Molecular simulation of ab initio protein folding for a millisecond folder NTL9(1-39).
ABSTRACT To date, the slowest-folding proteins folded ab initio by all-atom molecular dynamics simulations have had folding times in the range of nanoseconds to microseconds. We report simulations of several folding trajectories of NTL9(1-39), a protein which has a folding time of approximately 1.5 ms. Distributed molecular dynamics simulations in implicit solvent on GPU processors were used to generate ensembles of trajectories out to approximately 40 micros for several temperatures and starting states. At a temperature less than the melting point of the force field, we observe a small number of productive folding events, consistent with predictions from a model of parallel uncoupled two-state simulations. The posterior distribution of the folding rate predicted from the data agrees well with the experimental folding rate (approximately 640/s). Markov State Models (MSMs) built from the data show a gap in the implied time scales indicative of two-state folding and heterogeneous pathways connecting diffuse mesoscopic substates. Structural analysis of the 14 out of 2000 macrostates transited by the top 10 folding pathways reveals that native-like pairing between strands 1 and 2 only occurs for macrostates with p(fold) > 0.5, suggesting beta(12) hairpin formation may be rate-limiting. We believe that using simulation data such as these to seed adaptive resampling simulations will be a promising new method for achieving statistically converged descriptions of folding landscapes at longer time scales than ever before.
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ABSTRACT: Protein folding has been viewed as a difficult problem of molecular self-organization. The search problem involved in folding however has been simplified through the evolution of folding energy landscapes that are funneled. The funnel hypothesis can be quantified using energy landscape theory based on the minimal frustration principle. Strong quantitative predictions that follow from energy landscape theory have been widely confirmed both through laboratory folding experiments and from detailed simulations. Energy landscape ideas also have allowed successful protein structure prediction algorithms to be developed. The selection constraint of having funneled folding landscapes has left its imprint on the sequences of existing protein structural families. Quantitative analysis of co-evolution patterns allows us to infer the statistical characteristics of the folding landscape. These turn out to be consistent with what has been obtained from laboratory physicochemical folding experiments signalling a beautiful confluence of genomics and chemical physics. Copyright © 2014. Published by Elsevier B.V.Biochimie 12/2014; · 3.14 Impact Factor
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ABSTRACT: We describe an innovative protocol for ab initio prediction of ligand crystallographic binding poses and highly effective analysis of large datasets generated for protein-ligand dynamics. We include a procedure for setup and performance of distributed molecular dynamics simulations on cloud computing architectures, a model for efficient analysis of simulation data, and a metric for evaluation of model convergence. We give accurate binding pose predictions for five ligands ranging in affinity from 7 nM to > 200 μM for the immunophilin protein FKBP12, for expedited results in cases where experimental structures are difficult to produce. Our approach goes beyond single, low energy ligand poses to give quantitative kinetic information that can inform protein engineering and ligand design.Scientific reports. 01/2015; 5:7918.
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ABSTRACT: Data reporting on structure and dynamics of cellular constituents are growing with increasing pace enabling, as never before, the understanding of fine mechanistic aspects of biological systems and providing the possibility to affect them in controlled ways. Nonetheless, experimental techniques do not yet allow for an arbitrary level of resolution on cellular processes in situ. By consistently integrating a variety of diverse experimental data, molecular modeling is optimally poised to enhance to near-atomistic resolution our understanding of molecular recognition in large assemblies. Within this integrative modeling context, we briefly review in this chapter the recent progresses of molecular simulations at the atomistic and coarse-grained level of resolution to explore protein-protein interactions. In particular, we discuss our recent contributions in this field, which aim at providing a robust bridge between novel optimization algorithms and multiscale molecular simulations for a consistent integration of experimental inputs. We expect that, with the ever-growing sampling ability of molecular simulations and the tireless progress of experimental methods, the impact of such dynamic-based approach could only be more effective with time, contributing to provide detailed description of cellular organization. © 2014 Elsevier Inc. All rights reserved.Advances in Protein Chemistry and Structural Biology 01/2014; 96:77-111. · 3.74 Impact Factor
Molecular Simulation of ab Initio Protein Folding for a Millisecond Folder
Vincent A. Voelz,†Gregory R. Bowman,§Kyle Beauchamp,§and Vijay S. Pande*,†,‡,§
Departments of Chemistry and Structural Biology, Stanford UniVersity, Stanford, California 94305, and Biophysics
Program, Stanford UniVersity, Stanford, California 94305
Received October 28, 2009; E-mail: firstname.lastname@example.org
A complete understanding of how proteins fold, i.e. self-assemble
to their biologically relevant “native state,” remains an unattained
goal.1Computer simulation, validated by experiment, is a natural
means to elucidate this. There is over a million-fold range in folding
rates, suggesting a possible diversity in mechanisms between slow
and fast folding proteins.2Very fast (microsecond time scale)
folding proteins3,4appear to fold via a large number of heteroge-
neous, parallel paths,5-7potentially key for folding on such fast
time scales. Does the folding of much slower proteins change this
To date, the slowest-folding proteins folded ab initio by all-atom
molecular dynamics simulations with fidelity to experimental
kinetics have had folding times in the range of nanoseconds to
microseconds. These include the designed mini-protein Trp-cage
(∼4.1 µs),8the villin headpiece domain (∼10 µs),9a fast-folding
variant of villin (<1 µs),7and Fip35 WW domain (∼13 µs).10In
this communication, we report simulations of several folding
trajectories, each from fully unfolded states, of the 39-residue
protein NTL9(1-39), which experimentally has a folding time of
MD Simulation. Trajectories were simulated via the Folding@
Home distributed computing platform12at 300, 330, 370, and 450
K from native, extended, and random-coil configurations using an
accelerated version of GROMACS written for GPU processors,13
for an aggregate time of 1.52 ms. GPUs play a key role here,
allowing for dramatically longer trajectories than previously pos-
sible. The AMBER ff96 force field14with the GBSA solvation
model15was used, a combination previously shown to give good
results folding Fip35 WW domain,10and shown to exhibit a good
balance of native-like secondary structure for a set of small helical
and ?-sheet peptides studied by replica exchange.17
Prediction of ab Initio Folding and Folding Rates. We find
that the native state (taken from the N-terminal domain of the crystal
structure of ribosomal protein L918) is stable in this force field at
300 K, exhibiting decreasing stability with increasing temperature
(Figure 1a). Rmsd-CRdistributions after 10 µs show well-defined
native and collapsed unfolded basins near 3 and 5 Å, respectively.
Of the ∼3000 trajectories started from unfolded (extended and coil)
states at 370 K (Figure 1b), two reach an rmsd-CR< 3.5 Å and
eight reach an rmsd-CR< 4 Å. No productive folding trajectories
were observed at lower temperatures, consistent with the enhanced
forward folding rate expected by Arrhenius kinetics. Higher
temperature trajectories (450 K) exceed the melting temperature
of NTL9 in the force field.
The observed number of folding events n is consistent with
expectations from a simple model of parallel uncoupled folding
simulations19in which folding is modeled as a two-state Poisson
process: 〈n〉 ) ∫M(t)k exp(-M(t) kt) dt, is the number of simulations
that reach time t (Figure 1b) and k is the experimental folding rate
(∼640/s).11This theory predicts (on average) ∼1.8 folding trajec-
tories for the amount of sampling performed, in agreement with
the two folding trajectories found in practice. Posterior distributions
of folding rates given the amount of simulation time and number
of folding trajectories were computed using a Bayesian approach,16
which yield expectation values within an order of magnitude of
the experimental folding rate.
In addition to native-like conformations, we see near-native
configurations, which show heterogeneity in hydrophobic packing,
most notably in alternative side chain arrangements in the ?-sheet
structure (Figure 2). Most common of these is a non-native
hydrophobic core involving residues I4, I18, and I37 (which
normally contact the C-terminal helix in the full-length protein)
with F5 solvent-exposed.
Insight into Folding Mechanisms. To describe the kinetics and
mechanistic aspects of folding, we employ a new paradigm for
sampling the global free energy landscape of folding, using Markov
State Models (MSMs). MSM approaches, by automatically iden-
tifying a set of kinetically metastable states (such as foldons20) and
efficiently sampling transitions between these states, can model
long-time scale kinetics from much shorter trajectories.21-24
Our strategy for simulating slow-folding proteins is first to
generate an initial series of kinetically connected states from both
the folding and unfolding directions and then to use adaptive
resampling techniques25to produce statistically converged estimates
of metastable basins and the transition rates between them. In the
†Department of Chemistry.
‡Department of Structural Biology.
Figure 1. (a) Distributions of rmsd-CR for native-state simulations of
NTL9(1-39) after 10 µs. The arrows indicate thresholds defined for the
native basin at 3.5 and 4 Å. (b) The number of parallel simulations M(t)
started from unfolded states at 370 K that reach time t. (c) Posterior
predictions of the folding rate given the amount of simulation time and
observed folding events for 3.5 Å (dashed) and 4 Å (solid) thresholds, using
uniform (black) and Jeffrey’s (gray) priors, using methods from ref 16. In
red is a Gaussian distribution representing the experimental rate mean and
Published on Web 01/13/2010
10.1021/ja9090353 2010 American Chemical Society
1526 9 J. AM. CHEM. SOC. 2010, 132, 1526–1528
remainder of this communication, we report progress toward the
first goal, by constructing an MSM from the entire set of 370 K
trajectory data,26,27which we will use to seed future rounds of
transition sampling. While additional rounds of adaptive sampling
could likely aid in increasing the quantitative power of this model,
there are several notable observations which can be made with the
current data set.
Key to accurately identifying metastable states is the clustering
of trajectory conformations into microstates fine-grained enough
to be used for lumping into groups of maximally metastable
macrostates.26100 000 microstate clusters were calculated using
an approximate k-centers algorithm,28each with an average radius
of 4.5 Å rmsd-backbone. Lag times ranging from 1 to 32 ns were
used to build a series of MSMs. The implied time scales predicted
by these models (obtained by diagonalizing the rate matrix) show
a clear spectral gap separating the slowest relaxation time scale
from the rest, indicative of single-exponential kinetics (see Figure
S1). The implied time scale of the model levels off beyond a lag
time of ∼10 ns to an implied time scale of ∼1 ms, close to the
experimental folding time.
An important strength of MSMs is their ability to gain insight
at coarser scales by “lumping” the kinetic transitions into a simpler
model with fewer states. To gain a mesoscopic view of the folding
free energy landscape, we lumped our 100 000- microstate MSM
into a 2000-macrostate model. From this view, we find that the
metastable states are diffuse collections of conformations over which
multiple possible folding pathways can occur, indicating a vast
heterogeneity of folding substates that need to be understood in
greater detail. At the same time, we can identify highly populated
“native” (state n) and “unfolded” (state a) macrostates that dominate
the observed relaxation rates (Figures 3 and S2).
The 10 pathways with the highest folding flux from macrostate
a to n were calculated by a greedy backtracking algorithm (see
Supporting Information (SI)) from the macrostate transition matrix
using transition path theory29,30(TPT). The diversity of pathways
demonstrates the power of the MSM approach: although we observe
only a few folding trajectories directly, a network of many possible
pathways can be inferred from the overlapping sampling of local
While NTL9(1-39) folds quickly for a two-state folder, it is
similar in size to many ultrafast (submillisecond) folders that appear
to exhibit so-called “downhill” folding. Hence, we would like to
understand the structural features that limit the overall folding rate.
As in a macroscopic two-state model, the highest-flux pathways in
our mesoscopic model are afmfn and aflfn direct routes from
disordered to structured macrostates, reminiscent of nucleation-
condensation. These pathways by themselves, however, account
for only ∼10% of the total flux, and the structural diversity seen
in all pathways is reminiscent of more hierarchical folding models
such as diffusion-collision. Thus, we sought to more fully study
the 14 macrostates transited by the top 10 folding pathways.
To examine structural changes along the folding reaction, we
considered three main native structural elements: the central helix
(R), the pairing of strands 1 and 2 (?12), and the pairing of strands
1 and 3 (?13). To quantify the extent of native-like structuring for
each of these elements we calculated QR, Q?12, and Q?13, respectively
(see SI for details). The Q-value is a number between 0 and 1 that
quantifies the extent of native-like contacts. We then examined,
for each macrostate, the Q-values in relation to the pfold value
(committor), a kinetic reaction coordinate. The pfold value is
computed from the macrostate transition matrix.24,29,30
This analysis yields several key insights into the folding
mechanism of NTL9(1-39) on the mesoscale. We find the
“unfolded” state a is compact and contains a baseline level of
residual native-like structure, with QRnear 0.5, and Q?12and Q?13
near 0.2. In general, across the 14 macrostates studied, Q-values
increase as pfoldvalues increase, although the relative balance of
QR, Q?12, and Q?13varies, indicating pathway heterogeneity: i.e.,
native-like structures can form in different orders (Figures 4, S4,
and S5). An exception to this, however, is observed for ?12strand
Figure 2. (a) A snapshot from a folding trajectory (dark blue) achieves an
rmsd-CRof 3.1 Å compared to the native state (cyan). (b) Non-native (top)
and native-like (bottom) hydrophobic core arrangements observed in low-
rmsd conformations of folding trajectories. Highlighted are side chains of
residues F5 (magenta), V3,V9,V21 (tan), and L30,L35 (pink).
Figure 3. A 2000-state Markov State Model (MSM) was built using a lag
time of 12 ns. Shown is the superposition of the top 10 folding fluxes,
calculated by a greedy backtracking algorithm (see Supporting Information).
These pathways account for only ∼25% of the total flux and transit only
14 of the 2000 macrostates (shown labeled a-n, for convenient discussion).
The visual size of each state is proportional to its free energy, and arrow
size is proportional to the interstate flux.
Figure 4. The 14 macrostates involved in the top 10 folding pathways,
plotted along structural and kinetic reaction coordinates. The balance
between native-like helix and sheet structure is quantified by QR- (Q?12
+ Q?13)/2 (vertical axis), and progress along the folding reaction is quantified
by the pfold(committor) value (horizontal axis). It can be seen that the
“unfolded” state (a) contains residual native-like helical propensity, and
that pathways involving various ordering of native-like helix and sheet
formation are possible.
J. AM. CHEM. SOC. 9 VOL. 132, NO. 5, 2010
pairing. Only for macrostates with pfold> 0.5 (states g-n) does
appreciable ?12strand pairing occur (Figure 5). This suggests that
the formation of a local strand pair (?12), rather than a nonlocal
strand pair (?13), is rate-limiting. This effect is not predicted by
strictly topological models of folding in which loop closure entropy
loss dominates31but instead may result from sequence-specific
details. Unlike the ?13strand pair, which has a small interaction
surface stabilized by hydrophobic contacts, the ?12hairpin contains
seven of the protein’s eight lysine residues and three of its five
glycine residues in a flexible loop region, features which may imbue
?12with larger barriers to folding. This proposed role of ?12is also
consistent with the large changes in kinetics and stability seen
experimentally for mutations in the ?12hairpin.11
It is natural to compare our results with previous unfolding
simulations of NTL9(1-39) K12 M by Snow et al.32In that work,
a detailed characterization of the transition state ensemble required
the definition of strand-pairing reaction coordinates corresponding
to ?12and ?13formation. In our MSM analysis, no such predefinition
is required. Snow et al. also note the difficulty in resolving kinetic
intermediates not captured by the chosen order parameters. Indeed,
our structural analysis can resolve subtle kinetic intermediates within
the native basin, corresponding to alternative rearrangements of the
?12hairpin loop (Figure S6).
Conclusion. The above results suggest that existing force field
models using implicit solvent are indeed accurate enough to fold
proteins ab initio at long time scales (milliseconds), opening
the door to simulating more structurally complex proteins.
Moreover, our work demonstrates that there need not be a single
pathway or single, dominant mechanism for the folding of a
given protein: since the theories proposed for how proteins fold
are based on broadly relevant physical principles, it is natural
to imagine that multiple mechanisms could be simultaneously
present but that the sequence of the protein, coupled with the
chemical environment, would control the balance to which each
mechanistic pathway is seen.
Acknowledgment. We thank the NSF for support through FIBR
Grant EF-0623664, the NIH through R01-GM062868 and Simbios
U54-GM072970, and NSF Award CNS-0619926 for computer
Supporting Information Available: Detailed description of simu-
lation methods, results, analysis, and Supporting Figures S1-S7.
This material is available free of charge via the Internet at http://
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1528J. AM. CHEM. SOC. 9 VOL. 132, NO. 5, 2010