Ensemble-Based Computational Approach Discriminates
Functional Activity of p53 Cancer and Rescue Mutants
O¨zlem Demir1, Roberta Baronio2,3, Faezeh Salehi3,4, Christopher D. Wassman3,4, Linda Hall2, G. Wesley
Hatfield3, Richard Chamberlin1,5, Peter Kaiser2,3, Richard H. Lathrop3,4, Rommie E. Amaro1,3,4,5*
1Department of Pharmaceutical Sciences, University of California, Irvine, California, United States of America, 2Department of Biological Chemistry, University of
California, Irvine, California, United States of America, 3Institute for Genomics and Bioinformatics, University of California, Irvine, California, United States of America,
4Department of Computer Science, University of California, Irvine, California, United States of America, 5Department of Chemistry, University of California, Irvine,
California, United States of America
The tumor suppressor protein p53 can lose its function upon single-point missense mutations in the core DNA-binding
domain (‘‘cancer mutants’’). Activity can be restored by second-site suppressor mutations (‘‘rescue mutants’’). This paper
relates the functional activity of p53 cancer and rescue mutants to their overall molecular dynamics (MD), without focusing
on local structural details. A novel global measure of protein flexibility for the p53 core DNA-binding domain, the number of
clusters at a certain RMSD cutoff, was computed by clustering over 0.7 ms of explicitly solvated all-atom MD simulations. For
wild-type p53 and a sample of p53 cancer or rescue mutants, the number of clusters was a good predictor of in vivo p53
functional activity in cell-based assays. This number-of-clusters (NOC) metric was strongly correlated (r2=0.77) with
reported values of experimentally measured DDG protein thermodynamic stability. Interpreting the number of clusters as a
measure of protein flexibility: (i) p53 cancer mutants were more flexible than wild-type protein, (ii) second-site rescue
mutations decreased the flexibility of cancer mutants, and (iii) negative controls of non-rescue second-site mutants did not.
This new method reflects the overall stability of the p53 core domain and can discriminate which second-site mutations
restore activity to p53 cancer mutants.
Citation: Demir O¨, Baronio R, Salehi F, Wassman CD, Hall L, et al. (2011) Ensemble-Based Computational Approach Discriminates Functional Activity of p53
Cancer and Rescue Mutants. PLoS Comput Biol 7(10): e1002238. doi:10.1371/journal.pcbi.1002238
Editor: Michael Gilson, University of California San Diego, United States of America
Received May 31, 2011; Accepted September 5, 2011; Published October 20, 2011
Copyright: ? 2011 Demir et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was funded in part by the National Institutes of Health through the NIH Director’s New Innovator Award Program 1-DP2-OD007237 and
through the NSF TeraGrid Supercomputer resources grant LRAC CHE060073N to R.E.A.; and NIH Natl. Cancer Institute BISTI grant CA-112560 to PK and RHL. The
funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: firstname.lastname@example.org
The tumor suppressor protein p53 is a transcription factor that
plays a major role in preventing cancer initiation and progression.
Cellular stress conditions such as hypoxia or DNA damage activate
p53, which induces cell cycle arrest, DNA repair, senescence, or
apoptosis [1,2,3]. In most, if not all, human cancers, the p53
apoptosis pathway is inactivated, and p53 itself is mutated in about
half of all human cancers. About three-quarters of tumors with
mutant p53 express full-length p53 with single missense mutations
in the p53 DNA-binding core domain. These mutations may cause
partial or global protein destabilization, loss of zinc coordination, or
disruption of DNA contacts, and thus inactivate the tumor
suppressor function of p53 (www-p53.iarc.fr) . These missense
mutations (‘‘cancer mutations’’ or ‘‘oncogenic mutations’’) are
widely distributed throughout the core domain (Figure 1). They
have been classified based on their physical location within the
protein: (i) DNA-contact mutants (e.g., R248Q, R273H), (ii)
structural mutants in the DNA binding surface (e.g., R175H,
G245S, R249S, R282W), (iii) b-sandwich mutants (e.g., Y220C),
and (iv) zinc-binding domain mutants (e.g., C242S, R175H).
Pharmacological rescue of p53 function in cancer tissues is an
attractive therapeutic target . Recently, two independent studies
on transgenic mice demonstrated that restoration of p53 activity
enables tumor regression in vivo [6,7]. p53 reactivation is especially
promising in regression of advanced stage cancers [8,9]. The p53
function of some oncogenic mutants has been rescued in vivo by a
handful ofsmallmolecules [10,11,12,13,14]aswellasbysecond-site
suppressor (‘‘cancer rescue’’) mutations [15,16,17,18]. The second-
site mutations provide easily-studied cases of p53 cancer rescue.
The effect of oncogenic and rescue mutations in p53 has been of
great interest. Many detailed structural studies have been pursued,
including X-ray crystal structures of individual oncogenic and
rescue mutants of p53 [19,20,21]. The loss or gain of hydrogen-
bonding interactions, salt bridges and other minute stabilizing or
destabilizing effects upon different missense mutations have been
investigated to develop a more complete understanding of the
inactivation mechanisms by the oncogenic missense mutations
and, correspondingly, the mechanisms by which restoration of
activity for rescue mutations occur [22,23]. At 310 K, wild-type
p53 is estimated to be only 3.0 kcal/mol more stable than the
denatured state , and thus missense mutations can easily shift
the delicate balance of p53 stability.
The present study quantifies the effect of oncogenic and rescue
mutations on the overall dynamics of p53 without focusing on
local structural details. The core DNA-binding domain of p53 was
PLoS Computational Biology | www.ploscompbiol.org1October 2011 | Volume 7 | Issue 10 | e1002238
used, as it dictates the stability of the overall protein . The
overall protein flexibility of the p53 DNA-binding domain for the
wild-type, cancer mutants, rescue mutants and non-rescue mutants
was compared in explicitly-solvated all-atom molecular dynamics
(MD) trajectories, which are well suited to investigate the local
conformational space sampled by each particular mutant. A single
discriminating metric, the measure of flexibility of p53 in terms of
the number of clusters obtained at a certain RMSD cutoff, was
able to predict the functional activity of various mutant p53
The coordinates for the starting structure were obtained from
the wild-type p53 coordinates of chain B in pdbID 1TSR .
Each mutant system was prepared from this structure by
rebuilding the mutated side chain(s) with the AMBER suite .
Crystallographic waters were retained. Histidine, asparagine and
glutamine side chains which were mis-fit during structure
characterization were determined and flipped by 180u using the
Molprobity web server . Histidine protonation states were
determined using the Whatif Web Interface  and manually
verified. Zinc coordination residues (Cys176, Cys238, Cys242 and
His179) were modeled following the cationic dummy atom
method of Pang et al . Missing atoms and hydrogens were
added using the Leap module of Amber10 . Each system was
solvated in a TIP3P  water box. The buffer between the
protein and the periodic boundary was not closer than 8 A˚in any
direction. The wild-type p53 system has a charge of +1. Chloride
ions were added as needed to neutralize the different mutant
systems studied. The topology and coordinate files of the systems
were constructed using Amber FF99SB force field . The final
wild-type p53 system consisted of 27,264 atoms.
Each system was first relaxed by 36,000 steps of minimization
and a standard relaxation procedure using restrained MD. In the
first 2,000 steps of minimization only the hydrogen atoms were
relaxed, leaving all other atoms fixed. In the second 2,000 steps, all
water atoms and ions were minimized in addition to the hydrogen
atoms. In the third 2,000 steps, zinc-coordinating residues Cys176,
Cys238, Cys242 and His179 as well as all hydrogens, water atoms,
Figure 1. p53 DNA-binding core domain. A) p53 DNA-binding domain mutations studied in this work. The zinc ion, destabilizing cancer
mutations and stabilizing rescue mutations focused are depicted in purple, blue and red spheres, respectively. B) Different types of mutations in the
p53 DNA-binding core domain. b-sheet residues, zinc-binding residues and DNA contact residues are depicted in purple, yellow and green,
respectively. The zinc ion is depicted as an orange sphere.
p53 is a tumor suppressor protein that controls a central
apoptotic pathway (programmed cell death). Thus, it is the
most-mutated gene in human cancers. Due to the
marginal stability of p53, a single mutation can abolish
p53 function (‘‘cancer mutants’’), while a second mutation
(or several) can restore it (‘‘rescue mutants’’). Restoring p53
function is a promising therapeutic goal that has been
strongly supported by recent experimental results on
mice. Understanding of the effects of p53 cancer and
rescue mutations would be helpful for designing drugs
that are able to achieve the same goal. The challenge is
that cancer and rescue mutations are distributed widely in
the protein, and experimental testing of all possible
combinations of mutations is not feasible. This paper
describes a simple computational metric that reflects the
overall stability of the p53 core domain and can
discriminate which second-site mutations restore activity
to p53 cancer mutants.
Discrimination of p53 Functional Activity
PLoS Computational Biology | www.ploscompbiol.org2 October 2011 | Volume 7 | Issue 10 | e1002238
and ions were minimized. In the following 10,000 steps, all atoms
were minimized except backbone atoms, which were held fixed. In
the last 20,000 steps, the entire system was minimized. Following
the minimizations, restrained MD simulation at 310 K was carried
out for 1 nanosecond to prevent structural artifacts from
introducing kinetic energy into the system. For this purpose,
positional restraints for the heavy atoms of the protein backbone
were gradually decreased from 4.0 to 1.0 kcal/(mol * A˚2) in four
consecutive 250-picosecond-long MD simulations.
Thereafter, unrestrained MD was performed in explicit solvent
for 30 nanoseconds at 310 K using a time step of 1 femtosecond.
Temperature was maintained constant at 310 K by Langevin
dynamics with a collision frequency of 5 ps21, and pressure was
maintained at 1 atm by the Nose Hoover-Langevin piston method
[30,31] using period and decay times of 100 and 50 femtoseconds,
respectively. Long-range electrostatics was treated by the Particle
Mesh Ewald method  and a nonbonded cutoff of 10 A˚was
used. The interatomic distances within the water molecules were
fixed using the SHAKE algorithm [33,34]. A multiple-time step
algorithm was employed, in which bonded interactions were
computed at every time step, short-range non-bonded interactions
were computed at every second time step, and full electrostatics
was computed at every fourth time step.
All minimizations and MD simulations were performed using
NAMD2.7  on the Teragrid Ranger cluster. The simulations
scaled as 0.10 days per nanosecond using 64 processors. Root-
mean-square-deviation (RMSD) traces over the course of the MD
trajectories are depicted in Figure S1.
We considered all four structural classes of p53 mutants: (i)
DNA-contact mutants, (ii) structural mutants in the DNA binding
surface, (iii) b-sandwich mutants, and (iv) zinc-binding domain
mutants. We did not attempt to characterize any direct zinc-
binding residue mutations (e.g., C242S), as rigorous parameter-
ization of the partial charges on the metal ion and coordinating
groups would be required for proper treatment of any mutations in
Mutants simulated included the wild-type p53, the six most-
frequent cancer mutants (R175H, G245S, R248Q, R249S,
R273H, R282W), cancer mutant Y220C for which some
stabilization (although not enough to restore p53 activity) was
achieved recently with a small-molecule filling the location of the
mutated tyrosine side chain , four rescue mutants and three
non-rescue mutants for the R273H cancer mutant [17,18], two
rescue mutants and one non-rescue mutant for the G245S cancer
mutant (G245S_N239Y, G245S_T123P and G245S_E286D)
[18,37], two rescue mutants and one non-rescue mutant for
the Y220C cancer mutant (Y220C_A138G, Y220C_L137R
and Y220C_L114G),the superstable
M133L_V203A_N239Y_N268D , and stabilizing mutant
Conformational clustering was performed using the gromos
algorithm  with GROMACS4.0.5 analysis software . For
each of the mutants, atomic coordinates were extracted at 10 ps
intervals over the 30 ns MD simulation. The resulting 3000
structures were superimposed with respect to all Ca atoms to
remove overall translation and rotation, then clustered at various
RMSD cutoff values (i.e., 0.95, 1.05, and 1.15 A˚) based on atomic
coordinates of all Caatoms of the protein. After calculating an
RMSD-distance matrix of atomic positions between all pairs of
MD snapshots in a trajectory, the gromos clustering algorithm
counts the number of similar MD snapshots for which the
calculated RMSD is less than or equal to the determined RMSD
cutoff for each MD snapshot. The MD snapshot with the highest
number of neighbors (e.g. the structure with the smallest RMSD
between all the other structures in the cluster) is determined to be
the center of the first cluster. Thus this structure is also referred to
as the ‘‘cluster centroid.’’ Subsequently, this entire cluster (i.e. the
cluster centroid and its neighbors) is eliminated from the pool of
MD snapshots, and the same process is repeated until all MD
snapshots are assigned to a cluster.
As another potential flexibility metric, root-mean-square-
fluctuation of all Ca atoms of p53 in the trajectories were
calculated using AMBER suite.
Two alternative clustering methods available in GROMACS
package, namely single-linkage clustering and Jarvis-Patrick
clustering, were also performed for comparison. A cutoff of
0.65 A˚was used for single-linkage clustering. In Jarvis-Patrick
clustering, the RMSD cutoff used to determine the number of
nearest neighbors considered for Jarvis-Patrick algorithm was set
to 0.80 A˚, and the snaphots that have at least 3 identical nearest
neighbors were assigned to the same cluster.
Functional activities of rescue and non-rescue mutants for which
no published experimental p53 activity result exists (R273H_N239S,
R273H_R282S, R273H_L114G, G245S_E286D, Y220C_A138G,
Y220C_L137R and Y220C_L114G) were verified using yeast assays
(Figure 2). Wild-type p53 and relevant cancer mutants R273H and
Y220C were also included in the assays as controls. For this purpose,
p53-tester yeast strain RBy379 (1cUASp53::URA3 his3D200 a/
alpha) [17,41] expressing the URA3 gene under control of a p53-
dependent promoter was transformed with centromeric pTW300
plasmids  (HIS3 selection marker) expressing either wild-type
human p53 or the mutants indicated under control of the ADH1
promoter. Yeast strains were grown in YEPD (10% yeast extract,
20% pepton, 20% dextrose) and transformed with the relevant
plasmids using a LiAc-based transformation protocol . Trans-
formants were selected on SC plates lacking histidine and incubated
at 30uC. Serial dilutions of mid-log phase cells (10,000; 2,000; and
400 cells) were spotted onto agar plates lacking either histidine or
growth on plates lacking uracil is dependent on expression of the
URA3 gene and is a measure of p53 activity .
In order to compare the dynamical effects of different mutations
on p53, MD-generated trajectories of various p53 mutants were
clustered based on overall structural similarity. Explicitly solvated
MD simulations were run in the isothermal-isobaric (NPT)
ensemble for 30 ns, after which RMSD-based clustering was
performed on the resulting trajectories with the gromos clustering
algorithm . The RMSD distance matrix was computed in a
pairwise fashion over all of the alpha carbons for each structure
extracted every 10 ps from a particular trajectory (i.e., 3000
structures representing each trajectory). A large range of RMSD
cutoff values were tested, including 0.95, 1.05, 1.15, 1.25, 1.35 and
1.60 A˚. RMSD cutoffs larger than 1.15 A˚caused loss of sensitivity
of NOCs to the effect p53 mutations. The low optimal RMSD
cutoff is an indication of a well-behaved system sampling
configurations within a single energetically low-lying substate, as
well as a reflection of the small size and low flexibility of the p53
core domain. The NOCs observed for p53 wild-type and its
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various mutants using a cutoff of 1.15 A˚are shown in Tables 1
through 3. Clustering results at several other RMSD cutoff values
are presented in Table S1.
Cancer mutants are more flexible than wild-type p53
The number of clusters was significantly higher for the cancer
mutants (in bold in Table 1) compared to the wild-type p53, which
suggests that oncogenic mutations increase the overall plasticity of
the p53 core domain. This result is consistent with Rauf et al. ,
who investigated the effects of different oncogenic mutations on
the flexibility of the p53 DNA-binding domain using a graph
theoretical approach. Here, the oncogenic property for all four
structural classes of p53 mutants has been quantified by a single
The flexibility of the structural and zinc-binding domain mutant
R175H, as characterized by the number of clusters, was
remarkably higher compared to the wild-type and other cancer
mutants (Table 1). The especially high degree of flexibility
exhibited by this system may explain why so far it has not been
possible to rescue the R175H mutant with second-site suppressor
mutations, even though all possible single point core domain
mutations of R175H were tested exhaustively for p53 function
The number-of-clusters (NOC) metric presented here correctly
locates the DNA-contact mutant R273H as the closest cancer
mutant to the wild-type in terms of structural variability over the
30 ns trajectories. R273H has been previously demonstrated to be
the easiest to rescue among the most-frequent cancer mutants
. The thermodynamic stability of the R273H mutant is the
closest to the wild-type p53 among the 19 cancer mutants
considered by Bullock et al . The number of rescue mutants
Figure 2. Biological validation of p53 cancer rescue mutations.
A p53 tester yeast strain expressing the URA3 gene under control of a
p53-dependent promoter was transformed with centromeric plasmids
(HIS3 selection marker) expressing either wild-type human p53, or the
mutants R273H, R273H_N239S, R273H_R282S, R272H_L114G,
G245S_E286D, Y220C, Y220C_A138G, Y220C_L137R, and Y220C_L114G.
Serial dilutions cells (10,000; 2,000; and 400) grown to mid-log phase
were spotted onto agar plates lacking either histidine or uracil. Plates
were incubated for 2 days at 37uC. Growth on plates lacking uracil is
dependent on expression of the URA3 gene and is a measure of p53
activity, while the growth on plates lacking histidine is selective for the
presence of the plasmid.
Table 1. Number of clusters (NOC) at 1.15 A˚RMSD cutoff for
the MD simulations and experimental thermodynamic
stability data for wild-type p53 and its cancer mutants.
R273HI 27 0.4560.03a
R249SI 30 1.9260.04a
Y220CI 32 3.9860.06a
R282WI 36 3.3060.10a
R248QI 40 1.8760.09a
R175HI 42 3.5260.06a
aRef 23. Cancer mutants are typed in bold letters. A: active, I: inactive.
Table 2. Number of clusters (NOC) at 1.15 A˚RMSD cutoff for
the MD simulations of wild-type p53 and its functional and
R273H_N263V R273HA 19
R273H_N200Q_D208T R273HA 19
R273H R273HI 27
R273H_R282S R273HI 31
Cancer mutants are typed in bold letters, rescue mutants are italicized, and non-
rescue mutants are underlined. Wild-type p53 is presented as the first line of
table. Cancer mutants and their relevant second-site mutants are grouped
according to their relevant cancer mutants and sorted in ascending number of
clusters in each group. A: active, I: inactive. The thermodynamic stability of
G245S_N239Y mutant is given in Ref 36 to be 20.14 kcal/mol.
Discrimination of p53 Functional Activity
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known to reactivate R273H mutant is large compared to few or no
rescue mutants known to reactivate each of the other hot spot
cancer mutants [17,18,37,38].
Rescue mutations decrease the flexibility of cancer
Comparison of the number of clusters for the R273H rescue
mutants (in italics in Table 2) with those for the R273H cancer
mutant (in bold in Table 2) indicated a significant decrease
in flexibility for the rescue mutants. The restoration of stability
to the protein was especially remarkable in the case of the
R273H_N263V and R273H_N200Q_D208T rescue mutants, for
which the number of clusters was even lower than the wild-type
p53. Although thermodynamic stability data is not available for
these rescue mutations, our results suggest that such values would
be lower than wild-type p53.
As there are no single point mutations that can strongly rescue
cancer mutants R175H, R248Q, R249S or R282W, the generality
of this finding was tested on rescue mutants for which
experimental data is available (in italics in Table 2). More
specifically, the known rescue mutants G245S_N239Y and
G245S_T123P were considered for the class of structural mutants
in the DNA binding surface. Similarly, two rescue mutants for the
b-sandwich mutant Y220C, Y220C_A138G and Y220C_L137R,
were investigated with the same approach. Functional activity of
the latter two rescue mutants, for which no published experimental
p53 activity results exist, was verified using yeast assays and
depicted in Figure 2. Three out of these four rescue mutants
showed decreased flexibility compared to their relevant cancer
mutant (Table 2). The only exception in this test set was
G245S_T123P, which exhibited more clusters than its cancer
Nonrescue mutations introduce even more flexibility to
As negative controls, we tested experimentally confirmed non-
rescue second-site mutations (underlined in Table 2) of relevant
cancer mutants with the same approach. Functional inactivity of
all the nonrescue mutants was verified using yeast assays and
depicted in Figure 2. All non-rescue mutants that we simulated
were more flexible, as compared to their relevant cancer mutant,
indicating destabilization introduced to the cancer mutant by these
second-site mutations. Thus, our method can successfully
discriminate rescue mutants from non-rescue mutants.
More stable p53 mutants follow a similar trend
We extended the same analysis on stabilizing mutant N239Y
and the ‘‘superstable’’ quadruple mutant M133L_V203A_
N239Y_N268D  (Table 3). The N239Y mutant exhibited a
significant decrease in flexibility compared to the wild-type. In
contrast, the superstable mutant did not follow the same trend
(Table 3). This may be due to the need for longer relaxation in
MD simulations in order to account for the greater extent of
structural change introduced by four point mutations. To explore
this hypothesis, we extended the quadruple mutant MD simulation
for an additional 30 ns (for a total of 60 ns of production
dynamics). In the second 30 ns, its number of clusters decreased
significantly to a value much lower than that of the wild-type p53
At least 30 ns MD simulation is required
The data presented in tables 1–3 relies on the full 30 ns
trajectories of p53. In order to determine what is the shortest MD
simulation necessary to discriminate between the functional and
nonfunctional forms of p53 mutants, shorter segments of the full
production MD trajectories were analyzed. At the RMSD cutoff of
1.15 A˚, the number of clusters for each p53 mutant calculated at
5, 10, 20, 25 and 30 ns of MD simulations were separately
depicted as column graphs in Figure S2. In this set of graphs,
active and inactive p53 mutants were grouped and designated with
a green arrow and a red arrow, respectively. In Figure 3, the
percentage of mutants for which p53 function was correctly
predicted by our flexibility metric are depicted for 5, 10, 20, 25
and 30 ns of MD simulations. The success of function prediction
increased from 74% to 91% while our simulation time increased
from 5 ns to 30 ns. This analysis indicated that at least 30 ns of
MD simulation is required for a successful prediction of function of
NOC metric correlates with experimental thermodynamic
The thermodynamic stability values of several p53 cancer
mutants are available in the literature (Table 1), as measured by
urea-induced unfolding experiments at 283 K [23,37,38]. There
are no comparable experimental data for the rescue or non-rescue
mutants, which were not included in this part of the analysis. All
cancer mutants evaluated experimentally exhibit differential
experimental destabilization compared to wild-type p53 (Table 1).
Figure 4 depicts the correlation between the available
thermodynamic stability values of p53 cancer mutants and the
number of clusters observed in the MD simulations at the RMSD
cutoff of 1.15 A˚(r2=0.77). The r2values for p53 single mutants at
RMSD cutoff values of 0.95 A˚and 1.05 A˚are both 0.74. The
number of clusters at these cutoffs for each mutant is tabulated in
Table S1. The number of clusters in the second 30 ns MD
simulation of the superstable mutant was used for this correlation
analysis. If the NOCs in the initial 30 ns MD simulation of the
superstable mutants was used instead, the r2value decreased from
Table 3. Number of clusters (NOC) at 1.15 A˚RMSD cutoff for the MD simulations and experimental thermodynamic stability data
for wild-type p53 and its more-stabilized forms.
NOC Thermodynamic stability (kcal/mol)
M133L_V203A_N239Y_N268D (first 30 ns of MD simulation) 25
M133L_V203A_N239Y_N268D (second 30 ns of MD simulation)9
bRef 37. Both mutants are functionally active.
Discrimination of p53 Functional Activity
PLoS Computational Biology | www.ploscompbiol.org5 October 2011 | Volume 7 | Issue 10 | e1002238
0.77 to 0.55. If the average of the two was used, the r2value
became 0.70. Excluding the superstable mutant form the data set
gave an r2value of 0.66. Remarkably, the number of clusters
metric alone explains about three-quarters of the variance in
experimentally measured thermodynamic stability values of p53
To compare the NOC metric with another simple flexibility
metric, the root-mean-square-fluctuation (RMSF) values of all Ca
atoms of p53 were calculated using the AMBER suite for each
mutant (Table S2). The correlation of the RMSF values with the
thermodynamic data gave an r2value of 0.62, which showed the
superiority of the NOC method comparing to its r2value of 0.77.
To compare the performance of other clustering methods, single-
linkage clustering and Jarvis-Patrick clustering were performed on
the MD trajectories (Table S3). Both methods resulted in a lower
correlation with the thermodynamic stability values versus the
RMSD-based clustering method, with r2values of 0.44 and 0.45,
p53 is an inherently unstable protein, as reflected by its low
melting temperature of ,42–44uC . It has been shown that
the main reason of p53 instability is neither poor packing density
nor the presence of unusually large void volumes in the protein.
Instead, an analysis of the solution structure of p53 core domain
obtained by NMR has revealed several reasons for instability of
p53 . First, this study indicated that p53 has buried hydroxyl
and sulfhydryl groups that form sub-optimal hydrogen-bonding
networks. Second, high flexibility of loop regions, especially of L1
loop, is observed in p53. Lastly, some buried tyrosine residues were
found to be involved in temperature-dependent dynamic processes
possibly indicating presence of alternative hydrogen-bonding
networks in p53. Based on all of these factors, Canadillas et al
concluded that ‘‘the p53 structure is more flexible than is apparent
from crystal structures’’ .
In an effort to capture this intrinsic structural flexibility, we have
focused on finding a computational method to measure the overall
flexibility of the p53 core domain and the effect of mutations, be
they cancer mutations, rescue mutations or non-rescue mutations,
on the flexibility. This work presents a new method in which the
number of structural clusters representing an explicitly solvated all-
atom MD trajectory can be used as a single robust measure of
overall flexibility in the p53 core domain. All hot-spot cancer
mutants we studied demonstrated higher flexibility compared to
the wild-type p53, in line with the results of an earlier graph-
theoretical approach that assessed the flexibilities of wild-type p53
and several cancer mutants . Testing rescue and non-rescue
mutants for particular cancer mutants, the number of clusters for
functional p53 mutants was found to differ significantly from the
nonfunctional p53 mutants. Remarkably, the NOC metric is able
to predict which second-site mutations may restore p53 activity to
cancer mutants and which will leave p53 functionally defective.
It is also notable that such a simple metric reflecting system
flexibility or entropy can account for three-quarters of the variance
in experimentally measured thermodynamic stability values of p53
mutants. MD simulations thus promise to be a robust tool to
predict thermodynamic stability of p53 mutants of interest. The
NOC metric could further be used to discover new rescue mutants
that restore p53 activity, and thus kill the cancer cell. Additionally,
whether binding of a small-molecule can achieve enough
stabilization to restore p53 function to cancer mutants could be
tested with this metric. The computational cost of performing
classical MD simulations could be decreased by using alternative
methods such as accelerated MD, which may achieve increased
sampling of conformational states over significantly shorter
simulation timescales. Experimental efficiencies could be achieved
through an integrated strategy that is guided by use of the NOC
metric as a predictive measure for p53 function.
atoms of p53 wild-type and mutant systems during
Root mean square deviations (RMSD) of Ca
Figure 3. Time evolution of the predictive ability of flexibility
metric during molecular dynamics (MD) simulations. Shorter
segments of MD trajectories were analyzed in order to find out the
predictive ability of the flexibility metric in terms of discriminating
between the functional and nonfunctional forms of p53 mutants. The
percentage of succesful predictions using the number of clusters metric
for the p53 mutants calculated at each of 5, 10, 20, 25 and 30 ns of MD
simulations are plotted in the form of a column graph. The green and
red column bars at each MD segment represents the percentage of
successful and unsuccessful predictions, respectively. The exact
percentage values are printed in each column bar.
Figure 4. The correlation of the number of clusters and the
thermodynamic stability for p53 mutants. The number of clusters
is computed at RMSD cutoff of 1.15 A˚. The predicted linear regression
line is also shown (y=4.376+22.79). r2value is 0.77.
Discrimination of p53 Functional Activity
PLoS Computational Biology | www.ploscompbiol.org6 October 2011 | Volume 7 | Issue 10 | e1002238
during molecular dynamics simulations. Shorter segments
of MD trajectories were analyzed in order to find out what is the
shortest MD simulation necessary to discriminate between the
functional and nonfunctional forms of p53 mutants. Number of
clusters for the p53 mutants calculated at each of 5, 10, 20, 25 and
30 ns of MD simulations are graphed separately in the form of
column graphs. Functionally active p53 mutants are grouped and
designated with a green arrow while nonfunctional p53 mutants
were designated with a red arrow. The clustering results at RMSD
cutoff of 1.15 A˚indicates that at least 30 ns of MD simulation is
Time evolution of the number of clusters
cutoffs for p53 mutants.
The number of clusters at different RMSD
mutants in MD trajectories.
The root-mean-square-fluctuations for p53
MD trajectories using single-linkage algorithm and
The number of clusters for p53 mutants in
Simulations were run at the Texas Advanced Computing Center. We also
thank Lane Votapka, who wrote a script to automate the clustering
Conceived and designed the experiments: O¨D RB CDW PK RHL REA.
Performed the experiments: O¨D RB LH. Analyzed the data: O¨D RB FS
CDW PK RHL REA. Contributed reagents/materials/analysis tools: O¨D
RB FS CDW LH GWH RC PK RHL REA. Wrote the paper: O¨D PK
RHL REA. Contributed to scientific discussions: O¨D RB FS CDW LH
GWH RC PK RHL REA. Critically reviewed the paper: O¨D RB FS
CDW LH GWH RC PK RHL REA.
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