Particle segmentation algorithm for flexible single particle reconstruction
Particle segmentation algorithm for flexible single particle reconstruction
Qiang Zhou 0 1
Niyun Zhou 0
Hong-Wei Wang 0
0 Ministry of Education Key Laboratory of Protein Science, Tsinghua-Peking Joint Center for Life Sciences, Center for Structural Biology, School of Life Sciences, Tsinghua University , Beijing 100084 , China
1 State Key Laboratory of Biomembrane and Membrane Biotechnology, Center for Structural Biology, School of Life Sciences, Tsinghua University , Beijing 100084 , China
As single particle cryo-electron microscopy has evolved to a new era of atomic resolution, sample heterogeneity still imposes a major limit to the resolution of many macromolecular complexes, especially those with continuous conformational flexibility. Here, we describe a particle segmentation algorithm towards solving structures of molecules composed of several parts that are relatively flexible with each other. In this algorithm, the different parts of a target molecule are segmented from raw images according to their alignment information obtained from a preliminary 3D reconstruction and are subjected to single particle processing in an iterative manner. This algorithm was tested on both simulated and experimental data and showed improvement of 3D reconstruction resolution of each segmented part of the molecule than that of the entire molecule.
Single particle reconstruction; Cryo-EM; Particle segmentation; Local reconstruction
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Single particle cryo-electron microscopy (cryo-EM) is a
powerful structural biology tool being developed in the
past several decades and becoming more matured in
recent years (Bai et al. 2015a; Carazo et al. 2015; Cheng
2015; Cheng et al. 2015; Nogales and Scheres 2015). By
quickly freezing biological macromolecules in a thin film
of vitreous ice, cryo-EM preserves the molecules as they
are in solution immediately before the freezing. This
stipulates cryo-EM the unique advantage to reveal the
Qiang Zhou and Niyun Zhou have contributed equally to this work.
Electronic supplementary material The online version of this
article (doi:10.1007/s41048-017-0038-7) contains
supplementary material, which is available to authorized users.
molecular structure in their close-to-native states and
the possibility to examine structures in action. The most
recent development of new direct-electron detection
device and image processing algorithms has
dramatically boosted the capability of this technique so that
three-dimensional (3D) structures of biological
macromolecules can be solved to near atomic resolution from
averaging many individual images without
crystallization (Bai et al. 2013; Liao et al. 2013; Bartesaghi et al.
2015). This has led to a resolution revolution of the
cryo-EM technology and is transforming the field of
structural biology (Kuhlbrandt 2014).
Despite the major technical progresses,
compositional and conformational heterogeneity still imposes a
major obstacle on high-resolution single particle
cryoEM structural determination. Different from
crystallography where the macromolecules are constrained
within a crystalline lattice, single particle molecules in
solution are more flexible in changing their ternary and
quaternary structures which may cause conformational
or compositional heterogeneity among the molecules. In
cases where the heterogeneity is relatively subtle and
localized, single particle 3D reconstruction of a
macromolecule complex is an averaged structure of the
common region of all the molecules but with a low
resolution at the flexible region. Algorithms based on
multivariate statistical analysis were developed to
classify molecules into different states (van Heel and
Frank 1981). The maximum likelihood algorithm was
developed to classify molecule images with low signal to
noise ratio (Scheres et al. 2007). Methods such as
random conical tilt and orthogonal tilt reconstruction were
developed to obtain 3D models of different molecular
states (Radermacher et al. 1987; Leschziner and
Nogales 2006). Using statistical classification approach,
these algorithms sort the heterogeneous particle images
into different classes based on the level of similarity
among them and treat each class of images as a
homogeneous set of molecules. The classification thus
generates multiple structures each reflecting a different
state of the biological sample in vitreous ice. The above
methods all assume common structure within the same
class of molecules. While these methods have been
proved to be very successful on the structural studies of
many macromolecular complexes and revealed
important mechanistic insight to the conformational switch of
important molecular machines, there are still a lot of
complexes with more complicated conformational
heterogeneity that cannot be easily studied. In a severe
conformational heterogeneity such as a global variation
within the molecule or a continuous domain–domain
movement at large scale, a correct 3D reconstruction
cannot even be obtained using the conventional
classification approach.
Several algorithms without classification strategy
have been introduced to single particle analysis of
macromolecular complexes with continuous
conformational changes. These include the normal-mode analysis
(Ma and Karplus 1997; Brink et al. 2004; Ma 2005; Jin
et al. 2014), energy landscape analysis and manifold
embedding (Dashti et al. 2014; Frank and Ourmazd
2016), 3D variance analysis (Penczek et al. 2006; Zhang
et al. 2008), covariance analysis (Anden et al. 2015;
Katsevich et al. 2015; Liao et al. 2015), and eigen
analysis-based methods (Penczek et al. 2011; Tagare
et al. 2015). These algorithms can provide quantitative
description of the conformational variation mode in the
complex to guide further processing of the dataset. More
recently, local masking technique was used in
reconstructing the rigid body within a complex or further
classifying local subtle conformational heterogeneity in
a focused region of the molecule. This has been quite
successful in improving the local resolution significantly
of different rigid portions within a complex (Amunts
et al. 2014; Brown et al. 2014; Chang et al. 2015; Yan
et al. 2015).
Further implementation of algorithms that can
separate the relative mobile parts within a flexible
molecule and reconstruct the different parts separately
will be more useful. Because the electron micrograph
of a molecule reflects the 2D projection of the molecule
along the electron beam illumination direction,
different parts of the complex superimpose with each other
in the 2D image. So simply masking the 2D image or
3D model does not eliminate the influence by the
signal of the mobile portion on the 3D reconstruction.
A clearer way should be to remove the signal of mobile
portion from the 2D image entirely so a reconstruction
of the interesting part can be done with greater
fidelity. Such kind of separation has been realized in
Fourier–Bessel space for the reconstruction of a
double-layered helical assembly of tubulin (Wang and
Nogales 2005). Recently, separation and
reconstruction of icosahedral viral genomic structure from the
capsid structure were achieved by subtracting the
capsid signal from the raw images of virus particles
(Liu and Cheng 2015; Zhang et al. 2015). In our most
recent work, we have developed a segmentation
algorithm to separate the SNAP–SNARE structure from 20S
particle by subtracting the hexameric NSF complex in
the raw image of 20S particle and thus overcome the
symmetry mismatch and severe conformational
heterogeneity in the 20S particles. This allowed us to
reconstruct the SNAP–SNARE complex with higher
resolution than using the whole particle images (Zhou
et al. 2015). At nearly the same time, Bai et al. (2015b),
Ilca et al. (2015), and Shan et al. (2016) developed
similar algorithms independently. A recent
development in RELION software (Scheres 2012a, b) makes it
possible to subtract certain portions within a complex
from the raw 2D images without introducing major
artifact. This allowed much better classification of the
interested portion to further sort the heterogeneous
particle images to even higher resolution than the
overall average (Bai et al. 2015b).
In this work, we further expand the particle
segmentation algorithm that we have developed for the
analysis of 20S particles to other samples. The
successful application of this algorithm to different systems
with conformational heterogeneity indicated its
generality. We also incorporated the image subtraction
algorithm at micrograph level so it not only overcomes the
potential artifact from interpolation and contrast
transfer function, but more importantly also provides
new opportunities to analyze micrographs of crowding
particle images.
THEORY AND ALGORITHM
Particles segmentation
In the current algorithm, we consider a scenario where
the being-studied macromolecule is composed of two
rigid bodies that are relatively mobile with each other.
In a cubic volume with N 9 N 9 N voxels, the 3D
densities of the two rigid bodies are V1 and V2, respectively.
For a certain conformation of the macromolecule, its 3D
density V thus can be written as
where E1 and E2 are the Euler matrix of V1 and V2,
respectively. The Euler matrices are functions of Euler
angles and translational vectors
The different combinations of E1 and E2 define a
heterogeneous conformation among the molecules. Our
goal is to determine the high-resolution structure of the
two rigid bodies, V1 and V2. During the process, we
should also be able to reveal all the E1 and E2
combinations therefore the conformational distribution within
the specimen.
For a particle i in a transmission electron microscope,
its 2D image as a N 9 N array is
where F and F-1 are Fourier transform and reverse
Fourier transform operation, respectively; CTFi is the
contrast transfer function for particle i; AEk;i is the slicing
operation on the 3D Fourier transform according to Ek,i,
k = 1,2; Ni is the noise of the particle i.
In this 2D image, the projection of V1 or V2 is
If we know V1 and V2 and their exact corresponding
Euler matrices, we should be able to subtract the signal
of either V1 or V2 from the raw particle or micrograph
and then segment the other part according to its
location for further analysis (Fig. 1A).
where rh;i is the location of Vh and Win rh;i; b is a
function to re-window an image with box size b at rh;i,
h = 1,2. This operation thus calculates a new image
with most of the signal of Vk removed.
In situations where the flexibility between the two
rigid bodies is within certain range, i.e., the 20S particle,
a global low-resolution reconstruction from all the
images may serve as a starting model. The initial Vk can
be obtained from this global reconstruction through 3D
segmentation. The initial Ek,i can be roughly estimated
as the Euler matrix obtained from the global
reconstruction. These initial values can also be obtained by
further focused 3D refinement with corresponding local
mask applied. The initial location rh;i for Vh can be
obtained from its location in the global 3D
reconstruction (r3D;h;i) and corresponding Euler matrix Eh,i
where PXY is an operation to project vector to XY plane.
More specifically, we can first subtract V2 and
generate images for V1. Then we can get an updated volume
and Euler matrix for V1 with which we can generate
images for V2. These procedures can be iterated
between V1 and V2 for several rounds until convergence
(Fig. 1B).
Because the true value of Vk (Vk,true) is unknown and
can only be estimated with Vk at the resolution of the 3D
reconstruction, the projection subtracting residual
should be:
where R is spatial frequency and Rk is the 3D
reconstruction resolution. If the initial estimated volume
function of Vk can be of enough high resolution, the
intensity of DPk,i can be neglected.
Segmentation algorithm improves the resolution
of simulated 20S particle dataset
From the 48 simulated micrographs of 20S particles
(Fig. 2, Table 1 for simulating parameters), we extracted
the 20S particle images and performed 2D classification
and 3D reconstruction of the whole particle images.
These showed overall shape of the 20S particle
comprising two fuzzy parts corresponding to the SNARE/
SNAP (SS) and the D1–D2 domain of NSF (DD),
respectively (Supplementary Fig. S1A, Fig. 3A). While
the FSC of this overall reconstruction reported a
resolution of 5.8 Å, the EM map lacks clear features
especially in the SS region. We performed additional 3D
reconstruction refinements with local masks around SS
or DD, resulting in slightly better-defined SS at 5.7 Å
resolution (Fig. 3B) and much better DD at 3.4 Å
resolution (Fig. 3C), respectively. The 3D auto-refinements
with sub-particles generated with relion_project
resulted in similar resolution of 5.45 Å for SS and 3.35 Å for
DD (Supplementary Fig. S2, Table 2). Alternatively, we
Fig. 1 Flowchart of particle
segmentation and 3D
reconstruction. A The V2 part
of a particle is re-windowed
and centered from the raw
particle image according to its
location r2, meanwhile the V1
part is subtracted from the
raw particle image. B The
flowchart of iterative
segmentation and
reconstruction. The raw
particles are composed of two
rigid parts flexible to each
other: V1 and V2. Firstly, the
whole 3D volume of initial
model is segmented into V1
and V2. Then V2 is subtracted
from raw particle images or
micrographs, from which the
V1 particle images are
rewindowed and subjected to
3D reconstruction, resulting in
a refined V1. This process is
repeated again with V1
subtracted from raw particle
images or micrographs,
obtaining V2 particle images
and a refined V2. The
procedure can be repeated
until convergence
Fig. 2 An area of simulated micrograph. Three simulated 20S
particles in various views are marked by circles
applied the segmentation algorithm to the dataset
(Supplementary Fig. S1B–D) and obtained a
betterdefined reconstruction of SS than the previous two SS
volumes at 4.59 Å resolution even in the first round of
segmentation (Fig. 3D). After second round of
segmentation, the map quality was further improved (Fig. 3E,
F) although the apparent FSC value didn’t change
significantly from the first round reconstruction (Fig. 3J).
The segmentation algorithm also resulted in a DD
(Fig. 3G–I, Supplementary Fig. S1E) better than those in
the overall 3D reconstruction.
It is notable that the image box size of the windowed
particle has an effect on the reconstruction resolution of
DD particles. The 3D reconstruction resolution of the
segmented DD with a box size of 160 and 256 pixels
was 3.52 Å and 3.41 Å, respectively (Fig. 3G, I, J,
Table 2). Because the signal of particles is proportional
to the molecular weight and the noise is proportional to
the box size (Rosenthal and Henderson 2003), using too
large box size will decrease the signal to noise ratio of
particles. But on the other hand the too small box size
Table 1 Parameters for micrograph simulation
results in too large reciprocal pixel size, which may limit
the CTF correction and interpolation in Fourier space
(Penczek et al. 2014). The optimal box size used for 3D
reconstruction may be variable for particles with
different sizes and/or symmetry.
Segmentation algorithm improves
the reconstruction quality of influenza RdRP
Our previous work has shown that the influenza RdRP
tetramer contains two homo-dimers interacting with
each other in a flexible manner (Chang et al. 2015). We
were able to obtain a 3D reconstruction of the RdRP
dimer at resolution of 4.3 Å by applying a mask around
one of the dimer density during the refinement (Fig. 4A).
In this practice, each particle image lost half of its
structural information in the final reconstruction. The
segmentation algorithm provides the opportunity to include
the other dimer in the final 3D reconstruction thus double
the effective dataset. We segmented the RdRP dimers
from all the tetramer dataset and performed 2D
classification (Supplementary Fig. S3) and 3D refinement. The
3D reconstruction obtained in this way showed a similar
apparent resolution as the previous one (Fig. 4B). But
closer look at the FSC curves indicated an elevated signal
at medium-resolution range from 10 to 5 Å-1 in the latter
reconstruction (Fig. 4C). The EM density obtained by the
segmentation reconstruction algorithm showed
betterdefined feature and higher local resolution than that
obtained by the local masking reconstruction algorithm
(Fig. 4D–F). As a control, the 3D auto-refinements with
dimer sub-particles generated with relion_project also
resulted in similar resolution of 4.45 Å (Supplementary
Fig. S4, Table 2).
It is well-known that there is a ratchet motion between
the 30S and 50S subunits within a 70S ribosome.
Former analysis of 70S ribosomes using supervised
classification, maximum likelihood classification, and local
masking reconstruction can all separate the different
conformers and reconstruct the 30S and 50S portions of
the complex. We tested the segmentation algorithm in
separating and reconstructing the two portions of 70S
ribosome. As a control, we firstly performed 3D
reconstruction of the entire 70S particle images and obtained
a structure at 3.4 Å resolution. Using local masking
approaches, the 30S and 50S subunits can be further
refined to 3.4 Å and 3.2 Å resolutions, respectively
(Fig. 5A, B). We applied the segmentation algorithm on
the dataset and reconstructed the 30S and 50S subunits
separately, resulting in final reconstructions at 3.3 Å
and 3.2 Å resolutions, respectively (Fig. 5C, D). The 3D
auto-refinements with sub-particles generated with
relion_project also resulted in similar resolution of 3.4 Å
for 30S and 3.2 Å for 50S (Supplementary Fig. S5,
Table 2). In summary, both the local masking
refinement and segmentation algorithm improved the
resolution than the whole particle refinement procedure
(Fig. 5E). For both 30S and 50S subunits, the 3D
reconstructions using local masking refinement and
segmentation algorithm have very similar resolution
(Fig. 5E). The reason that there was no improvement is
probably due to the rather small motion between the
30S and 50S subunits for which local masking in an
auto-refinement obviously restored the orientation of
the subunits effectively.
Because we were using segmentation reconstruction,
we could calculate the relative rotating angles between
30S and 50S subunits for each individual particle by
comparing their Euler angles after the reconstructions.
The distribution of the rotation angles showed two
peaks, in agreement to the fact that there are two major
populations of conformers in the ratchet switch of the
70S ribosome (Fig. 5F). When we aligned the two
classes of 3D reconstructions of 70S ribosome based on
the 50S subunit, the 30S subunit has a rotation of about
3.8 (Fig. 5G).
We noted that the segmentation algorithm can be
directly applied to segment particle images from raw
micrographs. As we have discussed previously, the
segmentation of raw particle images may suffer from
the loss of information due to the point spread function
Fig. 3 Comparison of 3D reconstructions from simulated 20S particles. A 3D reconstruction of whole particles without local mask. B 3D
reconstruction of whole particles with a local mask around the SS portion. Only SS is shown. C 3D reconstruction of whole particles with a
local mask around the DD portion. Only DD is shown. D 3D reconstruction of the SS particles after the first round of segmentation. E 3D
reconstruction of the SS particles after the second round of segmentation. F An a-helix from the 3D density of E with the corresponding
atomic model docked in. This corresponds to the amino acid residues 138–156 of the a-SNAP. G 3D reconstruction of the DD particles
after the first round of segmentation. H An a-helix from the 3D density of G with the corresponding atomic model docked in. This
corresponds to the amino acid residues 511–531 of the NSF. I 3D reconstruction of the DD particles after the first round of segmentation
with a box size of 256 pixels. J FSC curves of the 3D reconstructions. The FSC curve of segmented SS is the one after the second round of
segmentation
caused by the CTF. After aligning each of the raw
particle images with the reference calculated from the
partial volume, we should be able to subtract reference
projections from the raw micrographs directly. Because
there is no cutoff of the CTF fringes around the raw
particle images in the whole micrograph, we don’t need
Table 2 Summary of 3D reconstruction
to worry about the information loss caused by the
windowing. In our simulated micrographs, we can easily
subtract the projections of DD from each of the 20S
particles (Fig. 6A, B). This can also be done in a real
electron micrograph that contains relatively crowded
20S particle images (Fig. 6C, D). This provided
opportunities for processing of wider range of cryo-electron
micrographs.
Sample heterogeneity is still a major technical obstacle
in single particle cryo-EM 3D reconstruction. The source
of heterogeneity includes but is not limited to the
following aspects: compositional diversity and
conformational flexibility. The conformational variation that
molecules undergo can be continuous or discrete.
Compositional heterogeneity and conformational
heterogeneity with discrete states usually lead to a finite
number of classes that current 3D classification
algorithms can handle reasonably well. In contrast,
continuous conformational change within a molecule would
lead to an almost infinite number of classes.
3D refinement and reconstruction with an adaptive
local mask around the relatively rigid portion of the
Resolution after
post-processing
molecule has shown to be successful in some cases to
solve high-resolution structure of certain part of the
whole molecule. But in most cases, the overlapped
structures in 2D projections interfere correct alignment
of the common portion of the molecule. Using the
particle segmentation algorithm, we can separate the
relatively mobile portions within a molecule image and thus
perform single particle analysis of the separated
portions without the interference from each other. The
image after segmentation has much cleaner signals for
more precise alignment and further analysis. Our
example of the 20S particle analysis presented in this
work indicates the particular advantage of segmentation
algorithm in analyzing complexes with internal
symmetry mismatch. The further refinement with local
angular searching may result in artifact in some cases.
In the example of simulated 20S particle, the
asymmetric feature of SS part was lost after local angular
searching. However, this feature can be well recovered
by the segmentation algorithm.
In our segmentation algorithm, after projecting the
3D partial density, it is critical to subtract the projection
from raw particles with correct operation. There have
been several attempts (Wang and Sigworth 2009; Bai
et al. 2015b; Ilca et al. 2015; Liu and Cheng 2015; Zhang
et al. 2015) to subtract the projection of a 3D
Fig. 4 Comparison of 3D reconstructions of influenza RdRP. A 3D reconstruction of influenza RdRP tetramer particles with a local mask
around the dimer portion (EMD ID: 6202). B 3D reconstruction of the influenza RdRP dimer after the first round of segmentation from
the tetramer particle images. C FSC curves of 3D reconstructions. D and E Enlarged views of an a-helix density with the corresponding
atomic models from A and B, respectively. The a-helix corresponds to the amino acid residue 454–476 of polymerase basic protein 1 of
RdRP. F Central slice of the maps colored by local resolution computed with ResMap
reconstruction or 3D model from raw particles. We
found that the absolution gray scale feature of the 3D
reconstruction within RELION makes the subtraction
easy and intuitive. This operation, which removes most
of the low frequency signals of one macromolecule part
from the raw particle images, immediately allows the
alignment of the other macromolecule part more
precisely. This is proved by the fact that reference-free 2D
classes of segmented particles show more detailed
features than the entire particle but are free of
contaminated features from the subtracted references.
Furthermore, while we can use the iterative approach
Fig. 5 Comparison of 3D reconstructions of 70S ribosome. A and B are the 3D reconstruction maps of 70S ribosome particles with a
local mask of 30S and 50S, respectively. C and D are the 3D reconstruction maps of 30S and 50S ribosomes after the particle
segmentation, respectively. E FSC curves of 3D reconstructions. F Distribution of the difference of Euler angle theta between the 30S and
50S subunits. Inset is an enlarged view corresponding to the range of theta from 0 to 10 . G Comparison between 30S subunit of the 70S
ribosome 3D reconstructed from dataset fraction #1 (blue) and fraction #2 (purple) using the alignment parameters from the 3D
autorefinement of segmented 50S subunit
(Fig. 1B) to improve the segmentation and alignment of
each portion of the molecule, at most two iterations are
enough to result the convergence of the solutions in
practice (Table 2). This proved that our approximation
in Eq. 7 is reasonable for practical purpose.
Besides solving the high-resolution structure of each
compositional rigid parts of a complex, the
segmentation algorithm provides additional information of the
spatial relationship between the rigid parts within each
individual particle image. Although in the examples of
this work, we mainly focused at the molecules made of
two rigid components, the concept can be extended to
molecules composed of three or even more rigid bodies
that are mobile to each other. Such information of the
whole dataset can then be summarized for statistical
analysis to reflect the distribution of various
Fig. 6 Particle segmentation from raw micrographs. A An area of
simulated micrograph of the 20S particles. B The same
micrograph in A from which DD particles were subtracted. C An area of
a raw micrograph of 20S particles. D The same micrograph in
C from which the 20S particles were subtracted. Some typical
particles are marked with black circles
conformational states within the flexible molecule. The
conformational distribution is of important biological
relevance beyond what the static structure can provide,
thus realizing the unique power of single particle
analysis.
MATERIALS AND METHODS
Computation implementation
The particle segmentation algorithm described above
was implemented as a new program
‘‘subtract_micrograph’’ and its mpi version ‘‘subtract_micrograph_mpi’’
within the RELION 1.4 package. Part of the source code
was copied or adapted from RELION 1.3 or 1.4. We also
incorporated this program in a GUI version of RELION
1.4 (Fig. 7).
Generation of simulated dataset
Previous works (Zhao et al. 2015; Zhou et al. 2015)
showed that human 20S particle functioning in
membrane fusion processes in eukaryotic cells is composed
of two parts relatively flexible to each other: the SS
complex with pseudo four-fold symmetry and the
hexameric NSF complex. We used the 20S particle as a
testing model to generate simulated dataset. For
convenience of the simulation, we built a model of the SS
complex without symmetry and a hexameric model of
DD imposed with a C6 symmetry using the Modeller
software package (Eswar et al. 2006). The two atomic
models were converted to MRC format with
e2pdb2mrc.py in EMAN2 package (Tang et al. 2007). The two
MRC volumes with voxel size of 1.32 Å representing the
SS and DD portions of 20S particle were then assembled
together to resemble the overall architecture of 20S
particle. Heterogeneous conformational states were
generated by randomly tilting the two portions
independently with a standard deviation of 10 for all three
Euler angles and translating the two parts with a
standard deviation of 2 pixels in coordinates. Subsequently,
we used the full set of simulated 3D MRC volumes to
generate simulated electron micrographs using a
program genRandomImage.py written with EMAN2
package. A total of 48 simulated electron micrographs each
containing 150 particle images at random orientations
and locations were generated. In each of these
micrographs, CTF-independent Gaussian white noise was
superimposed and CTF-dependent water noise was
generated by randomizing the Fourier phase of the
atomic model of water molecules simulated with NAMD
and VMD (Humphrey et al. 1996). The noise level and
CTF parameters in these simulated micrographs were
chosen to mimic the real micrographs obtained by a
Gatan K2-Summit electron counting camera on a Titan
Krios microscope operated at 300 kV. More details of
the parameters for simulation are listed in Table 1.
Processing of simulated dataset
A total of 7200 SS/DD particle images were extracted
from simulated micrographs with a box size of
256 pixels. These particle images were first 3D refined
with RELION 1.3 against an initial model of 20S particle
low-pass filtered at 60 Å resolution. As a control, we
refined the 3D reconstruction with local angular search
range of 30 , during which a SS or DD mask was applied,
resulting in a SS or DD volume, respectively. As another
control, we also generated SS or DD sub-particles with
relion_project and performed 3D auto-refinement with
these sub-particles with a local angular search range of
30 . Alternatively, using our implemented segmentation
algorithm, the SS particles were segmented by
subtracting the DD density from the whole particle images.
The segmented and re-windowed SS particles with a
box size of 160 pixels were subjected to 2D
classification to select the good SS particle images for further 3D
refinement in RELION 1.3. After the 3D refinement of
segmented SS particles, DD particles were segmented
Fig. 7 The GUI interface of
the segmentation algorithm
embedded in RELION package.
The segmentation algorithm
was embedded in RELION
and re-windowed from the whole particle images by
subtracting the SS density calculated from the new SS
3D volume. The DD particle images were then subjected
to 2D classification and 3D refinement, resulting in an
updated DD 3D volume, which was then used for the
next cycle of SS segmentation and 3D reconstruction.
Processing of influenza RdRP
The 3D reconstruction of influenza RdRP tetramer and
dimer was described previously (Chang et al. 2015). The
RdRP dataset from the previous work was used in this
study. Each raw particle image containing a tetramer
has a pixel size of 1.32 Å and a dimension of 256 pixels.
Two RdRP dimer particles were segmented and
rewindowed from each raw tetramer particle image with a
box size of 180 pixels. Therefore, the particle number of
RdRP dimer was doubled after segmentation from the
tetramers. The segmented RdRP dimer particles were
subsequently used for 2D classification and 3D
refinement analysis. As a control, we also generated dimer
sub-particles with relion_project and performed 3D
auto-refinement with all of the dimer sub-particles.
Processing of 70S ribosome
We used a cryo-EM dataset of 70S ribosome comprising
68,543 particle images with box size of 280 pixels and a
pixel size of 1.32 Å from Prof. Ning Gao’s group. These
micrographs were taken from a Titan Krios microscope
equipped with a Gatan K2-Summit electron counting
camera. We firstly reconstructed a 3D volume of the
entire 70S ribosome following the conventional way.
This 3D reconstruction was further refined with a local
angular search range of 15 , during which a 30S or 50S
mask was applied, resulting in the 3D map of 30S or 50S
subunit, respectively. We then segmented the 30S
subunit from the dataset with a box size of 280 pixels by
subtracting the 50S subunit with the segmentation
algorithm. The segmented 30S particles were subjected
to 2D classification to select good particles for further
3D auto-refinement. The 50S subunit was subsequently
segmented from the 70S ribosome images by
subtracting the 30S signal using the segmentation algorithm.
The segmented 50S subunit images were then refined to
reconstruct a 3D volume. As a control, we also
generated 30S or 50S sub-particles with relion_project and
performed 3D auto-refinement with these sub-particles.
The rotating angles between segmented 30S and 50S
subunits were calculated with a program
CompareDataStars_data.py written with EMAN2 package.
The micrograph of 20S particle was obtained as
described in our previous paper (Zhou et al. 2015). 2D
classification, 3D reconstruction, and auto-refinement
were performed with RELION 1.3. CTF parameters were
determined with CTFFIND3 (Mindell and Grigorieff
2003). Reconstruction resolution was estimated with
high-frequency noise substituted gold-standard FSC
(Scheres and Chen 2012; Chen et al. 2013). Local
resolution was calculated with ResMap (Kucukelbir et al.
2014). Corresponding masks were also applied during
the 3D auto-refinement of the segmented particles if not
particularly indicated. 3D volume segmentation and
atomic model docking were performed with UCSF
Chimera (Pettersen et al. 2004). The 3D refinements
mentioned above are summarized in Table 2.
Acknowledgements Open access. The software and scripts used
in the work can be accessed via https://github.com/zhouqiang00/
Particle-Segmentation. We thank Prof. X. Li, S.-F. Sui for helpful
discussions, Dr. D.P. Sun and Dr. J. Wang for kindly providing the
RdRP dataset, and Prof. N. Gao and Dr. Y.X. Zhang for kindly
providing the ribosome dataset. This work was supported by Grant
(2016YFA0501100 to H.W.) from the Ministry of Science and
Technology of China and Grant (Z161100000116034 to H.W.)
from the Beijing Municipal Science & Technology Commission. Q.Z.
was supported by CLS Postdoctoral Fellowship Foundation.
Compliance with ethical standards
Conflict of interest Qiang Zhou, Niyun Zhou, and Hongwei Wang
declare that they have no conflict of interest.
Human and animal rights and informed consent This article
does not contain any studies with human or animal subjects
performed by any of the authors.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium,
provided you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license, and
indicate if changes were made.
Amunts A , Brown A , Bai XC , Llacer JL , Hussain T , Emsley P , Long F , Murshudov G , Scheres SH , Ramakrishnan V ( 2014 ) Structure of the yeast mitochondrial large ribosomal subunit . Science 343 : 1485 - 1489
Anden J , Katsevich E , Singer A ( 2015 ) COVARIANCE ESTIMATION USING CONJUGATE GRADIENT FOR 3D CLASSIFICATION IN CRYO-EM . Proceedings. IEEE Int Symp Biomed Imaging 2015 : 200 - 204
Bai XC , Fernandez IS , McMullan G , Scheres SH ( 2013 ) Ribosome structures to near-atomic resolution from thirty thousand cryo-EM particles . Elife 2:e00461
Bai XC , McMullan G , Scheres SH ( 2015a ) How cryo-EM is revolutionizing structural biology . Trends Biochem Sci 40 : 49 - 57
Bai XC , Rajendra E , Yang G , Shi Y , Scheres SH ( 2015b ) Sampling the conformational space of the catalytic subunit of human gamma-secretase . Elife 4:e11182
Bartesaghi A , Merk A , Banerjee S , Matthies D , Wu X , Milne JL , Subramaniam S ( 2015 ) 2. 2 A resolution cryo-EM structure of beta-galactosidase in complex with a cell-permeant inhibitor . Science 348 : 1147 - 1151
Brink J , Ludtke SJ , Kong Y , Wakil SJ , Ma J , Chiu W ( 2004 ) Experimental verification of conformational variation of human fatty acid synthase as predicted by normal mode analysis . Structure 12 : 185 - 191
Brown A , Amunts A , Bai XC , Sugimoto Y , Edwards PC , Murshudov G , Scheres SH , Ramakrishnan V ( 2014 ) Structure of the large ribosomal subunit from human mitochondria . Science 346 : 718 - 722
Carazo JM , Sorzano CO , Oton J , Marabini R , Vargas J ( 2015 ) Threedimensional reconstruction methods in single particle analysis from transmission electron microscopy data . Arch Biochem Biophys 581 : 39 - 48
Chang S , Sun D , Liang H , Wang J , Li J , Guo L , Wang X , Guan C , Boruah BM , Yuan L , Feng F , Yang M , Wang L , Wang Y , Wojdyla J , Li L , Wang M , Cheng G , Wang HW , Liu Y ( 2015 ) Cryo-EM structure of influenza virus RNA polymerase complex at 4.3 A resolution . Mol Cell 57 : 925 - 935
Chen S , McMullan G , Faruqi AR , Murshudov GN , Short JM , Scheres SH , Henderson R ( 2013 ) High-resolution noise substitution to measure overfitting and validate resolution in 3D structure determination by single particle electron cryomicroscopy . Ultramicroscopy 135 : 24 - 35
Cheng Y ( 2015 ) Single-particle cryo-EM at crystallographic resolution . Cell 161 : 450 - 457
Cheng Y , Grigorieff N , Penczek PA , Walz T ( 2015 ) A primer to single-particle cryo-electron microscopy . Cell 161 : 438 - 449
Dashti A , Schwander P , Langlois R , Fung R , Li W , Hosseinizadeh A , Liao HY , Pallesen J , Sharma G , Stupina VA , Simon AE , Dinman JD , Frank J , Ourmazd A ( 2014 ) Trajectories of the ribosome as a Brownian nanomachine . Proc Natl Acad Sci 111 : 17492 - 17497
Eswar N , Webb B , Marti-Renom MA , Madhusudhan MS , Eramian D , Shen MY , Pieper U , Sali A , ( 2006 ). Comparative protein structure modeling using Modeller . Current protocols in bioinformatics/editoral board , Andreas D. Baxevanis … [et al.] Chapter 5, Unit 5.6
Frank J , Ourmazd A ( 2016 ) Continuous changes in structure mapped by manifold embedding of single-particle data in cryo-EM . Methods 100 : 61 - 67
Humphrey W , Dalke A , Schulten K ( 1996 ) VMD: visual molecular dynamics . J Mol Graph 14 ( 33 - 38 ): 27 - 38
Ilca S , Kotecha A , Sun X , Poranen M , Stuart D , Huiskonen J ( 2015 ) Localized reconstruction of subunits from electron cryomicroscopy images of macromolecular complexes . Nat Commun. doi:10.1038/ncomms9843
Jin Q , Sorzano CO , de la Rosa-Trevin JM , Bilbao-Castro JR , NunezRamirez R , Llorca O , Tama F , Jonic S ( 2014 ) Iterative elastic 3D-to-2D alignment method using normal modes for studying structural dynamics of large macromolecular complexes . Structure 22 : 496 - 506
Katsevich E , Katsevich A , Singer A ( 2015 ) Covariance matrix estimation for the cryo-EM heterogeneity problem . SIAM J Imaging Sci 8 : 126 - 185
Kucukelbir A , Sigworth FJ , Tagare HD ( 2014 ) Quantifying the local resolution of cryo-EM density maps . Nat Methods 11 : 63 - 65
Kuhlbrandt W ( 2014 ) Cryo-EM enters a new era . Elife 3:e03678
Leschziner AE , Nogales E ( 2006 ) The orthogonal tilt reconstruction method: an approach to generating single-class volumes with no missing cone for ab initio reconstruction of asymmetric particles . J Struct Biol 153 : 284 - 299
Liao M , Cao E , Julius D , Cheng Y ( 2013 ) Structure of the TRPV1 ion channel determined by electron cryo-microscopy . Nature 504 : 107 - 112
Liao HY , Hashem Y , Frank J ( 2015 ) Efficient estimation of threedimensional covariance and its application in the analysis of heterogeneous samples in cryo-electron microscopy . Structure 23 : 1129 - 1137
Liu H , Cheng L ( 2015 ) Cryo-EM shows the polymerase structures and a nonspooled genome within a dsRNA virus . Science 349 : 1347 - 1350
Ma J ( 2005 ) Usefulness and limitations of normal mode analysis in modeling dynamics of biomolecular complexes . Structure 13 : 373 - 380
Ma J , Karplus M ( 1997 ) Ligand-induced conformational changes in ras p21: a normal mode and energy minimization analysis . J Mol Biol 274 : 114 - 131
Mindell JA , Grigorieff N ( 2003 ) Accurate determination of local defocus and specimen tilt in electron microscopy . J Struct Biol 142 : 334 - 347
Nogales E , Scheres SH ( 2015 ) Cryo-EM: a unique tool for the visualization of macromolecular complexity . Mol Cell 58 : 677 - 689
Penczek PA , Frank J , Spahn CM ( 2006 ) A method of focused classification, based on the bootstrap 3D variance analysis, and its application to EF-G-dependent translocation . J Struct Biol 154 : 184 - 194
Penczek PA , Kimmel M , Spahn CM ( 2011 ) Identifying conformational states of macromolecules by eigen-analysis of resampled cryo-EM images . Structure 19 : 1582 - 1590
Penczek PA , Fang J , Li X , Cheng Y , Loerke J , Spahn CM ( 2014 ) CTER-rapid estimation of CTF parameters with error assessment . Ultramicroscopy 140 : 9 - 19
Pettersen EF , Goddard TD , Huang CC , Couch GS , Greenblatt DM , Meng EC , Ferrin TE ( 2004 ) UCSF Chimera-a visualization system for exploratory research and analysis . J Comput Chem 25 : 1605 - 1612
Radermacher M , Wagenknecht T , Verschoor A , Frank J ( 1987 ) Three-dimensional reconstruction from a single-exposure, random conical tilt series applied to the 50S ribosomal subunit of Escherichia coli . J Microsc 146 : 113 - 136
Rosenthal PB , Henderson R ( 2003 ) Optimal determination of particle orientation, absolute hand, and contrast loss in single-particle electron cryomicroscopy . J Mol Biol 333 : 721 - 745
Scheres SH (2012a) A Bayesian view on cryo-EM structure determination . J Mol Biol 415 : 406 - 418
Scheres SH ( 2012b ) RELION: implementation of a Bayesian approach to cryo-EM structure determination . J Struct Biol 180 : 519 - 530
Scheres SH , Chen S ( 2012 ) Prevention of overfitting in cryo-EM structure determination . Nat Methods 9 : 853 - 854
Scheres SH , Gao H , Valle M , Herman GT , Eggermont PP , Frank J , Carazo JM ( 2007 ) Disentangling conformational states of macromolecules in 3D-EM through likelihood optimization . Nat Methods 4 : 27 - 29
Shan H , Wang Z , Zhang F , Xiong Y , Yin CC , Sun F ( 2016 ) A localoptimization refinement algorithm in single particle analysis for macromolecular complex with multiple rigid modules . Protein Cell 7 : 46 - 62
Tagare HD , Kucukelbir A , Sigworth FJ , Wang H , Rao M ( 2015 ) Directly reconstructing principal components of heterogeneous particles from cryo-EM images . J Struct Biol 191 : 245 - 262
Tang G , Peng L , Baldwin PR , Mann DS , Jiang W , Rees I , Ludtke SJ ( 2007 ) EMAN2: an extensible image processing suite for electron microscopy . J Struct Biol 157 : 38 - 46
van Heel M , Frank J ( 1981 ) Use of multivariate statistics in analysing the images of biological macromolecules . Ultramicroscopy 6 : 187 - 194
Wang HW , Nogales E ( 2005 ) An iterative Fourier-Bessel algorithm for reconstruction of helical structures with severe Bessel overlap . J Struct Biol 149 : 65 - 78
Wang L , Sigworth FJ ( 2009 ) Structure of the BK potassium channel in a lipid membrane from electron cryomicroscopy . Nature 461 : 292 - 295
Yan C , Hang J , Wan R , Huang M , Wong CC , Shi Y ( 2015 ) Structure of a yeast spliceosome at 3.6-angstrom resolution . Science (New York, N.Y.) 349 : 1182 - 1191
Zhang W , Kimmel M , Spahn CM , Penczek PA ( 2008 ) Heterogeneity of large macromolecular complexes revealed by 3D cryo-EM variance analysis . Structure 16 : 1770 - 1776
Zhang X , Ding K , Yu X , Chang W , Sun J , Zhou ZH ( 2015 ) In situ structures of the segmented genome and RNA polymerase complex inside a dsRNA virus . Nature 527 : 531 - 534
Zhao M , Wu S , Zhou Q , Vivona S , Cipriano D , Cheng Y , Brunger A ( 2015 ) Mechanistic insights into the recycling machine of the SNARE complex . Nature 518 : 61 - 67
Zhou Q , Huang X , Sun S , Li XM , Wang HW , Sui SF ( 2015 ) Cryo-EM structure of SNAP-SNARE assembly in 20S particle . Cell Res 25 : 551 - 560