doi:10.1016/j.jmb.2006.03.021
J. Mol. Biol. (2006) 359, 202–214
Computational Sampling of a Cryptic Drug Binding Site
in a Protein Receptor: Explicit Solvent Molecular
Dynamics and Inhibitor Docking to p38 MAP Kinase
Tamara Frembgen-Kesner and Adrian H. Elcock*
Department of Biochemistry
University of Iowa, Iowa City
IA 52242, USA
An increasing number of structural studies reveal alternative binding sites in
protein receptors that become apparent only when an inhibitor binds, and
correct prediction of these situations presents a significant challenge to
computer-aided drug design efforts. A striking example is provided by recent
crystal structures of the p38 MAP kinase, where a 10 Å movement of the
Phe169 side-chain creates a new binding site adjacent to the ATP binding site
that is exploited by the diaryl urea inhibitor BIRB796. Here, we show that this
binding site can be successfully and repeatedly identified in explicit-solvent
molecular dynamics (MD) simulations of the protein that begin from an
unliganded p38 crystal structure. Ligand-docking calculations performed on
5000 different structural snapshots generated during MD indicate that the
conformations sampled are often surprisingly competent to bind the inhibitor
BIRB796 in the crystallographically correct position and with docked energies
that are generally more favorable than those of other positions. Similar
docking studies with an ATP-binding site-directed inhibitor suggest that it
may be possible to develop hybrid inhibitors that target both the ATP and
cryptic binding sites simultaneously. Intriguingly, both inhibitors are
occasionally found to dock correctly even with p38’s “DFG” motif in the
“wrong” conformation and BIRB796 can successfully dock, albeit infrequently, without significant displacement of the Phe169 side-chain; this
suggests that the inhibitor might facilitate the latter’s conformational change.
Finally, two quite different conformations of p38’s DFG motif are also sampled
for extended periods of time during the simulations; these may provide new
opportunities for inhibitor development. The MD simulations reported here,
which total 390 ns in length, therefore demonstrate that existing computational methods may be of surprising utility in predicting cryptic binding
sites in protein receptors prior to their experimental discovery.
q 2006 Elsevier Ltd. All rights reserved.
*Corresponding author
Keywords: p38 MAP kinase; BIRB796; molecular dynamics simulations;
AutoDock; cryptic binding sites
Introduction
A major obstacle to the development of truly
predictive ligand–receptor docking methods is the
tendency for conformational changes in the
receptor to accompany ligand or substrate binding.1
Often these conformational changes are only very
limited;2 however, there are cases where a novel and
drastically different receptor backbone conformation
Abbreviation used: MD, molecular dynamics.
E-mail address of the corresponding author:
[email protected]
is only revealed once a ligand binds.3,4 Since the
newly formed receptor binding sites are often difficult
or impossible to anticipate based on visual or
computational examination of previously solved
structures, they effectively constitute “cryptic” binding sites. Importantly, the ability to predict cryptic
binding sites in pharmaceutically relevant targets
could be of considerable use to structure-based
inhibitor design efforts, since binding of ligands at
such sites often results in allosteric inhibition of the
receptor’s activity.5 Examples of such sites, and the
methods that might be used to identify them in
other systems, have recently been the subject of a
comprehensive review.5
0022-2836/$ - see front matter q 2006 Elsevier Ltd. All rights reserved.
Computational Sampling of p38 MAP Kinase
A good illustration of both the identification and
exploitation of a cryptic binding site has been
provided by a recent study of diaryl urea inhibitors
binding to p38 MAP kinase,3 a target in the
treatment of inflammatory diseases.6 Binding of
the inhibitors causes a major structural rearrangement of the kinase’s conserved Asp-Phe-Gly (DFG)
motif, shifting the phenylalanine (Phe169) sidechain from its usual, buried location (the “DFG-in”
conformation), to a location w10 Å away that
sterically interferes with ATP binding (the “DFGout” conformation; Figure 1(a)). The departure of
the phenylalanine side-chain leaves a vacant
hydrophobic pocket (the cryptic binding site) that
is filled by the t-butyl group of the inhibitors, one of
which (BIRB796) also makes limited contact with
the nearby ATP binding site. BIRB796 has slow
dissociation kinetics,3,8 appears to be selective for
p38 MAP kinases, and additionally, drug-resistant
mutants of BCR-ABL,9 and was in phase III clinical
trials as a treatment for rheumatoid arthritis as of
early 2005.9
The p38 system provides an excellent test case for
computational methodologies aimed at identifying
cryptic ligand binding sites formed by local
rearrangement of the receptor backbone. To assess
perhaps the simplest possible approach we have
used conceptually straightforward but computationally intensive explicit-solvent MD simulations
to sample alternative conformations of p38’s
activation loop. Specifically, we have attempted to
answer three questions. First, can the cryptic, DFGout conformation of the kinase be identified or
sampled in dynamics simulations that start from an
unliganded, DFG-in crystal structure of the kinase?
Second, if such a conformation is observed in
simulations, is it sufficiently well defined to be
competent to bind the diaryl urea inhibitors? Third,
can the simulations predict other novel conformations that might also be useful in rational ligand
design? We report here that the answer to each of
these questions is, perhaps surprisingly, “yes”:
conventional high-temperature MD simulations
repeatedly sample the DFG-out conformation,
structural snapshots from the simulations can
successfully dock inhibitors using the ligand docking program AutoDock,10 and at least two novel,
probably catalytically incompetent conformations
(“pseudo-DFG-in” and “pseudo-DFG-out”) are
identified that may be useful targets for virtual
screening efforts. These results suggest that existing
computational methods might be usefully
employed to identify potential cryptic binding
sites in therapeutically interesting targets in
advance of their experimental discovery.
Results
MD simulations
The initial (DFG-in) conformation of the protein
used in all MD simulations is shown in Figure 1(b).
203
Figure 1. (a) The DFG-in (blue) and DFG-out (red)
conformations. Residues of the cryptic binding site
surrounding the Phe169 side-chain of DFG-in are
shown; hydrophobic residues are colored orange. Important residues of the ATP binding pocket (Met109, Thr106,
and Lys53) are shown in green. (b) Starting conformation
of p38 in the MD simulations. Residues subject to
restraints are shown in gray, with the unrestrained loop
residues highlighted in blue. For reference, the position
that would be occupied by the ligand BIRB796 in the
cryptic binding site (t-butyl group circled) is shown; note
however, that no ligands were included in the simulations. (c) Sampling of the activation loop during one
60 ns simulation; snapshots shown are sampled at 500 ps
intervals. These Figures were prepared with Rasmol.7
Starting from this conformation, ten independent
high-temperature MD simulations were conducted
in the presence of explicit solvent and with
harmonic restraints applied to atoms outside the
kinase activation loop (see Methods). Of these ten
simulations, seven remained close to this initial
204
conformation for a full 30 ns despite the simulated
temperature being 1000 K. The three other simulations, however, all made significant conformational transitions during the 30 ns (see below) and so
were each continued to 60 ns.
Figure 1(c) shows an overlay of structures taken
at 500 ps intervals from one of these 60 ns simulations; from this it is clear that the activation loop
samples a wide range of conformations during
the simulation. Conformational transitions of the
crucial DFG motif of the activation loop can be
conveniently measured using the available crystal
structures of the DFG-in (PDB code 1P38)11 and
DFG-out (PDB code 1KV1)3 conformations as
reference points. The root-mean-square deviation
(RMSD) of the atoms of the DFG motif from their
positions in the DFG-in and DFG-out conformations are shown as a function of time for the
three 60 ns simulations (denoted simulations A, B
and C) in Figure 2. In each simulation, conformational transitions that moved the DFG motif
away from its DFG-in conformation also tended
to move it toward the DFG-out conformation
Figure 2. Conformational sampling during three independent 60 ns MD simulations. The RMSD of the DFG motif
from the DFG-in conformation as a function of the
simulation time is shown in blue; the RMSD of the DFG
motif from the DFG-out conformation is shown in red.
Computational Sampling of p38 MAP Kinase
(i.e. increases in the RMSD from DFG-in are
accompanied by decreases in the RMSD from
DFG-out). Importantly, all three simulations, having started with RMSD values of 8 Å from DFG-out,
sampled conformations with RMSD values of only
1.5–3 Å before returning to DFG-in; simulation A
even did so on two separate occasions (Figure 2).
This indicates that the conformational changes
necessary to expose the cryptic binding site can
be sampled effectively with conventional hightemperature MD. Visual analysis of the eight
(forward and reverse) transitions that occurred
between DFG-in and DFG-out conformations
showed that seven followed the pathway illustrated
in Figure 3(a); the single exceptional transition
pathway is illustrated in Figure 3(b).
In addition to sampling the DFG-in and DFG-out
conformations, two other unusual conformations
were observed for extended periods of time. At
w33 ns in simulation B, a set of conformations that
was w6 Å from DFG-in and w7 Å from DFG-out
was formed and sampled continuously for 19 ns
(Figure 2(b)). The structures sampled during this
period form a tightly clustered group in which,
despite the high RMSD from the initial conformation, the Phe169 side-chain remains buried in its
hydrophobic pocket: we therefore refer to this set of
Figure 3. MD snapshots illustrating the two transition
pathways. (a) The major pathway, followed in seven of
eight transitions. (b) The minor pathway. Snapshots are
colored in chronological order on a rainbow scale with
blue and red indicating the DFG-in and DFG-out
conformations, respectively.
205
Computational Sampling of p38 MAP Kinase
conformations as pseudo-DFG-in. The backbone of
the DFG motif assumes an orientation that is
approximately perpendicular to its DFG-in orientation, and forms part of a larger b-turn-like
structure in which transient hydrogen bonds are
formed amongst residues of the turn (Asp168–
Leu171) and between the amide nitrogen of Gly170
and the carboxylate side-chain of Glu71. The latter
hydrogen bond appears to be a defining characteristic of the pseudo-DFG-in conformation as it is
found only rarely elsewhere in the three 60 ns
simulations; it is therefore worth noting that an inhouse alignment of 493 kinase sequences shows
that Glu71 is conserved in w78% of kinases. An
additional striking feature of the pseudo-DFG-in
conformations is the position of the Leu171 sidechain: it occupies essentially the same position
occupied by the Phe169 side-chain in the DFG-out
conformation. Notably, by doing so, it partially
obstructs the ATP binding site and therefore may
make the pseudo-DFG-in set of conformations
catalytically incompetent (Figure 4(a)).
The second unusual set of conformations
observed in the simulations was formed at w30 ns
in simulation C: conformations that were w7 Å
from DFG-in but w5.5 Å from DFG-out were
sampled for 13 ns (Figure 2(c)). Again, structural
snapshots taken from this period of the simulation
form a closely clustered group. Since in these
structures the Phe169 side-chain assumes approximately the same position occupied in the DFG-out
conformation, we refer to this set of conformations
as pseudo-DFG-out. Importantly, the Phe169 sidechain’s position in these conformations appears to
be incompatible with ATP binding, thus likely
making this set of conformations also catalytically
incompetent. The backbone of the DFG motif again
Figure 4. The pseudo conformations. DFG-in (blue) and
DFG-out (red) are shown for reference; the pseudo
conformations are colored in a CPK scheme. (a) PseudoDFG-in; (b) pseudo-DFG-out.
forms a b-turn-like structure with transient hydrogen bonds amongst residues of the turn (Asp168–
Leu171) prominent, but assumes an orientation
approximately perpendicular to that observed in
DFG-out (Figure 4(b)). Again, the position of the
Leu171 side-chain is notable: it is buried in the
hydrophobic pocket of the cryptic binding site, and
therefore mimics the position assumed by the
Phe169 side-chain in DFG-in conformations.
While the conformations adopted by the DFG
motif in the three simulations tend to fall into a few
relatively well defined clusters, the rest of the
activation loop, and in particular the residues
beyond Leu171, shows much greater conformational heterogeneity. The extent of conformational
freedom in the rest of the activation loop does not
appear to be affected by the specific conformation
assumed by the DFG motif: loop structures sampled
during periods of the simulations when pseudoDFG-in and pseudo-DFG-out conformations are
adopted all have ranges similar to those sampled
during periods in which DFG-in conformations are
assumed (Supplementary Data). This suggests,
superficially at least, that the relative stabilities of
these various DFG conformations should not be
drastically affected by differences in the conformational entropy of the rest of the flexible loop.
Inhibitor docking
As noted above, the three 60 ns simulations all
sampled conformations in which the DFG motif
was within 1.5–3 Å of its position in the DFG-out
crystal structure. In order to determine whether any
of the sampled structures might be competent to
bind known inhibitors of the kinase, a series of
docking experiments was performed with the
program AutoDock.10 Five inhibitors for which
p38 co-crystal structures are available (Figure 5(a))
were rigidly docked to each of 5000 structural
snapshots sampled from the first 50 ns of simulation A. For each snapshot and each inhibitor, ten
independent docking runs were performed and the
resulting docking pose with the most favorable
binding energy selected; in all cases docking was
conducted within a cubic space large enough to
comfortably encompass both the cryptic and ATP
binding sites (Figure 5(b)). Two of the selected
inhibitors, a simple diaryl urea inhibitor and
BIRB796, bind crystallographically to the DFG-out
conformation.3,8 The remaining three ligands, a
pyridinylimidazole inhibitor,12 a dihydroquinazolinone inhibitor,13 and a 4-azaindole inhibitor,14 all
bind crystallographically to the ATP binding
pocket, with the DFG motif in its more usual
DFG-in conformation.
The results of docking BIRB796 to the 5000
snapshots are shown in Figure 6(b) (results for the
smaller diaryl urea inhibitor are very similar and
therefore not shown); the conformational state of
the DFG motif in these same snapshots, indicated
by its RMSD from the crystallographic DFG-in
conformation, is shown for comparison in
206
Figure 5. (a) The five inhibitors used in the docking
calculations. Top row, from left: diaryl urea inhibitor and
BIRB796. Middle: pyridinylimidazole and 4-azaindole
inhibitors. Bottom: dihydroquinazolinone inhibitor.
(b) The 27 Å!27 Å!27 Å grid in which docking was
performed encompassed both the hydrophobic pocket/
cryptic binding site (surrounding the phenylalanine sidechain of DFG-in (blue)) as well as the ATP binding site
(residues Met109, Thr106 and Lys53 shown in green). For
reference, the DFG-out conformation is shown in red.
Computational Sampling of p38 MAP Kinase
Figure 6. Inhibitor docking results. (a) RMSD of the
DFG motif from the DFG-in conformation for the 5000
structural snapshots. (b) RMSD of the most energetically
favorable docked pose of BIRB796 from its crystal
structure position for each snapshot. (c) Same for the
dihydroquinazolinone inhibitor. (d) An example of
BIRB796 and dihydroquinazolinone correctly docked to
the same snapshot.
207
Computational Sampling of p38 MAP Kinase
Figure 6(a). With most snapshots, the docking of
BIRB796 was “unsuccessful” in the sense that the
docked poses with the best energies had high
RMSDs (6–17 Å) from the inhibitor’s crystallographic position: this result is not surprising given
that in this particular simulation the protein spends
most of its time in DFG-in conformations. With 139
of the snapshots, however, docking was successful
(with “successful” being defined here as the
inhibitor having an RMSD !2 Å from its crystallographic position). As a comparison of Figure 6(a)
and (b) indicates, these successful docking events
occurred most frequently with snapshots in which
the DFG motif was significantly displaced from its
DFG-in conformation (DFG RMSDs O7 Å). This
relationship can be more usefully displayed by
grouping snapshots according to their RMSDs from
the DFG-out conformation and plotting the fraction
of snapshots in each group that successfully docked
BIRB796 (filled bars in Figure 7); such a plot clearly
indicates that snapshots that are more similar to
DFG-out are more likely to bind BIRB796 correctly.
Other aspects of the same Figure deserve comment.
First, it is clear that adoption of a conformation close
to DFG-out is not by itself a sufficient condition for
successful docking of the inhibitor: of the 107
snapshots with RMSDs !3 Å from DFG-out, only
w10% are actually found to correctly dock BIRB796;
visual examination of a sample of the structures that
fail to properly bind the inhibitor suggests that they
do so primarily because of clashes with the sidechains of Asp168 and Phe169. Second, although
snapshots that are very similar to DFG-out are the
most likely to successfully dock BIRB796, successful
docking is also achieved with snapshots in which a
transition to DFG-out is incomplete (3 Å!RMSD!
6 Å); these same snapshots are, however, also distant
from DFG-in, which indicates that the exit of the
Phe169 side-chain from its hydrophobic pocket is
sufficient to produce a binding site that is recognized
by the inhibitor (and AutoDock). Third, and perhaps
most interestingly, BIRB796 can (very occasionally)
successfully dock even to snapshots that have RMSDs
from DFG-out of O8 Å. Since these same snapshots
are all classified as DFG-in snapshots, this result
indicates that the inhibitor can, according to AutoDock, bind in its crystallographic position even
without a major conformational change in the DFG
motif; a similar result is also obtained with the smaller
diaryl urea inhibitor. Examination of these snapshots
indicates that even small adjustments in the sidechains that form the hydrophobic pocket (including
that of Phe169) can enlarge it sufficiently that the
t-butyl group of the diaryl urea inhibitors fits
alongside the Phe side-chain without incurring
clashes with other protein atoms. The likelihood of
this being a real result and its potential implications
for the mechanism of BIRB796’s binding are considered in Discussion.
Of the three inhibitors studied that bind crystallographically to DFG-in conformations, only the
dihydroquinazolinone inhibitor could be successfully docked with AutoDock: both the pyridinylimidazole and 4-azaindole inhibitors failed to dock even
to their own p38 crystal structures (best RMSD values
of 7.2 Å and 13.2 Å, respectively). The dihydroquinazolinone inhibitor, which did successfully dock to its
own p38 crystal structure (best RMSD value of
0.58 Å), also docked repeatedly with RMSDs of
!2 Å to the p38 snapshots sampled from simulation
A. As expected, docking in the crystallographically
correct position occurred primarily to DFG-in conformations (Figure 6(c)), but again, it was occasionally
achieved even with structural snapshots that differed
considerably from the DFG-in conformation (open
bars in Figure 7). Even more surprisingly, six out of
the 5000 snapshots were able to successfully dock
both BIRB796 and the dihydroquinazolinone inhibitor in their crystallographic orientations, although
because of steric overlaps, it would not be possible to
bind both drugs simultaneously (Figure 6(d)).
One final result of the docking calculations that is
worth noting is that although correct binding
orientations were often returned as having the most
favorable binding energy for a particular structural
snapshot, it was only in the case of BIRB796 that the
snapshots to which correct binding occurred had
consistently more favorable energies than snapshots
to which “incorrect” binding occurred (Figure 8(a)):
for both the small diaryl urea inhibitor and the
dihydroquinazolinone inhibitor, the binding energies
for correct and incorrect orientations were effectively
indistinguishable (Figure 8(b)). Focusing specifically
on the correctly docked BIRB796 poses, it was found
that there was no clear significant dependence of the
computed binding energies on the RMSD of the DFG
motif (Figure 8(c)).
Discussion
Figure 7. Fraction of snapshots that correctly docked
inhibitors plotted as a function of the snapshots’
similarity to the crystallographic DFG conformation.
Filled bars, BIRB796; RMSD measured from DFG-out.
Open bars, dihydroquinazolinone inhibitor; RMSD
measured from DFG-in.
MD simulations successfully sample p38’s
cryptic binding site
MD simulations are now routinely used in the
field of ligand–receptor docking as a means of
208
Figure 8. Inhibitor docking results, energies. (a) Plot of
the computed binding energy of the docked poses of
BIRB796 versus their RMSD from the crystallographic
position. (b) Plot of the computed binding energy of the
docked poses of the dihydroquinazolinone inhibitor
versus their RMSD from the crystallographic position.
(c) Plot of the computed binding energies of correctly
docked poses (ligand RMSD!2.0 Å) of BIRB796 versus
the RMSD of the DFG motif from DFG-in.
sampling the accessible conformations of protein
receptors. The conformations thus generated have
been used in attempts to construct more realistic
pharmacophore models15 and, for some years now,
to provide discrete sets of alternative protein
models to which ligands can be docked (in lieu of
explicitly modeling protein flexibility during docking).16–23 Conceptually, the goal of the present MD
simulations was similar to these previous applications, but the range of receptor conformations
intended to be sampled here, and the timescale over
which sampling was performed, was more extreme.
The fact that activation loop conformations within
3 Å of DFG-out (and O10 Å from the initial DFG-in
conformation) were repeatedly sampled indicates
that this goal was successfully achieved. It would
clearly be inappropriate to claim that complete
sampling of all accessible conformations of the
activation loop was also achieved but it is worth
noting that all three of the simulations that made
excursions to DFG-out conformations subsequently
returned to DFG-in conformations (with simulation
Computational Sampling of p38 MAP Kinase
A doing so on two separate occasions; Figure 2). The
perhaps more important question of whether these
sampled DFG-out conformations could be identified as being important in a truly predictive
scenario is discussed further below.
While the MD simulations effectively sampled
the DFG-out conformations necessary to reveal
p38’s cryptic binding site, it would be misleading
to suggest that they did so efficiently. The simulations were extremely computationally intensive,
each of the 30 ns simulations required approximately four months of computer time, and even
then, only three out of ten simulations made
significant excursions from the initial DFG-in
conformation, despite the simulated temperature
being 1000 K. High-temperature, explicit-solvent
MD is routinely and successfully used to simulate
protein unfolding transitions, and unfolding typically occurs within a few nanoseconds at temperatures of only w500 K.24 That the much smaller
conformational change studied here is only
occasionally observed even after 30 ns of simulation
is probably due to the fact that a large portion of the
protein is restrained; simulations that more weakly
restrain the protein might be capable of undergoing
more frequent transitions, though they may of
course also tend to undergo unintended (i.e.
unfolding) transitions. Certainly, there are more
sophisticated simulation techniques currently available, or in development, that might be used for
modeling these kinds of conformational transitions.25,26 Such methods may have the additional
advantage of allowing the relative stabilities of
alternative conformations to be properly estimated,
something that is not straightforward to do with the
present approach.
MD-generated conformations are competent
to bind inhibitors
Although one can use RMSD measurements to
determine that DFG-out conformations are sampled
during the simulations, a perhaps more important
test of the authenticity of such conformations is
whether they are capable of correctly docking the
inhibitors that are known to bind to this conformation experimentally.3 This question was
answered in the affirmative by docking BIRB796 to
MD snapshots taken from simulation A: of those
snapshots for which the RMSD from DFG-out was
!3 Å, w10% returned docked BIRB796 structures
that were correct (i.e. within 2 Å of the inhibitor’s
crystallographic position). Although this success
rate might seem rather low, it should be noted that it
actually compares well with the success rate
obtained for the dihydroquinazolinone inhibitor
docking to DFG-in snapshots (18%; see Figure 7).
For both inhibitors, the failure of the remaining
snapshots to correctly dock the inhibitor can be
attributed, in part, to certain critical side-chains
being slightly “misplaced” even though the backbone conformation is basically correct; an exaggerated sensitivity to the positions of side-chains is
Computational Sampling of p38 MAP Kinase
a common feature of docking methods that do
not explicitly model the flexibility of the receptor.27
A second reason why the success rate for docking
is not higher is that the space within which
docking was conducted was considerably larger
than was strictly required to simultaneously cover
both the cryptic and ATP binding sites: pilot
docking calculations with a smaller grid (though
still large enough to cover both established binding
sites) resulted in considerably higher success
rates (data not shown). Regardless of the apparently low rate of successful dockings, it should be
remembered that the fact that correct docking
occurs at all for BIRB796 is really the major result:
it suggests that MD may be useful for sampling
protein conformations that are competent to bind
inhibitors even when they are structurally quite
distant from the reference (crystal) structure.
Studies on other challenging systems4,5 will of
course be required to determine whether this is a
general result.
Although docking methods are not the focus of
this study, it is worth noting that of the three ATPbinding site inhibitors studied here, successful
docking to the MD-generated conformations was
obtained only with the dihydroquinazolinone
inhibitor. Failure with the two other ATP-binding
inhibitors was not due to the imposition of MD
restraints on key side-chain atoms in the ATP
binding site, since neither inhibitor could be
successfully docked even to its own crystal
structure receptor with AutoDock. Nor was the
failure due to the conformation of the unliganded
p38 crystal structure being structurally dissimilar
from these inhibitor-bound conformations: in fact,
the three inhibitors for which successful docking
was achieved were those whose crystal structures
showed the greatest backbone deviation from the
unliganded p38 structure (Figure 9; also, see
Methods). The fact that useable results are obtained
with three out of five inhibitors is consistent with
AutoDock’s success rate in other studies: the
Abagyan & Brooks groups have obtained successful
docking in w50% of applications of AutoDock,28
and the Thornton group has found the method to be
successful in identifying a docking conformation
with best energy within 3 Å of the crystal structure
59% of the time.29 There are of course many other
docking programs that could equally well be used
to assess the abilities of MD-sampled snapshots to
bind inhibitors.30,31
Successful docking to “wrong” conformations
It is intriguing that both BIRB796 and the
dihydroquinazolinone inhibitor are occasionally
found to bind in their crystallographic positions
even when the protein’s DFG motif is in the wrong
conformation. In the case of BIRB796, this is a result
of minor shifts in a few side-chains enlarging the
hydrophobic pocket sufficiently to allow binding
of the inhibitor alongside the buried Phe169
209
Figure 9. Overlay of six crystal structures of p38 MAP
kinase. The structure used for the simulations (1P38) is
shown in blue. The two structures with the DFG motif in
the DFG-out conformation (1KV1 and 1KV2) are shown in
red. The structure with the dihydroquinazolinone inhibitor bound (1M7Q) is shown in yellow. The remaining two
structures, 1A9U (pyridinylimidazole inhibitor) and
1OZ1 (4-azaindole inhibitor) are shown in gray. Met109
of the ATP binding pocket is circled.
side-chain. Although this is a surprising result,
important evidence indicating that it is actually
reasonable can be found in a recent crystal structure
of an inhibitor-bound MEK kinase in which a diaryl
amine inhibitor assumes a position flanking MEK’s
corresponding phenylalanine side-chain;32 the
structural similarities between the simulated inhibitor–p38 structures obtained here and the experimental inhibitor–MEK structure are quite striking
(Figure 10). If correct, the present result would
suggest that a prior conversion to the DFG-out
conformation may not be a prerequisite for p38’s
binding of the inhibitor: instead, it may be that the
initial binding of BIRB796 occurs while the protein
is still in the DFG-in conformation, and that this in
turn facilitates a subsequent conversion to DFG-out.
In support of this scenario, it is worth noting that
the entire DFG-in/DFG-out transition shown in
Figure 3(a) can be superimposed on a crystallographically bound BIRB796 molecule with only
minimal steric clashes.
Of course, readers might reasonably question the
significance of successful docking of BIRB796 to
DFG-in snapshots, since it might be interpreted as
simply indicating that the docking program is
incapable of discriminating between receptor conformations. There is certainly some truth to this
idea: we have already noted for example that there
is no significant difference between the binding
energies of BIRB796 docked in its crystallographic
position to DFG-in and DFG-out snapshots
(Figure 8(c)) and there is, in addition, a wider
issue with discrimination, since for the dihydroquinazolinone inhibitor there is not even a clear
210
Computational Sampling of p38 MAP Kinase
further the fact that six of the 5000 structural
snapshots subjected to docking calculations proved
capable of binding both BIRB796 and the dihydroquinazolinone inhibitor, even though the two
would not be able to bind simultaneously. This
suggests that it may be possible to develop a hybrid
molecule that merges functionality from both of the
existing inhibitors, thereby yielding an inhibitor
that stretches from one site to the other with
potentially increased affinity for the p38 target.
Engineering covalent links between complementary molecular scaffolds is a common strategy for
developing inhibitors with greater affinity and
specificity;33 the present study suggests that such
efforts might be worth pursuing even when the
receptor conformations used to bind the two
scaffolds appear to be sterically incompatible.
New conformations predicted
Figure 10. Comparison of inhibitors binding to the
hydrophobic pocket/cryptic binding site (hydrophobic
residues colored orange). (a) Snapshot from simulation A
to which AutoDock was able to bind BIRB796 (red) in the
hydrophobic pocket even with the DFG motif (blue) in
the DFG-in conformation. (b) Crystallographic results of
the binding of an inhibitor to the hydrophobic pocket of
MEK1.32 The diaryl amine inhibitor is shown in red and
the DFG motif in blue.
difference between the binding energies of crystallographically correct and incorrect orientations
(Figure 8(b)). That said, it would be a mistake to
conclude that the docking results are totally devoid
of discriminative abilities: Figure 7 makes it very
clear that crystallographically correct docking of
both BIRB796 and the dihydroquinazolinone inhibitors is far more likely to occur when the DFG motif
is in the conformation appropriate to that inhibitor’s binding mode. Moreover, since there is, in the
particular case of BIRB796, a relationship between
the binding energy and the RMSD of the inhibitor
from its crystallographic position, it would be
possible to use this information, together with that
shown in Figure 7, to identify it unequivocally as a
binder of DFG-out conformations.
If one chooses not to dismiss any unusual
docking results out of hand, it is worth considering
A further interesting result of this study is the
discovery of two new potential conformational
states of the DFG motif, dubbed here pseudoDFG-in and pseudo-DFG-out; these conformations
were sampled for w19 ns and w13 ns, respectively.
Since both conformations, if they exist as predicted,
would likely render the kinase catalytically inactive,
stabilization of either conformation through ligand
binding might prove to be an effective means of
inhibiting the protein, and as such, both conformations may be attractive vehicles for virtual
screening studies. In keeping with the need to be
skeptical of simulation results; however, it is of
course worth asking whether these simulated
structures are likely to be realistic. Obviously, the
most compelling way of answering this question
would be to find similar conformational states in
existing kinase crystal structures (though this
would also destroy any claims of novelty!). Since
we have been unable to locate clearly similar
examples, we can only assess the credibility of the
structures indirectly. One important indication may
be that both pseudo conformations are characterized by b-turn-like structures at residues Asp168–
Leu171 (Figure 4), and though neither appears to
fall into established structural classifications, the
local DFGL sequence is at least consistent with a
high b-turn propensity.34
A second interesting aspect of the pseudo
structures, albeit one that does not necessarily
reflect on their likely validity, is the behavior of
the Leu171 side-chain. In pseudo-DFG-in, Leu171
occupies the same position as that assumed by
Phe169 in DFG-out; in pseudo-DFG-out, Leu171
occupies the same position as that assumed by
Phe169 in DFG-in. It appears therefore that these
two hydrophobic side-chains exchange between
only two preferred locations. In effect, Leu171
seems to act as a kind of “counterweight” to
Phe169, and its tendency to swap places with
Phe169 may be important in stabilizing alternative
conformations of the DFG motif. For example, in
pseudo-DFG-out, the free energy penalty that is
Computational Sampling of p38 MAP Kinase
likely to be incurred when Phe169 is extracted from
the hydrophobic pocket, and becomes partially
exposed to solvent, is likely to be balanced to
some extent by the free energy reward of simultaneously burying Leu171 in the same hydrophobic
pocket. If this behavior does indeed result from
compensating hydrophobic interactions it may be
worth investigating whether it is dependent on the
inclusion of explicit water molecules in the MD
simulations.
Use of the same approach in a predictive setting
A final important issue to consider is whether the
approach adopted here could reasonably be used in
a truly predictive setting to find cryptic binding
sites in other protein receptors; after all, this study
was performed in full knowledge of (a) the
existence of a DFG-out conformation,3 and (b) the
fact that the extent of the conformational changes
outside the activation loop was known to be rather
limited. Clearly, a prerequisite for using the present
approach is the ability to identify the region(s) of
the receptor that might exhibit significant conformational plasticity. The issue of identifying conformationally adaptable regions in proteins is similar,5
but not identical, to the question of identifying
functionally important regions in proteins and
while the latter has been the focus of a number of
computational studies,35,36 only a subset of these
methods is likely to be useful in the present context.
Criteria that can signify regions where substantial
conformational rearrangements might occur
include (a) residues that are in energetically
unfavorable environments,37,38 (b) backbone dihedral angles that are in strained conformations,39 and
(c) residues for which the predicted secondary
structure falls with equal probability into two or
more of the three basic categories.40 Alternatively,
methods based on graph theory can be used to
predict the relative flexibility (or rigidity) of
elements of a protein’s structure.41 An interesting
application of some of these criteria to rationalize
the presence of a buried allosteric binding site in
b-lactamase has recently been reported.42
In the present case, a clear signal that the
conformational states accessible to the activation
loop would be worth investigating is that the
available crystallographic data indicate that it is
highly flexible,43,44 with the residues beyond the
DFG motif often being completely disordered.11
Although the DFG motif itself is usually better
resolved, a characteristic that indicates that it also
might assume alternative backbone conformations
is that while its backbone NH and C]O atoms
could potentially participate in a total of six
backbone hydrogen bonds, in the unliganded 1P38
crystal structure it actually participates in only one
(Gly170, via its NH group). Finally, a justification for
applying restraints to all other regions of the kinase,
which is necessary in high-temperature MD in
order to prevent unfolding, is to be found in the fact
that an overlay of several p38 crystal structures
211
shows little variation in the protein backbone
outside the activation loop (Figure 9). Now, whether
such a simple partitioning of a receptor’s structure
into flexible and restrained regions will be possible
with other proteins is difficult to say, and since it is
likely that there will be cases where it is not possible
there will clearly remain a need for developing
more generally applicable conformational sampling
methods.45,46
No matter what method of sampling is used,
there is still another issue that remains to be
resolved in a predictive setting: how to identify
those sampled conformations that are worth
pursuing for inhibitor design. Obviously a first
criterion to consider would be whether binding of a
small molecule to a sampled conformation would
be likely to inhibit the protein’s activity, either
directly, by occupying space normally occupied by
substrates or cofactors, or indirectly, by stabilizing a
conformation that is not competent to bind substrates or carry out catalysis. In the present case, in
which the non-DFG-in conformations that are
sampled during MD fall into three major clusters
(DFG-out, pseudo-DFG-in and pseudo-DFG-out),
all three conformations appear to fit the requirement of being inhibitory to binding.
A second criterion that might be considered is
whether a sampled conformation is likely to be
reasonably stable relative to other known conformations. One practical difficulty with this idea is
that we are unlikely to be able to sample
conformations completely enough to accurately
estimate their thermodynamics. It should be
recalled for example that the pseudoconformations
described here were each observed only once in ten
independent simulations (and that this contrasts
intriguingly with the fact that DFG-out conformations were sampled on four different occasions).
Moreover, even if we could sample sufficiently to
estimate relative stabilities, we may not be able to
rely on current simulation force fields to give
stabilities that are in the right order. A more
important conceptual difficulty, however, is that
even if it did prove possible to correctly rank
sampled conformations in order of their stability it
is not necessarily the case that the most stable
conformations will be those that are of most interest
for inhibitor design purposes. In fact, what are
really being sought are conformations likely to give
stable complexes with inhibitors, and since these
will usually be ones that form binding sites by
exposing hydrophobic groups they are actually
unlikely to be intrinsically the most stable conformations. There is therefore a fundamental difficulty
in identifying conformations competent to bind
inhibitors when the inhibitor is absent from the
simulations used to sample the conformations. One
interesting avenue for the future may be to examine
whether sampling of cryptic binding sites might be
improved by including model inhibitors in the
simulations that can be restrained near potential
sites, poised to exploit and stabilize bindingcompetent transient conformations.
212
Methods
The work reported here employed two common
computational methods: (a) high-temperature, explicitsolvent MD simulations were performed in an attempt
to extensively sample the conformational space of the
p38 kinase activation loop (including its DFG motif);
and (b) ligand docking calculations were conducted to
investigate the competence of the kinase conformations
generated during MD to bind a number of inhibitors
(for a review of kinase–inhibitor structures see Noble
et al.47).
Molecular dynamics simulations
The 1P3811 crystal structure of p38a MAP kinase in its
inactive, unliganded, DFG-in state was used as the starting
conformation for all simulations; this structure has the
advantage of containing a completely resolved activation
loop. To convert this murine p38 MAP kinase into the
human form for which the DFG-out conformation was
observed crystallographically,3 the two residues differing in
the two sequences, His48 and Ala263, were converted to
Leu and Thr, respectively, using the program WHAT IF;48
neither of these residues is close to the activation loop.
Ionizable residues were assigned standard protonation
states corresponding to pH 7; the N and C termini were
both charged, so the “humanized” protein had an overall
charge of K9e. To construct a solvated system, the
protein was immersed in an equilibrated 1 g/ml box of
SPC water molecules49 such that the boundaries of the
box extended 5 Å beyond the furthest edges of the
protein; the resulting system contained 9437 water
molecules. We note that this 5 Å distance to the box
boundary is shorter than typically used; it was chosen,
however, because it significantly reduced the computation time and, since most of the protein atoms were
subjected to position restraints (see below), it did not
result in artefactual cross-boundary collisions of the
protein with itself.
All dynamics simulations were performed with the
GROMACS suite of programs version 3.050,51 using the
OPLS all-atom force field.52,53 Non-covalent interactions
(van der Waals and short-range electrostatics) were
truncated at 12.5 Å; longer-range electrostatic interactions were computed using the particle-mesh Ewald
(PME) method54 with an interpolation order of 4. Prior
to MD, energy minimization of the system was
performed with 100 steps of steepest descent and 500
steps of conjugate gradient optimization. Ten independent but otherwise identical MD simulations were then
started.
High-temperature MD was used to accelerate conformational sampling. During a 1 ns equilibration period, each
system was brought to 1000 K in 50 K increments at 25 ps
intervals and then maintained at 1000 K for 525 ps; this
equilibration phase was not included in any subsequent
analysis. To prevent unfolding of the protein, harmonic
restraints with a force constant of 1000 kJ molK1 ÅK1 were
applied to all non-hydrogen protein atoms during this
initial period. At the end of the 1 ns period, all restraints
were removed from the residues of the activation loop plus
2–4 residues at either end (residues Leu164 through
Thr185), allowing completely free motion of the loop, and
MD of each independent system was continued at 1000 K
for a further 30 ns. For three of the ten simulations the
production phase of the MD was extended to 60 ns
(see Results); in total then, the conformational dynamics
Computational Sampling of p38 MAP Kinase
of p38’s activation loop were simulated for 390 ns.
Simulations were performed under constant volume
conditions with the temperature controlled using the
Nosé–Hoover method.55,56 All covalent bonds were constrained with the LINCS algorithm,57,58 thus allowing a
time step of 2 fs to be used. Structural snapshots from each
simulation were recorded every 1 ps. Each nanosecond of
simulation required approximately three days CPU time on
a single Pentium 4 (3.0 GHz) processor.
Inhibitor docking calculations
Five inhibitors that have been solved crystallographically
in complex with p38 were selected for study: a simple diaryl
urea inhibitor (PDB code 1KV1),3 BIRB796 (1KV2),3 a
pyridinylimidazole inhibitor (1A9U),12 a dihydroquinazolinone inhibitor (1M7Q),13 and a 4-azaindole inhibitor
(1OZ1).14 In order to test whether structures sampled
during the MD simulations were competent to bind any of
these inhibitors, docking calculations were performed for
each inhibitor with 5000 structural snapshots sampled at
10 ps intervals from the first 50 ns of one production MD
trajectory (simulation A; see Results). All docking calculations were performed using AutoDock version 3.0.10
AutoDock input files (coordinate files with torsions and
charges added) for each inhibitor were generated using the
PRODRG webserver,59 and three-dimensional interaction
grids of dimensions 27 Å!27 Å!27 Å and spacing 0.25 Å
were computed for each structural snapshot using the
AutoGrid10 utility. Partial charges were added to the protein
using the CHARMM 27 parameter set.60 For each snapshot
and for each inhibitor, ten independent docking runs were
then performed with AutoDock’s genetic algorithm. In each
run, a population size of 200 was used, the maximum
number of energy evaluations was set at 10,000,000, and the
maximum number of generations set at 27,000. To accelerate
the calculations, the AutoDock source code was modified so
that runs were terminated when the lowest energy of any
member of the population remained unchanged for three
consecutive generations. All inhibitors were treated as rigid
during docking. Flexible dockings of BIRB796 and the
dihydroquinazolinone inhibitor were also conducted on a
smaller subset of 500 snapshots but, since they produced
results that were not significantly better or worse than
those obtained with rigid inhibitor models, they were
not pursued further.
To quantify how closely inhibitors docked to their
crystallographic positions, it was first necessary to
establish reference positions; this was achieved by
superimposing each inhibitor-bound crystal structure
onto the 1P38 crystal structure using the Ca atoms of
residues outside the flexible activation loop. The Ca
RMSDs of each of the proteins in these superpositions
were as follows: 1KV1, 1.24 Å (using 322 Ca atoms in the
superposition); 1KV2, 1.40 Å (318 Ca atoms); 1A9U,
0.44 Å (329 Ca atoms); 1M7Q, 0.91 Å (326 Ca atoms);
and 1OZ1, 0.59 Å (322 Ca atoms).
Acknowledgements
This work was supported using start-up funds
from the University of Iowa and the Irene Wells
Medical Research Fund. T.F.K. is a Presidential
Graduate Fellow at the University of Iowa.
Computational Sampling of p38 MAP Kinase
Supplementary Data
Supplementary data associated with this article
can be found, in the online version, at doi:10.1016/
j.jmb.2006.03.021
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Edited by B. Honig
(Received 10 June 2005; received in revised form 12 December 2005; accepted 9 March 2006)
Available online 29 March 2006