Article

The structural dynamics of macromolecular processes

Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, 1700 4th Street, San Francisco, CA 94158-2330, USA.
Current opinion in cell biology (Impact Factor: 8.47). 03/2009; 21(1):97-108. DOI: 10.1016/j.ceb.2009.01.022
Source: PubMed

ABSTRACT

Dynamic processes involving macromolecular complexes are essential to cell function. These processes take place over a wide variety of length scales from nanometers to micrometers, and over time scales from nanoseconds to minutes. As a result, information from a variety of different experimental and computational approaches is required. We review the relevant sources of information and introduce a framework for integrating the data to produce representations of dynamic processes.

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A
vailable online at www.sciencedirect.com
The structural dynamics of macromolecular processes
Daniel Russel
1
, Keren Lasker
1,2
, Jeremy Phillips
1,3
,
Dina Schneidman-D uhovny
1
, Javier A Vela
´
zquez-Muriel
1
and Andrej Sali
1
Dynamic processes involving macromolecular complexes are
essential to cell function. These processes take place over a
wide variety of length scales from nanometers to micrometers,
and over time scales from nanoseconds to minutes. As a result,
information from a variety of different experimental and
computational approaches is required. We review the relevant
sources of information and introduce a framework for
integrating the data to produce representations of dynamic
processes.
Addresses
1
Department of Bioengineering and Therapeutic Sciences, Department
of Pharmaceutical, Chemistry, and California Institute for Quantitative
Biosciences, Byers Hall, Suite 503B, University of California at San
Francisco, 1700 4th Street, San Francisco, CA 94158-2330, USA
2
Blavatnik School of Computer Science, Raymond and Beverly Sackler
Faculty of Exact Sciences, Tel Aviv University, Tel-Aviv 69978, Israel
3
Graduate Group in Biological and Medical Informatics, University of
California at San Francisco, USA
Corresponding author: Sali, Andrej (sali@salilab.org)
Current Opinion in Cell Biology 2009, 21:1–12
This review comes from a themed issue on
Cell structure and dynamics
Edited by Daniel J. Lew and Michael Rout
0955-0674/$ see front matter
Published by Elsevier Ltd.
DOI 10.1016/j.ceb.2009.01.022
Introduction
To understand the processes that maintain and replicate a
living cell, we need to describe the structural dynamics of
a few hundred core macromolecular processes [1]
(Figure 1), such as DNA replication by the replisome
[2], transcription of DNA into RNA by RNA polymerase
[3
!
], protein synthesis by the ribosome [4
!!
,5], protein
folding by chaperonins [6,7], nucleocytoplasmic transport
through the nuclear pore complex [8], active transport
with molecular motors [911], assembly pathways of large
complexes [1214], and protein degradation in the pro-
teasome [1517]. These processes take place over a wide
variety of length scales from nanometers to micrometers,
and over time scales from nanoseconds to minutes. More-
over, the macromolecular systems can exist in different
structural states (conformational heterogeneity) and fol-
low different kinetic pathways during a single process
(kinetic heterogeneity) (Figure 2 and Table 1).
No single technique, computational or experimental, is
able to span all relevant spatial and temporal scales
(Figure 3). For static complexes, for example, X-ray
crystallography can generate atomic structures of the
components, while single particle cryo-electron micro-
scopy (cryo-EM) can provide average mass density maps
of the whole assembly at nanometer resolution for the
whole assembly. For processes, computer simulations are
beginning to reach the microsecond time scale, while
various single molecule and stopped-flow techniques
come into play on the millisecond time scale. Thus, a
key challenge is to integrate different kinds of static and
dynamic characterizations at different resolutions to
obtain a comprehensive description of a process. As for
descriptions of static structures [18
!!
,19], such an integ-
ration of data will have to be achieved through compu-
tational approaches.
We expect that inspiration for the needed computational
approaches will come from a wide range of fields that
model dynamic systems. Examples include motion cap-
ture techniques for movies, where the motions of markers
on an actor are tracked and used to restrain a general
model of locomotion to make an animated character move
like the actor [20]; kinematics in robotics, where motions
are designed to connect a set of states subject to con-
straints, such as driving a vehicle from one point to
another [2123]; the master equation in chemical kinetics
that captures rates of transitions between different states
[24]; molecular dynamics simulations, where every
attempt is made to make the trajectories correspond to
reality [25
!
,26]; simplified physical simulations, such as
Brownian dynamics [25
!
,26] and modeling of transitions
[27]; and diffusion-based models of biological processes
[28]. However, none of these computational approaches
are always accurate, applicable on all time and size scales
of interest, capable of describing all properties of interest,
and able to include all available experimental data and
theoretical considerations. Such an integrative approach
still needs to be developed.
In this review, we des cri be a pr ocess a s a set of key states
connected by transit ion s (Figure 2 and Table 1). Such a
model is s imilar to the owchart diagrams that are typi-
cally used to prov ide high-l evel views of processes. A
representation of the process ca n vary in resolution,
ranging from schemes involving two key states to
high-resolution schemes with many key states and tran-
sitions be twe en them. S ome processes may be mor e
concisely and efficiently modeled at a given resolution
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by a continuum diffusion model or molecular d yna mic s,
particularly at high resolution w her e the numbe r of states
becomes large. As in the static structure case, w e can u se
experimental and theor etical data to cons tru ct restraints
that limit the set of possible process models [18
!!
]. Fo r
example, a restraint can act to limit the distance between
two components of the system as a function of time. More
restraints are added as the evid enc e accumulates, redu-
cing the number of acceptable models.
Important tasks required to build a structural dynamics
model of a process are first, discovering key states and
determining their structures; second, finding which pairs
of key states interconvert; third, determining rates of
transitions between interconverting key states; and
fourth, computing trajectories for the transitions between
key states. The next four sections review how these four
challenges can be addressed by different techniques
(techniques referred to in italics are described in
Table 2). Thes e challenges are inter-related and progress
toward resolving one may help resolve others.
Discovering key states and determining their
structures
Structural modeling of a dynamic process generally begins
with the determination of key states and their structural
characterization. If a conformationally homogenous sample
of a key state can be purified, the whole arsenal of
traditional structural biology techniques can be applied.
These techniques are reviewed from a computational
perspective in Ref. [18
!!
]. Key states that are not suffi-
ciently stable can sometimes be stabilized by removing,
adding, or modifying parts of the system (eg, by adding
ligands) to block the transition to another state. For
example, in studies of the ribosome-bound nascent chain
by NMR spectroscopy, the RNA transcript was prepared
without a stop codon. This modification led to translation
arrest and allowed measurements to be taken on the partly
2 Cell structure and dynamics
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Figure 1
Examples of dynamic macromolecular processes: (a) Locomotion of a cell is enabled by a reversible rotary propeller of the bacterial flagellum [119]. (b)
Nucleocytoplasmic transport of macromolecules occurs in a regulated fashion through the nuclear pore complex [120
!
]. (c) A number of cellular
functions, including muscle contraction, cell motility, cell division, and cytokinesis, depend on the assembly and maintenance of branched actin
filaments (http://www.cgl.ucsf.edu/chimera/ImageGallery/). (d) The folding of many proteins is catalyzed inside the chaperonin cavity [7](http://
www.cgl.ucsf.edu/chimera/ImageGallery/). (e) The HIV-1 core assembles inside the maturing virion [121]. (f) Synthesis of ATP in mitochondria and
chloroplasts is catalyzed by ATP synthase (http://www.mrc-dunn.cam.ac.uk/research/atp_synthase).
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The structural dynamics Russel et al. 3
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Figure 2
A representation of a process. (a) Several terms used in the text are illustrated by a process of four key states (circled solid shapes) connected by
transitions (arrows). (b) Conformational heterogeneity is illustrated by a sample consisting of complexes of varying composition and conformation. (c)
Illustration of kinetic heterogeneity, showing two (black and blue) of the many paths through the graph in (a). Definitions of the terms can be found in
Table 1.
Figure 3
An overview of the spatial and temporal coverage of the various methods. The x-axis represents the size of the systems th at can be explored by
each method in nanometers. The y-axis represents the time scales t hat can be reached. The methods a nd abbrevi ations are described in
Table 2.
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produced protein [29]. In other work, the kiromycin anti-
biotic was used to stall translation in the Escherichia coli
ribosome to take cryo-EM snapshots of the elongation
factor Tu in complex with the ribosome [30]. Methods
that can be used to characterize transient states and hetero-
geneous states are discussed next.
Characterizing transient states
Key states of the system often exist only for a brief time as
the syst em changes from one stable state to another. To
provide structural information about such a transient key
state, the method must either quickly immobilize the key
state or have high enough temporal resolution to take the
measurement as the system changes. In the former
category, hydroxyl radical footprinting breaks a structure
into pieces over a few milliseconds, thus terminating the
time evolution of the system. This approach has been
used to monitor the early stages of ribosome assembly
after rRNAprotein encounter, following changes in the
structure of the 30S subunit [31
!!
]. The results show that
the initial RNAprotein complexes refold during the
process. Fluorescent affinity tag purification allows visual-
ization of the target protein in live cells, followed by
extraction and detection of interacting macrom olecular
partners. It has been used to localize specific interactions
of viral proteins with hostcell interaction partners at
different stag es during a viral infection [32].
NMR spectroscopy and fluorescence-based methods can
monitor the assembly as it changes over time. Relaxa-
tion-dispersion NMR spectroscopy has been used to observe
transient states of small compl exes of proteins that only
exist on the time scales of seconds [33,34,35
!!
]. This new
method has not yet been applied to macromolecular
assemblies. Fluorescence tagging allows different types of
measurements to be taken and has been used extensively
in the determination of attributes of static structures. For
example, the stoichiometry of a particular subunit in an
assembly can be estimated by monitoring the fluctuations
in intensity as complexes with tagged subunits move
through the observed volume [36,37]. FRET can be used
for structur e determination by successively tagging pairs
of proteins [38]. The detection of the addition or removal
of a subunit from a single complex can be accomplished
via fluorescence labeling of proteins [39] or polarization
fluorescence spectroscopy [40].
Disentangling conformationally heterogeneous states
Methods that measure average properties of the system are
typically comparatively easy to apply, but their precision is
limited by conformational and kinetic heterogeneity in the
sample. It is often difficult to create a sample containing
only a single key state, owing to multiple kinetic pathways
or inability to synchronize each step of a single pathway. To
avoid averaging over various states in a sample, single
molecule methods such as FRET and optical tweezers are
required [41
!
,42]. Unfortunately, single molecule methods
provide information about only a few variables at a time,
thus making it difficult to reproduce the state of the whole
assembly. Methods, such as cryo-electron tomography [43],
not traditionally includedin the category ofsingle molecule
techniques, also provide information about individual
copies of the system. For example, the expectation-max-
imization algorithm, together with a maximum likelihood
scoring function, is able to classify single particle cryo-EM
images corresponding to different states of the complex.
The classified images can then be used to produce struc-
tures of each of the well-populated states in the sample.
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Table 1
Brief descriptions of important terms used in the review
State A state is described by a three-dimensional structure of an assembly at some resolution. The structure may
be flexible and its description may be incomplete.
Key state The set of key states and transitions between them capture the essence of the process. Key states need not be
stable and can correspond to transition states.
Transition A transition occurs between a pair of key states that can interconvert directly without passing through other key states.
Trajectory A trajectory is a detailed sequence of states describing a transition between two key states, like frames in a movie.
Rate of transition Transition rates can be expressed in a variety of ways such as the probability of moving from one state to another
in a given period of time or rate constants.
Conformational
heterogeneity
Conformational heterogeneity implies that multiple states exist in a single sample of the system. Such heterogeneity
complicates bulk experimental measurements, often requiring single-molecule experiments.
Kinetic heterogeneity Kinetic heterogeneity results from different copies of the system following different transitions. For example,
different parts of the secondary structure of a protein can form independently and asynchronously before the
tertiary structure forms [110] and, during ribosome assembly, different interactions between proteins and RNA
can stabilize independently of one another [13].
Restraint A restraint restricts geometric and/or temporal properties of an assembly, such as the distance between two
components, the overall shape of the complex, or the time interval between two key states. A restraint is a scalar
function that quantifies the agreement between a restrained feature and the data.
Process representation A process is represented as a set of key states connected by transitions with associated trajectory and rate information.
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This technique hasbeen applied tothe E. coli ribosomeand
the large T antigen of Simian Virus 40 [44].
Finding which pairs of key states interconvert
Given the set of key states, the allowed transitions need to
be determined. These transitions are between the pairs of
key states that can interconvert directly (i.e. without
passing through other states). The effort to determine
the connected key states involves using experiment ally
measured time series or computational searches.
Using experimental time series
When the process involves rapid transitions between a
number of relatively stable key states, any temporal data
that can distinguish between different mixtures of states
can be used to determine connectivity. Such data fitting
approaches enumerate sets of connections and determine
if rates exist that reproduce the experimental data for
each choice of connectivity. Suc cessful applications in-
clude finding the topology of RNA folding using hydroxyl
radical footprinting [45] and monitoring virus capsid
assembly using time resolved SAXS [46
!!
]. In the former
work, a five-state model of the folding process of a large
RNA molecule was proposed. The number of key states
was determined by clustering the rate at which various
parts of the molecule were protected during folding. To
find the best model, all possible graphs of five key states
and assignments of protected regions to states were
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Table 2
A brief over view of the various experimental and computational methods mentioned in this review
Time resolved SAXS [46
!!
,111] In time resolved small angle X-ray scattering (SAXS), a time course of the scattering profile is collected by
repeatedly exposing a sample in solution. The scattering profile provides low-resolution information about
the distribution of interatomic distances in the sample.
TROSY NMR [63,112] Transverse relaxation optimized spectroscopy (TROSY) is a variant of nuclear magnetic resonance (NMR)
spectroscopy that can be applied to large systems. This method isolates part of the system by replacing
the remaining hydrogen atoms with deuterium atoms. The chemical shifts of the hydrogen atoms can
then be monitored to measure local conformat ion and its changes.
PC/QMS [13] In pulse-chase monitored by quantita tive mass spectrometry (PC/QMS), a complex is allowed to assemble
for some period, followed by a rapid dilution of nonbound proteins in solution with
14
N labeled proteins.
Quantitative mass spectrometry then measures the relative populations of the heavy and light molecules,
producing an association rate estim ate for accumulation in the complex.
Time resolved pullouts [113] The cells are rapidly frozen and the media is ground. The ground substrate is thawed and the protein pulled
out by affinity chromatography, bringin g non-covalently attached along. These attached proteins can be
identified with mass spectrometry or other methods.
Time resolved hydroxyl radical
footprinting [31
!!
]
A brief pulse of synchrotron radiation is used to create radicals near the RNA that cleave the solvent
exposed backbone. Sequencing of the resulting fragments allows the cleavage sites to be identified,
and hence determines which parts of the backbone were exposed. Exposure information, coupled
with secondary structure prediction, can be sufficient to reconstruct the shape of the RNA.
Flourescent tags [49
!!
] Fluorescent tags are attached to particles and the system is observed through a microscope. When the
marked particles are separated by at least tens of nanometers, the individual dyes can be located.
Several different colors can be used at once to provide measurements of proximity. Fluorescent dyes suffer
from photo-bleaching that limits how long a single dye molecule can be used.
FRET [49
!!
,114] In Fo
¨
rster resonance energy transfer (FRET) spectroscopy, two particles are tagged with appropriate
fluorophores. When the dyes are close to one another (several nanometers), they become coupled and
excitation of one, the FRET donor, causes emission by the other, the FRET acceptor. The strength of
this coupling depends on distance, allowing changes in distance to be detected.
Optical tweezers [115] A micron-sized polystyrene bead attache d to part of the system is held in an optical trap. The trap can
be used either to hold the bead at a specified force or, alternatively, to set the displacement over time.
By restraining another part of the system (e.g. by immobilizing a bead attached to another part of the system),
a distance can be measured. Some recent setups allow a second bead to be trapped and manipula ted
independently. Optical tweezers can apply forces of up to hundreds of piconewtons.
Molecular dynamics
[25
!
,26,71,74,75
!
]
The Newton’s equations of motion are integrated for the atoms of the system, relying on a molecular
mechanics force field. The result is a trajectory of the system, sampled with time steps on the order
of femtoseconds. The longest simulations approach microseconds in duration. Coarse-grained
representations, combining multiple atoms into a single particle, can reach millisecond time scales.
Normal modes dynamics
[116,117]
The assembly is represented as a collection of points connected by springs. The local dynamics of such
an object is approximated by a linear combination of a small basis set of harmonic motions, each with a
characteristic frequency. The trajectory is generated by an iterative extrapolation of local dynamics.
Motion planning [21,118] Motion planning algorithms are a large family of techniques taken from robotics that search for
noncolliding trajectories between two known states of the system. The most advanced
techniques can handle hundreds of parameters.
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enumerated and scored. The assignment of backbone
protection in each key state was sufficient to uniquely
determine the RNA conformations.
Using simulations of system dynamics
Various computational methods can be used to find both
key states and transitions between them. The most direct
approach is to simply simulate the assembly with molecu-
lar dynamics, and connect key states on the basis of the
molecular dynamics trajectories. Such approaches require
a high-resolution representation of the starting state. In
addition, the methods suffer from an inability to connect
states distant from each other, because of limitations in
the accuracy and length of molecular dynamics trajec-
tories. Nevertheless, this approach has been used exten-
sively for building models of processes occurring on
smaller spatial and temporal scales, such as protein fold-
ing [47] and lipid vesicle formation [48]. In both cases,
many simulations were run and the frames from the
resulting trajectories were clustered to give a small num-
ber of highly populated key states connected by less
populous transitions.
Determining rates of transitions between
interconverting states
Knowledge of how key states are connected captures
much of what we want to know about dynamic processes,
but a complete descr iption requires determining the
transition rates between directly connected states in
the model. Different techniques can be applied to this
problem, depen ding on the properties of the system.
Monitoring an order parameter
An order parameter is a simple structural feature of the
system that changes during the transitio n of interest.
Fluorescent tags are useful for following individual particles
[49
!!
], monitoring the accumulation in the target key
state [50
!!
], tracking the orientation of a molecule [51],
and measuring relative distances [52
!
,53,54]. For
example, FRET fluctuations have been used to determine
transition rates between RNA folding states [55,56]. Dyes
were attached to immobilized RNA molecules so that the
two key states had different FRET efficiency [56].
Photons were then gathered from single molecules and
averaged over thousands of transitions. By using the
FRET efficiency measurements in narrow windows
around transitions, it was possible to monitor transitions
as short as the average photon emission interval as well as
to measure the time spent in each key state. Atomic force
microscopy can be used to monitor the height of an
assembly attached to a support at millisecond time scale
and nanometer resolution, as was done on GroEL [57].
Optical tweezers can be used to measure a single distance in
a single copy of the complex; for example, monitoring the
rate of RNA unfolding by a helicase [58] or translating by
the ribosome [59
!!
]. Such experiments involve holding
part of the system under tension and monitoring how the
distance changes as the process proceeds. When applied
to translation, single translational steps could be observed
to occur with a median interval of 2.8 s and to take less
than a tenth of a second to complete. NMR spectroscopy can
use changes in the local environment of certain atoms,
such as their solvent accessibility, to measure rates of
conformational transitions on the microsecond to milli-
second time scale [60,61
!
]. NMR-based methods for
monitoring enzyme kinetics on the picosecond to seconds
time scale were reviewed in Ref. [61
!
].
Determining rates of addition of components to a
system
The process of assembling a complex is particularly
amenable to rate measurements as the transitions be-
tween key states involve primarily involve the accumu-
lation of new species. Pulse chase monitored by quantitative
mass spectrometry can measure the rate of addition of new
subunits to the assembly. The main application so far has
been elucidation of the ribosome assembly process [13].
SAXS can monitor the size of an assembly as it is built, and
was applied, for example, to measuring the rate of virus
capsid assembly [46
!!
].
Computing trajectories for the transitions
between states
Trajectories connecting one key state to another can
contain a great deal of information, especial ly for models
with sparse key states. Generating a complete trajectory
typically requires computation, because experimental
methods generally cannot monitor all structural coordi-
nates for each molecule in an ensemble. The compu-
tational methods range from highly physically accurate,
but expensive methods (e.g. molecular dynamics simu-
lation) to less physically realistic but faster approaches
that allow us to compute trajectories between key states
more separated in time and space (e.g. normal modes
dynamics and motion planning).
Experimental restraints on trajectories
Hydroxyl radical footprinting has been used to monitor the
assembly process of the ribosome where the solvent
accessibility of the RNA backbone could be determined
with 10 ms precision in vivo [31
!!
]. The resulting solvent
accessibility information was sufficient to determine the
folding nucleation sites and rates. While time resolved
SAXS can generally monitor only coarse shape of the
assembly, under certain circumstances with regular and
well-defined structures, the time-resolved data can be
used to reconstruct the whole trajectory. Examples in-
clude formation of insulin fibrils [62] and virus capsids
[46
!!
]. Recently introduced TROSY NMR spectroscopy
allows detecting changes in the local en vironment of a
small part of the assembly, typically methyl groups,
during a dynamic process [63]. Applications include
monitoring conformational changes of a protein in
the GroEL-GroES chaperonin [64] and following
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the transition between two conformations of the ClpP
protease [65
!!
].
Computing trajectories
Computational approaches are generally needed to obtain
trajectories between key states. The gold standard is to
perform all atom molecular dynamics simulations of the sys-
tem in solvent. Such simulations require detailed structural
information about the components of the system and their
starting state, and can only simulate processes lasting less
than microseconds. Fortunately, molecular dynamics soft-
ware parallelizes efficiently and so can handle large systems
consisting of millions of atoms [66
!!
,67,68]. Coarse graining
and multiscale methods can extend the reachable time
scales to fractions of a millisecond by representing many
atoms with a single particle and using force fields derived
from more detailed all-atom computations [69
!
,7072,73
!
].
Adding intermediate key states along the transition can
make it easier to explore longer time scales [74].
An ortho gonal approach to speeding simulations, at the
risk of losing the physical accuracy, is to add forces that
guide the evolution of the system. These forces can
minimize the time the simulation spends stuck in dead
ends and local minima. For example, each atom can be
constrained inside a ball centered at the final position of
the atom, as given by an X-ray crystallographic structure
[75
!
]. The radius of the ball is initially large enough to
include the initial position of the atom, and gradually
shrinks to zero during the simulation, forcing the atom to
converge on its final location.
Techniques such as normal modes dynamics and motion
planning further sacrifice physical realism to handle long
time scales. Normal modes dynamics have been applied
to ribosomes [76,77], viruses [7880], myosin [81,82], and
chaperonins [83] at time scales up to 10
"8
s[84]. Motion
planning approaches have been applied to connecting the
open and closed confor mations of the K-channel [85],
studying the folding pathways of proteins [86] and RNA
[87], and computing large-amplitude motions [88].
Example process: the ubiquitinproteasome
protein degradation pathway
To illustrate our perspective on dynamic processes out-
lined above, we review recent studies toward elucidating
the dyn amics of the 26S proteasome as part of the
ubiquitinproteasome pathway (Figure 4). This pathway
plays a key role in regulating protein levels in eukaryotes
[17,89]. The ubiquitinproteasome pathway involves tag-
ging the substrate protein by covalently attaching
multiple ubiquitin molecules, followed by degradation
of the tagged protein inside the 26S proteasome and the
release of the ubiquitin molecules. A number of key
biological questions remain unanswered, such as how
tagged substrates are recognized by the proteasome for
degradation, whether the proteasome disassembles
during the catalytic cycle, and how the substrate is
degraded within the proteasome. Addressing these ques-
tions is challenging, as some key states are transient and it
is difficult to prepare sufficient quantities of tagged sub-
strates.
The 26S proteasome consists of a 28-protein 20S core
particle chamber that is capped on both sides by a #20
proteins 19S regulatory particle. We chose to describe the
degradation process using four key states, as suggested by
the ‘chew and spew’ model [90], although alternative
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Figure 4
A model of the substrate degradation by the proteasome. The degradation process is part of a larger pathway, consisting of activation of ubiquitin,
conjugation of the protein substrate and ubiquitins, degradation of the tagged protein, and deubiquitination to recycle the ubiquitins. The degradation
process is modeled by four key states and transitions between them, discussed in more detail in the text. The modeled system involves the 26S
proteasome, the E3 ligase enzyme, and the ubiquinated substrate protein. The four key states of the model are (a) 26S proteasome disassociated from
substrate, (b) recruitment of polyubiquinated substrate, (c) storage of substrate inside the proteasome, and (d) disassociated and disassembled 19S
regulatory particle. Arrows show transitions between states. As more information is obtained, the model can become more detailed by adding key
states, increasing the spatial resolution of each key state, and mapping trajectories between key states.
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Page 7
models have also been proposed [91]. The ‘chew and
spew’ model proposes that ATP hydrolysis in the pre-
sence of product peptides triggers controlled disassocia-
tion and disassembly of the 19S regulatory particle from
the 20S core particle (Figure 4). Next, we describe each of
the key states and the experimental and computational
techniques used for their characterization.
The first key state of the model consists of the ubiqui-
tinated substrate bound to the E3 ligase enzyme and a
26S proteasome. The structure of the 20S core particle
was solved by X-ray crystallography [92]. The ident ities
and interactions of proteins in the 19S regulatory particle
were revealed by affinity purification studies followed by
mass spectrometry [ 93 97 ]. A low-resolution structure of
the entire 26S molecule was determined by cryo-EM [98].
In the second key state, the ubiquinated-substrate/E3
complex is bound to the 26S proteasome via one of two
known ubiquitin receptors, Rpn10 or Rpn13 subunits of
the 19S regulatory particle [99102]. Biochemical studies
have revealed that Rpn10 is bound to the polyubiquitin
chain [103] and recognizes targets via its ubiquitin-binding
motif [104]. The amino-terminal domain of Rpn13 shows
no similarity to Rpn10 ubiquitin-binding motif, as revealed
by NMR spectroscopy and X-ray crystallography.
In the third key state of our model, the protein substrate is
located within the 20S outer chamber, before degra-
dation. The existence of this key state is suggested by
cryo-EM and tandem mass spectrometry that determined
the stoichiometry and location of substrates within the
26S proteasome [16]. This study also observed structural
differences between the free and substrate-bound 26S
structure.
In the fourth key state, the 19S particle is disassembled
and disassociated from the 20S and the peptides have
been released. The disassembled complex has been
mapped by negative stain EM [90].
The transitions between the key states are less well
characterized. Between key states two and three, sub-
strates enter the 20S core particle through a gated channel
after being unfolded by energy-dependent translocation
through the ATPase ring [102,105]. The residues
involved in this gate as well as its open and closed
structure have been localized in the 20S core particle
of an archaeal proteasome using cryo-EM [106,107]. The
mechanism appears to be conserved in mammals [108].
The transition from the third to fourth key state involves
degradation of the substrate and disassociation of the
complex. NMR spectroscopy has been used to monitor
the 20S core particle during the degradation process. The
study revealed motions o ccurring on the tens of nanose-
conds time scale in the outer chamber walls that are
correlated with much slower motions on the millisecond
time scale in the catalytic chamber [109
!!
]. The coupling
between substrate degradation and disassociation of 19S
regulatory particle is likely to be linked to conformational
changes in its AAA-ATPase ring. These changes have
been observed using biochemical studies [90].
Conclusions
Understanding macromolecular processes requires a wide
range of experimental and computational techniques. As
a result, we expect that integrative approaches will be
critical, even more so than in the static structure case.
Most existing models of dynamic processes have been the
result of ad hoc integrat ion of experimental results via
simple models or mental constructions. But moving to
higher accuracy, precision, coverage, and efficiency
through incorporating all the available information will
require novel computational approaches.
Acknowledgements
We thank Yifan Cheng, Friederich Fo
¨
rster, Joerg Gsponer, Avner
Schlessinger, and Elizabeth Villa for helpful discussions. KL, JVM, and
DSD have been funded by the Clore Foundation Predoctoral Fellowship,
Spanish Ministry of Education Postdoctoral Fellowship, and Weizmann
Institute Advancing Women in Science Postdoctoral Fellowship,
respectively. We also acknowledge support from the Sandler Family
Supporting Foundation, NIH/NCRR U54 RR022220, NIH R01 GM083960,
NIH PN2 EY016525, NSF IIS 0705196, and Pfizer Inc. And we are grateful
for computer hardware gifts from Ron Conway, Mike Homer, Intel,
Hewlett-Packard, IBM, and Netapp.
References and recommended reading
Paper of particular interest, published within the period of review, have
been highlighted as:
! of special interest
!! of outstanding interest
1. Sali A, Chiu W: Macromolecular assemblies highlighted.
Structure 2005, 13:339-341.
2. van Oijen AM: Single-molecule studies o f complex systems:
the replisome. Mol Biosyst 2007, 3:117-125.
3.
!
Herbert KM, Greenleaf WJ, Block SM: Single-molecule studies
of RNA polymerase: motoring along. Annu Rev Biochem 2008,
77:149-176.
An overview of the workings of the RNA polymerase is presented,
focusing on the various single molecule experiments that have helped
elucidate the process.
4.
!!
Marshall RA, Aitken CE, Dorywalska M, Puglisi JD: Translation at
the single-molecule leve l. Annu Rev Biochem 2008, 77:177-203.
A review of single molecule experiments used to study translation in the
ribosome.
5. Kaczanowska M, Ryden-Aulin M: Ribosome biogenesis and the
translation process in Escherichia coli. Microbiol Mol Biol Rev
2007, 71:477-494.
6. Dunn AY, Melville MW, Frydman J: Review: cellular substrates of
the eukaryotic chaperonin TRiC/CCT. J Struct Biol 2001,
135:176-184.
7. Clare DK, Stagg S, Quispe J, Farr GW, Horwich AL, Saibil HR:
Multiple states of a nucleotidebound group 2 chaperonin.
Structure 2008, 16:528-534.
8. Rout MP, Aitchison JD: The nuclear pore complex as a transport
machine. J Biol Chem 2001, 276:16593-16596.
9. Block SM: Kinesin motor mechanics: binding, stepping,
tracking, gating, and limping. Biophys J 2007, 92:2986-2995.
8 Cell structure and dynamics
COCEBI-649; NO OF PAGES 12
Please cite this article in press as: Russel D, et al. The structural dynamics of macromolecular processes, Curr Opin Cell Biol (2009), doi:10.1016/j.ceb.2009.01.022
Current Opinion in Cell Biology 2009, 21:112 www.sciencedirect.com
Page 8
10. Berg HC: The rotary motor of bacterial flagella. Annu Rev
Biochem 2003, 72:19-54.
11. Smith DE, Tans SJ, Smith SB, Grimes S, Anderson DL,
Bustamante C: The bacteriophage straight phi29 portal motor
can package DNA against a large internal force. Nature 2001,
413:748-752.
12. D’Angelo MA, Hetzer MW: Structure, dynamics and function of
nuclear pore complexes. Trends Cell Biol 2008, 18(10):456-466.
13. Talkington M, Siuzdak G, Williamson J: An assembly landscape
for the 30S ribosomal subunit. Nature 2005, 438:628-632.
14. Misra N, Lees D, Zhang T, Schwartz R: Pathway complexity of
model virus capsid assembly systems. Comput Math Models
Med 2008, 9:277-2 93.
15. Pickart CM, Cohen RE: Proteasomes and their kin: proteases in
the machine age. Nat Rev Mol Cell Biol 2004, 5:177-187.
16. Sharon M, Witt S, Felderer K, Rockel B, Baumeister W,
Robinson CV: 20S proteasomes have the potential to keep
substrates in store for continual degradation. J Biol Chem
2006, 281:9569-9575.
17. Glickman MH, Ciechanover A: The ubiquitinproteasome
proteolytic pathway: destruction for the sake of construction .
Physiol Rev 2002, 82:373-428.
18.
!!
Alber F, Forster F, Korkin D, Topf M, Sali A: Integrating diverse
data for structure determination of macromolecular
assemblies. Annu Rev Biochem 2008, 77:443-477.
The paper describes an approach for integrating experimental and
computational methods toward determination of static macromolecular
assembly structures.
19. Robinson CV, Sali A, Baumeister W: The molecular sociology of
the cell. Nature 2007, 450:973-982.
20. Moeslund T, Granum E: A survey of computer vision-based
human motion capture. Comput Vis Image Und 2001,
81:231-268.
21. Latombe J-C: Motion planning: a journey of robots, molecules,
digital actors, and other artifacts
. Int J Robotics Res 1999,
18:1119-1128.
22. Agarwal PK, Guibas LJ, Edelsbrunner H, Erickson J, Isard M, Har-
Peled S, Hershberger J, Jensen C, Kavraki L, Koehl P et al.:
Algorithmic issues in modeling motion. ACM Comput Surv
2002, 34:550-572.
23. Thrun S, Montemerlo M, Dahlkamp H, Stavens D, Aron A, Diebel J,
Fong P, Gale J, Halpenny M, Hoffmann G et al.: Stanley, the robot
that won the DARPA grand challenge . J Robot Syst 2006,
23:661-692.
24. MacNamara S, Burrage K, Sidje RB: Multiscale modeling of
chemical kinetics via the master equation. Multiscale Model
Simul 2008, 6:1146-1168.
25.
!
McGuffee SR, Elcock AH: Atomically detailed simulations of
concentrated protein solutions: the effects of salt, pH, point
mutations, and protein concent ration in simulations of 1000-
molecule systems. J Am Chem Soc 2006, 128:12098-12110.
The diffusion and interaction of a large number of densely packed rigid
proteins are simulated. Such simulations are able to reproduce many bulk
properties of the systems of interest.
26. Cheng Y, Chang CE, Yu Z, Zhang Y, Sun M, Leyh TS, Holst MJ,
McCammon JA: Diffusional channeling in the sulfate-activating
complex: combined continuum modeling and coarse-grained
Brownian dynamics studies. Biophys J 2008, 95:4659-4667.
27. Elber R: A milestoning study of the kinetics of an allosteric
transition: atomically detailed simulations of deoxy Scapharca
haemoglobin. Biophys J 2007, 92:L85-87.
28. Sosinsky GE, Deerinck TJ, Greco R, Buitenhuys CH, Bartol TM,
Ellisman MH: Development of a model for microphysiological
simulations: small nodes of ranvier from peripheral nerves of
mice reconstructed by electron tomography. Neuroinformatics
2005, 3:133-162.
29. Hsu ST, Fucini P, Cabrita LD, Launay H, Dobson CM,
Christodoulou J: Structure and dynamics of a ribosome-bound
nascent chain by NMR spectroscopy. Proc Natl Acad Sci U S A
2007, 104:16516-16521.
30. Stark H, Rodnina MV, Rinke-Appel J, Brimacombe R,
Wintermeyer W, van Heel M: Visualization of elongation factor
Tu on the Escherichia coli ribosome. Nature 1997, 389:403-406.
31.
!!
Adilakshmi T, Bellur DL, Woodson SA:
Concurrent nucleation of
16S folding and induced fit in 30S ribosome assembly. Nature
2008, 455:1268-1272.
In this study, the authors use hydroxyl radical footprinting to investigate
the ribosome assembly process.
32. Cristea IM, Carroll JW, Rout MP, Rice CM, Chait BT,
MacDonald MR: T racking and elucidating alphavirushost
protein interactions. J Biol Chem 2006, 281:30269-30278.
33. Vallurupalli P, Hansen DF, Kay LE: Structures of invisible,
excited protein states by relaxation dispersion NMR
spectroscopy. Proc Natl Acad Sci U S A 2008, 105:11766-11771.
34. Sugase K, Lansing JC, Dyson HJ, Wright PE: Tailoring relaxation
dispersion experiments for fast-associating protein
complexes. J Am Chem Soc 2007, 129:13406-13407.
35.
!!
Suh JY, Tang C, Clore GM: Role of electrostatic interactions in
transient encounter complexes in proteinprotein association
investigated by paramagnetic relaxation enhancement.
J Am Chem Soc 2007, 129:12954-12955.
The authors use paramagnetic relaxation enhancement to detect tran-
sient macromolecular interactions.
36. Chen Y, Muller JD: Determining the stoichiometry of protein
heterocomplexes in living cells with fluorescence fluctuation
spectroscopy. Proc Natl Acad Sci U S A 2007, 104:3147-3152.
37. Sykora J, Kaiser K, Gregor I, Bonigk W, Schmalzing G, Enderlein J:
Exploring fluorescence antibunching in solution to determine
the stoichiometry of molecular complexes. Anal Chem 2007,
79:4040-4049.
38. Muller EG, Snydsman BE, Novik I, Hailey DW, Gestaut DR,
Niemann CA, O’Toole ET, Giddings TH Jr, Sundin BA, Davis TN:
The organization of the core proteins of the yeast spindle pole
body. Mol Biol Cell 2005, 16:3341-3352.
39. Dange T, Grunwald D, Grunwald A, Peters R, Kubitscheck U:
Autonomy and robustness of translocation through the
nuclear pore complex: a single-molecule study. J Cell Biol
2008, 183:77-86.
40. Vrabioiu AM, Mitchison TJ: Structural insights into yeast septin
organization from polarized fluorescence microscopy. Nature
2006, 443:466-469.
41.
!
Moffitt J, Chemla Y, Smith S, Bustamante C: Recent advances in
optical tweezers. Annu Rev Biochem 2008, 77
:205-228.
A review of the current state of the art in optical tweezer experiments is
presented.
42. Moerner WE: New directions in single-molecule imaging and
analysis. Proc Natl Acad Sci U S A 2007, 104 :12596-12602.
43. Lucic V, Forster F, Baumeister W: Structural studies by electron
tomography: from cells to molecules. Annu Rev Biochem 2005,
74:833-865.
44. Scheres SH, Nunez-Ramirez R, Gomez-Llorente Y, San Martin C,
Eggermont PP, Carazo JM: Modeling experimental image
formation for likelihood-based classification of electron
microscopy data. Structure 2007, 15:1167-1177.
45. Laederach A, Shcherbakova I, Liang MP, Brenowitz M, Altman RB:
Local kinetic measures of macromolecular structure reveal
partitioning among multiple parallel pathways from the
earliest steps in the folding of a large RNA molecule. J Mol Biol
2006, 358:1179-1190.
46.
!!
Tuma R, Tsuruta H, French KH, Prevelige PE: Detection of
intermediates and kinetic control during assembly
of bacteriophage P22 procapsid. J Mol Biol 2008,
381:1395-1406.
In the paper, time resolved SAXS is used to detect unstable intermediate
states during virus capsid formation. By assuming that intermediates are
spherically symmetric, the authors are able to determine the structure of
the intermediates.
The structural dynamics Russel et al. 9
COCEBI-649; NO OF PAGES 12
Please cite this article in press as: Russel D, et al. The structural dynamics of macromolecular processes, Curr Opin Cell Biol (2009), doi:10.1016/j.ceb.2009.01.022
www.sciencedirect.com Current Opinion in Cell Biology 2009, 21:112
Page 9
47. Chodera JD, Singhal N, Pande VS, Dill KA, Swope WC: Automatic
discovery of metastable states for the construction of Markov
models of macromolecular conformational dynamics. J Chem
Phys 2007, 126:155101.
48. Kasson PM, Pande VS: Control of membrane fusion mechanism
by lipid composition: predictions from ensemble molecular
dynamics. PLoS Comput Biol 2007, 3:e220.
49.
!!
Joo C, Balci H, Ishitsuka Y, Buranachai C, Ha T: Advances in
single-molecule fluorescence methods for molecular biology.
Annu Rev Biochem 2008, 77:51-76.
The paper reviews single-molecule fluorescence techniques including
FRET and single particle tracking.
50.
!!
Jovanovic-Talisman T, Tetenbaum-Novatt J, McKenney AS,
Zilman A, Peters R, Rout MP, Chait BT: Artificial nanopores that
mimic the transport selectivity of the nuclear pore complex.
Nature 2008, doi:10.1038/nature07600 (available online 21
December 2008, in press)
A mimic of the central channel of the nuclear pore complex in a membrane
was designed. This artificial system reproduces key features of trafficking
through the NPC, including transport-factor-mediated cargo import.
51. Nishizaka T, Oiwa K, Noji H, Kimura S, Muneyuki E, Yoshida M,
Kinosita K: Chemomechanical coupling in F1-ATPase revealed
by simultaneous observation of nucleotide kinetics and
rotation. Nat Struct Mol Biol 2004, 11:142-148.
52.
!
Roy R, Hohng S, Ha T: A practical guide to single-molecule
FRET. Nat Methods 2008, 5:507-516.
A practical guide to setting up and performing FRET experiments is
provided, focusing on the study of immobilized molecules that allow
measurements of single-molecule reaction trajectories from 1 ms to many
minutes.
53. Weiss S: Fluorescence spectroscopy of single biomolecules.
Science 1999, 283:1676-1683.
54. Schuler B, Lipman EA, Steinbach PJ, Kumke M, Eaton WA:
Polyproline and the ‘‘spectroscopic ruler’’ revisited with
single-molecule fluorescence. Proc Natl Acad Sci U S A 2005,
102:2754-2759.
55. Kim HD, Nie nhaus GU, Ha T, Orr JW, Williamson JR, Chu S: Mg
2+
-
dependent conformational change of RNA studied by
fluorescence correlation and FRET on immobilized
single molecules. Proc Natl Acad Sci U S A 2002,
99:4284-4289.
56. Lee NK, Kapanidis AN, Koh HR, Korlann Y, Ho SO, Kim Y,
Gassman N, Kim SK, Weiss S: Three-color alternating-laser
excitation of single molecules: monitoring multiple
interactions and distances. Biophys J 2007, 92:303-312.
57. Yokokawa M, Wada C, Ando T, Sakai N, Yagi A, Yoshimura SH,
Takeyasu K: Fast-scanning atomic force microscopy reveals
the ATP/ADP-dependent conformational changes of GroEL.
EMBO J 2006, 25:4567-4576.
58. Dumont S, Cheng W, Serebrov V, Beran RK, Tinoco I Jr, Pyle AM,
Bustamante C: RNA translocation and unwinding mechanism
of HCV NS3 helicase and its coordination by ATP. Nature 2006,
439:105-108.
59.
!!
Wen JD, Lancaster L, Hodges C, Zeri AC, Yoshimura SH,
Noller HF, Bustamante C, Tinoco I: Following translation by
single ribosomes one codon at a time. Nature 2008,
452:598-603.
The authors used optical tweezers to monitor translation in the ribosome.
The resolution of the experiment was sufficient to observe three substeps
in each translocation and conclude that there are two rate-limiting steps in
the process.
60. Kern D, Eisenmesser EZ, Wolf-Watz M: Enzyme dynamics during
catalysis measured by NMR spectroscopy. Methods Enzymol
2005, 394:507-524.
61.
!
Henzler-Wildman K, Kern D: Dynamic personalities of proteins.
Nature 2007, 450:964-972.
Understanding the dynamics of proteins is key to understanding how they
function. The dynamics and a variety of experimental methods that help
elucidate the dynamics are discussed, focusing on enzymes.
62. Vestergaard B, Groenning M, Roessle M, Kastrup JS, van de
Weert M, Flink JM, Frokjaer S, Gajhede M, Svergun DI: A helical
structural nucleus is the primary elongating unit of insulin
amyloid fibrils. PLoS Biol 2007, 5:e134.
63. Mittermaier A, Kay LE: New tools provide new insights in
NMR studies of protein dynamics. Science 2006,
312:224-228.
64. Horst R, Bertelsen EB, Fiaux J, Wider G, Horwich AL, Wuthrich K:
Direct NMR observation of a substrate protein bound to the
chaperonin GroEL. Proc Natl Acad Sci U S A 2005,
102:12748-12753.
65.
!!
Sprangers R, Gribun A, Hwang PM, Houry WA, Kay LE:
Quantitative NMR spectroscopy of supramolecular
complexes: dynamic side pores in ClpP are important
for product release. Proc Natl Acad Sci U S A 2005,
102:16678-16683.
TROSY NMR experiments were used to describe the interactions of
inhibitors with the 670 kDa 20S proteasome.
66.
!!
Sanbonmatsu KY, Tung CS: High performance computing in
biology: multimillion atom simulations of nanoscale systems.
J Struct Biol 2007, 157:470-480.
A review of the current capabilities of molecular dynamics and the
challenges in the field.
67. Sotomayor M, Schulten K: Single-molecule experiments in vitro
and in silico. Science 2007, 316:1144-1148.
68. Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E,
Villa E, Chipot C, Skeel RD, Kale L, Schulten K: Scalable
molecular dynamics with NAMD. J Comput Chem 2005,
26:1781-1802.
69.
!
Ayton GS, Voth GA: Multiscale simulation of transmembrane
proteins. J Struct Biol 2007, 157:570-578.
Atomic resolution simulations of membrane proteins are coupled with a
coarse grained model of the membrane to investigate the behavior of key
gating residues in a influenza A virus M2 proton channel.
70. Nakamura T, Sasa S-i: Systematic derivation of coarse-grained
fluctuating hydrodynamic equations for many Brownian
particles under nonequilibrium conditions. Phys Rev E
(Statistical, Nonlinear, and Soft Matter Physics) 2006,
74:031105-031114.
71. Tozzini V, Trylska J, Chang CE, McCammon JA: Flap opening
dynamics in HIV-1 protease explored with a coarse-grained
model. J Struct Biol 2007, 157:606-615.
72. Arkhipov A, Yin Y, Schulten K: Four-scale description of
membrane sculpting by BAR domains. Biophys J 2008,
95:2806-2821.
73.
!
Marrink SJ, Risselada HJ, Yefimov S, Tieleman DP,
de Vries AH: The MARTINI force field: coarse grained
model for biomolecular simulations. J Phys Chem B 2007,
111:7812-7824.
A coarse grained force field is described, representing each residue with
several particles. The coarse grained structures generated with the force
field can typically be used to generate equivalent all atoms structures.
74. West AM, Elber R, Shalloway D: Extending molecular dynamics
time scales with milestoning: example of complex kinetics in a
solvated peptide. J Chem Phys 2007, 126:145104.
75.
!
Sanbonmatsu KY, Joseph S, Tung CS:
Simulating movement of
tRNA into the ribosome during decoding. Proc Natl Acad Sci U
SA2005, 102:15854-15859.
The ribosome (2.5 million atoms) is simulated at atomic resolution during
the cognate tRNA selection step of translation. The study observes
several novel features of the process as well as providing a template
for other simulations of large biological systems.
76. Wang YM, Rader AJ, Bahar I, Jernigan RL: Global ribosome
motions revealed with elastic network model. J Struct Biol
2004, 147:302.
77. Tama F, Valle M, Frank J, Brooks CL: Dynamic reorganization of
the functionally active ribosome explored by normal mode
analysis and cryo-electron microscopy. Proc Natl Acad Sci U S
A 2003, 100:9319.
78. Rader AJ, Vlad DH, Bahar I: Maturation dynamics of
bacteriophage HK97 capsid. Structure 2005, 13:413.
10 Cell structure and dynamics
COCEBI-649; NO OF PAGES 12
Please cite this article in press as: Russel D, et al. The structural dynamics of macromolecular processes, Curr Opin Cell Biol (2009), doi:10.1016/j.ceb.2009.01.022
Current Opinion in Cell Biology 2009, 21:112 www.sciencedirect.com
Page 10
79. Tama F, Brooks CL III: Diversity and identity of mechanical
properties of icosahedral viral capsids studied with elastic
network normal mode analysis. J Mol Biol 2005, 345:299.
80. van Vlijmen HWT, Karplus M: Normal mode calculations of
icosahedral viruses with full dihedral flexibility by use of
molecular symmetry. J Mol Biol 2005, 350:528.
81. Li GH, Cui Q: Analysis of functional motions in Brownian
molecular machines with an efficient block normal mode
approach: myosin-II and Ca
2+
-ATPase. Biophys J 2004,
86:743.
82. Navizet I, Lavery R, Jernigan RL: Myosin flexibility: structural
domains and collective vibrations. Proteins 2004, 54:384.
83. Keskin O, Bahar I, Flatow D, Covell DG, Jernigan RL: Molecular
mechanisms of chaperonin GroEL-GroES function.
Biochemistry 2002, 41:491.
84. Tama F, Brooks CL: SYMMETRY, FORM, AND SHAPE: guiding
principles for robustness in macromolecular machines. Annu
Rev Biophys Biomol Struct 2006, 35:115-133.
85. Enosh A, Raveh B, Furman-Schueler O, Halperin D, Ben-Tal N:
Generation, comparison and merging of pathways between
protein conformations: gating in K-channels. Biophys J 2008,
95(8):3850-3860.
86. Thomas S, Song G, Amato NM: Protein folding by motion
planning. Phys Biol 2005, 2:S148-155.
87. Tang X, Kirkpatrick B, Thomas S, Song G, Amato NM: Using
motion planning to study RNA folding kinetics. J Comput Biol
2005, 12:862-881.
88. Cortes J, Simeon T, Ruiz de Angulo V, Guieysse D, Remaud-
Simeon M, Tran V: A path planning approach for computing
large-amplitude motions of flexible molecules. Bioinformatics
2005, 21(Suppl 1):i116-125.
89. Hershko D, Bornstein G, Ben-Izhak O, Carrano A, Pagano M,
Krausz MM, Hershko A: Inverse relation between levels of
p27(Kip1) and of its ubiquitin ligase subunit Skp2 in colorectal
carcinomas. Cancer 2001, 91:1745-1751.
90. Babbitt SE, Kiss A, Deffenbaugh AE, Chang YH, Bailly E,
Erdjument-Bromage H, Tempst P, Buranda T, Sklar LA, Baumler J
et al.: ATP hydrolysis-dependent disassembly of the 26S
proteasome is part of the catalytic cycle. Cell 2005,
121:553-565.
91. Kleijnen MF, Roelofs J, Park S, Hathaway NA, Glickman M,
King RW, Finley D: Stability of the proteasome can be
regulated allosterically through engagement of its
proteolytic active sites. Nat Struct Mol Biol 2007,
14:1180-1188.
92. Groll M, Ditzel L, Lowe J, Stock D, Bochtler M, Bartunik HD,
Huber R: Structure of 20S proteasome from yeast at 2.4 A
resolution. Nature 1997,
386:463-471.
93. Fu H, Reis N, Lee Y, Glickman MH, Vierstra RD: Subunit
interaction maps for the regulatory particle of the 26S
proteasome and the COP9 signalosome. EMB O J 2001,
20:7096-7107.
94. Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, Knight JR,
Lockshon D, Narayan V, Srinivasan M, Pochart P et al.: A
comprehensive analysis of proteinprotein interactions in
Saccharomyces cerevisiae. Nature 2000, 403:623-627.
95. Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y: A
comprehensive two-hybrid analysis to explore the yeast
protein interactome. Proc Natl Acad Sci U S A 2001,
98:4569-4574.
96. Hartmann-Petersen R, Tanaka K, Hendil KB: Quaternary
structure of the ATPase complex of human 26S proteasomes
determined by chemical cross-linking. Arch Biochem Biophys
2001, 386:89-94.
97. Sharon M, Taverner T, Ambroggio XI, Deshaies RJ, Robinson CV:
Structural organization of the 19S proteasome lid:
insights from MS of intact complexes. PLoS Biol 2006,
4:e267.
98. Walz J, Erdmann A, Kania M, Typke D, Koster AJ, Baumeister W:
26S proteasome structure revealed by three-dimensional
electron microscopy. J Struct Biol 1998, 121:19-29.
99. Husnjak K, Elsasser S, Zhang N, Chen X, Randles L, Shi Y,
Hofmann K, Walters KJ, Finley D, Dikic I: Proteasome
subunit Rpn13 is a novel ubiquitin receptor. Nature 2008,
453:481-488.
100. Schreiner P, Chen X, Husnjak K, Randles L, Zhang N, Elsasser S,
Finley D, Dikic I, Walters KJ, Groll M: Ubiquitin docking at the
proteasome through a novel pleckstrin-homology domain
interaction. Nature 2008, 453:548-552.
101. Deveraux Q, Ustrell V, Pickart C, Rechsteiner M: A 26 S protease
subunit that binds ubiquitin conjugates. J Biol Chem 1994,
269:7059-7061.
102. Elsasser S, Finley D: Delivery of ubiquitinated substrates
to protein-unfolding machines. Nat Cell Biol 2005,
7:742-749.
103. Verma R, Oania R, Graumann J, Deshaies RJ: Multiubiquitin
chain receptors define a layer of substrate selectivity
in the ubiquitin proteasome system. Cell
2004,
118:99-110.
104. Hofmann K, Falquet L: A ubiquitin-interacting motif
conserved in components of the proteasomal and
lysosomal protein degradation systems. Trends Biochem Sci
2001, 26:347-350.
105. Navon A, Goldberg AL: Proteins are unfolded on the surface of
the ATPase ring before transport into the proteasome. Mol Cell
2001, 8:1339-1349.
106. Rabl J, Smith DM, Yu Y, Chang SC, Goldberg AL, Cheng Y:
Mechanism of gate opening in the 20S proteasome by the
proteasomal ATPases. Mol Cell 2008, 30:360-368.
107. Forster A, Masters EI, Whitby FG, Robinson H, Hill CP: The 1.9 A
structure of a proteasome-11S activator complex and
implications for proteasome-PAN/PA700 interactions. Mol Cell
2005, 18:589-599.
108. Smith DM, Chang SC, Park S, Finley D, Cheng Y, Goldberg AL:
Docking of the proteasomal ATPases’ carboxyl termini in the
20S proteasome’s alpha ring opens the gate for substrate
entry. Mol Cell 2007, 27:731-744.
109
!!
. Sprangers R, Kay LE: Quantitative dynamics and binding
studies of the 20S proteasome by NMR. Nature 2007,
445:618-622.
In this study, a special isotope labeling scheme is used in combination
with methyl-TROSY NMR spectroscopy to revea l functionally important
motions of the 670 kDa 20S proteasome. These assemblies are an order
of magnitude larger than ones that could be studied using earlier meth-
odologies.
110. Baldwin RL: Protein folding. Matching speed and stability.
Nature 1994, 369:183-184.
111. Lipfert J, Doniach S: Small-angle X-ray scattering from RNA,
proteins, and protein complexes. Annu Rev Biophys Biomol
Struct 2007, 36:307-327.
112. Ohki S, Dohi K, Tamai A, Takeuchi M, Mori M: Stable-isotope
labeling using an inducible viral infection system in
suspension-cultured plant cells. J Biomol NMR 2008,
42:271-277.
113. Cristea IM, Williams R, Chait BT, Rout MP: Fluorescent
proteins as proteomic probes. Mol Cell Proteomics 2005,
4:1933-1941.
114. Hohng S, Joo C, Ha T: Single-molecule three-color FRET.
Biophys J 2004, 87:1328-1337.
115. Moffitt JR, Chemla YR, Izhaky D, Bustamante C:
Differential
detection of dual traps improves the spatial resolution
of optical tweezers. Proc Natl Acad Sci U S A 2006,
103:9006-9011.
116. Van Wynsberghe AW, Cui Q: Interpreting correlated
motions using normal mode analysis. Structure 2006,
14:1647-1653.
The structural dynamics Russel et al. 11
COCEBI-649; NO OF PAGES 12
Please cite this article in press as: Russel D, et al. The structural dynamics of macromolecular processes, Curr Opin Cell Biol (2009), doi:10.1016/j.ceb.2009.01.022
www.sciencedirect.com Current Opinion in Cell Biology 2009, 21:112
Page 11
117. Miyashita O, Onuchic JN, Wolynes PG: Nonlinear elasticity,
proteinquakes, and the energy landscapes of functional
transitions in proteins. Proc Natl Acad Sci U S A 2003,
100:12570.
118. Latombe JC: Probabilistic roadmaps: a motion planning
approach based on active learning. The 5th IEEE International
Conference on Cognitive Informatics, 2006. 2006:1-2.
119. Minamino T, Imada K, Namba K: Molecular motors of the
bacterial flagella. Curr Opin Struct Biol 2008, 18:693-701.
120
!
.Beck M, Lucic V, Forster F, Baumeister W, Medalia O: Snapshots
of nuclear pore complexes in action captured by cryo-electron
tomography. Nature 2007, 449:611-615.
Cryo-electron tomography is used to study the structure of nuclear pore
complexes in vivo. Transport is monitored by tagging cargo with gold
particles resulting in a map of transport pathways through the nuclear pore.
121. Briggs JA, Grunewald K, Glass B, Forster F, Krausslich HG,
Fuller SD: The mechanism of HIV-1 core assembly: insights
from three-dimensional reconstructions of authentic virions.
Structure 2006, 14:15-20.
12 Cell structure and dynamics
COCEBI-649; NO OF PAGES 12
Please cite this article in press as: Russel D, et al. The structural dynamics of macromolecular processes, Curr Opin Cell Biol (2009), doi:10.1016/j.ceb.2009.01.022
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    • "While single-molecule wet-laboratory techniques have made great strides in revealing transitions [7], in principle, wetlaboratory techniques cannot obtain a complete picture, as dwell times at successive structural states in a transition may be too short to be detected in the wet laboratory. Neither wet-nor dry-laboratory techniques can on their own span all spatial and temporal scales in protein dynamics [8]. The presence of disparate temporal scales challenges methods that simulate dynamics (known as Molecular Dynamics – MD – methods) by iteratively solving Newton's equation of motion on a finely discretized time scale [9]. "
    [Show abstract] [Hide abstract] ABSTRACT: Proteins are macromolecules in perpetual motion, switching between structural states to modulate their function. A detailed characterization of the precise yet complex relationship between protein structure, dynamics, and function requires elucidating transitions between functionally-relevant states. Doing so challenges both wet and dry laboratories, as protein dynamics involves disparate temporal scales. In this paper we present a novel, sampling-based algorithm to compute transition paths. The algorithm exploits two main ideas. First, it leverages known structures to initialize its search and define a reduced conformation space for rapid sampling. This is key to address the insufficient sampling issue suffered by sampling-based algorithms. Second, the algorithm embeds samples in a nearest-neighbor graph where transition paths can be efficiently computed via queries. The algorithm adapts the probabilistic roadmap framework that is popular in robot motion planning. In addition to efficiently computing lowest-cost paths between any given structures, the algorithm allows investigating hypotheses regarding the order of experimentally-known structures in a transition event. This novel contribution is likely to open up new venues of research. Detailed analysis is presented on multiple-basin proteins of relevance to human disease. Multiscaling and the AMBER ff12SB force field are used to obtain energetically-credible paths at atomistic detail.
    Full-text · Conference Paper · Sep 2015
    • "The molecular architecture of INO80 was determined with a 17-A ˚ resolution cryo-electron microscopy (EM) map and 212 intra-protein and 116 inter-protein crosslinks (Russel et al., 2009). The molecular architecture of Polycomb Repressive Complex 2 (PRC2) was determined with a 21-A ˚ resolution negative-stain EM map and 60 intra-protein and inter-protein crosslinks (Shi et al., 2014). "
    [Show abstract] [Hide abstract] ABSTRACT: Structures of biomolecular systems are increasingly computed by integrative modeling that relies on varied types of experimental data and theoretical information. We describe here the proceedings and conclusions from the first wwPDB Hybrid/Integrative Methods Task Force Workshop held at the European Bioinformatics Institute in Hinxton, UK, on October 6 and 7, 2014. At the workshop, experts in various experimental fields of structural biology, experts in integrative modeling and visualization, and experts in data archiving addressed a series of questions central to the future of structural biology. How should integrative models be represented? How should the data and integrative models be validated? What data should be archived? How should the data and models be archived? What information should accompany the publication of integrative models? Copyright © 2015 Elsevier Ltd. All rights reserved.
    No preview · Article · Jun 2015 · Structure
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    • "The function of proteins, and the multi-component complexes they assemble into, is directly related to the structures they adopt and the motions that facilitate their inter-conversion (Robinson et al., 2007; Russel et al., 2009). The twin fields of structural biology and structural genomics have met with considerable success over the last two decades, however many significant structures remain elusive and the conformational heterogeneity important for function remains challenging to access experimentally (Ward et al., 2013). "
    [Show abstract] [Hide abstract] ABSTRACT: Ion mobility mass spectrometry (IM-MS) allows the structural interrogation of biomolecules by reporting their collision cross sections (CCSs). The major bottleneck for exploiting IM-MS in structural proteomics lies in the lack of speed at which structures and models can be related to experimental data. Here we present IMPACT (Ion Mobility Projection Approximation Calculation Tool), which overcomes these twin challenges, providing accurate CCSs up to 10(6) times faster than alternative methods. This allows us to assess the CCS space presented by the entire structural proteome, interrogate ensembles of protein conformers, and monitor molecular dynamics trajectories. Our data demonstrate that the CCS is a highly informative parameter and that IM-MS is of considerable practical value to structural biologists. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Full-text · Article · Mar 2015 · Structure
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