Is Not Predicted To
Contain Membrane-Destabilizing Sequences
and Does Not Restructure Mitochondrial-like Membranes
Fis1
(∼16 kDa) is a highly conserved tail-anchored protein in the
mitochondrial outer membrane that is believed to be involved in mitochondrial
fission with Dnm1. Fis1 has a cytosolic N-terminal domain that contains
a six-helix array with two tandem tetratricopeptide repeat motifs
(TPR) and a C-terminal transmembrane (TM) domain serving as an anchor
into the mitochondrial outer membrane[18,85] (Figure A).
Figure 4
Helix-rich Fis1 is not
predicted to destabilize membranes and does
not restructure model mitochondrial membranes. (A) 3D structures of
yeast Fis1[86] (PDB: 3O48) (right) and human
hFis1[100] (PDB: 1NZN) (left) show strongly conserved helical
content. Both proteins contain a six-helix array. (B) The six helical
subsequences of Fis1 were screened for membrane activity using the
machine-learning classifier. A table of the Fis1 helical subsequences
with their corresponding outputs σ and P(+1)
from the machine-learning screen is reported. Predictions suggest
that helical subsequences of Fis1 have a low probability (P(+1) < 0.95) of membrane-destabilizing activity and
are unlikely to generate NGC. (C) Fis1 at P/L = 1/500 showed minimal membrane activity and did not significantly
restructure either 75/15/10 or 75/5/20 PE/PC/CL membranes (no generation
of NGC). Their SAXS profiles closely resembled those of control samples
containing only lipid vesicles (Figure S2).
Helix-rich Fis1 is not
predicted to destabilize membranes and does
not restructure model mitochondrial membranes. (A) 3D structures of
yeastFis1[86] (PDB: 3O48) (right) and humanhFis1[100] (PDB: 1NZN) (left) show strongly conserved helical
content. Both proteins contain a six-helix array. (B) The six helical
subsequences of Fis1 were screened for membrane activity using the
machine-learning classifier. A table of the Fis1 helical subsequences
with their corresponding outputs σ and P(+1)
from the machine-learning screen is reported. Predictions suggest
that helical subsequences of Fis1 have a low probability (P(+1) < 0.95) of membrane-destabilizing activity and
are unlikely to generate NGC. (C) Fis1 at P/L = 1/500 showed minimal membrane activity and did not significantly
restructure either 75/15/10 or 75/5/20 PE/PC/CL membranes (no generation
of NGC). Their SAXS profiles closely resembled those of control samples
containing only lipid vesicles (Figure S2).With the machine-learning classifier,
we evaluated Fis1 for potential
membrane-destabilizing helical sequences. We ran a moving window scan
on the protein sequence (UniProtKB: P40515) for subsequences of 10–25
residues and compared the outputs with the determined protein crystal
structure (PDB: 3O48).[86] While the algorithm predicted a few
helical sequences to have a positive σ score, none were identified
with a high probability (P(+1) > 0.95) for membrane
activity (Figure B).
Overall, these results indicate that Fis1 is unlikely to be membrane-destabilizing
or have a direct role in the membrane restructuring that occurs during
mitochondrial fission.Using SAXS, we examined SUVs incubated
with Fis1 at a P/L molar ratio of
1/500 and indeed found that the
protein did not restructure the lipid vesicles. Instead, the SAXS
profile for each model membrane, 75/15/10 and 75/5/20 PE/PC/CL (Figure C), exhibited a broad
feature that is consistent with the form factor of unilamellar vesicles
and resembled the spectra observed for the control SUVs (Figure S2). More specifically, Fis1 does not
generate NGC and, therefore, would not be expected to have direct
membrane activity that mediates membrane fission in the manner of
Dnm1, which is consistent with our machine-learning predictions.
Fis1 Inhibits Dnm1-Mediated Membrane Disruption
That
Fis1 does not restructure membranes in the manner of Dnm1, by inducing
NGC, raises interesting questions on precisely what its physical role
is, given extant results on its unambiguous contributions to the fission
process. To expand on the observation that Fis1 does not disrupt model
mitochondrial membranes by generating NGC, we investigate how Fis1
interacts with Dnm1, and do so in the context of membrane interactions,
since direct interactions between Dnm1 and Fis1 have been previously
studied.[18,68,73] Specifically,
we explored how Dnm1 in combination with Fis1 impacts Dnm1 membrane
remodeling.SUVs were incubated with both Dnm1 (P/L = 1/1000) and Fis1 (P/L = 1/500) and measured using SAXS. Model membrane 75/15/10
PE/PC/CL exhibited an HII phase with a lattice parameter
of 7.94 nm, while membrane 75/5/20 PE/PC/CL possessed an HII phase and a coexistence of Im3m and Pn3m QII phases,
characterized by lattice parameters of 7.83, 24.09, and 19.00 nm,
respectively (Figure ). Comparing these SAXS measurements to our previous results, we
noted changes in the phase identity and magnitude of membrane curvature
induced by Dnm1 while in the presence of Fis1. Specifically, for membrane
75/15/10 PE/PC/CL, the Pn3m QII phase induced by Dnm1 alone (Figure A,C) is now no longer apparent (Figure ). Similarly, the Im3m QII phase that resulted
from MBP-Dnm1 alone is also now absent (Figure S3). These disappearances of cubic phases imply a marked reduction
in the ability of Dnm1 to generate NGC in mitochondrial-like membranes
while in the presence of Fis1. Furthermore, we found that the lattice
parameter of the hexagonal phase increased from 7.88 to 7.94 nm. Larger
lattices were also observed for all coexisting phases in model membrane
75/5/20 PE/PC/CL, with unit-cell spacing increasing from 7.70 to 7.83
nm (HII), 20.65 to 24.09 nm (Im3m QII), and 16.15 to 19.00 nm (Pn3m QII). Importantly, these increased
lattice parameters correspond to decreased magnitudes of induced membrane
curvature for each associated phase, as the two characteristics are
inversely related.[71,87,88] For instance, while a hexagonal phase is characterized by having
a Gaussian curvature of zero, it has a mean curvature of H ≈ −1/a (radius of hexagonal lipid
cylinder ≈ 0.5a), and cubic phases have an
average Gaussian curvature of ⟨K⟩ =
(2πχ)/(A0a2). Both of these quantities decrease in magnitude with
an increasing lattice parameter a. Specifically,
the increased lattice parameters for the Im3m and Pn3m cubic phases
correspond quantitatively to a decrease in NGC magnitude from ⟨K⟩ = −2.5 × 10–2 nm–2 for Dnm1 alone to ⟨K⟩
= −1.8 × 10–2 nm–2 for Dnm1 in the presence of Fis1. These findings indicate that Fis1
limits the ability of Dnm1 to generate membrane curvature that would
facilitate mitochondrial fission. That the interactions between Fis1
and Dnm1 are complex from extant studies suggests that the action
of Fis1 in this context may be pleiotropic, impacting Dnm1 through
multiple channels of activity such as protein–protein binding
or direct induction of complementary membrane curvature to “cancel”
NGC. Yeast cell biological findings are congruent with this idea in
that Fis1 may act at two distinct stages in fission.[68,89] Thus, the combined membrane activity of Dnm1 and Fis1 in principle
affords the ability to control the equilibrium diameter of the fission
neck, in a way that is complementary to the mechanical constriction
model.
Figure 5
Dnm1 exhibits reduced ability to generate NGC in the presence of
Fis1. (A) Dnm1 (P/L = 1/1000) in
combination with Fis1 (P/L = 1/500)
restructured both 75/15/10 and 75/5/20 PE/PC/CL model membranes but
with decreased membrane curvature induction compared to Dnm1 alone
(Figure ). Correlation
peaks corresponding to assigned reflections are indicated for hexagonal
(green) and cubic (black lines) phases. (B) Indexing of generated
HII, and Im3m and Pn3m QII phases for 75/15/10
(red) and 75/5/20 (blue) PE/PC/CL membranes reveals increases in the
lattice parameters for all induced phases compared to membranes incubated
only with Dnm1 (Figure C). These larger lattice parameters and no observed Pn3m QII phase for 75/15/10 PE/PC/CL indicate
a marked attenuation in overall curvature induction and a reduced
ability of Dnm1 to generate NGC.
Dnm1 exhibits reduced ability to generate NGC in the presence of
Fis1. (A) Dnm1 (P/L = 1/1000) in
combination with Fis1 (P/L = 1/500)
restructured both 75/15/10 and 75/5/20 PE/PC/CL model membranes but
with decreased membrane curvature induction compared to Dnm1 alone
(Figure ). Correlation
peaks corresponding to assigned reflections are indicated for hexagonal
(green) and cubic (black lines) phases. (B) Indexing of generated
HII, and Im3m and Pn3m QII phases for 75/15/10
(red) and 75/5/20 (blue) PE/PC/CL membranes reveals increases in the
lattice parameters for all induced phases compared to membranes incubated
only with Dnm1 (Figure C). These larger lattice parameters and no observed Pn3m QII phase for 75/15/10 PE/PC/CL indicate
a marked attenuation in overall curvature induction and a reduced
ability of Dnm1 to generate NGC.
Membrane Activity Fitness Landscape of the Dnm1 N-Terminal Helix
and Evolution of Membrane Activity in the Dynamin Superfamily
Now that we have experimentally validated the ability of Dnm1 to
directly generate NGC in mitochondrial-like membranes, we apply the
machine-learning classifier to explore the membrane activity fitness
landscape of the Dnm1 N-terminal helix, and trace the evolution of
the membrane-remodeling ability of this helical domain in members
of the dynamin protein superfamily. In our previous work, we combined
machine learning with experimental SAXS measurements to demonstrate
that a positive, monotonic relationship exists between σ and
the NGC-generating ability of membrane-active helical peptides.[41] Conveniently, the observed NGC induced by Dnm1
(−2.5 × 10–2 nm–2)
and its σ score (1.36) fall exactly within the predictive range
established by previous SAXS experiments. This relationship thus allows
us to evaluate the local membrane activity fitness landscape of Dnm1
and its related family members without the cost of experimentally
screening hundreds of mutants (this strategy has been previously used
in the context of protein stability and viral fitness[90,91]).First, we conducted an in silico mutational
analysis of the N-terminal helical sequence corresponding to the top
hit from the initial machine-learning screen (LEDLIPTVNKLQDVMYD, Figure A,B). 323 unique
single mutants (17 × 19) were generated and screened using the
SVM-based membrane activity classifier.[41] The σ values of all single mutants were quantitatively compared
to the wild type (WT) sequence by the metric Δσ = σmutant – σWT. An increase in σ
upon mutation (Δσ > 0) corresponded to conservation
of
NGC-generating activity, and a decrease in σ upon mutation (Δσ
< 0) predicted a loss of NGC-generating activity. The sign and
magnitude of Δσ for each single mutant is plotted on a
heat map (Figure A),
and the distribution of Δσ is quantified (Figure B). Interestingly, residues
N9 and K10 were found to be especially important for membrane activity,
with all 19 possible mutations leading to large reductions in σ.
Other important residues include L1, L4, L11, D13, and D17 (Figure S4). This demonstrates that the majority
of single mutations led to reductions in σ and the predicted
ability to generate NGC. The average value of Δσ for all
single mutants was −0.29, and the distribution of Δσ
values is heavily skewed to negative values (skewness = −1.43)
(Figure B). In fact,
a third of the single mutants (108) led to drastic reductions in σ
(Δσ ≤ – 0.3). Overall, this mutational analysis
of the Dnm1 N-terminal helix demonstrates that the WT sequence is
well-optimized for membrane-restructuring activity.
Figure 6
Membrane activity fitness
landscape of the Dnm1 N-terminal helix
and evolution of membrane activity in the dynamin superfamily. (A)
2D heat map of the membrane activity fitness landscape of the Dnm1
N-terminal helix. Fitness of 323 unique single mutants is calculated
as Δσ = σmutant – σWT. Mutations predicted to reduce membrane activity are shown
in magenta, while those that conserve membrane activity are shown
in green. (B) Distribution of Δσ shows that the mutational
landscape is skewed toward reduced σ (negative Δσ),
indicating that the Dnm1 N-terminal helix is well-optimized for membrane
activity. (C) A phylogram is constructed via multiple sequence alignment
of 33 dynamin superfamily members, including yeast Dnm1 (DNM1_Sc)
and human Drp1 (DRP1_Hs) (Figure S5). The
lengths of the branches are proportional to the phylogenetic distance
from the nearest ancestral branch. Machine-learning screens for membrane
activity were carried out on an aligned 16-amino-acid span corresponding
to the conserved N-terminal helix in Dnm1 and Drp1. The members shown
in green are predicted to be able to generate NGC (σ > 0),
while
those in black are not (σ < 0). All family members shown
in green except for MFN2_Dr are amphitropic (lacking a TM domain),
while most family members shown in black contain TM domains (except
for MXA_Hs, MXB_Hs, MXA_Dr, and MXB_Dr). (D) Phylogenetic distance
from the reference human dynamin DYN1_Hs is inversely related to the
predicted ability to generate NGC (σ). Dynamin family members
are color coded by phylogenetic distance, in units of number of substitutions
per site (red = closet, blue = furthest). As phylogenetic distance
increases, ability to generate NGC is decreased (RPearson = −0.728 [−0.859, –0.587], P < 10–5, RSpearman = −0.750 [−0.863, –0.560], P < 10–5, N = 33). The density
plot shows two clusters of sequences, with the upper left cluster
roughly corresponding to the amphitropic members and the lower right
cluster roughly corresponding to the membrane-tethered members. The
locations of DNM1_Sc and DRP1_Hs are labeled in white. Raw data are
found in Table S1.
Membrane activity fitness
landscape of the Dnm1 N-terminal helix
and evolution of membrane activity in the dynamin superfamily. (A)
2D heat map of the membrane activity fitness landscape of the Dnm1
N-terminal helix. Fitness of 323 unique single mutants is calculated
as Δσ = σmutant – σWT. Mutations predicted to reduce membrane activity are shown
in magenta, while those that conserve membrane activity are shown
in green. (B) Distribution of Δσ shows that the mutational
landscape is skewed toward reduced σ (negative Δσ),
indicating that the Dnm1 N-terminal helix is well-optimized for membrane
activity. (C) A phylogram is constructed via multiple sequence alignment
of 33 dynamin superfamily members, including yeastDnm1 (DNM1_Sc)
and humanDrp1 (DRP1_Hs) (Figure S5). The
lengths of the branches are proportional to the phylogenetic distance
from the nearest ancestral branch. Machine-learning screens for membrane
activity were carried out on an aligned 16-amino-acid span corresponding
to the conserved N-terminal helix in Dnm1 and Drp1. The members shown
in green are predicted to be able to generate NGC (σ > 0),
while
those in black are not (σ < 0). All family members shown
in green except for MFN2_Dr are amphitropic (lacking a TM domain),
while most family members shown in black contain TM domains (except
for MXA_Hs, MXB_Hs, MXA_Dr, and MXB_Dr). (D) Phylogenetic distance
from the reference humandynamin DYN1_Hs is inversely related to the
predicted ability to generate NGC (σ). Dynamin family members
are color coded by phylogenetic distance, in units of number of substitutions
per site (red = closet, blue = furthest). As phylogenetic distance
increases, ability to generate NGC is decreased (RPearson = −0.728 [−0.859, –0.587], P < 10–5, RSpearman = −0.750 [−0.863, –0.560], P < 10–5, N = 33). The density
plot shows two clusters of sequences, with the upper left cluster
roughly corresponding to the amphitropic members and the lower right
cluster roughly corresponding to the membrane-tethered members. The
locations of DNM1_Sc and DRP1_Hs are labeled in white. Raw data are
found in Table S1.Next, to examine the membrane activity of Dnm1 in the global
scope
of dynamin superfamily proteins, we conducted machine-learning screens
on the homologous helical domains of family members. A sequence alignment
(Figure S5) and phylogenetic reconstruction
of 33 members of the dynamin superfamily from various organisms (Figure C) revealed that
dynamins fall into two previously unrecognized groups: membrane-bound
(containing a TM domain) and amphitropic, which are freely soluble
proteins that reversibly interact with membranes (lacking a TM domain).
We calculated σ for the homologous segments of the 33 dynamin
superfamily members (Table S1) and found
that 15 of the sequences were predicted to have NGC-generating activity
(σ > 0) while 18 did not (σ < 0) (Figure C,D). Based on sequence alignment
and secondary structure prediction, these conserved regions are likely
helical. While 14 of the 15 positive hits belonged to amphitropic
dynamins, 14 of the 18 proteins that were predicted to lack NGC-generating
domains belonged to the membrane-tethered dynamins (MGM1_Sc is a member
of both families since it has a long membrane-bound form and a short
amphitropic form[92]). Based on this finding,
we hypothesized that amphitropic dynamin superfamily members evolved
the ability to generate NGC to augment their membrane-remodeling functions.
To probe the quantitative relationship between the ability to generate
NGC and the evolutionary history of dynamin-related proteins, we calculated
the strength of correlation between σ and the phylogenetic distance
of each family member from the human classical dynamin DYN1_Hs (Figure D). We calculated
Pearson and Spearman correlations, as well as the newly developed
nonlinear correlation metrics distance correlation (dCor)[93] and maximal information coefficient (MIC).[94] Surprisingly, we find a strong, statistically
significant negative correlation between phylogenetic distance and
predicted ability to generate NGC (σ) (RPearson = −0.728 [−0.859, –0.587], P < 10–5; RSpearman = −0.750 [−0.863, –0.560], P < 10–5). Nonlinear correlations
suggest a strong statistical dependence between σ and phylogenetic
distance (RdCor = 0.781 [0.678, 0.883], P < 10–5; RMIC = 0.832 [0.786, 0.999], P < 10–5). Indeed, these results suggest that amphitropic members of the
dynamin superfamily evolved the ability to generate NGC, and the predicted
strength of NGC generation scales inversely with evolutionary distance.
A density analysis of σ versus phylogenetic distance reveals
two clusters of dynamin superfamily members, which closely recapitulates
the classification of the proteins as amphitropic (Figure D, upper left) and membrane-tethered
(Figure D, lower right).
Conclusions and Prospects
In summary, results from machine
learning and synchrotron SAXS
indicate that the fission protein Dnm1 may catalyze mitochondrial
fission by both its GTPase hydrolysis-driven mitochondrial mechanical
pinching activity and its synergistic membrane-remodeling activity.
The observations here provide a framework to reconcile diverse extant
results. The NGC thus generated by Dnm1 can reach fission neck sizes
of 12.6 nm in diameter, which is smaller than the observed diameters
of ∼120 nm and ∼70 nm, in the absence and presence of
nucleotide, respectively, from mechanical constriction,[24,27] suggesting that membrane remodeling may amplify effects from motor
activity alone. The existence of strong membrane-remodeling activity
provides a point of contact with recently observed roles for specific
membrane lipids in mitochondrial fission: The ability of Dnm1 to form
oligomeric spirals and induce NGC suggests that the protein drives
mitochondrial membrane constriction through a combination of both
mechanical pinching effects and membrane remodeling by creating the
curvature needed for scission neck formation. We note that it is possible
that the two effects are synergistic, since mechanical constriction
can locally enrich negative spontaneous curvature PElipids in the
neck region,[95] which will in turn lower
the absolute value of the Gaussian modulus and thereby make it even
easier to form narrower necks.[56] Finally,
that the sequence domain for membrane curvature generation falls within
a highly conserved region between Dnm1 and Drp1 suggests that such
membrane activity may be a general feature of dynamin superfamily
GTPases.[96]The findings presented
here also suggest that Fis1 may regulate
the ability of Dnm1 to induce membrane-destabilizing curvature that
would promote mitochondrial fission. Indeed, our measurements demonstrate
that Fis1 alone does not disrupt bilayer membranes at the stoichiometries
examined. However, it is possible that the protein induces membrane
curvature that counteracts the NGC-inducing activity of Dnm1. In support
of this notion, previous studies have demonstrated that Fis1 binds
directly with membranes and that this interaction can lead to vesicle
clustering that is not membrane-disruptive, as the vesicles retain
their original shape.[97] Previous experiments
on yeast cells have also shown that Dnm1-mediated mitochondrial fission
can be inhibited by Fis1.[98] However, the
landscape of interactions is likely complex, given that expression
of Fis1 and Dnm1 alone is insufficient for fission,[20,77] which can occur upon expression of the adaptor Mdv1.[20,72,77] Based on these results, we hypothesize
that the regulation of Dnm1 activity by Fis1, as an inhibitor, may
play a role in regulating mitochondrial fission by controlling the
effective diameter of the fission neck in a parallel channel of activity
in addition to mechanoconstriction.
Methods
Dnm1 and
Fis1 proteins were expressed and purified as previously
described.[99] We used a previously published
SVM-based machine-learning tool[41] in conjunction
with sequence alignment, homology modeling, and secondary structure
prediction to screen for membrane-active sequences. Proteins were
incubated with model mitochondrial membranes and characterized with
SAXS. A phylogenetic reconstruction of 33 dynamin superfamily members
was generated, and aligned sequences were screened for membrane activity.Further details can be found in the Supporting Information.
Authors: Anna V Shnyrova; Pavel V Bashkirov; Sergey A Akimov; Thomas J Pucadyil; Joshua Zimmerberg; Sandra L Schmid; Vadim A Frolov Journal: Science Date: 2013-03-22 Impact factor: 47.728
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