A Computational Approach to Finding Novel Targets for
Existing Drugs
Yvonne Y. Li*, Jianghong An, Steven J. M. Jones
Canada’s Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, British Columbia, Canada
Abstract
Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a
computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against
protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes
removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and
specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as
4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through
stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8
as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to
20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor in vitro (IC50 40 nM),
suggesting a potential use for this drug in treating inflammatory diseases. The published literature indicated experimental
evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions
discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target’s associated disease, added
insight into the drug’s mechanism of action, and added insight into the drug’s side effects.
Citation: Li YY, An J, Jones SJM (2011) A Computational Approach to Finding Novel Targets for Existing Drugs. PLoS Comput Biol 7(9): e1002139. doi:10.1371/
journal.pcbi.1002139
Editor: Philip E. Bourne, University of California San Diego, United States of America
Received April 11, 2011; Accepted June 14, 2011; Published September 1, 2011
Copyright: ß 2011 Li 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: SJMJ was supported by MSFHR (http://www.msfhr.org/) as a scholar. YYL was supported by NSERC (http://www.nserc-crsng.gc.ca/index_eng.asp). 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: yli@bcgsc.ca
two diseases [3]. In order to rationally reposition drugs, novel
target-disease or drug-target relationships must first be elucidated.
By screening compounds against a panel of proteins, there is
potential to discover novel drug-target interactions. Drug candidates are routinely screened against a small panel of similar proteins
to determine their specificity to the intended target. Large panels
with hundreds of kinase proteins have been developed to assess
kinase inhibitor specificity [5], especially since we now know that
many kinase drugs are multi-targeting. However, the druggable
proteome is much larger than just the kinome, so larger and more
varied protein panels are needed to truly assess drug specificity.
With the availability of massively parallel DNA sequencing
technology, recurrently mutated proteins in diseases – such as
EZH2 in certain lymphomas [6] and FOXL2 in certain ovarian
cancers [7] - are now being rapidly determined and are also relevant
drug targets. However, testing all drugs against all targets
experimentally is extremely costly and technically infeasible.
Recent computational endeavors to predict novel drug
repositioning candidates have used methods incorporating protein
structural similarity [8], chemical similarity [9], or side effect
similarity [10]. One study also incorporated some molecular
docking to help filter interactions predicted through protein
binding site similarity [8]. Here we present a large-scale molecular
docking analysis of known drugs against known protein targets for
the prediction of novel drug-target interactions. Molecular docking
is a computational method that predicts how two molecules
interact with each other in 3-dimensional space. It is well
Introduction
The continuing decline of drug discovery productivity has been
documented by many studies. In 2006, only 22 new molecular
entities were approved by the Food and Drug Administration
(FDA) despite research and development expenditures of $93
billion USD by biotech companies and large pharmaceutical
companies, and this low productivity has not improved since [1].
From discovering, developing to bringing one new drug to market,
clinical trials are the most expensive step, accounting for 63% of
the overall cost [2]. To this end, drug repositioning - finding new
therapeutic indications for existing drugs - represents an efficient
parallel approach to drug discovery, as existing drugs already have
extensive clinical history and toxicology information.
Many of today’s repositioned drugs were discovered through
serendipitous observations, including high profile drugs sildenafil
by Pfizer - first developed for angina but later approved for erectile
dysfunction - and thalidomide by Celgene - first marketed for
morning sickness, then approved for leprosy and recently for
multiple myeloma [3]. Repositioned drugs have also been
discovered through rational observations, including imatinib
(Gleevec), which was first approved for chronic myeloid leukemia
by targeting the BCR-Abl fusion protein but was subsequently
approved for gastrointestinal stromal tumor due to its ability to
potently inhibit c-KIT [4]. Another example is the anti-depressant
duloxetine (Cymbalta) that is also indicated for stress urinary
incontinence based on a shared mechanism of action between the
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we collected all 3D structures available for each drug target,
determined binding pockets in the structures, and docked drugs to
each pocket. Results were collected and thresholds were applied to
select the top predicted interactions, which were then visually
inspected.
Author Summary
Most drugs are designed to bind to and inhibit the
function of a disease target protein. However, drugs are
often able to bind to ‘off-target’ proteins due to similarities
in the protein binding sites. If an off-target is known to be
involved in another disease, then the drug has potential to
treat the second disease. This repositioning strategy is an
alternate and efficient approach to drug discovery, as the
clinical and toxicity histories of existing drugs can greatly
reduce drug development cost and time. We present here
a large-scale computational approach that simulates threedimensional binding between existing drugs and target
proteins to predict novel drug-target interactions. Our
method focuses on removing false predictions, using
annotated ‘known’ interactions, scoring and ranking
thresholds. 31 of our top novel drug-target predictions
were validated through literature search, and demonstrated the utility of our method. We were also able to identify
the cancer drug nilotinib as a potent inhibitor of MAPK14,
a target in inflammatory diseases, which suggests a
potential use for the drug in treating rheumatoid arthritis.
Known interactions docking
We first docked 3570 known protein-drug interactions annotated by DrugBank, between 678 unique human proteins and
1309 small molecule drugs. We used the docking software ICM
developed by Molsoft [13], which ranks ligands using a MonteCarlo based docking procedure and an empirical, energetics-based
docking score. Like most docking software, ICM recommends a
standard score cut-off for virtual screening efforts: 232 [14],
where more negative scores represent more likely binding
interactions. However, studies have used different cut-offs
depending on the protein target [15]. Here we used a score of
230 as the threshold for ‘good’ dockings scores. Of the 3570
known interactions docked, 1116 (31%) had a good ICM docking
score. 252 proteins had at least one known interaction predicted
by docking – these formed the ‘reliable’ set of proteins that we
believe are more suited for docking purposes. A breakdown of
protein classifications for this reliable set revealed that 67% of
targets were enzymes, of which 12% were protein kinases. In
contrast, there were few G-protein coupled receptors in our
database due to lack of crystal structures, which reflects both the
current state of solved protein crystal structure space as well as
popular drug targets.
established as a virtual screening method in drug discovery [11],
where typically many chemicals are docked against a specific
protein binding site, in order to discover novel inhibitors of that
target. Compared to similarity analyses, docking has the potential
to find drugs that bind to proteins with novel scaffolds as well as
off-targets that may be structurally dissimilar to the known targets.
Large-scale docking of many targets to many drugs is now
feasible when run on powerful computer clusters. However,
limitations in scoring methods result in high false positive
prediction rates [12], and large-scale studies amplify these low
prediction accuracies. Our method emphasizes removing false
positive predictions using scoring and ranking thresholds, and
retaining only the highest confidence interactions as drug
repositioning candidates.
Known interactions docking evaluation
In high-throughput molecular docking, it is common to hold
protein structures rigid during the simulation. With this restriction,
re-docking a PDB ligand back to its native PDB structure (cognate
docking) is a simpler task than docking a different ligand to the
structure (non-cognate docking) because in the former case the
protein is already in a specific ligand-bound conformation.
Cognate-docking situations occur frequently and previous studies
show that they can be docked well in 60–80% of cases [16]. In
contrast, the more useful non-cognate docking is only successful in
20–40% of cases [16].
We analyzed the 1116 known interactions to examine whether
those that docked well were only due docking cognate ligands. For
each interaction, we observed whether the drug bound 1) a holo
Results
Computational pipeline
A computational pipeline was developed for large-scale
molecular docking of drugs to protein targets (Figure 1). Briefly,
Figure 1. The computational molecular-docking pipeline.
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per protein and the multiple binding pockets per structure, there
were a total of 1514 crystal structures and 2923 binding pockets.
Each drug was docked to all binding pockets of a protein and
whichever pocket gave the best docking score for the drug
determined the final protein-drug score. This method allowed
multiple conformations of a protein to be accounted for during
docking and provided a simple model of protein flexibility.
In total, we docked 1.2 million protein-drug interactions.
104,625 (0.9%) had ICM docking scores (icm-score) of 230 or
better, encompassing all1116 known interactions in the reliable
data set. Since the fraction of known interactions in the predicted
set was so low, we assumed that the vast majority of predictions
were false positives. Though we believed that novel drug-target
interactions existed and were enriched within these 104,625, there
was clearly a need for more stringent score thresholds.
We investigated various methods of selecting top drug-target
interactions. The standard software-recommended icm-score is
based on a weighted sum of various binding energy terms [13].
The pmf-score, or potential of mean force score, is a measure of
the statistical probability for the drug and protein to interact with
each other (for example, it examines interatomic distances and
atom types of the docked interaction and compares that to existing
interactions in PDB) [14]. A consensus score was developed that
uses both icm- and pmf- scores and allows us to select the x% of
top interactions for each protein; it is described in more detail in
case studies below. We also ranked interactions in two ways. The
drug-rank is the rank of this drug compared to all drugs docked to
this protein (from 1–4621), and the protein-rank is the rank of this
protein when the drug is docked to all proteins (from 1–252).
Requiring high drug and protein ranks (i.e. a low value when the
two ranks are summed together) enforces a mutual specificity
criterion. We hypothesized that by choosing interactions with
good scores and ranks, we would better filter out false positive
predictions.
To assess performance, we measured the positive predictive
value (PPV), defined as the proportion of predicted interactions
that are known binding interactions. The premise is that a better
threshold would yield a set of predictions more enriched with
known interactions, and novel interactions that are more likely be
true binding events. Figure 4a shows that as the stringency of a
threshold increased (i.e. icm-score of 240 versus 230), fewer
interactions are predicted; however, the PPV increased due to a
higher proportion of known interactions in the predicted set. This
behavior is consistent for all thresholds, and the highest PPVs are
generally observed within the top 100 predicted interactions. It is
important to note that each of the 4621 drugs will always have a
top-ranked protein (interactions with protein-rank of 1), and each
of the 252 proteins will always have a top-ranked drug
(interactions of drug-rank 1). Thus, the protein-rank threshold
particularly is not sensitive alone.
(unliganded) protein structure, 2) an apo (liganded) structure with a
same or similar ligand as the drug (the cognate-docking scenario),
or 3) an apo structure with a chemically different ligand from the
drug. Chemical similarity was defined as having a Tanimoto
coefficient less than 0.54. Figure 2 shows that cognate docking
occurred in 380 of the 1116 interactions. Of these, only 56 were
drugs docked to an apo protein with the same ligand (Tanimoto
coefficient of 0). The majority of drugs docked well to holo
structures as well as apo structures with dissimilar ligands. In short,
the ICM docking method was able to predict known interactions
for both cognate and non-cognate docking scenarios.
Aside from the docking score, it was also important to verify that
the ligands were docked in correct binding conformations. We
further examined the 380 cognate dockings and found that the
docked drug conformation was close to the known drug
conformation (RMSD value #2 Å) in 69% of cases. The other
31% fell into two categories: 1) partly symmetrical ligands like
NAD and 2) ligands that bound to a small pocket. In the first case,
the molecule was incorrectly determined to be flipped, causing a
high RMSD; however, its central portion was docked correctly
due to symmetry. In the second case, the region of ligand bound in
the pocket was docked correctly, but the region free in solvent
contributed to a poor RMSD value. Overall, this analysis showed
that when a known interaction was docked with a good score, the
binding conformation was also reasonably predicted.
Known drug-target network
We gathered the known protein-drug interactions into a network
(Figure 3) with proteins as rectangular nodes, drugs as circular
nodes, and interactions as edges. Interaction edges with good
docking scores were highlighted in red. Proteins from the same
family were often grouped close together and shared many drug
interactions, such as the retinoid X and retinoic acid receptors and
the matrix metalloproteinases. Proteins having the most known drug
interactions in the network included the transport proteins serum
albumin and the phosphatase PTPN1. The most highly-connected
chemicals in the network were metabolites: ATP, NAD, and NADP.
For some proteins such as MAPK14, 13 of 14 known inhibitors
were well predicted by docking, whereas for others such as ACE,
only one of its nine known inhibitors scored well. For 426 of the 678
protein targets not included in Figure 3, none of their known
interacting drugs could be docked well, reflecting the limitations of
current molecular docking methods. To this end, we chose the
subset of 252 protein targets for which at least one known drug
docked well (from the 1116 interactions that docked well), deemed
as more ‘reliable-for-docking’ compared to the other proteins.
Large scale cross-docking & score thresholds
We proceeded to dock the 252 reliable protein set against the
database of 4621 drugs. Considering the multiple crystal structures
Figure 2. Evaluating the known drug-target docking. 1116 (31%) of 3570 known interactions docked with a good score. Two-thirds of the
1116 were ligands docking to non-cognate protein structures, showing that the method could do more than re-dock existing drug-target structures.
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The protein-rank and pmf-score thresholds appeared to be the
worst based on both the PPV plot (Figure 1) and on enrichment
factors (Table 1). However, they showed better PPVs when
combined with other thresholds. For example, the drug rank and
protein rank measure performs much better than drug-rank alone,
and the consensus score (combining icm- and pmf-score) also
performs better than the icm-score alone. We measured the
enrichment factor for each type of threshold, at its most stringent
setting (leftmost points of Figure 4a) and found that the pmf-score
and protein-rank were the least effective at predicting known drugs
(Table 1). Instead, combinations of score and rank criteria
provided a 100–5006 enrichment of known interactions compared to a random algorithm, and a10–506 enrichment compared to a standard binding energy-based ICM score cut-off of
230. Interestingly, the drug-rank 1 and protein-rank 1 (basically
the sum of ranks is 2) combination threshold performs surprisingly
well; however, adding the consensus score clearly improves PPV
for the top ,300 interactions (Figure 4b) which are the most
interesting to us for manual inspection.
Another threshold method is to use the scores of known binders
as the score cut-off for each protein. We investigated this using the
best and worst icm- and pmf-scores of known drugs. Table 2 shows
that this did not result in a higher enrichment, nor did it help
narrow down the number of predicted interactions.
Overall, the combination of consensus score with the two ranks
gave the highest PPV and enrichment values: in the top 50
predicted interactions, 49% are known. This gave us confidence
that many of the other 51%, all novel interactions, are real.
Figure 3. Network of known protein-drug interactions. Proteins
are shown as rectangular boxes (nodes), drugs are shown as pink
(approved) and blue (experimental) circles, and edges represent known
interactions annotated by DrugBank. Edges colored red denote known
interactions that were docked with a good icm-score. Here we show
only the 252 proteins for which at least one known drug docked well –
the ‘reliable-for-docking’ set. The proteins at the bottom of the graph
are not connected to other proteins through shared binding drugs.
doi:10.1371/journal.pcbi.1002139.g003
Case study: MAPK14
Two examples are presented to illustrate the utility of
combining rank and scoring criteria. The first is for the signaling
protein MAPK14 (also known as p38 alpha), an integral
component in numerous cellular processes. It is a drug-target for
inflammatory diseases [17]. MAPK14 is known to be a challenging
Figure 4. Score thresholds assessment. Various combinations of score and rank thresholds were assessed using the positive predictive value
(PPV). A) shows the PPVs for thresholds predicting less than 7000 interactions. B) is a zoomed in version showing clearer PPV separation for the top
500 predicted interactions.
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Table 1. A comparison of various threshold methods based on their ability to predict a high percentage of known interactions
(PPV) and enrich the predicted interaction set for known interactions.
threshold
# predicted
interactions
# known in
predicted interactions
# proteins in
interactions
% known in
predicted set (PPV)
enrichment factor
versus random
random
1,164,492
1116
252
0.1%
1
icm-score of 230
104,625
1116
252
1.1%
11
pmf-score of 2300
150
3
20
2.0%
21
protein-rank of 1
4621
234
206
5.1%
53
consensus score 0.05%
437
45
238
10.3%
107
icm-score of 2100
72
9
17
12.5%
130
drug-rank of 1
252
42
252
16.7%
174
icm-score 2100
& pmf score 2140
48
8
13
16.6%
174
drug rank 1 & protein rank 1
53
16
53
30.2%
315
consensus score 0.05% & sum
(drug rank, protein rank)#4
45
22
39
48.8%
510
Thresholds are listed by increasing enrichment. It is also important to consider the size of the predicted set and how many proteins are included.
doi:10.1371/journal.pcbi.1002139.t001
docking target due to its structural flexibility [18] and its shallow
binding pocket [19]. However, these docking studies used only one
3D structure. In our dataset, there are 35 crystal structures of
MAPK14 in different conformations, providing a simple view of
protein flexibility.
The consensus score is based on the observation that when
docking a large number of diverse compounds to any target, most
compounds have poor icm- or pmf- scores, and few compounds
have both good icm- and pmf- scores. Therefore, we chose a linear
threshold that eliminated the densest area of points in the poor
scoring region (top-right) of a score plot like Figure 5, and selects
the compounds in the best scoring region (bottom-left) as potential
interaction hits. As seen in Figure 4a and Table 1, the consensus
score performed better for PPVs and enrichments compared to a
simple icm- and pmf- score combination.
Figure 5 plots the icm- versus pmf- scores of the 4621 drugs
docked to MAPK14. Each drug is a point on the graph, where the
5% of drugs passing a consensus threshold are shown in orange,
and the 1% passing a consensus threshold are shown in purple.
For 67 drugs, MAPK14 was one of the top 5 scoring targets; they
are circled in green. Table 3 shows that a combination of the
consensus and protein rank criteria resulted in the best enrichment
(1106) of known drugs. There were 15 annotated known binders
of MAPK14 in DrugBank, but we disregarded 2-chlorophenyl due
to it being a very small molecule with a very weak MAPK14binding affinity (.1 mM). 10 of 14 known drugs were predicted
through our stringent thresholds. Though 4 true positive binders
were lost, 99.99% of points were eliminated, presumably
consisting mostly of non-binders. Through literature search, we
found that imatinib and quercetin have been previously tested
against MAPK14 and did not show any inhibition [20]. This
suggested that the 5% consensus threshold was too lenient for
MAPK14, whereas the 1% was more appropriate. Within the
other approved drugs predicted to bind MAPK14, we found
literature validation for sorafenib, a multi-kinase inhibitor approved for renal cell carcinoma [21], and gefitinib, a EGFR inhibitor
approved for late stage non-small cell lung cancer [22].
Previous high-throughput studies have shown varying results
regarding nilotinib-MAPK14 inhibition. Some enzymatic assays to
MAPK14 showed weak inhibition: 570 nM or 2.2 mM depending
on the assay type [23]. Direct binding assays have shown 100 nM
Kd [23] or no binding at all in peptide pulldown experiment [20].
Since nilotinib was one of our top approved drugs predicted to
bind MAPK14, we decided to further experimentally validate the
Table 2. A comparison of various threshold methods based on their ability to predict a high percentage of known interactions
(PPV) and enrich the predicted interaction set for known interactions compared to other methods.
# predicted
interactions
# known in
predicted
interactions
# proteins in
interactions
% known in
predicted set (PPV)
enrichment
factor
versus random
use icm- score of worst scoring
known binder
62337
1117
252
1.8%
20
use icm- & pmf- scores of worst
scoring known binder
28840
716
252
2.5%
27
use icm- score of best scoring
known binder
16412
253
252
1.5%
17
use icm- & pmf- scores of best
scoring known binder
7859
253
252
3.2%
35
Threshold
These thresholds use the best and worst scores of known binders for each protein.
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Figure 5. A score-plot containing docking ICM- and pmf- scores for 4621 drugs to MAPK14. Each point represents a drug. The top 5% of
the drugs as determined by the consensus scoring threshold are shown as orange dots. These drugs were also docked to the 252 other drug targets
in our database, and circles denote the drugs for which this protein was one of the top 5 targets for the drug. The circle colors denote whether the
protein rank was based on the ICM score (green) or the pmf score (purple). Finally, drugs that are known to bind MAPK14 are shown in red boxes, and
it can be seen than most of these red boxes pass both the consensus and protein rank thresholds.
doi:10.1371/journal.pcbi.1002139.g005
issues [24]. Though it may seem underwhelming to use a cancer
drug with potentially serious side effects to treat inflammation,
nilotinib is noted to have a much milder adverse effects profile
compared to its similar drug dasatinib [20]. Another similar drug
imatinib has shown promise in treating rheumatoid arthritis in
mouse models [25] and specific patients [26,27], speculated due to
its inhibition of mast cell c-Kit and PDGFRB. Nilotinib also
inhibits these two proteins, and its extra inhibition of MAPK14
may render it a better choice for arthritis models. Recently,
nilotinib was tested in a glucose-6-phosphate-isomerase-induced
arthritis mouse model and found to significantly prevent paw
inflammation – to a greater extent than imatinib [28]. This study
also suggested that the two drugs acted through some distinct
mechanisms. Overall, these findings seem to agree with our
observations that nilotinib potently inhibits MAPK14, unlike
imatinib, and thus has added potential as an anti-inflammatory
drug.
interaction. We performed MAPK14 ATP-competitive binding
assays for two inhibitors that were available for purchase:
zafirlukast, and nilotinib. As seen in Figure 6, both drugs exhibited
inhibition of MAPK14 at therapeutically relevant concentrations
(,10 mM) in a dose dependent manner. Zafirlukast (AstraZeneca)
is an oral leukotriene inhibitor that reduces inflammation of
breathing passage in asthma patients. We found that it does inhibit
MAPK14 weakly, and this may contribute to its inflammation
reducing effect. The chronic myeloid leukemia drug nilotinib was
especially potent with an IC50 of 40 nM.
Despite their appeal as an inflammatory disease target,
MAPK14 drug candidates to date have failed due to drug toxicity
Table 3. Enrichment factors of various thresholds for
MAPK14.
all docked
drugs
known drugs
ligands
enrichment
factor versus
random
# docked to
MAPK14
4621
14
1
# passing icm
score #230
970
14
5
# passing 5%
consensus score
225
10
15
# passing 5%
consensus &
protein rank #5
67
10
49
# passing 1%
consensus score
45
6
44
# passing 1%
consensus &
protein rank #5
18
6
110
Case study: BIM-8
A second example is the Protein Kinase C inhibitor BIM-8. We
docked BIM-8 to the set of 252 reliable targets, and the results are
plotted in Figure 7. Each point on the graph represents a protein
target, and targets for which BIM-8 passes the 5% consensus
threshold are shown in orange.
We compared our results to three previous studies. Two studies
performed protein kinase assays with radioactive ATP and
substrate peptides, where inhibitor binding decreases the amount
of radioactive peptide produced [29,30]. The third study
performed thermal shift assays where inhibitor binding increased
the kinase stability and thus the melting point [31]. BIM-8 targets
discovered by these papers are shown in shades of red in Figure 7,
and non-binders in these papers are shown green. The only
annotated target of BIM-8 in DrugBank is PDPK1. GSK3B and
PIM1, which are in the top 5 protein rank and top 5% consensus
threshold, were also validated as inhibitors. PDPK1 was not found
to be an inhibitor by the first two studies but was confirmed as a
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target, and a blue dot denotes an experimental drug docked to the
target. Only protein-drug interactions that docked with a score
passing the consensus threshold and had a protein-rank #5 are
shown.
Overall, the known drugs (black crosses) had better scores than
other drugs for a given target. This was expected, as many of these
known drugs were chemically optimized for their targets. For a
number of targets, the known drug was the only predicted
interaction. None of the approved and experimental drugs from
DrugBank were able to dock well, despite a reliable protein
structure, suggesting that virtually screening larger chemical
databases may be the only way to discover novel inhibitors by
docking. For most targets, at least one experimental drug showed a
better score than the known drugs; however, experimental drugs
are often unavailable for purchase or experimental testing.
Instead, we were most interested in cases with approved drugs
such as the MAPK14-sorafenib example which was verified by the
literature, and the MAPK14-nilotinib example which we verified
with an in vitro kinase assay.
Through literature search, we found experimental support for
many of our top drug-target predictions that scored better than
known interactions (Table 5). These all pass the 1% consensus
threshold and are observed to have high drug and protein ranks
for the most part. It is important to note that the drug-rank
depends on the number of known binders for the protein; thus,
since ESR1 had 39 annotated drugs in DrugBank, a drug-rank of
16 is not as low. In contrast, a drug-rank of 16 would be low for
MMP13, which has only seven annotated drugs in DrugBank.
One type of validated interaction includes drugs that are close
analogs of known drugs for that target; for example, the estrogen
analog ERA-923 is a known selective estrogen receptor modular
(SERM) [32]. Genistein is known to bind both ESR1 and ESR2
[33]. Becocalcidol and ED-71 are vitamin-D analogs and bind the
vitamin D receptor [34,35]. Drosiprenone is a synthetic progestin
with anti-mineralocorticoid receptor (MR, NR3C2) effects and has
potential for reducing cardiovascular risk in women taking oral
contraceptives or postmenopausal hormone treatment [36]. Due
to the many in depth studies on kinase inhibitor specificity, we
were able to find collaborating evidence for some of our kinase
protein interaction predictions. For example, vatalanib is a known
pan-VEGFR inhibitor [37], nilotinib is a potent KIT inhibitor
[38], and other inhibitors of MAPK14 and targets of kinase
inhibitor BIM-8 were discussed in previous sections. Docosahexanoic acid (DHA, DB03756) is an endogenous ligand for brain
fatty acid binding protein (B-FABP) that is essential for brain
growth and function [39]. We predicted that it binds the transport
protein human serum albumin; indeed, this interaction has been
validated and found to confer neuroprotection in animal models of
ischemia [40]. This finding suggested that DHA might have
potential repositioning value for ischemic stroke.
Overall, we were able to find literature support for 30 of our top
predicted interactions, which validated our computational method
as useful for finding novel drug-target interactions.
Figure 6. Testing nilotinib and zafirlukast in ATP-competitive
enzymatic assays against MAPK14. Results are plotted as percent
inhibition of activity versus drug concentration. The nilotinib-MAPK14
IC50 was calculated to be 40 nM.
doi:10.1371/journal.pcbi.1002139.g006
binder by the third study with a kinase assay and crystal structure.
Overall, if we count that there are 4 known binders (PIM1,
PDPK1, GSK3B, LCK, since CDK and MAPK14 are probably
weak or nonbinders), we can see that applying a 5% consensus
threshold and protein rank criteria gave us a 63-fold enrichment
over random selection, and a 63/10.5 = 46 fold enrichment over
using a standard ICM score threshold of 230 (Table 4).
Drug-target interaction map
For a global and quantitative review of the predicted proteindrug interactions, we plotted the icm scores of drugs docked to
established drug targets (Figure 8). Each protein is represented by
a column, on which a black line denotes a known drug docked to
the target, a red dot denotes an approved drug docked to the
Discussion
The binding of a small molecule drug to its target protein in a
cell is much more complex than a single docking calculation. For
example, an ATP-competitive kinase drug would have hundreds of
ATP-binding sites to choose from due to the large size of the
kinome. Cancer drugs such as sunitinib are now known to potently
inhibit many more kinase targets than previously expected [41]. In
addition, non-kinase targets of kinase drugs have also been found:
NQO2 was the first non-kinase target discovered for imatinib
Figure 7. Docking icm- and pmf- scores for BIM-8 docked to
252 reliable-for-docking protein targets. Each point represents a
protein target. Targets for which BIM-8 passed a consensus threshold
are shown as orange dots (top 5%) and brown dots (top 1%). Targets
with experimental support are enclosed in red colors. Targets that have
shown no binding activity with BIM-8 in the literature are shown in
shades of green. It can be seen that most of the actual targets of BIM-8
pass stringent consensus score thresholds.
doi:10.1371/journal.pcbi.1002139.g007
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September 2011 | Volume 7 | Issue 9 | e1002139
Computational Drug Repositioning
target search space for any inhibitor should be the entire
druggable proteome.
Our strategy was to find novel drug targets of existing drugs by
computationally screening the druggable proteome. For this
purpose, we chose molecular docking due to its speed, low cost,
and detailed three-dimensional simulation. Moreover, docking can
evaluate any protein with a solved structure due to its virtual
nature, without the need for tailoring enzymatic assays or
collecting drugs in solutions. However, docking is known to have
a high false positive prediction rate, due to limitations such as
incomplete binding pocket prediction, inadequate ligand conformation sampling, inaccurate scoring functions, lack of protein
flexibility, and lack of water and cofactor molecules during the
simulation. As evidenced in this study, only 31% of the 3570
known interactions docked with a good score. One review states
that 10–50% of a set of diverse compounds can be expected to be
docked correctly for a given target [12]. We are well within this
range, and believe our method performs quite well considering the
large variety protein targets involved and the automated nature of
the pipeline. However, the other 69% of known interactions were
not predicted due to docking limitations.
Our method attempted to address these limitations. First, we
manually included binding pockets that were present in PDB
structure complexes but not predicted by the binding pocket
search. Second, we docked each interaction 10 times to better
sample ligand conformations. Third, we applied consensus score
Table 4. Enrichment factors of various thresholds for BIM-8.
all docked
proteins
known protein
targets
enrichment
factor versus
random
# proteins BIM-8
was docked to
252
4
1.0
# passing default
score #230
24
4
10.5
# passing 5%
consensus score
20
4
12.6
# passing 1%
consensus score
6
3
31.5
# passing 5%
consensus &
protein rank #5
3
3
63
# passing 1%
consensus &
protein rank #5
3
3
63
doi:10.1371/journal.pcbi.1002139.t004
[20,42], and several cytotoxic LIM kinase inhibitors were found to
be actually inhibiting tubulin [43]. Such studies imply that the
Figure 8. Quantitative interaction map of drugs docked to protein targets, according to their ICM docking score. Each protein is
represented by a column, on which a black cross denotes a known drug docked to the target, a red dot denotes an approved drug docked to the
target, and a blue dot denotes an experimental drug docked to the target. Only the top predictions for established drug targets (at least one known
approved drug) that docked with a score passing the consensus threshold and had a protein-rank #5 are shown.
doi:10.1371/journal.pcbi.1002139.g008
PLoS Computational Biology | www.ploscompbiol.org
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Computational Drug Repositioning
Table 5. Top predicted hits that have literature support.
protein
drug
icm score
pmf score
drug rank
protein rank
notes
AIFM1
DB02332
279
2231
1
1
Flavin is a cofactor. [51]
ALB
DB03756
266
2163
1
2
Dosahexanoic acid (DHA) can form complex with
albumin and confers neuroprotective effects in rats.
[40]
ALB
DB06689
251
2130
84
3
Ethanolamine oleate promptly binds with albumin
in the blood [52]
AKT1
DB03265
281
295
2
1
Crystal structure of inositol 1,3,4,5tetrakisphosphate bound to AKT1. [53]
BTK
DB03344
269
299
1
3
[54] shows that inositol 1,3,4,5-tetrakisphosphate
binds to BTK. This compound is very similar: inositol
1,3,4,5,6 tetrakisphosphate.
CYB5R3
DB02332
271
2258
2
2
Flavin is a cofactor. [55]
ESR1
DB05414
247
2197
3
1
ERA-923 is a selective estrogen receptor modulator.
[32]
ESR1
DB01645
242
2109
16
1
Genistein is a selective estrogen receptor
modulator. [33]
GART
DB02223
263
2126
1
5
LY-231514 tetra-glu a known thymidylate synthase
inhibitor. LY-231514 is pemetrexed, a GART and
thymidylate sythase inhibitor. inhibitor. [56]
GART
DB02794
262
2147
2
4
Crystal structure of compound bound to E.coli
GART. [57]
GSR
DB02332
257
2211
KDR
DB04879
249
2152
1
1
Flavin is a cofactor. [61]
Vatalanib is a pan VEGFR inhibitor. IC50 37 nM. [37]
KIT
DB04868
244
2240
4
2
Nilotinib. [38]
MAPK10
DB00317
239
2183
72
3
Gefitinib binds MAPK10 weakly: Kd = 2–3 uM. [58]
MAPK14
DB00398
251
2161
2
2
Sorafenib IC50 0.057 uM. [59]
MMP2
DB02255
237
284
1
6
Illomastat is a broad-spectrum MMP inhibitor. Ki
0.5 nM (Chemicon International Inc, Temecula, CA)
MMP8
DB02255
244
267
2
1
Illomastat is a broad-spectrum MMP inhibitor. Ki
0.1 nM (Chemicon International Inc, Temecula, CA)
NR3C2
DB01395
248
2150
1
1
Drospirenone, a progestogen with
antimineralocorticoid properties. [60]
PPARD
DB03756
262
2144
1
4
DHA can activate PPARD. [61]
PPARG
DB06536
247
2130
9
1
Tesaglitazir is a dual PPARA/PPARG agonist [62]
RAC1
DB03532
2120
2145
1
1
RAC1 is a GTPase [51], and this compound is a
standard GTP analog.
RARG
DB02466
258
2216
1
1
BMS181156 binds RARG with Kd 0.6 nM. [63]
RARG
DB02258
256
2220
2
1
SR11254 is a RARG-selective ligand [64].
RARA
DB05076
245
2131
6
2
4-HPR is a highly selective activator of retinoid
receptors. [65]
RARG
DB05076
246
2134
6
1
4-HPR is a highly selective activator of retinoid
receptors. [65]
RARG
DB02741
252
2217
3
1
CD564 binds RARG with Kd 3 nM. [63]
RARG
DB03466
246
2208
11
1
BMS184394. [63]
RXRA
DB03756
254
2137
1
8
DHA. [66]
Arachidonic acid. lit support. [63]
RXRA
DB04557
253
2156
2
5
VDR
DB04891
249
2204
1
1
Becocalcidiol, a vitamin D analog. [34]
VDR
DB04295
244
2297
4
1
ED-71, a vitamin D analog. [35]
doi:10.1371/journal.pcbi.1002139.t005
and rank criteria to further narrow down top scoring docking hits.
Fourth, we used all available structures of a protein (versus
choosing one representative structure), to allow a simple view of
protein flexibility. We did not incorporate water and cofactor
molecules in our docking simulations due to the computational
complexity involved. However, by selecting proteins for which at
PLoS Computational Biology | www.ploscompbiol.org
least one known drug docked and scored well, we selected proteins
for which the limitations of the docking software were not critical
for a good prediction. In short, assuming the docked conformation
of the known ligand was correct, we used only proteins for which
the binding pocket was genuine, the scoring functions were
adequate, the protein was in a conformation amenable for drug
9
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Computational Drug Repositioning
mental screening. Due to differences in experimental methods,
assay settings, and protein panels, different studies may present
differing results. For example, small molecule affinity purification
methods that use whole cell lysates would give different results
from in vitro kinase assays that use a specific panel of proteins. In
the case of gefitinib, two such studies had distinct differences in
their proposed cellular targets [22,41]. Differences in methods are
also further compared in a study by Manley et al [23]. We
presented an example for BIM-8, which binds to PDPK1
differently in two similar in vitro experiments. For MAPK14, the
experimental results for nilotinib also varied. We experimentally
tested two purchasable approved drugs against MAPK14 and
found that nilotinib was a strong nanomolar inhibitor, and
zafirlukast was also an inhibitor, though not as potent. Thus,
interactions that are predicted to be very likely inhibitors
computationally may merit extra study even if experimental tests
are initially negative.
In short, we have developed a computational pipeline that can
run large-scale cross-docking of compounds to targets. We
developed stringent criteria to filter a large proportion of false
positive interactions. The two case studies presented were selected
based on known experimental binding assay data, so as to
demonstrate the notable enrichment of known interactions using
our scoring and ranking criteria. We hypothesized that predicting
a set of interactions with a higher PPV (enrichment of known
interactions) would also lend confidence to the other novel
interactions in the set. This appears to have worked, as we were
able to find validation for 31 predicted drug-target interactions
that were not previously annotated in DrugBank, as well as
validate two other inhibitors of MAPK14. Other drug-target
interaction predictions are currently undergoing experimental
validation; novel interactions discovered are potential drug
repositioning candidates, but also provide insight into a drug’s
mechanism of action and adverse effects profile.
inhibition, and the lack of water or cofactor molecules didn’t
drastically affect the prediction.
Virtual screening studies typically involve docking large
chemical databases to one protein target, selecting compounds
that score within the top 0.5–1% of the database and then further
prioritizing them by visual examination. When experimentally
validating these top candidates, a 5% hit rate can be considered a
successful endeavor (where a good hit is a predicted compound
showing an experimental binding affinity in the mM or lower
range) [44]. Depending on the target, the crystal structure, the
software used, post-docking criteria (such as chemical clustering),
and even the individual performing the visual examination, the hit
rate can be improved to 10–40% (Cavasotto et al. had 14% hit rate
from 50 tested compounds [15]; Sabio et al. had a 36% hit rate
from 56 tested compounds [45]).
In our case, both the standard scoring threshold and the knowninhibitor score were not sufficient. With a normal score threshold
of 230, docking 4621 drugs against 252 proteins resulted in
104,625 predicted interactions. This is roughly 1% of the docked
interactions, so even selecting the top 1% of the docking hits for
validation becomes prohibitive for large-scale studies. It is
important to note that each protein has different physiochemical
properties: for some proteins, hundreds of compounds pass the
230 cut-off, while for other proteins none pass. Thus, using the
known-inhibitor score as a cut-off allows for a threshold that is
tailored to each protein. However, this method still predicted
,8000 interactions at the most stringent. Our consensus threshold
allowed us to pick the top 1% (or any x%) of docked compounds
with the best icm- and pmf- scores for each protein and further
filter from there. Through testing many combinations, we found
that using the consensus score with rank information allowed us
the highest PPV – nearly 50% - and enrichment factor – 50 times
better than standard 230 score threshold and 490 times better
than random selection. This high enrichment for known
interactions suggests that many of the other predictions that have
not yet been experimentally tested may be true binding
interactions.
There are limitations to this scoring scheme. Since the pmfscore is a statistical score comparing the docked interaction to
known interactions in PDB, a chemical with a different scaffold or
novel binding conformation may have a poor pmf-score and
become predicted as a false negative. However, our foremost goal
in this study was to eliminate as many false positive predictions as
possible and obtain a high enrichment of true positives in our
predicted interaction set. Thus, it was acceptable to miss some
false negative predictions. In addition, the consensus score is quite
simple with a linear separation method, and may not be as
informative as a machine-learning algorithm that trains on known
ligand docking scores. However, we desired an automated scoring
method that did not depend upon the existence of known ligands.
That is, if a protein structure had just one, or no known binders,
our method would still be able to select the top 1% of docking hits.
To date, cross-docking of proteins to compounds has generally
been used for small datasets. As an example, Huang et al. docked
40 targets against 40 compounds to check whether their docking
method could distinguish between a target’s cognate ligands and
the other targets’ cognate ligands [19]. In this large-scale crossdocking study, our use of a 1000-processor cluster was essential to
completing the tens of millions of docking simulations in a timely
manner. In addition, the large number of crystal structures and
binding pockets involved required much of the docking pipeline be
automated.
High-throughput computational screening of drug-target interactions represents a parallel approach to high-throughput experiPLoS Computational Biology | www.ploscompbiol.org
Methods
Pocket database and drug database construction
We downloaded the DrugBank 2.5 database [46], containing
drug information and comprehensive information of their targets.
We extracted human protein drug targets from DrugBank and
retrieved their sequences from SwissProt [47]. Protein Data Bank
structures showing at least 95% sequence identity for proteins at
least 20 amino acids in size were downloaded. They were required
to be X-ray crystal structures with a minimum resolution of 2.8 Å.
Multiple chains were grouped into a set of non-redundant
sequences, based on PDB’s chain redundancy analysis at the
95% sequence identity level.
Preparing a target pocket database
We prepared protein structures for docking using Molsoft’s
ICM software version 3.4-9c [13], removing water molecules,
solvent ions, and other ligands from the structures. We added
hydrogen atoms to the structures then optimized their positions.
These prepared protein structure files can be downloaded from
http://www.bcgsc.ca/downloads/yli/. To predict pockets, or
potential binding sites, we used the PocketFinder [48] method in
ICM, which calculates a transformation of the van der Waals
energy for an aliphatic carbon probe on a grid map. For each
protein, the three largest pockets are retained in the database. If
metal ions were found near a pocket, we prepared two receptors
for docking, one of the protein with the metal ion and one without.
The receptor was defined as the box 3.5 Å surrounding the
pocket. If the pocket overlapped well with the ligand but the ligand
10
September 2011 | Volume 7 | Issue 9 | e1002139
Computational Drug Repositioning
extended out of the protein structure, we defined the receptor be
the box 3.5 Å around the pocket but also including 2.0 Å around
the ligand. This ensured that known ligand binding sites not
predicted by our automated method were also included in our
pocket database.
Applying and evaluating score thresholds
We applied several methods of score thresholding: applying cutoffs of the ICM docking score ranging from [225 to 2100];
applying cut-offs of the ICM potential of mean force score ranging
from [280 to 2200]; applying a drug rank cut-off ranging from [1
to 4500]; applying a protein rank cut-off ranging from [1 to 252];
applying a combined docking score and mean force score cut-offs.
For the consensus score thresholds, all slopes (from 21 to 240)
and intercept (from 0 to 2400) combinations were tested. For each
line, we calculated the density of the points eliminated in a
trapezoidal area delineated by the consensus line, the best icmscore for this protein, and the best pmf-score for this protein, the
midpoint between the worst icm-score and its mean, and the
midpoint between the worst pmf-score and its mean. For two
consensus thresholds that predicted the same number of
interactions, we used the one that eliminated a denser cloud of
points.
While evaluating PPV for combination thresholds, it was often
observed that two sets of thresholds resulted in the same number of
predicted interactions but different PPVs. In such cases, we
considered only the threshold combination that gave us the higher
PPV.
Docking
We docked drugs to target receptors using the ICM virtual
library screening (VLS) module. This method performs rigidreceptor flexible-ligand docking using a two-step Monte Carlo
minimization method and energy scoring function to sample
ligand conformations and select the best docking hits. MMFF
partial charges and ECEPP/3 force-field parameters are used.
Docking one interaction required on average 30 seconds to 1 min
per processor. A given protein may have several structures, each of
which with more than one pocket; in such cases we dock all
pockets to a drug, and the best scoring interaction is selected to be
the representative protein-drug score.
To ensure a sufficient coverage of the docking energy landscape,
we docked each drug-target interaction 10 times in the known
docking analysis and 5 times in the large-scale cross-docking
analysis. Docking was performed on a Linux cluster with 1000
processors – this level of throughput allowed us to complete 1–3
million dockings per day.
Large scale cross-docking
1,164,492 interactions between 252 proteins and 4621 drugs
were docked using ICM. Though there were actually 4854 drugs
small molecules, some were excluded being too small or too large
for docking (molecular weight under 100 or over 1000 g/mol).
Due to the multiple binding pockets per protein and multiple
crystal structures per protein, there were a total of 2923 binding
pockets. Each interaction was docked 5 times to better cover the
docking energy landscape and the best scoring conformation was
retained. Overall there were 29236462165 dockings or 68 million
docking calculations. The icm and pmf scores of each interaction
were gathered into large matrices for further analysis.
Known interactions docking
8867 known interactions between human protein targets and
drugs were culled from the DrugBank Drugcards database. Of
these, 3570 interactions with protein target crystal structures
present in our database were docked. Due to the Monte-Carlo
nature of the ICM method, each interaction was docked 10 times
to better cover the docking energy landscape. After 10 iterations,
the best scoring prediction was retained.
If the protein structure was solved in complex with a ligand, a
Tanimoto coefficient was used to determine if the docked drug was
similar to the complexed ligand. A coefficient less than 0.54
represented similar molecules [49], and thus cognate dockings.
Evaluation of static RMSD values of protein-drug interactions
representing 380 cognate interaction dockings was performed on a
case-by-case basis as the chemical numbering of PDB heteroatoms
and docked structures often differed, which caused incorrect
RMSD calculations. Each RMSD comparison was required to
match at least 30% of the docked ligand atoms to the cognate
crystal-structure ligand. 320 interactions pass this requirement, of
which 221 (69%) showed RMSDs under 2 Å. The other 99 (31%)
had RMSDs larger than 2 Å.
Cytoscape [50] was used to generate the known drug-target
interaction map. Networks were fitted to a force-directed layout
and manually edited for improved visibility. Drugs and protein
targets are nodes in the network, interconnected by interaction
edges. The edge lengths were not weighted, and are adjusted for
maximum visible understanding.
Kinase assays
Protein inhibition assays were performed by SignalChem
(Richmond, BC, Canada). Kinases assays consisted of 33P-ATP
at 25 mM, the protein kinase, peptide substrate, assay buffer, and
the drug. Blank assays without substrate or drug, and assays
without the drug, were used as controls. Staurosporine at 1 mM
was used as the positive control drug.
Author Contributions
Conceived and designed the experiments: YYL JA SJMJ. Performed the
experiments: YYL. Analyzed the data: YYL. Contributed reagents/
materials/analysis tools: YYL JA. Wrote the paper: YYL. Aided in
manuscript preparation: JA SJMJ. Supervised the study: SJMJ.
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