* Department of Clinical Veterinary Sciences, University of Bristol, Langford, Bristol, BS18 7DY, United Kingdom; and Biochemistry Department, Eastern Virginia Medical School, Norfolk, Virginia 23507
Electron microscopy of negatively stained myosin has previously revealed three discrete regions within the heads of the molecule. However, despite a probable resolution of ~2 nm, it is difficult to discern directly consistent details within these regions. This is due to variability in both head conformation and in staining. In this study, we applied single-particle image processing and classified heads into homogeneous groups. The improved signal-to-noise ratio after averaging these groups reveals substantially improved detail. The image averages were compared to a model simulating negative staining of the atomic structure of subfragment-1 (S1). This shows that the three head regions correspond to the motor domain and the essential and regulatory light chains. The image averages were very similar to particular views of the S1 model. They also revealed considerable flexibility between the motor and regulatory domains, despite the molecules having been prepared in the absence of nucleotide. This flexibility probably results from rotation of the regulatory domain about the motor domain, where the relative movement of the regulatory light chain is up to 12 nm, and is most clearly illustrated in animated sequences (available at http://www.leeds.ac.uk/chb/muscle/ myosinhead.html). The sharply curved conformation of the atomic model of S1 is seen only rarely in our data, with straighter heads being more typical.
MUSCLE force is derived from the interaction of
myosin and actin. The most likely mechanisms
involve a large conformational change in the attached head of the myosin molecule, either in the binding
angle made with actin (Huxley, 1969 The most detailed electron micrographs of myosin molecules have been obtained by negative staining and have
revealed three discrete regions within the heads. The most
common appearance consists of a large distal region connected to the head-tail junction by two smaller regions in
line (Walker et al., 1985 It is possible to recover more information from such micrographs by using the image processing technique known
as "single-particle analysis" (Frank, 1996 Initial attempts to classify the images based on the appearances of whole heads were difficult to interpret. It
emerged that heads not only adopt a variety of orientations on the grid, but also a variety of conformations between the motor and regulatory domains. Single particle
analysis has been used previously, mainly with invariant
structures. Therefore, we classified heads twice: first using
only the motor domains, and then using only the regulatory domains. The data from both rounds of classification
were then combined. The results demonstrate a large degree of flexibility within heads.
Specimen Preparation
Negative staining of myosin was carried out as described previously
(Walker et al., 1985 Electron Microscopy and Digitization
Grids were examined with an electron microscope (EM400T; Philips Electron Optics, Eindhoven, The Netherlands) operating at a magnification of
x46,000, and with a cold trap. A moderate dose (~105 electrons/nm2) was
used, but this was not accurately controlled. Whole micrographs were digitized as 16-bit images using a Leafscan-45 densitometer (Leaf Systems,
Inc., Southborough, MA) with a pixel size corresponding to 0.435 nm.
These data were then imported into the SPIDER program suite (Health
Research Inc., Rensselaer, NY) (Frank et al., 1981a Particle Selection and Alignment
Myosin molecules, showing at least one clearly visible head, were selected
interactively and windowed out as 128 x 128 pixel images; these were
large enough to contain both heads and a small portion of the tail. This selection from the original micrographs reduced the data from ~830 to
~100 megabytes. The digitized images were then scaled to adjust their
pixel values to a mean of 0 ± 1.0 SD. Individual heads, which would constitute the single particles in the analysis, were then windowed interactively as 64 x 64 pixel images and brought into approximate alignment by
programs using reference-free alignment (Penczek et al., 1992 Classification of Heads
The aligned heads were low pass filtered to 1/2.0 nm Negative Stain Model
To examine whether the new details revealed by image processing reflect
the real structure of the head, we constructed a simple negative stain
model using the atomic structure of chicken S1 (Rayment et al., 1993b This volume was then rotated to generate a quasi-uniform distribution
of orientations (Penczek et al., 1994 Resolution
Because myosin heads are attached to the flexible head-tail junction, estimating the resolution of the image averages by the differential phase residual method (Frank et al., 1981b Head Selection and Alignment
Negatively stained myosin molecules generally show two
comma shaped heads attached to a long thin tail (Fig. 1).
Flexibility about the head-tail junction is shown by the
wide variety of head orientations (Fig. 2). Variations in
head curvature and substructure are also evident.
1,512 such images were obtained from a data set comprising molecules stained in the absence of any nucleotide.
From these images, 2,492 individual heads were selected
and subjected to alignment. Removal of misaligned heads
left 2,289 images after the first round of alignment. Because we were aware that the heads showed curvature and
possibly flexibility, a second alignment was done using only
the motor domain; after this refinement there were 1,951 heads in the data set. The average and variance images from both rounds of alignment are shown in Fig. 3. The reduction in variance around the motor domain in Fig. 3 d
shows that the refinement was successful. Fig. 3 also shows
the three head regions identified previously much more
clearly than in the original micrographs (Walker et al.,
1985
Negative Stain Model
The negative stain model is illustrated in Fig. 4. This shows
how two different orientations of S1 are affected by simulated staining. In some orientations, part of S1 falls outside
the stain envelope and so does not contribute to the final
image (Fig. 4, f-j).
The appearance of S1 in the model was relatively insensitive to the choice of parameters used to determine both
the stain envelope profile and the relative electron densities of stain and protein (chosen as 100:1). However, the
appearance was altered dramatically by the overall stain
depth: this determined how much of the S1 structure fell
within the stain envelope and also affected the contrast of
protein, peripheral stain, and background stain in the final
image. We chose a depth (3.7 nm; Fig. 4, c and h) that best
reproduced the contrast in the real group averages (see Figs. 7 and 8).
Classification of Heads
Attempts to classify whole heads revealed that those with
similar motor domain appearances possessed a variety of
regulatory domain appearances. It therefore appeared that
the motor and regulatory domains might be moving relative
to one another. Since single particle analysis has mainly
been used with invariant structures, we divided the classification into two stages, based separately on the motor and
regulatory domains. Combining the results from both rounds
of classification would allow us to demonstrate unequivocally that heads with the same motor domain appearance
could display a variety of regulatory domain appearances.
The masks used to isolate these regions of the heads are
shown in Fig. 5.
It was also apparent that the data contained no distinct
groups or clusters, which indicates there were no strongly
preferred orientations on the grid. Thus, dividing the data
into groups suitable for averaging was somewhat arbitrary
and these would inevitably contain a range of orientations.
Extensive tests were made with several different classification strategies, involving various combinations of correspondence analysis, principal components analysis, and hierarchical ascendant classification, but these produced
unsatisfactory results in the way individual images were merged into groups. Finally, we found that the technique
known as K-means clustering was the most successful because it allowed us to control the division of the relatively
uniformly distributed data into groups of roughly similar
sizes. We examined the results of many trials, varying the
number of groups between 2 and 60. In deciding on the
optimum number, we needed to balance two conflicting trends: fewer groups contain more individual heads per
group, thereby allowing a more detailed examination of
their regulatory domains; whereas more groups can show
greater morphological diversity. We found that 20 groups
provided sufficient diversity, while maintaining large enough
group sizes to enable a second classification into four regulatory domain subgroups.
The results of classification are shown in Fig. 6. Only those
motor domain groups that compared favorably with the
model are included (see below). Both the motor domains
(Fig. 6, top row) and regulatory domains (Fig. 6, left column) show a surprising amount of detail, the essentials of
which are preserved in their corresponding subaverages
(where n > 8). Within each column, the appearance of the
motor domains is constant, but the regulatory domains
show considerable variability; this strongly suggests that
head conformation is variable.
K-means clustering was repeated a number of times because the outcome can depend upon the initial choice of
seeds used in the iterative process (Frank, 1996 Comparison between Data and Model
Rotating S1 in the model about a single axis at 36° intervals produced good matches with the regulatory domains
in the data (Fig. 7). Thus the regulatory domains appear to
be related to one another by rotation through an angle of
about 110°. However, agreement with the motor domains
was good in only some cases. This suggests that the motor
and regulatory domains may also have rotated with respect
to one another.
Good matches with the motor domains were found for
10 of the 20 group averages in the data (Fig. 8, a and b).
From the model (Fig. 8 b) it is clear that the motor domain
is highly asymmetric and different orientations account for
many of the appearances seen in the data, even when the
model images contain orientations within ±20°. A close
match with the remaining 10 groups could not be found so
we excluded them.
Having determined the approximate orientations of the
motor domains, we combined the data into a single, three-dimensional volume to summarize our findings. Because
of the limited number of views available, we chose to construct a simple envelope encompassing all the conformations found in the data. First, two-dimensional envelopes
were generated for each of the motor domain groups (Fig. 8 c). Using these and the corresponding orientations of the
S1 model, we determined a three-dimensional volume using
a simple back-projection method (for review see Frank,
1996 The relationship between this envelope and S1 is shown
in Fig. 9. The wire frame motor domain is larger than S1,
probably due to flattening during specimen preparation,
but the extent of the envelope around the regulatory domain is much larger, indicating the magnitude of the movement in this part of the head. Using the S1 orientation on
actin given by Whittaker et al. (1995)
The most striking feature of the averaged head images is
the amount of detail they reveal (Fig. 6). Whereas three
regions were previously identified within the heads, image
processing now shows considerable substructure within
these regions. Classification has also enabled us to demonstrate directly that, even in the absence of nucleotide,
heads display a variety of conformations.
Head Structure
The availability of the atomic structure of S1 has allowed
us to construct a negative-stain model that we have used to
examine the fidelity of the image averages from the micrographs. So far as we are aware, this is the first time that
negatively stained, single particles have been explored in
this way. The model is simple in that it includes neither
stain migration (Unwin, 1974 Single-particle image processing was previously applied
to myosin molecules shadowed with platinum by Vibert
(1988) Head Flexibility
Each of the columns in Fig. 6 shows a constant motor domain appearance but considerable variability in the regulatory domain. This suggests strongly that there were
movements between the motor and regulatory domains;
however, the exact nature of this flexibility is difficult to
establish. Each of the four rows in Fig. 6 has a relatively
constant regulatory domain and these are similar to the
appearances generated by rotating the S1 model at 36° intervals (Fig. 7). Thus it appears that some of the head flexibility can be accounted for by relative rotation of the motor
and regulatory domains, but caution is necessary because
the restricted size of the data set necessitated division of
the heads into a small number of groups. Classification into
more groups also suggested flexibility, but the regulatory
domains were less similar to the group averages shown in
Fig. 7 (data not shown). Thus we cannot rule out other
motions such as flexing of the regulatory domain.
The average regulatory domain conformations (Fig. 8 a)
are straighter than their modeled counterparts (Fig. 8 b).
Thus the sharply curved conformation of S1 determined
by crystallography is relatively uncommon among stained
heads. This difference may occur because staining tends to
straighten heads; alternatively, crystallization may impose
a curved conformation on a structure that is much more
mobile in solution. The extent of flexibility is such that the
regulatory light chain can move by up to 12 nm in both axial and circumferential directions (Fig. 9). However, given
the number of images in each of the subgroup averages
(Fig. 6), the incidence of such large movements is rare.
Several reports suggest that heads are capable of substantial flexibility, even when not hydrolyzing ATP. Craig
et al. (1980) Head flexibility may be affected by actin binding or the
presence of nucleotide and we cannot rule out effects of
the staining method. Nevertheless, the present work shows
directly that myosin heads are capable of substantial changes
in shape, achieved by movements between the motor and
regulatory domains, and such movements are consistent
with the predicted properties of cross-bridges.
), or within the head
itself (Rayment et al., 1993a
). Attached heads may also deform elastically in response to stress, allowing storage of
strain energy derived from ATP or stretching. Information
about the structure and flexibility of the myosin head is
therefore important in exploring the molecular mechanism of force generation.
; Walker and Trinick, 1986
, 1988
).
After the subsequent elucidation of the atomic structure
of myosin subfragment-1 (S1)1 by X-ray crystallography
(Rayment et al., 1993b), it seemed likely that the three regions identified correspond to the motor domain and the
essential and regulatory light chains. However, although the micrographs show many details, probably to a resolution of ~2 nm, further analysis is hindered by the wide
variations in head appearance and by the considerable
noise caused by the size and variability of the stain crystallites.
). This allows images with similar appearances to be classified into homogeneous groups; these can then be averaged, thus improving the signal-to-noise ratio. This type of approach was
used previously with the heads of shadowed myosin molecules (Vibert, 1988
). Here we describe its application to
negatively stained molecules, which reveals a considerable
amount of new detail. To confirm the validity of our results we developed a negative stain model and simulated
staining of the atomic structure of S1. This showed that the
three regions in stained myosin heads are indeed the motor domain and the essential and regulatory light chains
(the regulatory domain).
Materials and Methods
; Walker and Trinick, 1986
). Rabbit myosin, purified as
described by Perry (1955)
, was diluted with 0.6 M Na-acetate, 5 mM
MgCl2, 1 mM EGTA, and 6 mM K-phosphate, pH 7.0, to a final concentration of ~4 µg/ml and applied to carbon-coated electron microscope
grids. The films were prepared by resistive evaporation from carbon fibers
onto freshly cleaved mica. Immediately before application of myosin, grids
were irradiated for 40 min with a UV mercury vapor lamp (type R51; UV
Products Inc., Pasadena, CA). The carbon was as thin as possible without
being unacceptably fragile after the UV irradiation. Once on the grid, the
myosin was washed with dilution buffer at 37°C before being stained with freshly prepared 1% uranyl acetate at 4°C (Walker and Trinick, 1986
).
) running on a Silicon
Graphics Indigo workstation (Silicon Graphics Computer Systems, Mountain
View, CA). All subsequent image processing was performed using SPIDER.
). Images
found to have been misaligned were discarded: rotational misalignments by 180° were identified visually and particles requiring translational vectors of 16 pixels or more (~7 nm) were automatically removed. The alignment was then refined based on features within the motor domains. After
this, images with rotations >±50° or shift vectors >6 pixels (~3 nm) were
also discarded.
1 and classified directly (i.e., without prior treatments, such as correspondence analysis or
principal components analysis) using "K-means clustering" (Frank, 1990
)
as follows. The data were classified in two independent rounds, using different masks to isolate the motor and regulatory domains. Thus each head
was assigned to a particular motor domain and regulatory domain group.
These two sets of results were then combined to produce a table in which
each entry contained heads with consistent motor and regulatory domain
appearances. All heads assigned to the same motor domain and regulatory domain subgroup were therefore similar in appearance and were averaged.
).
Firstly, a SPIDER volume was generated from the C
atom coordinates of
the atomic structure, with the amino acids represented by uniform density
spheres of 0.6 nm diam. Cavities within this structure smaller than 2 nm
were filled, to simulate exclusion of the 2-nm diam stain crystallites. This
was achieved by low pass filtering the volume to 1/2.0 nm
1, followed by
thresholding to create a second volume. Adding this to the original volume created a new volume in which all invaginations <2 nm had been
filled, leaving protrusions unaffected.
) on a "carbon film" at 10° angular increments. Negative stain was simulated by low pass filtering this volume
(S1 structure and carbon film), followed by thresholding and contrast inversion. The "stained" volume was then projected in a direction perpendicular to the carbon film to simulate the negative stain image. Finally,
model images with similar orientations (±20°) were aligned and averaged, to simulate the effect of image classification and averaging (see Results).
These model views were then compared to the real group averages by inspection.
) is difficult, due to the choice that must
be made about the shape of the mask used to isolate the head. However,
with various masks designed to exclude the head-tail junction to greater
and lesser extents, estimates in the region of 2.5 nm were common (for the
larger subgroup averages). This is typical for the negative staining method.
Results
Fig. 1.
Examples of negatively stained myosin molecules
prepared in the absence of nucleotide. The two heads and tail
are clearly visible. Bar, 20 nm.
[View Larger Version of this Image (103K GIF file)]
Fig. 2.
Gallery of typical myosin heads used in this study.
The heads display various degrees of curvature and occasionally three discrete structural
regions can be visualized (arrowheads). Individual heads were
identified interactively from such
images for use in the single-
particle analysis. Bar, 20 nm.
[View Larger Version of this Image (120K GIF file)]
; Walker and Trinick, 1988
).
Fig. 3.
(a) Global average after the first round of alignment (n
= 2,289). Despite the multitude
of head appearances making up
this image, three structural regions corresponding to the motor domain and both light chains
are clearly visible. (b) Variance
image from a. White indicates
high variance. As is typical in
negative stain images, most variability is in the periphery of the
molecule where stain collects.
(c) Global average of the heads
after refinement (n = 1,951),
and corresponding variance image (d). Note the reduction in
variance around the motor domain and the increase in the region of the light chains.
[View Larger Version of this Image (103K GIF file)]
Fig. 4.
S1 modeled in negative
stain. Two orientations of S1 are
shown (a-e, and f-j). (a) Simple
projections. Arrowheads (a and
f) indicate the position of the
sections shown in (b and g),
made perpendicular to the carbon film (b and g, white line). (c
and h) Negative stain envelopes.
The relative electron densities of stain and protein were chosen to
be 100:1. (d and i) Projections of
"stained" S1. The regulatory
light chain contributes very little to the image (i) because it falls
outside the stain envelope. When
images with similar orientations
are aligned and averaged (e and
j) there is a small degradation in
the overall contrast of the images, and in particular, the regulatory light chain (e) becomes
weaker.
[View Larger Version of this Image (28K GIF file)]
Fig. 7.
Comparison of the
regulatory domain group averages (a) with the model (b). The
model images were derived by
rotating S1 about a vertical axis
(in the plane of the figure) at 1°
intervals and averaging them
into bins of ±18°. The overall
position of the regulatory domain with respect to the motor
domain is consistent in all cases,
as are morphological features of
the regulatory domains, particularly the appearance of the essential light chains (arrowheads).
The corresponding motor domains do not correlate as well,
suggesting that the heads in our
data are not rigid structures.
[View Larger Version of this Image (51K GIF file)]
Fig. 8.
Comparison with modeled
S1. The 10 motor domain groups (a,
shown also in Fig. 6) and their corresponding best matches with S1 modeled in negative stain (b). The motor
domains in the group averages are
generally larger than in the model but
many features are shared. Note the
difference in position of the regulatory domain in the model and the average position in each motor domain group. A two-dimensional envelope
was derived for each motor domain group (c) encompassing all regulatory domain positions. Using these
envelopes and the orientations derived from the S1 model, a simple
three-dimensional envelope was calculated that, when equivalently projected, produced the images shown in d.
[View Larger Version of this Image (69K GIF file)]
Fig. 6.
Results of classification. The data were classified twice, once according to
the motor domain (top row),
and once according to the
regulatory domain (left column). The table of images
shows the subaverages of
those heads allocated to each
combination of motor domain and regulatory domain.
The number of images belonging to each group or subgroup is indicated in the top
right corner. Note the uneven distribution in the positions of the regulatory domains among different motor
domain groups. Only 10 of
the 20 motor domain groups
showing the greatest similarity to the S1 model are shown
here.
[View Larger Version of this Image (104K GIF file)]
Fig. 5.
Masks used in image
classification. (a) Refined average and (b) variance images
masked to isolate the motor domains. The mask must be sufficiently large to contain all motor
domain morphologies (indicated
by the high variance), yet small
enough to exclude as much of
the background as possible. (c)
Average and (d) variance images masked to isolate the regulatory domains.
[View Larger Version of this Image (35K GIF file)]
). However,
the results were consistent from trial to trial and those presented in Fig. 6 are typical. Furthermore, heterogeneity of
the regulatory domain was a consistent finding regardless
of the number of motor domain groups chosen (up to 50, data not shown), although the improvement in signal-to-noise ratio in these was, of course, poorer.
). To illustrate the accuracy of the resulting envelope,
it is shown projected in the same direction as each of the
two-dimensional projections used in its determination
(Fig. 8 d).
, an imaginary actin
filament would be vertical in the plane of the page in Fig. 9 a,
and perpendicular to it in Fig. 9 b. Thus it can be seen that
the flexibility of the regulatory domain appears to be both
axial and circumferential. The maximum width of the envelope around the regulatory light chain is about 12 nm.
Also, the regulatory domain of S1 lies at the periphery of
this envelope rather than near its center.
Fig. 9.
Stereo pair images of the
S1 atomic structure (red) and the
negative stain envelope (blue wire
mesh), superimposed. The extent of
the envelope around the regulatory
domain is large compared to that
around the motor domain. In the orientation shown in a, the actin filament would lie in the plane of the figure (see Results), and in b would lie
perpendicular to the plane of the figure. The conformation of S1 lies at
the periphery of the regulatory domain envelope, suggesting that it is
rarely seen among stained heads.
[View Larger Version of this Image (52K GIF file)]
Discussion
), nor positive staining (Steven
and Navia, 1982
), nor flattening (Seymour and De Rosier,
1987), but its similarity to the data demonstrates that the
heads were exceptionally well preserved (Figs. 7 and 8).
The three distinct morphological regions in the heads, reported by Walker et al. (1985)
and Walker and Trinick (1988)
are confirmed here (Fig. 6) and shown to correspond to the motor domain, and the essential and regulatory light chains (Figs. 7 and 8). Not only are these regions
resolved in the averages, but details within them show
many similarities to the model (Figs. 7 and 8). Having once
seen these averages, particular details can be identified in
the original micrographs (Figs. 1 and 2).
. Shadowing myosin molecules shows fewer features
than negative staining, because platinum grains are substantially larger than uranyl crystallites; thus the resolution
of the shadowed averages was estimated to be ~4 nm. A
large distal domain was identified, but this was cusp shaped,
possibly as a result of the low shadowing angle used (5°). A neck region corresponding to the regulatory domain
was seen, but this was not resolved into two regions.
showed micrographs in which the heads in individual heavy meromyosin molecules curved differently
when bound to adjacent actin filament subunits. Irving et al.
(1995)
used fluorescence polarization to show that the regulatory domain is distorted when rigor fibers are stretched,
and Schmitz et al. (1996)
reconstructed insect flight muscle, in which rigor heads showed a variety of conformations distinct from the S1 structure. Even in the absence of
externally imposed force, the regulatory domains of frozen
S1-decorated actin filaments are poorly defined (Jontes et al.,
1995
; Milligan and Flicker, 1987
; Whittaker et al., 1995
);
this result was attributed to disorder, presumably resulting
from Brownian motion. Such head flexibility may underlie
cross-bridge compliance, as proposed by Huxley and Simmons (1971)
, and is probably an essential property of cross-bridges, allowing strain energy to be stored and dissipated gradually.
Received for publication 30 May 1997 and in revised form 30 July 1997.
Address all correspondence to J. Trinick, at his current address, Department of Human Biology, Leeds University, Leeds LS2 9JT, United Kingdom. Tel.: 44-113-233-4350. Fax: 44-113-233-4344. E-mail: j.trinick{at}leeds.ac.ukWe thank P. Knight and G. Offer for comments on an earlier version of the manuscript.
This work was supported by National Institutes of Health grant AR40964 and the Medical Research Council (UK).
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