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Introduction
Transmission electron microscopy (TEM) is a structural
technique that has existed for many years in biology to study
the ultra-structure of cells. However, it has only been more
recently that outstanding technical advances have
consolidated the prospect of observing single protein molecules at
the electron microscope level with such sufficient structural
details to use these data to reconstruct the molecule in three
dimensions (3D). These methodological improvements lie at
the level of the instrument itself, that is, better microscopes,
but most significantly, more powerful algorithms and
software platforms, and, importantly, dramatically increased
speed of computers to deal with the noisy images of
proteins obtained with the microscope. As a result of these
advances, analysis of macromolecules using single-particle
electron microscopy (EM) can be widely noticed in a
fast-increasing number of publications. Hence, the need rapidly
emerged to store and make accessible all of this 3D
information to the scientific community, and the Macromolecular
Structure Data Base (at the European Bioinformatics
Institute, Cambridge, UK) has been created to this end
(http://www.ebi.ac.uk/msd/). Most scientific journals now make mandatory
the deposition of any 3D structure obtained by EM into this
data base, importantly using standard formats to guarantee
the interchange of the data. This will certainly stimulate
both the flow of EM structures among scientists (as already
happens with atomic coordinates) as well as the quality of
the EM work deposited.
As EM spreads out, its interaction and dependence on
other structural techniques has deepened. A modern
approach to the exploration of macromolecular structures
requires a wise combination of molecular biology, biochemistry,
biocomputing, X-ray crystallography, nuclear magnetic
resonance (NMR), EM and any other structural technique
(ultracentrifugation, small-angle neutron scattering,
etc). An important challenge will therefore be to develop methods to
combine all of this multi-resolution information in a
comprehensive way.
In the following sections, I will describe the major
methods that EM employs today, how relevant structural
information is extracted, and the present limitations of these
approaches. Supplementary and more in-depth information
can be found elsewhere[1-9] and on the web (eg the
3D-electron microscopy data base at
http://3dem.ucsd.edu/index.html, the Electron Microscopy Yellow Pages at
http://cimewww.epfl.ch/EMYP/comp.html, or the SPIDER web site
at
http://www.wadsworth.org/spider-doc/spider/docs/spider.html).
Basics of single-particle electron microscopy
The protein of interest must be purified to homogeneity
prior to any EM analysis, as image processing is most
generally based on the assumption that every single image we
take derives from the same specimen (Figure 1A). The
sample is then applied to EM grids covered by a thin carbon
support film (which can contain small holes in the case of
cryo-EM, where specimens are vitrified) and visualized
under the electron microscope (Figure 1B). Molecules of the
protein adsorb to the carbon film in orientations determined
by the charges on their surface and their overall shape.
Ideally, a random distribution of orientations is desirable,
because this will allow recording images of the protein from
many different angles, a requirement to obtain a correct
3D reconstruction. Before insertion into the microscope, the
sample must be prepared to withstand the incident radiation
(electrons). For this purpose, the protein on the EM grid can
be either stained with heavy-atom salts, known as negative
staining (uranyl acetate, uranyl formiate and ammonium
molibdate, as the most widely used staining agents), or
quickly vitrified into liquid ethane and kept under liquid nitrogen
temperatures (cryo-EM). Each method has its own
advantages and limitations widely discussed before and beyond
the scope of this review[5,7,10]. Briefly, negative staining
provides a higher contrast at the expense of resolution and only
the surface or topography of the molecule is actually defined.
Performing cryo-EM experiments is technically more
demanding for the microscopist, but perfectly preserves the
structure of the protein at high resolution within the vitrified buffer.
Nevertheless, contrast is strongly reduced, thus small
proteins might only be analyzed with the help of staining
agents[10-12].
Images obtained with the electron microscope are
projections of the molecules along the direction of the electron
beam. In a very simplified view, we can state that as the
beam encounters more atoms along its path within the protein,
the fewer electrons get into the detector, either photographic
film or CCD, therefore integrating the 3D information of the
molecule along the beam direction. Large collections of
images from single molecules are selected and boxed out from
each micrograph (after digitalization) or CCD frames
(Figure 1C), which become the starting data set for image
processing. These single particle images are always noisy,
because low levels of electrons are used during imaging to
reduce radiation damage and so minimize the destruction of
the structural information. Furthermore, single particle
images are intentionally under focus to secure sufficient contrast of the protein over the background, so molecules can
be identified within the micrographs. Both the high noise
levels and the under focus of the micrographs are
responsible for the experimental limitations to reach high resolution
in 3D reconstructions using EM. Consequently, the aims of
image processing (Figure 1D) are 2-fold: first, to reduce the
noise present in the images by averaging similar projections
in 2 dimensions and, later on, into a 3D volume; second, to
correct the consequences of under focus and other optical
effects during the generation of images in the microscope
(globally known as contrast transfer function, CTF). This
second aspect of image processing will not be discussed in
this review, but it has been nicely introduced
elsewhere[7,13,14]. Generally speaking, image processing in 2D is required at
some stage in order to classify those images corresponding
to similar projections of the molecules (therefore, similar
"shape") and to align them in 2D, this is, to place them into
register, so that they can be averaged pixel-by-pixel to
improve their signal-to-noise ratio. How is a 3D structure then
reconstructed from the 2D data recorded? It is demonstrated
that 2D projections along a 3D object contain sufficient
information to restore the original object if the orientation
angles of each projection are known, and several algorithms
and approximations can easily perform this task. For
instance, in medical tomography a radiation source is used
to acquire projections of the patient along a set of
established directions and then a 3D reconstruction is generated.
Just a word of caution to point out that several algorithms
exist to reconstruct a 3D structure from its projections at
known orientations and the mathematics behind them and
its relevance to the correctness of the resulting structure is
not insignificant[15-17]. In single-particle EM, different views
of the same protein are contained within each micrograph as,
frequently, molecules interact with the support film or are
enclosed within the vitrified ice (in cryo-EM) at many
different orientations. In order to generate a "correct" 3D structure,
all these projections of the molecule must evenly fill Fourier
space, meaning we are merging in 3D images from all
possible angles. Nevertheless, there are cases where the shape
of the molecule can make it mathematically redundant to
collect all possible views. For instance, GroEL, a molecular
chaperon made up of 2 back-to-back stacked rings can be
reconstructed just from its side views because the protein
rotates along its longitudinal axis filling all Fourier space
without the need to incorporate top views during image
processing[18,19]. Therefore, once a sufficient data set is collected,
the only requirement to resolve the 3D structure of the
protein is to establish the orientation of each projection image
with respect to a common set of reference coordinates. The
problem of reconstructing a volume from projections is mostly
reduced to that of angular assignment. This is actually the
most time- and effort-consuming task during single particle
EM and the heart of the image processing itself (Figure 1D).
Several software platforms are commonly used, each one
with a specific vision on how to approach the problem of
angular assignment and 3D reconstruction, which in all cases
incorporates some type of iterative refinement of the data
(Figure 1D).
These procedures described above require images from
the same molecule at the same conformation taken in several
orientations. In some cases, such data cannot be collected
either because the protein binds to the grid in a preferred
view, or because conformational flexibility exists in the
protein, and therefore different views cannot be unambiguously
assigned to a specific conformation. In such situations, the
random conical tilt method can deliver a 3D structure for each
type of view and generate a volume without a
template[20-22].
Several commonly used software packages can do the job
Along the already relatively extended history of electron
microscopy and image processing, several groups have
deposited a lot of effort into the development of theory,
methodological approaches, algorithms and complete platforms
for the analysis of single-molecule images taken under the
electron microscope. It is an outstanding effort that all
microscopists should thank because they provide us with the
tools we need in our everyday work. Original work by
Crowther and colleagues developed the first methods to
combine images of the same specimen lying at different
orientations and applied them to icosahedral
viruses[23,24]. Moreover, works performed on the structural determination
of viral capsids have led the way in the possibilities of 3D
EM. Accordingly, in 1997 2 groups, led by Crowther at
Cambridge (UK) and Steven at the National Institutes of Health
in Bethesda (USA), managed to visualize secondary
structural elements in the 3D structure of hepatitis B viral cores at
7.4 Å and
9 Å[25,26], a major breakthrough at the time and the
beginning of today¡¯s improvements in the EM field.
At present, some kind of implicit standard has been
reached and most EM work is performed using 1 of 3 distinct
software platforms: SPIDER[27],
IMAGIC[28] and EMAN[16]. As a note of prudence, other popular platforms and
programs exist, many of them dedicated to the processing of
particles with icosahedral symmetry[29], which will not be
discussed in this review. SPIDER was developed by the group
of Joachim Frank in Albany (NY, USA), IMAGIC by Marinvan Heel¡¯s group in London (UK) and EMAN by Steve Ludtke and Wah Chiu in Houston (TX, USA). EMAN is the
most recent platform and it is available completely free of
charge. More information about each package is found on
their respective web sites: SPIDER
(http://www.wadsworth.org/spider-doc/spider/docs/spider.html), IMAGIC
(http://www.imagescience.de/imagic/) and EMAN
(http://ncmi.bcm.tmc.edu/ncmi/).
Other research groups are also intensively contributing
to software development and implementation for EM analysis, whose strength lies in that they mostly attend to
aspects or approaches in image processing not sufficiently
looked after by the previous platforms. This software can
therefore add force to the potential of the most commonly
used packages. This is the case with XMIPP, which
includes a good repertoire of classification and alignment
algorithms[30-32] (http://www.cnb.uam.es/~bioinfo/),
FREALIGN
(http://emlab.rose2.brandeis.edu/grigorieff/downloads.html)
designed for extracting high-resolution features at the final stages of refinement, and BSOFT
(http://www.niams.nih.gov/labbranch/lsbr/software/bsoft)
containing, among other tools, a good algorithm to estimate
and correct the CTF of the micrographs. To this day, the
most common way for EM groups to make use of all of these
computational possibilities is to choose 1 or 2 of the above
main platforms while using other software to complement
them for specific tasks.
It is as well worth mentioning that these efforts in
software development for EM processing have been matched
by spectacular improvements in the programs needed to
render and visualize the 3D data. Many different programs are
now available, all of them very good, each displaying
advantages in specific features. Just a few of the most typically
used by the EM community are AMIRA
(http://www.amiravis.com/), CHIMERA (http://www.cgl.ucsf.edu/chimera/), VMD
(visual molecular dynamics; REF;
http://www.ks.uiuc.edu/Research/vmd/) or PYMOL (http://pymol.sourceforge.net/).
In the following paragraphs I will summarize the basic
features of each one of the main platforms (SPIDER, IMAGIC
and EMAN)[16,27,28], pointing out those aspects that make
each software package exceptional (Figure 2). Globally, the
main differences among them center on (i) the use of either
single particles or their 2D averages to build the volumes;
and (ii) the means for angular assignment, either "angular
reconstitution" or "projection
matching"[7]. With respect to the first point, all 3 platforms use the images from single
particles as input data, but only SPIDER directly utilizes these
to reconstruct the volume, because both IMAGIC and EMAN
classify and average single images from similar views of the
protein in order to produce a 2D average with improved
signal-to-noise ratio. These averages then constitute the input
to reconstruct the 3D structure. With respect to the second
point, and as mentioned earlier, the fundamental aspect of
image processing corresponds to the determination of the
orientation angle of each image (or average) with respect to
a common set of coordinates. SPIDER and EMAN define
these angles by comparison with projections of preliminary
volumes that act as templates of known angles. Within each
cycle of refinement the reconstructed volumes and their
projections are improved, so that angular assignment is also
iteratively improved. This strategy is known as "projection
matching". Alternatively, IMAGIC defines orientation angles
using "common lines", an algorithm that can potentially find
the angular relation between projections without additional
input. I will not get into the principles that underlie common
lines but this requires a high signal-to-noise ratio to
diminish false solutions and, consequently, IMAGIC spends much
of its efforts in particle classification, alignment and
averaging. Its great conceptual advantage is that angles
come directly from the data, thus the name "angular
reconstitution"[7]. Model bias in the assignment of angles is
therefore greatly reduced, though some bias still exists because
projections from iteratively improved 3D models are used to
increase the accuracy in particle alignment and classification.
It is imperative to point out that, besides these differences in
the general approaches among several platforms, each of
these contains the tools required to perform almost any
operation with the images from the electron microscope, and
are consequently intrinsically very flexible. Therefore, for
instance, a "projection matching" strategy can be perfectly
carried out using tools provided by IMAGIC.
Figure 2 outlines a generalised flow-through during
image processing with each platform.
SPIDER[27] initiates from a rough starting model to generate projections of defined
angular spacing (Figure 2A). Each single particle is
compared with all projections so that it receives those angles of
that template with which it better fits. This preliminary
angular assignment is used to build a new 3D model that acts as a
new source for projections. As this process is repeated
iteratively ("angular refinement"), projections better match the
real data and, at the end, the angles assigned to the particles
allow reconstruction of the structure. Refinement in
IMAGIC[28] (Figure 2B), on the other hand, makes use of the projection
templates just to align the particles, so classification and
averaging can be iteratively improved, but angles are
defined using the 2D averages and common lines. Finally,
EMAN[16] (Figure 2C) has adopted a scheme somehow in
between those of SPIDER and IMAGIC. Angular assign
ment for each particle in EMAN is defined based in their
correlation with projection templates, as with SPIDER. But
instead of using particles directly to build the volumes, all
particles with a similar orientation constitute a group or class
to be averaged, and only these averages are then used to
reconstruct a volume. The process of averaging in EMAN
incorporates a very good set of parameters that can be tuned
to improve averaging and discard "bad" particles. An
especially interesting feature of EMAN is that particles within a
class are actually "refined" during averaging so that model
bias is strongly minimised and single images with a standard
deviation above a certain threshold are not incorporated into
the final average. Common to all 3 systems is that either
mechanism of angular assignment is repeated iteratively
(angular refinement) until the angles assigned to the
particles and the resulting 3D structure are stabilised. At the
end, if correctly used, any of these 3 software platforms can
construct an accurate structure. Nevertheless, it is extremely
important to note that image processing is far from a fully
automated method that does not require user intervention.
On the contrary, each processing platform just provides a
large number of computing tools to deal with the data from
the microscope, but evaluation of the output results and
decisions during processing are completely user dependent.
Consequently, an inexperienced user could end up with a
wrong structure.
The resolution problem or how to solve the resolution gap
Once we have the final 3D reconstruction of our
macromolecule, the last stage of the research involves the
in-depth inspection and description of the structure. This is
a crucial step because interpretation of the 3D data is the
source for the extraction of biologically relevant information,
and therefore the source of our conclusions about the
processes we are studying. In single-particle EM this task is
problematic because the structures are solved to resolutions
above those required to trace the polypeptide chain, due to
the difficulties still present during averaging and alignment
of the noisy images obtained with the microscope. Typically,
EM analysis provides structures ranging from 8 Å-10 Å to
30 Å-40 Å resolution, and the consequences of these
resolutions for the way in which a macromolecule is visualized
can be perceived in Figure 3C, where I have used as an
example the recent atomic model of DNA-PKcs, a kinase
implicated in DNA repair[33]. While at a resolution of 9 Å
secondary structural elements, such as alpha helices, can still be
distinguished in favorable cases[19,34], at poorer resolution
(>15 Å), rarely anything more than the overall shape of the
protein is apparent (Figure 3C).
To bridge the gap between the atomic information we
would wish to have in our structure and the medium or low
resolution of the actual EM reconstructions, 3
"multi-resolution" methods have been
proposed[35-39]. These suggest combining information at different levels of resolution in
order to investigate complex systems.
Rigid-body fitting A medium-resolution EM structure can
be depicted as a convolution of its atomic features; hence a
pseudo-atomic model can be obtained by computationally
placing ("fitting") atomic coordinates into the EM
map[36]. The atomic structure is considered to be a rigid body with no
conformational changes, whose density has to be located
and placed within the 3D reconstruction. Rigid-body fitting
is a very appropriate mode to map domains into a larger
complex that contains several domains or proteins, and it
has been extensively used already to propose pseudo-atomic
models of macromolecular complexes. However, it is
important to bear in mind that the accuracy of this computational
approach can be seriously hampered by a lack of resolution
of the target 3D model and by the size, shape and
conformational flexibility of the fitted atomic structure. Figure 3 shows
a recent example from our group where the 3D structure of
the DNA-PKcs kinase[33] was fitted with atomic structures of
individual domains (Figure 3A) to produce a pseudo-atomic
model for some portions of the molecule (Figure 3B). This
atomic model, when filtered to the resolution experimentally
obtained by EM, very much resembles the corresponding
segments of the 3D structure (Figure 3C). Many other
examples can be found in the recent literature where these
methods have been applied[40-42].
Several algorithms have been developed that consider
different sides of the problem[35,36], but this is still a very
active field where a consensus about the best approaches
has not yet been reached. Some of the most commonly used
tools are those implemented in
SITUS[43] (http://situs.biomachina.org/),
EMAN[16] (http://ncmi.bcm.tmc.edu/ncmi/)
and EM fit[44]. Nevertheless, other algorithms are also
frequently used[45-47]. At low resolution, several solutions could
comply with our fitting criteria, so one must be cautious with
the results, which, if much uncertainty persists, should
ideally be supported or validated with external information.
Flexible fitting It is very likely that an atomic structure
will not perfectly match one solved by EM, especially when
the portion represented by the atomic structure is part of a
larger complex solved by EM or it is at a different stage of its
functional cycle. In such cases, and if sufficiently good
resolution is present, the atomic coordinates can be modi
fied so that they better fit into the EM density. By doing so,
we can not only place a domain within a larger structure (as
in rigid-body fitting), but we can also actually identify a new
conformation of the protein. A nice example has been shown
recently in the 6 Å structure of GroEL, where some
displacement of helices were found compared to its atomic
coordinates[19]. Both
SITUS[48] and EMAN[16] contain algorithms
to perform flexible fitting.
Prediction of secondary structure elements and protein
folds In those cases where the resolution of an EM map is
very good (usually anything below 8 Å), instead of fitting
known atomic structures, a more powerful approach can be
carried out, and the actual recognition of secondary
structure elements within the map can be achieved. The AIRS
platform in EMAN has several commands (helixhunter,
ssehunter) to look for sheets and helices in the maps. A
score is supplied to help discriminate real from spurious
findings. In favorable cases, a whole fold type can be
defined and consequently a sufficiently realistic atomic model.
A brilliant example has been recently published describing a
pseudo-atomic model for the capsid of phi29
phage[34]. Other groups are also currently working on fold predictions from
EM reconstructions[49]. Importantly, results in the area of
secondary structure prediction need to be handled with great
care due to the still innovative nature of this field.
Some notable recent electron microscopy studies
The last 2-3 years have been characterized by a rapid
increase in EM publications. I wish to point out some
significant works recently published, which have been dealing
with some challenging applications of single-particle EM.
These works shed light on where the field is going in the
very near future. I strongly apologize to all whose work has
not been reflected in this review.
Small, asymmetric and flexible proteins Certainly, EM
has clear limitations with respect to the smallest size of the
molecules it can analyze. Proteins must be visualized above
a noisy background to be extracted and processed, and only
large macromolecules can therefore fit this criterion. There
are also deeper conceptual reasons that do not allow
reconstruction of very small
molecules[50]. As a result, proteins targeted by EM studies are usually above 200 kDa-300 kDa.
Having said that, some recent outstanding works have
challenged these difficulties and studied small proteins, many
with molecular weights around, or even below, 100 kDa, and
these have been reconstructed at decent resolutions (around
20 Å-30 Å):
geminin[51], separase[11], the Arp2/3
complex[52]
and the mammalian fatty acid
synthase[53], to name a few.
A common feature of many of these proteins is that they
participate in very relevant pathways in the biology of the
cell (eg signaling pathways, DNA repair, oncogenes and
tumor suppressors), and though they are not extremely large,
they are still difficult to purify in large quantities for X-ray
studies. This is the case, for instance, with Vav, an activator
of Rho/Rac GTPases, whose 3D structure has been solved
in the inactive and active conformations, plus a ~85 kDa
truncated mutant with oncogenic
potential[12], providing insights into its regulatory mechanisms. Another remarkable example
is the structure of the tetrameric KvAP voltage-dependent
K+ channel[54] with a mass of 100 kDa, which the authors
deliberately increased up to 300 kDa by addition of 4 Fab
fragments.
Generally speaking, all of these works have the
challenging difficulty of collecting good microscopy data and being
able to correctly align images of small molecules, which, on
top of that, frequently display no symmetry at all. But an
even further twist to these already difficult experiments comes
when the protein under study is flexible; several
conformations are present in the same micrographs whose
identification is not always straightforward. In some of these cases,
the method called "random conical
tilt"[20,22] (developed some time ago to obtain 3D structures from proteins bound to the
EM grids with preferential views) might be the only tactic
available to solve these structures without mixing several
conformations. In this regard, it is worth taking a look at
some recent excellent works by Tomas Waltz¡¯s
group[55,56]
Structures of the
ribosome The complex structure of
the ribosome and the process of mRNA translation have been
extensively studied by several groups in the last decade.
These authors regularly provide structures at resolutions
better than 12 Å and the wealth of biological information
obtained by combining EM and X-ray crystallography of
the ribosome is unprecedented. Two groups have been
leading this research: Joachim Frank at Albany (NY,
USA)[57-61] and Marin van Heel at London
(UK)[62-64].
Multi-protein macromolecular complexes or
"molecular
machines" The natural targets of EM are those
complexes that contain several proteins, are very large in
size, are heterogeneous in their composition, have complex
functional cycles and are difficult to purify; all of these qualities
making complicated a traditional analysis by
crystallography or NMR. Interestingly, these large and transient
complexes, sometimes named "molecular machines", have
been admitted by modern biology to comprise the basics for
a major number of cellular processes, and are therefore a
subject of great interest. Some EM works have started to
obtain 3D information on some of these complexes, such us
the spliceosome (implicated in mRNA
splicing)[65-68], the SAGA complex from
Saccharomyces cerevisiae[69]
, the apoptosome involved in procaspase-9 binding and
activation during apoptosis[70], and several complexes between
chaperonins with their substrates and
co-chaperons[71-74].
DNA-bound protein
complexes Determination of the 3D
structure of complexes between proteins and DNA substrates
has been accomplished, for instance, the large T antigen
bound to the simian virus 40 origin of
replication[75], the DNA-PKcs kinase bound to a double-stranded DNA fragment that
simulates a DNA repair signal[33,76], and the clamp-loading
complex for DNA replication[77].
High resolution
structures Averaging of the noise
images from single-particle EM has the potential to deliver 3D
structures with a resolution sufficient to trace the
polypeptide chain[50]. A Nature paper published in 2003 on the
flagellar structure[78] demonstrated that if good single images can
be accurately aligned, they are able to deliver atomic
information. The authors made use of a trick to align the
images according to their regular arrangement within the
flagella. Nevertheless, the outstanding merit of the work
was that no diffraction information was used during their
processing. It seems clear that reaching very high
resolution will only be possible for adequate specimens of large
size, high symmetry and by means of high-quality (ie high
signal-to-noise ratio) images, derived probably from
helium-cooled microscopes. However, the race is on to
increase the resolution of single-particle reconstructions both with
improved equipment and better algorithms. Some recent
examples are the GroEL structure at
6 Å[19] using EMAN, the Escherichia
coli large ribosomal subunit at
7.5 Å[79], and the 8 Å resolution structure of
microtubules[41] using SPIDER.
Other noteworthy works Other recent studies of
special interest have been the definition of the structural basis
of pore formation by the bacterial toxin pneumolysin, by the
group of Helen Saibil[80], and the pseudo-atomic model of the
capsid of phage phi29[34].
Future prospects and limitations of single-particle electron microscopy
Single-particle EM analysis has become a trendy
structural tool in biology despite not providing atomic resolution
information. This is due to some of the great advantages of
this method in comparison to more established atomic
resolution techniques: (i) small amounts of purified protein are
required, compatible with those ordinarily obtained for large
macromolecules or multi-subunit protein complexes, possi
bly its key advantage when compared to crystallography
and NMR as EM can deliver biological information from
modest quantities of material; (ii) macromolecules are trapped
in their native conformation in physiological buffers; and
(iii) preparations containing mixed populations or
contaminated samples could be potentially analyzed whenever the
distinct populations can be separated, either visually or
computationally. Consequently, single-particle EM is a
technique very suitable for determining the 3D structure of large
macromolecule complexes ("molecular machines"), which are
now known to be implicated in many cellular processes. This
is so because macromolecular complexes can be very large
and challenging to crystallize; they can frequently be
purified only in modest quantities, while at the same time being
very flexible and of variable composition.
Future developments of the technique are being directed
toward improved resolution and a more profound
examination of the volumetric data by either the fitting of atomic
structures or the identification of folds. All of these advances
will require new methods now under
development[7,13,81]. Automation of data collection and analysis is also becoming an
important goal for EM. The long learning process needed to
properly operate a modern electron microscope, to collect
good-quality data and to perform a flawless image
processing means that only those with extended experience in EM
can really do the job. Hence, making as many of those
processes as automatic as possible is a great need for the future
development of EM, especially when collecting the many
thousands of single images needed when high resolutions
are the goal. Some interesting approximations are already
under development[82-87], and there are no serious
conceptual reasons why automation of the most repetitive
microscope tasks should not be achieved in the near future.
However, one of the more important challenges for the
future will be to deal with conformational flexibility and the
heterogeneity of large protein complexes. As resolution
increases, more data on identical conformations must be
averaged, but the better the resolution, the more likely it is
that molecules will differ in their exact conformation.
Suggested solutions implicate the refinement of the data into
more than one possible 3D volume and the exploration of the
conformational space of a protein using normal-mode
analysis[88-91].
Still, EM analysis presents some important limitations
that are essential to keep in mind. To begin with, the
methods we use today are still very much dependent on the
expertise of the user to deliver the correct structure, especially
when dealing with small and low-symmetry molecules or
heterogeneous samples. Things can be done wrong, and an
inexperienced user can end up in local minima not
representing the real 3D structure. Consequently, a great challenge
for the near future should be to standardize methods and
controls as is done in modern X-ray crystallography. A
further limitation to the method is that, in some cases,
structures of macromolecular complexes are solved only to
moderate resolutions, which might provide few or no
biologically relevant information, especially when no atomic data of
any part of a complex is known. Nevertheless, in these cases,
EM structures can still be interpreted by calculating
difference maps among several reconstructions to then determine
the position of components in the complex. Besides these
limitations, single-particle EM will certainly cope with its
future challenges to become a widespread method to study
the 3D structures of macromolecules in conjunction with
other structural techniques.
Acknowledgement
I greatly acknowledge the constant support by Laurence
H Pearl at the Institute of Cancer Research (London, UK) in
the DNA-PK work presented in the figures of this review. I
am also very thankful for the work of Angel Rivera-Calzada
and Ernesto Arias-Palomo in my lab. I apologize to all those
groups working in the EM field whose studies have not been
reflected in this mini review, which was just intended as an
initiation for those outside of our discipline.
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