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Ron O. Dror,1 Robert M. Dirks,1 J.P. Grossman,1
Huafeng Xu,1 and David E. Shaw1,2
1
D. E. Shaw Research, New York, New York 10036; email: Ron.Dror@DEShawResearch.com;
David.Shaw@DEShawResearch.com
2
Center for Computational Biology and Bioinformatics, Columbia University, New York,
New York 10032

Annu. Rev. Biophys. 2012. 41:429–52 Keywords

The Annual Review of Biophysics is online at molecular dynamics, millisecond timescale, Anton, protein folding, drug
biophys.annualreviews.org
binding
This article’s doi:
10.1146/annurev-biophys-042910-155245 Abstract
Copyright  c 2012 by Annual Reviews. Molecular dynamics simulations capture the behavior of biological macro-
All rights reserved
molecules in full atomic detail, but their computational demands, combined
1936-122X/12/0609-0429$20.00 with the challenge of appropriately modeling the relevant physics, have his-
torically restricted their length and accuracy. Dramatic recent improvements
in achievable simulation speed and the underlying physical models have
enabled atomic-level simulations on timescales as long as milliseconds that
capture key biochemical processes such as protein folding, drug binding,
membrane transport, and the conformational changes critical to protein
function. Such simulation may serve as a computational microscope, re-
vealing biomolecular mechanisms at spatial and temporal scales that are
difficult to observe experimentally. We describe the rapidly evolving state
of the art for atomic-level biomolecular simulation, illustrate the types of
biological discoveries that can now be made through simulation, and discuss
challenges motivating continued innovation in this field.

429
 BB41CH19-Shaw ARI 3 April 2012 14:56

Contents
INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 430
RECENT ADVANCES IN SIMULATION METHODOLOGY . . . . . . . . . . . . . . . . . . . 433
Accessing Longer Timescales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433
Enhanced Sampling and Coarse-Graining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 436
Improving Force Field Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 436
SIMULATION AS A TOOL FOR MOLECULAR BIOLOGY . . . . . . . . . . . . . . . . . . . . . 437
Conformational Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437
Membrane Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439
Protein Folding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441
Ligand Binding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 442
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FUTURE FRONTIERS OF BIOMOLECULAR SIMULATION . . . . . . . . . . . . . . . . . . 444

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Drug Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444

Protein Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445
Modeling Nucleic Acids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445
Simulation of Complex Biological Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446
Enabling Longer and Cheaper Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446
More Accurate Biochemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446

INTRODUCTION
Over the past half-century, breakthroughs in structural biology have provided atomic-resolution
models of many of the molecules that are essential to life, including proteins and nucleic acids.
Although static structures determined through crystallography and other techniques are tremen-
dously useful, the molecules they represent are, in reality, highly dynamic, and their motions are
often critical to their function (Figure 1). Proteins, for example, undergo a variety of conforma-
tional changes that allow them to act as signaling molecules, transporters, catalysts, sensors, and
mechanical effectors. Likewise, they interact dynamically with hormones, drugs, and one another.
Static structural information might be likened to a photograph of a football game; to understand
more readily how the game is played, we want a video recording.
A variety of experimental techniques can provide information about the dynamics of proteins
Conformational and other biomolecules, but they are generally limited in their spatial and temporal resolution,
change: a transition and most report ensemble average properties rather than the motion of individual molecules
between two (Figure 2). An attractive alternative, in principle, is to model atomic-level motions computa-
alternative structures
tionally, based on first-principles physics. Although such simulations have been an active area of
of a flexible
biomolecule such as a research for decades (55), their computational expense, combined with the challenge of develop-
protein ing appropriate physical models, has placed restrictions on both their length and their accuracy.
Molecular dynamics The past few years have seen great progress in addressing these limitations, making simulations a
(MD) simulation: a much more powerful tool for the study of biomolecular dynamics. This review describes several
simulation in which important recent advances in simulation methodology and offers an overview of what is currently
the positions and possible with biomolecular simulation.
velocities of atoms are
The quantum mechanical behavior of molecules at a subatomic level is described by the time-
computed using
Newton’s laws of dependent Schrödinger equation, but a direct solution to this equation is in practice computation-
motion ally infeasible for biological macromolecules. The standard method for simulating the motions of
such molecules is a technique known as all-atom molecular dynamics (MD) simulation, in which

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a Transport across membrane b Ligand binding

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c Conformational change d Protein folding

Figure 1
Examples of biomolecular processes that have been examined using molecular dynamics (MD) simulations.
(a) Transport of small molecules across the cell membrane. (b) Binding of drugs to their target proteins.
(c) Conformational transitions in proteins. (d ) Protein folding.

the positions and velocities of particles representing every atom in the system evolve according
to the laws of classical physics. The forces acting on these particles are computed using a model
known as a force field, which is typically designed based on a combination of first-principles
physics and parameter fitting to quantum mechanical computations and experimental data.
Although MD simulation does not model the underlying physics exactly, it can provide a suf-
ficiently close approximation to capture a wide range of critical biochemical processes. The popu-
larity of such simulations is illustrated by the fact that they account for a majority of the computer Force field: energy
time devoted to biomedical research at National Science Foundation supercomputer centers. function used to
Although we briefly touch on approaches that represent molecules at a coarser or finer level of compute the forces
detail, or that evolve positions in a nonphysical manner, all-atom MD simulations constitute the acting on atoms (due
to interatomic
principal focus of this review.
interactions) during an
Historically, the timescales accessible to MD simulation have been shorter than those on MD simulation
which most biomolecular events of interest take place, thus limiting the applicability of these

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104

Cells
103 Electrophysiology
Organelles

102
Assemblies
Length (nm)

All-atom
101 molecular dynamics
simulations EM
FRET Proteins

10 0 AFM/
α-helices
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optical tweezers
β-sheets
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NMR
X-ray Atoms
10 –1

Increasing
resolution

10–15 10–12 10–9 10–6 10–3 100 10 3 Static

Time (s)

Light Proton Solute permeation

absorption transfer

Electron Side chain 2° structure Active

transfer flip formation transport

Channel Protein
gating translation

Domain Action
motion potential

Figure 2
Spatiotemporal resolution of various biophysical techniques. The temporal (abscissa) and spatial (ordinate)
resolutions of each technique are indicated by colored boxes. Techniques capable of yielding data on single
molecules (as opposed to only on ensembles) are in boldface. NMR methods can probe a wide range of
timescales, but they provide limited information on motion at certain intermediate timescales, as indicated
by the lighter shading and dashed lines. The timescales of some fundamental molecular processes, as well as
composite physiological processes, are indicated below the abscissa. The spatial resolution needed to resolve
certain objects is shown at the right. Adapted from Reference 19. Abbreviations: AFM, atomic force
microscopy; EM, electron microscopy; FRET, Förster resonance energy transfer; NMR, nuclear magnetic
resonance.

simulations. Events such as protein folding, protein–drug binding, and major conformational
changes essential to protein function typically take place on timescales of microseconds to mil-
liseconds (Figure 2). MD simulations, by contrast, were until recently generally limited in practice
to nanosecond timescales. Simulations of even a few microseconds required months on the most
powerful supercomputers available, and longer simulations had never been performed. Recent
advances in hardware, software, and algorithms have increased the timescales accessible to simula-
tion by several orders of magnitude, enabling the first millisecond-scale simulations and allowing
MD to capture many critical biochemical processes for the first time.
The other major factor limiting the applicability of MD has been the accuracy of the force field
models that underlie the simulations. A number of improved force fields have been introduced

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over the past several years, and the longer timescales now accessible to MD simulations have
allowed more extensive validation of these force fields against experimental data.
We begin by summarizing the fundamentals of MD simulation and certain recent methodolog-
Parallel
ical and technological advances that have expanded its applicability. We then review the state of computation: using
the art in terms of the types of biological discoveries one can make through simulation, providing multiple cooperating
a number of recent illustrative examples. Finally, we discuss several classes of important problems processors to perform
that MD could potentially address in the coming years and the methodological advances that may a computation faster
than would be possible
help solve them.
with a single processor
Moore’s law: a trend
RECENT ADVANCES IN SIMULATION METHODOLOGY dating back to 1960 in
which the logic density
Although the speed and accuracy of all-atom MD simulations has improved substantially over the on computer chips
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past few years, the basic form of such simulations has endured (1). Each atom in the system—for doubles approximately
example, a protein and the water surrounding it—is represented by a particle (or, in certain cases, every two years
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multiple particles). The simulation steps through time, alternately computing the forces acting
on each atom and using Newton’s laws of motion to update the positions and velocities of all the
atoms. Commonly used biomolecular force fields express the total force on an atom as a sum of
three components: (a) bonded forces, which involve interactions between small groups of atoms
connected by one or more covalent bonds; (b) van der Waals forces, which involve interactions
between all pairs of atoms in the system but which fall off quickly with distance and are generally
evaluated only for nearby pairs of atoms; and (c) electrostatic forces, which involve interactions
between all pairs of atoms and fall off slowly with distance. Electrostatic interactions are computed
explicitly between nearby pairs of atoms, whereas long-range electrostatic interactions are typically
handled via one of several approximate methods that are more efficient than explicitly computing
interactions between all distant pairs of atoms.

Accessing Longer Timescales

MD simulations are computationally demanding for two reasons. First, the force calculation at
each time step requires substantial computation—roughly one billion arithmetic operations for a
system with one hundred thousand atoms. Second, the force calculation must be repeated many
times. Individual steps are limited to a few femtoseconds by fast atomic vibrations, so simulating a
millisecond of physical time requires nearly one trillion time steps. On a single high-end processor
core, such a simulation would take thousands of years to complete.
To make matters worse, using many computer processors in parallel to accelerate a simulation
is challenging. Parallelizing a force calculation across multiple processors requires that those
processors communicate with one another, and the amount of communication required increases
with the number of processors. Beyond some point, adding more processors actually slows down
the calculation. Furthermore, the force calculations in a single simulation must be performed
sequentially, because the atom positions that serve as input for one force calculation depend on
the results of the previous one. The difficulty of parallelizing an MD simulation implies that the
transistor density improvements predicted by Moore’s law do not automatically lead to improved
MD performance, because in recent years higher transistor density has translated to more processor
cores per chip, not faster individual processor cores.
In spite of these challenges, the recent performance improvements in MD simulations have
far outpaced Moore’s law. In half a decade, the raw performance of state-of-the-art simulations
increased by over three orders of magnitude (Figure 3). As recently as 2007, the longest published
all-atom MD simulation of a protein was 2 μs; in 2009, the first millisecond-long simulation was

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10,000

(simulated ns day–1)
Fastest reported
1,000 all-atom MD

Performance
simulation

100

Moore's law
trend
10

1
2004 2005 2006 2007 2008 2009
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Year
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Figure 3
Fastest reported all-atom molecular dynamics (MD) simulations from 2004 to 2009 (blue line). The simulated
systems ranged from 14,000 to 92,000 atoms, and different simulations were performed using different
parameters, so this data is not intended to be a direct comparison of MD hardware and software systems.
Nonetheless, an overall performance trend is evident, substantially exceeding the Moore’s law growth trend
in processing power (black line). The leftmost data point is from a 512-processor simulation using NAMD
(65); the rightmost data point is from a 512-chip simulation on Anton (82). The remaining data points are
from simulations run using Blue Gene/L (25) and Desmond (9, 14).

published (Table 1). These improvements are attributable to a variety of hardware, software, and
algorithm innovations, which we discuss below.

Parallelization across general-purpose computer chips. Software that parallelizes MD force

calculations across multiple computer processors has existed for two decades (69) but has become
much more efficient and scalable in the past several years. IBM’s Blue Matter code for its Blue
Gene/L general-purpose supercomputer has been scaled up to 32,768 cores (25). The widely
used MD codes NAMD (65), GROMACS (31), and AMBER (12) have all substantially improved
their parallel performance in recent years. These packages can now deliver performance of over
100 ns day−1 on commodity computer clusters, with the number of processors required to achieve
this performance scaling roughly linearly with the number of atoms in the system. Desmond, a
software package for commodity clusters developed at D. E. Shaw Research, allowed simulations
nearly an order of magnitude faster than previously possible on the same hardware (9) and has
achieved performance approaching 500 ns day−1 (14).
These improvements in parallel scalability and efficiency have been possible thanks to a number
of algorithmic innovations, particularly in methods for reducing the communication requirements

Table 1 Longest reported all-atom molecular dynamics simulations from 2006 to 2009
Year Length (μs) Protein Platform Reference
2006 2 Rhodopsin Blue Gene/La 54
2007 2 Villin HP-35 GROMACSb 22
2008 10 WW domain NAMDb 27
2009 1,031 BPTI Anton 82

a
This simulation used IBM’s Blue Matter software.
b
These simulations were performed on a commodity computer cluster with the specified software.

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between chips during a simulation. The class of neutral territory methods (9, 10, 25, 81, 86), for
example, substantially reduces the amount of data that must be exchanged between processors in
order to compute range-limited particle interactions.
GPU: graphics
processing unit
Graphics processing units. Originally designed specifically to accelerate the rendering of three-
Special-purpose
dimensional graphics, graphics processing units (GPUs) have become increasingly popular for parallel
general-purpose scientific computation thanks to their ability to perform large numbers of iden- architectures:
tical computations in parallel on a single chip. Several MD implementations have been ported to computer
GPUs (2, 30, 29, 66), and a simulation on one or a few GPUs often rivals the performance of a architectures designed
for a specific task,
simulation on a small- to moderate-sized computer cluster. Unfortunately, efficiently parallelizing
often allowing such
across many GPUs is difficult, because communication between GPUs remains slower than com- computers to complete
munication between general-purpose processors; as a result, clusters of GPUs have been unable to that task much faster
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match the performance of large standard clusters. GPUs offer an excellent price-to-performance than general-purpose
ratio, however, enabling reasonably fast simulations at a cost substantially lower than that for a computers
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cluster of general-purpose processors. Anton: a special-

purpose parallel
supercomputer
Special-purpose parallel architectures. By far the greatest recent speedups have been achieved
designed by D. E.
through the use of special-purpose chips, in combination with new parallelization algorithms Shaw Research to
and software. In particular, a recently developed machine named Anton can perform all-atom enable fast MD
MD simulations at up to 20 μs day−1 , approximately two orders of magnitude faster than the simulations
simulation rates generally achieved by any other hardware/software combination (82).
Anton is able to achieve this speed because it is designed specifically for MD simulations. The
entire computation is performed on special-purpose chips developed at D. E. Shaw Research,
which are directly connected to one another in a custom network (Figure 4). Each Anton chip
contains an array of arithmetic units hardwired for computing particle interactions, enabling a
single Anton chip to compute interactions hundreds of times faster than a commodity proces-
sor core (82). Each chip also contains a dozen programmable processor cores, customized for
MD, which accelerate the remainder of the computation. In addition, specialized data-movement

a b

Figure 4
Anton, a special-purpose computer for molecular dynamics designed by D. E. Shaw Research, has performed all-atom protein
simulations over one hundred times longer than any published previously. (a) A single Anton chip. (b) The first Anton machine,
comprising 512 Anton chips connected through a specially designed network.

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 BB41CH19-Shaw ARI 3 April 2012 14:56

operations support fast communication between small on-chip memories, eliminating the memory
cache hierarchy that typically consumes the majority of the area on commodity chips.
Several algorithmic advances also contribute to Anton’s performance. A specific neutral terri-
Enhanced sampling:
algorithms devised to tory method (81) was designed for Anton and is directly implemented within its specialized parti-
speed up the cle interaction hardware. Anton computes long-range electrostatic forces using the Gaussian split
exploration of Ewald method (79) rather than the more commonly used particle mesh Ewald method, allowing
molecular a significant portion of the long-range electrostatics computation to be performed by the same
conformations by
specialized hardware that handles particle interactions. Finally, the communication patterns in
altering the physics of
the system Anton’s MD software, which differ significantly from those in other parallel MD software packages,
are designed to take advantage of Anton’s specialized low-latency mechanisms for communication
between and within chips (18).
Several previous projects, including FASTRUN (24), MD Engine (92), and MDGRAPE (90),
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have built special-purpose hardware to accelerate the most computationally expensive elements
of an MD simulation. Although such hardware reduces the effective cost of simulating a given
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period of biological time, the speedup achieved through parallelization across many such chips is
limited by the remainder of the computation as well as the communication required, precluding
individual simulations on multi-microsecond timescales.
Anton has enabled all-atom MD simulations of proteins of more than a millisecond in length,
over 100 times longer than any such simulation reported using other hardware. With Anton it
becomes possible, for the first time, to directly observe in simulation various important biochemical
processes that occur on timescales greater than a few microseconds.

Enhanced Sampling and Coarse-Graining

A variety of methods can be used in combination with MD simulation to investigate events that
occur on timescales that remain inaccessible to direct all-atom MD simulation. Zuckerman (106)
provided a thorough review of such enhanced sampling methods in Volume 40 of the Annual
Review of Biophysics, in which he concluded that “No other method can routinely and reliably
outperform MD by a significant amount.” These methods prove essential in certain situations,
however, as illustrated by several of the case studies in the following section.
One can also sidestep communication bottlenecks by performing many short simulations in
parallel. A particularly impressive example is the Folding@Home project (5), which has obtained
significant scientific results by using over 400,000 personal computers (PCs) to simulate many
separate molecular trajectories, each limited to the timescale accessible on a single PC. The
solution of many important problems, however, is greatly facilitated by the availability of long
individual trajectories.
Finally, one can often substantially accelerate simulations at the cost of reduced accuracy
by employing simplified system representations, such as coarse-grained models, in which each
simulated particle represents several atoms, or implicit solvent models in which water atoms are
replaced by a continuum representation. Models of both types have seen substantial development
in recent years (13, 58).

Improving Force Field Accuracy

Long-timescale simulations place more stringent demands on force fields; a force field that proves
sufficient for short-timescale simulations may not be sufficient at longer timescales. Fortunately,
the past several years have seen substantial improvements in force fields for biomolecular simula-
tion. Historically, force field parameters were determined using quantum mechanical calculations

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and condensed phase experimental data for small molecular fragments. Recently, force field de-
velopment has come to increasingly rely on experimental data for proteins and other biological
macromolecules, as improvements in both simulation speed and experimental methods have led
G-protein-coupled
to an overlap in the timescales accessible through the two approaches. receptors (GPCRs):
Although the functional forms of the most widely used force fields have remained largely a family of
unchanged, their parameters have recently undergone a number of adjustments. The Amber transmembrane
force field, for example, incorporated changes to parameters associated with torsional angles of proteins that transmit
signals into cells and
the protein backbone, first to improve fits to quantum calculations (32) and then to achieve
represent the largest
better agreement between secondary structure preferences observed in long MD simulations of class of drug targets
polypeptides and corresponding NMR measurements (7). Amber protein side chain torsions were
also adjusted to better match both quantum calculations and NMR data (51). Adjustments to
backbone and side chain torsions were also incorporated into the CHARMM force field (53, 67),
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as were modifications to the charge distributions of ionizable amino acid residues (67). Recent
studies have also resulted in improved parameters for lipids in CHARMM (42) and for small
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drug-like molecules in the CHARMM, Amber, and OPLS-AA force fields (4, 96, 98).
A recent study exploited long-timescale MD simulations on Anton to evaluate a number of
protein force fields through a systematic comparison with experimental data (49). Criteria in-
cluded the ability of each force field to fold small proteins to their native structures, to predict the
secondary structure propensities of polypeptides, and to reproduce NMR data reporting on the
structure and dynamics of folded proteins. The results indicated that the force fields examined
have consistently improved over the past decade, and that the most recent versions provide an accu-
rate description of many structural and dynamic properties of proteins. The study also highlighted
certain shortcomings: None of the force fields, for example, were able to accurately capture the
temperature dependency of the secondary structure propensities. It is an open question whether
the ongoing parameterization of existing functional forms will be sufficient to further improve
force fields. Substantial efforts are under way to develop force fields with more sophisticated
functional forms, including polarizable force fields (36, 70), which capture the redistribution of
electrons around each atom in response to changes in environment.

SIMULATION AS A TOOL FOR MOLECULAR BIOLOGY

In this section, we illustrate the utility of modern MD simulation as a biological research tool
through a number of recent case studies, many of which are drawn from our own work, involving
conformational changes in proteins, transport across membranes, protein folding, and the binding
of ligands to proteins (Figure 1).

Conformational Changes
Under physiological conditions, proteins and other biomacromolecules constantly move from
one structural state to another, and their function and regulation depend on these conformational
changes. MD simulations are often used to identify novel conformations, to capture the transi-
tional pathways between conformations, to determine equilibrium distributions among different
conformations, and to characterize changes in conformational distribution as a result of mutation
or ligand binding. We provide several examples involving G-protein-coupled receptors (GPCRs)
and kinases.
GPCRs represent the largest class of drug targets: One-third of all marketed drugs act by
binding to a GPCR and either triggering or preventing receptor activation, which involves
a transition from an inactive receptor conformation to an active conformation that causes

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G-protein-mediated signaling. The past few years have witnessed the determination of the first
several crystal structures of ligand-activated GPCRs, beginning with the β2 -adrenergic receptor
(β2 AR). MD has addressed several key questions raised by these structures about the conforma-
β2 -adrenergic
receptor (β2 AR): an tions of inactive states and the mechanism of receptor activation (16, 17, 52, 62, 72, 73, 95).
archetypal GPCR and An MD simulation study by Dror et al. (16) identified a previously unobserved inactive con-
a target of beta formation of β2 AR, resolving an apparent contradiction between experimental results: A network
blockers and beta of salt bridges known as the ionic lock, suggested by biochemical experiments to stabilize the
agonists
inactive state of β2 AR and other GPCRs, was disrupted in the inactive state crystal structures
(46). In microsecond-timescale simulations of inactive β2 AR, the receptor transitions repeatedly
between two conformational states, one with the lock broken and one with it formed. In simu-
lations of wild-type β2 AR, the lock-formed conformation predominates, in agreement with the
biochemical data, but in simulations of the modified protein used for crystallographic structure
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determination, the lock-broken conformation predominates, explaining the crystallographic re-

sult. Both lock-broken and lock-formed conformations were subsequently observed in a crystal
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structure of a closely related receptor (60), lending support to these computational predictions.
More recent simulations on Anton (73) captured spontaneous transitions of β2 AR from an active
to an inactive conformation, addressing a puzzle posed by two recent crystallographic structures of
β2 AR bound to two different agonists (ligands that cause activation). One of these structures, which
also has a G-protein-mimetic nanobody bound to its intracellular surface, appears to represent an
active conformation (71). The other, which is bound to an agonist but lacks the nanobody, is almost
identical to the previously solved inactive structure (73). Is this surprising structural difference due
to differences between the agonists or the crystallized receptor constructs, or might it be due to the
presence or absence of the G-protein-mimetic partner? In multi-microsecond simulations of β2 AR
initiated from the nanobody-bound active structure, but with the nanobody removed, the agonist-
bound receptor spontaneously transitioned to a conformation that closely matched the inactive
crystal structure. Taken together, these simulations and the crystal structures suggest that, even
with an agonist bound, the majority of the β2 AR population remains in an inactive-like conforma-
tion until a G-protein or G-protein-mimetic nanobody binds, stabilizing the active conformation.
These simulations—which represent the first in which a GPCR transitions spontaneously
between crystallographic conformations representing functionally distinct states—also served to
characterize the atomic-level activation mechanism of β2 AR (17). They showed that the extra-
cellular drug-binding site is connected to the intracellular G-protein-binding site via a loosely
coupled allosteric network, comprising three regions that can switch individually between dis-
tinct conformations. The simulations revealed a key intermediate conformation on the activation
pathway and suggested—somewhat counterintuitively—that the first structural changes during
activation often take place on the intracellular side of the receptor, far from the drug-binding site.
These results may provide a foundation for the design of drugs that control receptor signaling
more precisely by stabilizing specific receptor conformations.
MD simulations have also shed light on the function and regulation of kinases, a class of
enzymes that are actively pursued as therapeutic targets for cancer and autoimmune diseases. The
activity of a typical kinase is regulated by changes to its preference for the active and various
inactive conformations. Mutations to kinases often favor the “wrong” conformations, leading to
aberrant signaling and consequently disease. A number of studies have used MD simulations to
characterize conformational changes in kinases (6, 23, 80, 100), yielding predictions that were in
agreement with subsequent experimental measurements (80) and insights that led to the design
of new experimental methods (76).
Faraldo-Gómez & Roux (23) used MD simulations to characterize the regulation of Src family
tyrosine kinases, which depends on an inactivation process in which the auxiliary domains (known

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as SH2 and SH3) of a kinase assemble onto its catalytic domain, preventing catalysis. What
makes such assembly robust and fast, so that kinases can be reliably and quickly turned off? To
answer these questions, the authors used an enhanced sampling technique known as umbrella
Membrane
sampling (44) to characterize the relative free energies of various conformations connecting the transport:
disassembled and assembled states. The simulations indicated that the SH2–SH3 construct has the movement of
an intrinsic propensity to adopt conformations primed for association with the catalytic domain, molecules across a cell
thus favoring and accelerating formation of the assembled (inhibitory) state. Their results also membrane, usually
facilitated by a
suggested that the SH2–SH3 connector is more than a passive link between the domains; rather,
transmembrane
it is responsible for their propensity toward the assembly-ready conformation, explaining the protein
experimental observation that mutations in the connector region increase the constitutive activity
of the kinase.
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Membrane Transport
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Transport of various substrates across the cell membrane is vital both to maintaining a cell’s con-
stitution and to transmitting biochemical signals. The transport efficiency and substrate selectivity
of carrier proteins often depend critically on the detailed spatial configuration of the atoms along
the transport pathway as well as their subtle movement during the transport process. MD sim-
ulation, with its unique ability to simulate and record the movement of individual atoms at very
fine temporal and spatial resolutions, lends itself naturally to the study of transport processes. In
the past decade, MD simulations have been applied to investigate a number of transporters and
channels, including aquaporins (35, 91), ion channels (8, 34, 63), and active transporters (3, 21).
These studies have shed light on many mechanistic questions: How do the channels achieve a fast
rate of substrate permeation? How do the transporters affect selectivity for their substrates? How
is transport regulated in response to various stimuli?
Potassium channels, which allow potassium ions to move passively through the cell membrane,
are essential for the transmission of nerve impulses and represent an important target for the
treatment of diseases ranging from Alzheimer’s to diabetes. A longstanding puzzle about these
channels is why they let potassium ions, but not smaller sodium ions, pass through. Crystal struc-
tures suggest that the narrowest region of a potassium channel, known as the selectivity filter,
has a geometry that snugly fits potassium ions, but the difference between the radii of potassium
and sodium ions (0.38 Å) is smaller than the thermal fluctuations of the atomic positions in the
selectivity filter (0.75 Å). Noskov et al. (63) and Bostick & Brooks (8) addressed this question by
using MD simulations to examine several hypothetical variants of the real selectivity filter. In both
studies, the authors computed the difference in the binding free energies of sodium and potassium
ions to the selectivity filter and explored how this difference varied when they artificially adjusted
the physical properties of the filter. Both studies suggested that selectivity was a robust feature
of the filter that did not depend on its precise geometry. Instead, selectivity was a consequence of
the dipole moment of the carbonyls coordinating the ions in the selectivity filter, the coordination
number, and the thermal fluctuations in the filter.
Recent advances in simulation speed have allowed the first direct, atomic-resolution ob-
servations of ion permeation and pore domain closure in a voltage-gated potassium channel
(Figure 5). Using unbiased microsecond-timescale MD simulations at various transmembrane
voltages, Jensen et al. (34) followed the permeation of hundreds of potassium ions through the
channel. The authors identified the transitions between microscopic states that underlie the per-
meation of an individual ion, thereby supplying atomistic detail of the long-postulated “knock-on”
conduction mechanism, in which translocation of two selectivity-filter-bound ions is driven by a
third, incoming ion.

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 BB41CH19-Shaw ARI 3 April 2012 14:56

a Ion transport at positive transmembrane potential

10

Ion position along pore axis (Å)

H2O 5

K+
0
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–5
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–10
0.1 0.2 0.3 0.4
Time (μs)

b Negative transmembrane potential induces channel closure

Open and Closed and Closed and
wetted partially dewetted fully dewetted

Time
Figure 5
Simulation of ion permeation and gating in a potassium channel. (a) Potassium ions permeated outward (in
the figure, upward) through the selectivity filter when the transmembrane potential was positive. Individual
ions paused at well-defined sites within the filter, as shown by the representative traces in green. (b) When
the transmembrane voltage was reversed, the hydrophobic cavity dehydrated, causing it to collapse and thus
close the channel to conduction. Figure adapted from Reference 34.

Moreover, Jensen et al. observed channel closure—gating of the potassium channel pore
domain—at negative voltages (Figure 5). Closure took place by means of a previously hypoth-
esized, but unobserved (for ion channels) mechanism, called hydrophobic gating, in which the
hydrophobic cavity adjacent to the selectivity filter dehydrated, causing the open pore domain to
collapse into a closed conformation. This mechanism provides a molecular explanation for the
experimental observation that the channel conductance is sensitive to the osmotic pressure. In
particular, the change in volume upon channel closure has been measured experimentally, and
it corresponds to the volume of 40–50 water molecules, closely matching the number of water
molecules expelled from the pore cavity upon channel closure in the simulations (105).

440 Dror et al.

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MD simulations have also been used to deduce the mechanism of the sodium proton antiporter,
NhaA (3), a transporter that moves sodium ions and protons in opposite directions across the cell
membrane. Arkin et al. (3) performed a series of simulations in which they systematically varied
Folding pathway:
the initial position of the sodium ion, as well as the protonation states of two aspartate residues— a sequence of
Asp163 and Asp164—critical for antiporting function. These simulations suggested that Asp164 intermediate
serves as the binding site of sodium ion, with its protonation state determining whether the ion will structures visited by a
remain bound or be released into the membrane, and that Aps163 acts as an accessibility control protein as it transitions
from a disordered state
site, determining whether the ion will be released to the inside or outside of the cell. Although
to its native state
the simulations (≤100 ns each) were much shorter than the complete antiporting cycle (∼1 ms),
they allowed formulation of a complete transport mechanism, which was substantiated through
experiments on NhaA mutants.
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Protein Folding
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Protein folding actually represents two challenges: Given only a protein’s amino acid sequence,
(a) determine the native structure of the protein, and (b) elucidate the pathways by which it folds to
that structure. MD can potentially address both challenges (87), but it is particularly well suited for
revealing folding pathways. Many computational (43) and experimental methods directly predict
or determine protein structure, but few techniques allow direct observation of the dynamics of a
folding event in atomic detail. Given an accurate force field and sufficient simulation time, MD
can produce atomic-level trajectories of spontaneous folding events (22, 28, 37, 47, 83–85, 97), as
well as unfolding events (93). Such a microscopic view can shed light not only on the structure and
stability of the folded state, but also on the heterogeneity of folding pathways, the rate-limiting
steps on these pathways, the nature of misfolded states, and other complex features of the protein
folding process.
Improvements in both simulation speed and force field accuracy recently enabled Lindorff-
Larsen et al. (50) to simulate repeated folding and unfolding events for a structurally diverse set
of 12 small, fast-folding proteins, using a single force field. All 12 proteins folded to structures
closely resembling those determined experimentally (Figure 6). The ability of simulations to
identify the native structures is itself noteworthy, suggesting that MD may eventually serve as a
viable method for predicting or refining the structures of arbitrary proteins. The most immediate
utility of these simulations, however, is in allowing direct observation of the protein folding
process.
Comparing the behavior of these 12 proteins suggested unifying principles for protein folding,
at least for small, fast-folding proteins, and allowed the authors to address several long-standing
questions regarding the mechanisms of protein folding (88). Most of the proteins studied, for
example, consistently fold along a single dominant route, with local structures forming in an order
that largely corresponds to the stability of those structures in the unfolded ensemble. In addition,
a few long-range contacts typically form early in the folding process and help establish a nucleus
to guide formation of the rest of the structure.
MD can also help guide wet-lab protein folding experiments (45). Piana et al. (68), for exam-
ple, used insights gained from long simulations of a WW domain to suggest a triple mutation
that should reduce the main energy barrier on the folding pathway and thus accelerate fold-
ing. Temperature-jump experiments confirmed this prediction, establishing this mutant as the
fastest folding β-sheet protein known—a conclusion made more noteworthy because substantial
effort had previously been dedicated to maximizing the folding rate of this WW domain through
mutagenesis (61).

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 BB41CH19-Shaw ARI 3 April 2012 14:56

Chignolin Trp-cage BBA Villin

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WW domain NTL9 BBL Protein B

Homeodomain Protein G α3D λ repressor

Figure 6
In simulations with a single force field, 12 structurally diverse proteins fold spontaneously to a structure
(blue) closely resembling that determined experimentally (red ). The simulation snapshots were chosen
automatically based on a clustering analysis that did not exploit knowledge of the experimental structure.
The total simulation time per protein ranged from 104 to 2,936 μs, allowing observation of at least 10
folding and 10 unfolding events for each protein. Figure adapted from Reference 50.

Ligand Binding
Interactions between proteins and small-molecule ligands play a key role in intercellular signaling
and, when the ligands are drugs, in the treatment of disease. Ligands typically affect protein
function by directly blocking the active site of a protein or by causing the protein to adopt a
functionally altered conformational state.
Thanks to recent advances in accessible timescales, it is now possible to perform MD simula-
tions in which ligands bind spontaneously to proteins without any prior knowledge of the binding
site (20, 33, 78). In work by Shan et al. (78) on inhibitors binding to Src kinase, and by Dror et al.
(20) on beta blockers and a beta agonist binding to two GPCRs, simulated drug molecules diffused
extensively about the protein before discovering their binding site and binding in a location and
conformation that match crystallographic observations almost exactly (Figure 7). These results

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0 Time (μs) 5
a
HO
O NH2+
Extracellular space

(S)-dihydroalprenolol
1 2

Extracellular
vestibule
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4
5

0.51 μs 1 0.68 μs 2 0.80 μs 3 1.88 μs 4 3.68 μs 5

b 1
30
25 2
Ligand rmsd (Å)

20 3
15
4
10 5
5

0 1 2 3 4 5
Time (μs)

Figure 7
Beta blockers bind spontaneously to the β2 -adrenergic receptor (β2 AR) in molecular dynamics simulations, achieving the
crystallographic pose and revealing several metastable intermediate states on the binding pathway. (a, top left) Pins indicate successive
positions of a dihydroalprenolol molecule as it binds to β2 AR. The ligand moves from bulk solvent (pose ), into the extracellular
vestibule (poses  and ), and finally into the binding pocket (poses  and ). (a, bottom) These five poses are shown in purple, with
the crystallographic pose in gray. (a, top right) The path taken by the ligand as it diffuses about the receptor and then binds.
(b) Root mean square deviation (rmsd) of the ligand in simulation from its position in the alprenolol–β2 AR crystal structure. Figure
adapted from Reference 18.

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 BB41CH19-Shaw ARI 3 April 2012 14:56

raise the possibility of using simulation to identify novel binding sites. Indeed, both Shan et al. and
Dror et al. discovered alternative binding sites, suggesting possibilities for the design of allosteric
drugs with improved selectivity among kinases or GPCRs.
Such simulations also allow atomic-level characterization of the binding pathways and energetic
barriers that determine binding kinetics. Dror et al. (20) found that beta blockers visit a sequence
of metastable conformations en route to the binding pocket of the β2 AR (Figure 7). Surprisingly,
they found that the largest energetic barrier on the binding pathway often occurs much earlier than
receptor geometry would suggest, and appears to involve substantial dehydration that occurs as
the drug associates with a particular region on the receptor surface. Shan et al. (78) also identified
metastable conformations on the binding pathway, as did Buch et al. (11) in a study of an inhibitor
binding to trypsin. These studies are computationally intensive: Shan et al., Dror et al., and Buch
et al. performed multiple simulations totaling over 150 μs, 400 μs, and 50 μs, respectively.
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A common application of protein–ligand simulations is to compute the binding affinity of a

ligand, often a drug candidate, to a known binding site. Unbiased MD simulations of ligand binding
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are usually ill suited for this purpose, as precise estimation of ligand affinity would typically require
seconds to hours of simulated time in order to observe sufficiently many binding and unbinding
events. Fortunately, binding affinity calculations can be performed much more efficiently using
methods such as free energy perturbation (107) or thermodynamic integration (40), which involve
using a family of modified force fields to bias a series of simulations in ways that accelerate
the forming and breaking of protein-ligand interactions. These biasing forces can be physically
intuitive, such as forcibly pulling a ligand into or out of a known binding pocket (99), or more
abstract, such as gradually turning off all interactions between a ligand and its surroundings (38). If
the artificial energy functions are properly constructed, unbiased binding affinities can be efficiently
and quantitatively derived from the biased simulations.
One compelling example of simulation-based binding affinity calculations is recent work on
HIV reverse transcriptase. Starting with a weakly binding ligand that displayed no activity as a
reverse transcriptase inhibitor, Zeevaart et al. (103) used Monte Carlo simulations (closely related
to MD) to calculate the relative binding energies of a family of closely related molecules. By
selecting variants predicted to bind more tightly, they discovered several molecules that proved
experimentally active in protecting human T-cells from HIV infection.

FUTURE FRONTIERS OF BIOMOLECULAR SIMULATION

As MD simulations become faster and more accurate, they will almost certainly find applications
beyond the categories we have discussed. In this section, we speculate on other areas on which
biomolecular simulation may make an impact in the coming years, and highlight some of the
methodological problems whose solutions may help make these possibilities reality.

Drug Design
A major goal of structural biology in general, and biomolecular simulation in particular, has long
been to assist in the design of therapeutics. Simulations are already sometimes utilized as part of
the drug development process. Simulation-based binding affinity calculations, for example, guided
the design of HIV reverse transcriptase inhibitors mentioned above (103), as well as the subsequent
design of inhibitors that maintain high potency in the presence of a drug-resistance mutation (37).
The use of MD in mainstream drug discovery efforts, however, remains limited.
In the future, simulation may offer a number of opportunities for improving the drug discovery
process. Simulation-based methods can compute ligand–protein affinities more accurately than

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standard docking methods, aiding in the identification of lead compounds through virtual screen-
ing of drug candidates or through a fragment-based approach. Accurate evaluation of binding
affinities may prove even more useful in the subsequent process of lead optimization, or in avoid-
ing toxicity by ensuring that drug candidates do not bind to known antitargets. MD also has the
potential to discover novel binding sites, including pockets that are not present in existing crystal
structures (75, 78). In addition, simulations may allow refinement of low-resolution structural
models for proteins, thus enabling structure-based drug design.
MD also allows the examination of interactions between known drugs and genetic variants
of protein targets. If a disease becomes resistant to a drug, simulations of the mutated targets
may elucidate the mechanism of resistance and facilitate modifications that restore drug efficacy
(37). Simulations might even aid in the design of drugs or drug cocktails tailored to the genetic
makeup—and thus the unique protein variants—of a particular individual.
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Perhaps more importantly, the insights MD can provide into the functional mechanisms of
proteins involved in disease pathways may facilitate the identification of appropriate targets and
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the design of drugs that target those proteins. Many drugs may prove more effective if they bind
preferentially to a specific conformation of their target protein. Such conformational selectivity
could allow finer control of cellular signaling by stabilizing a particular conformation of a re-
ceptor, or reduce side effects by favoring binding to a protein when it is in a particular state of
activity.

Protein Design
By facilitating optimization of properties such as structure, ligand-binding affinity, or enzymatic
activity, MD may play a role in the design of proteins for use as biosensors, industrial catalysts,
or therapeutic antibodies, among other potential applications. MD has already been used to rank
candidate amino acid sequences on the basis of calculated properties such as binding affinity (41,
59, 101). It may be used in the future not only to test whether a protein binds a ligand, forms a
desired interface with another protein, or folds correctly, but to guide the design process in order
to achieve such properties. One might even imagine a simulation during which a protein gradually
evolves, favoring mutations that improve some measure of its fitness.

Modeling Nucleic Acids

Although nucleic acids are often viewed as static structures that primarily carry sequence infor-
mation, experimental investigations of both RNAs and DNAs, such as those found in ribosomes,
riboswitches, Holliday junctions, and nucleosomes, reveal diverse structures, functions, and dy-
namics. As with proteins, accurate, long-timescale simulations of nucleic acids should enrich our
understanding of important biological mechanisms and may help reveal promising drug targets
and binding sites.
To date, MD simulations of proteins have vastly outnumbered those of nucleic acids
(56). Reasons for this disparity include both the smaller number of available nucleic acid
structures and the relative immaturity of nucleic acid force fields. The Protein Data Bank
(http://www.pdb.org/) currently contains over 75,000 solved structures, whereas the Nucleic
Acid Database (http://ndbserver.rutgers.edu/) contains just over 5,000. Force fields for nucleic
acids are still undergoing extensive refinement (15, 64, 102, 104), and it is unclear whether the
high charge density of nucleic acids and commonly associated divalent cations can be accurately
simulated using simple point charges; polarizability may be essential.

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 BB41CH19-Shaw ARI 3 April 2012 14:56

Simulation of Complex Biological Structures

The increasing availability of high-resolution structures for biological units of greater size and
complexity—motor proteins, multiprotein complexes, ribosomes, whole organelles, and even sim-
ple organisms—offers the opportunity to investigate their dynamics through simulation. All-atom
MD simulations have already been employed to model a few such units. Using simulations of more
than two million atoms, Sanbonmatsu et al. (74) investigated how a ribosome selects transfer RNAs
(tRNAs) with high efficiency and accuracy and found that the flexibility of tRNAs was crucial to
maintaining the complementary codon–anticodon geometry. Freddolino et al. (26) conducted
MD simulations of the complete satellite tobacco mosaic virus, including a capsid consisting of
60 copies of a single protein and a 1,058-base RNA genome. These simulations, with more than a
million atoms, suggested that the presence of the RNA is necessary for the assembly and stability
of the capsid.
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Although such million-atom simulations are impressive, cellular organelles, let alone entire
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cells, are dramatically larger; a mitochondrion, for instance, is about half a micron in diameter and
comprises over ten billion atoms. Further, the functional timescales of large protein complexes
and organelles tend to be substantially longer than those of individual proteins, often extending
to seconds or more. Such spatial and temporal scales are well beyond those of even the most
advanced MD simulations. Fortunately, complex biological structures usually have a hierarchical
and modular organization; it may thus be especially productive to develop multiscale models that
use the most appropriate abstraction and representation for each temporal and spatial scale. The
challenge lies in integrating all-atom MD simulations seamlessly into such multiscale models.

Enabling Longer and Cheaper Simulations

Although the millisecond-scale simulations recently performed on Anton represent a significant
milestone, many biochemical events take place on timescales well above a millisecond and involve
systems larger than those currently being simulated. The efficiency of a parallel MD simulation
on a given hardware platform is determined roughly by the ratio of atoms to processors, so to first
approximation one can roughly maintain simulation speed for larger chemical systems by using
additional processors. In practice, however, cost often becomes a limiting factor. Accessing longer
timescales is even more difficult: Because of interprocessor communication limits, one cannot
increase the speed of today’s fastest simulations by simply adding more processors. Innovation
in some combination of algorithms, computer architecture, and enhanced sampling methods is
thus necessary to reach longer timescales and to reduce the cost of large simulations. Such work
is currently under way, both in our group and elsewhere.

More Accurate Biochemistry

Despite recent improvements in force fields, MD simulations still face a number of sources of
error. First, force fields remain imperfect. Second, most contemporary MD simulations do not
fully capture the detailed molecular composition of biological systems, in which many different
types of molecules are present, both in the aqueous phase and in lipid bilayers. In the past, force
field inaccuracies often overshadowed the effects of unrealistic composition, but as force fields
improve, accurately modeling molecular composition will become more important.
Third, classical MD simulations treat covalent bonds as unchanging. Chemical reactions, in
which covalent bonds break or form, are typically simulated using techniques such as quantum
mechanics/molecular mechanics simulations, in which the bulk of the system is simulated as

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in classical MD, but a small part is evaluated using more computationally intensive quantum
mechanical approaches (39, 77). Several methods are under development to handle chemical
reactions directly within an MD framework. Some of these concentrate on capturing changes to
the protonation states of ionizable amino acid residues (57, 89). Reactive force fields, which allow
covalent bonds between arbitrary pairs of atoms to break or form, have thus far been limited to
simple inorganic and organic molecules (94) but may eventually capture more general enzymatic
reactions.
A promising avenue to improve the accuracy of MD simulations is to incorporate experimen-
tal data directly into the simulations. NMR data, for example, has been used to restrain MD
simulations, biasing the protein conformations toward those compatible with the experimental
measurements (48). A general framework allowing incorporation of biophysical, biochemical, and
even evolutionary data into MD simulations may prove useful both in interpreting experimental
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data and in making simulation-based predictions.

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SUMMARY POINTS
1. MD simulation can serve as a computational microscope, revealing the workings of
biomolecular systems at a spatial and temporal resolution that is often difficult to access
experimentally.
2. Until recently, even the longest atomic-level MD simulations fell short of the microsec-
ond and millisecond timescales on which biochemical events such as protein folding,
protein–drug interactions, and major conformational changes typically take place. The
speed of the fastest MD simulations has increased 1,000-fold over a period of several
years, however, due to the development of specialized hardware and better paralleliza-
tion algorithms. All-atom simulations of proteins can now reach timescales in excess of
a millisecond.
3. These developments, combined with the improvements to the force field models that
underlie MD simulations, have allowed MD to capture in atomistic detail processes such
as the conformational transitions essential to protein function, the folding of proteins to
their native structures, the transport of small molecules across cell membranes, and the
binding of drugs to their targets.

FUTURE ISSUES
1. Many biochemical events still take place on long timescales that are inaccessible to atomic-
level MD simulations, or on large spatial scales that make atomic-level simulation inor-
dinately expensive. Further improvements in algorithms and computer architectures are
needed to make simulations faster and more cost-effective.
2. Multiscale models and enhanced sampling methods will likely also play an essential role
in capturing events at larger temporal and spatial scales.
3. Force fields require further improvement and validation, particularly for the modeling
of nucleic acids, certain ions, and some types of ligands.
4. Classical MD does not capture breaking and formation of covalent bonds, but it may be
possible to handle such reactive chemistry within a generalized MD framework.

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 BB41CH19-Shaw ARI 3 April 2012 14:56

5. Application of MD simulation to the design of drugs and proteins remains fertile ground
for future research.

DISCLOSURE STATEMENT
David E. Shaw is the beneficial owner of D. E. Shaw Research and serves as its Chief Scientist.

ACKNOWLEDGMENTS
We thank Anton Arkhipov, David Borhani, Michael Eastwood, Morten Jensen, John Klepeis,
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Kresten Lindorff-Larsen, Venkatesh Mysore, Albert Pan, Stefano Piana, and Yibing Shan for
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stimulating discussions, helpful comments, and assistance with figures, and Mollie Kirk for editorial
assistance.

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Annual Review of
Biophysics
Contents Volume 41, 2012

How Should We Think About the Ribosome?

Peter B. Moore p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 1
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Mechanisms of Sec61/SecY-Mediated Protein Translocation

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Across Membranes
Eunyong Park and Tom A. Rapoport p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p21
Racemic Protein Crystallography
Todd O. Yeates and Stephen B.H. Kent p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p41
Disulfide Bonding in Protein Biophysics
Deborah Fass p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p63
Prokaryotic Diacylglycerol Kinase and Undecaprenol Kinase
Wade D. van Horn and Charles R. Sanders p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p81
Allostery and the Monod-Wyman-Changeux Model After 50 Years
Jean-Pierre Changeux p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 103
Protein Structure in Membrane Domains
Arianna Rath and Charles M. Deber p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 135
Bacterial Mechanosensitive Channels—MscS: Evolution’s Solution
to Creating Sensitivity in Function
James H. Naismith and Ian R. Booth p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 157
Cooperativity in Cellular Biochemical Processes: Noise-Enhanced
Sensitivity, Fluctuating Enzyme, Bistability with Nonlinear
Feedback, and Other Mechanisms for Sigmoidal
Responses
Hong Qian p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 179
Network-Based Models as Tools Hinting at Nonevident
Protein Functionality
Canan Atilgan, Osman Burak Okan, and Ali Rana Atilgan p p p p p p p p p p p p p p p p p p p p p p p p p p p p 205
Filamins in Mechanosensing and Signaling
Ziba Razinia, Toni Mäkelä, Jari Ylänne, and David A. Calderwood p p p p p p p p p p p p p p p p p p p 227

vii
 BB41-Frontmatter ARI 11 April 2012 11:3

ATP Utilization and RNA Conformational Rearrangement

by DEAD-Box Proteins
Arnon Henn, Michael J. Bradley, and Enrique M. De La Cruz p p p p p p p p p p p p p p p p p p p p p p p p 247
Zero-Mode Waveguides for Single-Molecule Analysis
Paul Zhu and Harold G. Craighead p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 269
Single-Molecule Views of Protein Movement
on Single-Stranded DNA
Taekjip Ha, Alexander G. Kozlov, and Timothy M. Lohman p p p p p p p p p p p p p p p p p p p p p p p p p p p p 295
Extending Microscopic Resolution with Single-Molecule Imaging
and Active Control
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Michael A. Thompson, Matthew D. Lew, and W.E. Moerner p p p p p p p p p p p p p p p p p p p p p p p p p p p 321

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Metabolite Recognition Principles and Molecular Mechanisms

Underlying Riboswitch Function
Alexander Serganov and Dinshaw J. Patel p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 343
Biophysical Dynamics in Disorderly Environments
David R. Nelson p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 371
Radical Use of Rossmann and TIM Barrel Architectures
for Controlling Coenzyme B12 Chemistry
Daniel P. Dowling, Anna K. Croft, and Catherine L. Drennan p p p p p p p p p p p p p p p p p p p p p p p p 403
Biomolecular Simulation: A Computational Microscope
for Molecular Biology
Ron O. Dror, Robert M. Dirks, J.P. Grossman, Huafeng Xu, and David E. Shaw p p p 429
Recent Advances in Magnetic Tweezers
Iwijn De Vlaminck and Cees Dekker p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 453
Virus Maturation
David Veesler and John E. Johnson p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 473
Single-Molecule Mechanoenzymatics
Elias M. Puchner and Hermann E. Gaub p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 497
Forcing Stem Cells to Behave: A Biophysical Perspective
of the Cellular Microenvironment
Yubing Sun, Christopher S. Chen, and Jianping Fu p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 519
Receptor Signaling Clusters in the Immune Synapse
Michael L. Dustin and Jay T. Groves p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 543
Functional Architecture of the Nuclear Pore Complex
Einat Grossman, Ohad Medalia, and Monika Zwerger p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 557
Structural and Energetic Basis of Allostery
Vincent J. Hilser, James O. Wrabl, and Hesam N. Motlagh p p p p p p p p p p p p p p p p p p p p p p p p p p p p 585

viii Contents