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REVIEW
Systems biology analysis of protein–drug interactions
Jacques Colinge, Uwe Rix, Keiryn L. Bennett and Giulio Superti-Furga
Research Center for Molecular Medicine of the Austrian Academy of Sciences (CeMM), Jacques Colinge, Vienna,
Austria
Received: September 1, 2011
Revised: September 26, 2011
Accepted: September 27, 2011
Drugs induce global perturbations at the molecular machinery level because their cognate
targets are involved in multiple biological functions or because of off-target effects. The
analysis or the prediction of such systems level consequences of drug treatment therefore
requires the application of systems biology concepts and methods. In this review, we firstsummarize the methods of chemical proteomics that can measure unbiased and proteome-
wide drug protein target spectra, which is an obvious necessity to perform a global analysis.We then focus on the introduction of computational methods and tools to relate such target
spectra to global models such as pathways and networks of protein–protein interactions, and
to integrate them with existing protein functional annotations. In particular, we discuss how
drug treatment can be mapped onto likely affected biological functions, how this can helpidentifying drug mechanisms of action, and how such mappings can be exploited to predict
potential side effects and to suggest new indications for existing compounds.
Keywords:
Bioinformatics / Chemical proteomics / Drugs / Personalized medicine / Statistics
1 Introduction
Our knowledge of drug protein target profiles is often
limited by practical difficulties in obtaining such informa-
tion. Consequently, numerous compounds in clinical use
are orphan ligands or one target only is identified. Contin-uous and substantial progress in proteomic technologies [1]
have made it possible to develop chemical proteomic – or
chemoproteomic – approaches, where the protein targets of a
drug are affinity purified and identified by MS [2, 3]. Thismethodology empowers researchers to measure
compound–protein interactions in a biological context as
opposed to in vitro-binding assays. That is, drug–protein
interactions cannot only be determined proteome wide, but
also in a tissue- or cell type-dependent manner. The strength
of this approach is that all the proteins are expressed at truephysiological concentrations and bear correct posttransla-
tional modifications.
Understanding the mechanism-of-action (MoA) of
compounds and elucidating the origin of observed sideeffects are of great importance in drug discovery. Accessing
accurate and sensitive target spectra offers promising
perspectives that result in the derivation of more efficient
and safer compounds. Detrimental leads can be halted at anearlier stage of development and thus significantly reducing
costs [4]. Furthermore, the knowledge of multiple potent
targets creates boundless opportunities for drug repurpos-
ing. In general, this can potentially provide access to new
targets. It could also reveal unexpected synergistic effects
that explain the success of certain compounds [5].Reaching the promises of chemical proteomics is not a
straightforward task since the compounds used in patient
therapy induce more global changes in the molecular
machinery of cells than simply regulating a selected protein
[6]. Obviously, a targeted protein is involved in biochemicalreactions that take place within one or several biological
pathways. These in turn can interact with other pathways
Colour Online: See the article online to view Figs. 2, 4 and 5 in colour.
Abbreviations: ABPP, affinity-based protein profiling; CML,
chronic myeloid leukemia; FDR, false discovery rate; GO, gene
ontology; PPI, protein–protein physical interaction; TCM, tradi-
tional Chinese medicine
Correspondence: Dr. Jacques Colinge, Research Center for
Molecular Medicine of the Austrian Academy of Sciences
(CeMM), Jacques Colinge, AKH BT 25.3, Lazarettgasse 14,
A-1090 Vienna, Austria
E-mail: [email protected]
Fax: 143-1-40160-970030
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[7]. Therefore, modification of the activity of a single agent,which is part of a complex network, can have far-reaching
consequences on multiple biological functions. Moreover,
compounds often have more than one target and hence can
exhibit broad-ranging impact on cell biology. For instance,
the tyrosine kinase inhibitor imatinib, which is a hallmark
of targeted therapy against the chronic myeloid leukemia(CML) causing fusion protein BCR-ABL, was found to have
at least five additional potent targets, including the nonki-
nase (NQO2) [8].
To fully exploit the potential of chemical proteomics,combination with computational methods is required. In
this way, it now becomes feasible to extend beyond an ideal
case where the protein target spectrum reveals valuable
information directly, considering complex and indirectimplications. This combination of unbiased proteome-wide
target determination and computation naturally occurswithin the paradigm of systems biology, where units of
functions are all regarded as interconnected [9, 10].
Computations can integrate available information on biolo-
gical pathways, protein and gene interaction networks, andprotein functions to predict the biological processes that are
impacted by drug treatment. This assists in understanding
the mechanism of action of compounds and naturally
expands in the direction of identifying potential side effects
and new indications for existing molecules.In this review, we briefly recall the general principles of
chemical proteomics before presenting the current methodsof data analysis and available tools and algorithms. We will
also discuss how the presented computational methods can
be applied to the analysis of target spectra that are not
necessarily derived from chemical proteomics, e.g. currentefforts in traditional Chinese medicine (TCM) research. We
will conclude by presenting attractive future perspectives
toward personalized medicine and digital patient models.
2 Methods of chemical proteomics
Different approaches to discover drug targets exist that can
be subdivided into two classes [2, 3]. The first is classical
compound affinity purification [8, 11, 12] that requires theimmobilization of the compound of interest. The second is
affinity-based protein profiling (ABPP) [13, 14]. Via a generic
chemical probe, it profiles a whole class of compounds,
though in a biased manner. Additionally, either theexpression proteomic experiments or the global mapping of
chosen posttranslational modifications, e.g. acetylations for
histone deacetylase (HDAC) inhibitors, can be employed to
compare drug-treated versus untreated samples. Thereby,
information regarding drug-induced changes is obtained
[15–17]. Such broader methods do not measure compoundtargets directly and are hence not properly speaking
chemical proteomic methods. The potential to partially
reveal target spectra is evident, although a combination of
direct drug influence is usually combined with downstream
regulation and the data obtained represent the global‘‘integrated’’ impact of drugs.
In recent years, chemical proteomic applications have
aided in the elucidation of several important drug–protein
interactions [8, 13, 18–25]. The main steps of compound
immobilization-based and ABPP methods are introduced
below.
2.1 Compound immobilization approaches
To profile the protein targets of a chosen compound
requires the immobilization of the compound on a matrix.
This operation is achieved through a functional group, e.g.
sulfhydryl, amino, hydroxyl, or carboxyl, that binds to anactivated resin, e.g. sepharose or agarose beads. Small
molecules that do not contain an appropriate group must bechemically modified. The potency of the modified, linked
compound should be assayed to ensure that it is preserved
[26]. A cell or tissue extract is then incubated with the matrix
and washed extensively before elution. Finally, proteomicmethods are applied to identify the proteins that bound the
linked molecule. Current strategies are to either further
reduce sample complexity via one- or two-dimensional SDS-
PAGE [8], or use gel-free methods [27]. Both approaches are
then followed by MS. The process is shown in Fig. 1 andcommonly named drug pulldown [3, 27].
2.2 Noise in the signal?
Similar to any affinity purification methods, compoundimmobilization approaches suffer from the presence of
nonspecific interactions that consistently appear in the list
of proteins identified by MS. The causes of nonspecific
interactions are multiple, and appropriate solutions exist. Inorder to analyze the true target profile of a compound, it is
crucial to eliminate nonspecific binders without the loss of
any important target. This step constitutes the first stage of
bioinformatic analysis.
Some proteins might bind to the chemical linker between
the matrix support and the compound or even to the matrixitself. This category of nonspecific binders can be readily
identified in negative control experiments performed with
‘‘empty’’ blocked beads.
Abundant proteins that have a low affinity for either theimmobilized compound or the true interactors of the
compound cannot be eliminated completely at the washing
step. These proteins contribute largely to the identified
nonspecific binders. They are not identified in negative
control experiments with blocked beads. Nonetheless,
different approaches can be implemented to identify them.It is possible to perform a parallel experiment with an
unrelated compound that, with some confidence, does not
share any target with the compound of interest. To improve
the potential of identifying nonspecific binders, a chemically
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related compound, which is biologically inactive, is yet a
better tool, but the risk of overlapping target profiles,
however, is increased. One last variation of the same
approach is to compare a new drug pulldown with previousexperiments and to exclude frequently found proteins.
Determining the frequency threshold might require statis-
tical analysis or empirical validation. We have obtained
satisfying results with geometric tests (unpublished data). In
general, comparisons with previous experiments increasethe risk of discarding a correct target that was found to be
nonspecific in a different context. Thus, lists of ‘‘assumed’’
nonspecific binders, though useful as initial approxima-
tions, should be considered with care.
It is also possible to compare the proteins identified from
a drug pulldown with the proteome of the cells from whichthe experiment was performed. Due to the nature of MS, an
analysis at the whole proteome level will primarily detect the
abundant proteins, the so-called core proteome . Conversely,
the drug pulldown is an enrichment process resulting in thedetection of lower abundance proteins. As a first approx-
imation, proteins that are detected in both data sets are
considered suspicious and should be removed as true
interactors. The downside of this simplistic subtraction isthat abundant true targets, as recently exemplified by Hsp90
[23], are excluded. Furthermore, such an approach becomesquestionable since the emergence of highly sensitive MS
instrumentation that can now routinely detect medium-
abundance and even some low-abundance proteins. To
circumvent this difficulty, semi-quantitative MS indicatorssuch as spectral counts [28] can be exploited to detect
significant enrichments from a chemical proteomic experi-
ment. Identification of the minimum increase of spectral
count required in the pulldown versus the core proteome
can be achieved empirically, e.g. following known targets, orthrough proper statistical modeling [29, 30]. A final possi-
bility is to perform the desired pulldown again with a chosen
concentration of the free compound added to the cell
lysate prior to performing the drug pulldown. In this way,
true targets are bound by the free compound and are no
longer available in the lysate to interact with the immobi-lized drug (Fig. 2). Requiring the complete disappearance of
the targets or a significant reduction of the spectral counts
in the MS data, when the two data sets are compared,
identifies the correct proteins. In our hands, this concep-tually simple method gives reproducible, sensitive, and
reliable results [31].
Drug pulldowns retrieve complete or partial protein
complexes in certain cases and the elimination of nonspe-
cific binders leaves a list of proteins mixing direct and
indirect interactors (Fig. 3A). Depending on the compound,it is possible to recognize the direct binders with a good
confidence simply using the knowledge of the binding
mechanism. For instance, kinases are very likely to be direct
interactors of a kinase inhibitor. More generally, protein–protein binary interaction data such as measured by yeast 2-
hybrid experiments [32], which are available from public
databases, might shed light on a mixture of direct/indirect
drug interactions (Fig. 3B). Similar help can be obtained
through the knowledge of protein interaction domains and
predicted physical interactions [33]. Ultimately, complemen-tary protein–protein interaction experiments could be planed
to discover the structure of those interactions, and clarify the
position of the drug interactions in this network [29]. Table 1
provides a summary of noise elimination methods.
Figure 1. Overview of the entire process of measuring drug–-
protein interactions through chemical proteomic experiments
and of analyzing the generated data. A drug is coupled to
magnetic beads through a chemical linker and incubated with
the lysate of a biological sample. Different proteins bind the drug
with strong affinity (violet and orange) and some others might
have a low affinity for the compound (aqua). Additionally, some
proteins bind to drug strong binders (green). After washing and
elution, the strong binders are purified along with the indirect
binders and some abundant low-affinity proteins. MS detects the
purified proteins and provides the input data for the bioinfor-
matic analysis, which may exploit a wide range of additional
data sources to compute its results.
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2.3 Miniaturization toward individual patient
profiling
Until recently, relatively large quantities of protein material
were necessary to perform chemical proteomic experiments
successfully. This constraint might have limited the wide-
spread use of this powerful methodology, particularly when
analyzing clinical samples. Due to progresses in MS
instrumentation and the development of new experimentalprotocols involving more sensitive chromatography, it is
currently possible to perform drug pulldowns using as little
as 106 cells or even less [27]. These improvements provide
accessibility to individual patient sample analyses. Thus,
new opportunities both in research and, ultimately, inpersonalized medicine are created that are complementary
to next generation DNA-sequencing technologies.
2.4 Subproteome-focused chemical proteomics
To study an entire class of compounds that inhibit proteins
through a common binding mechanism, e.g. inhibitors that
interact with the ATP pocket of kinases, it is possible to
develop generic matrices that bind with a large range of thetargets with slightly reduced specificity. Through competi-
tion experiments, where the same matrix is employed alone
or with the presence of compounds to profile, protein targets
can be identified reliably through the same logic discussed
above for immobilized compounds. Such generic matriceshave been developed for kinases (Kinobeadss) [13] and
histone deacetylases [34] and offer powerful assay platforms
that do not need to be adapted to existing or future
compounds. On the other hand, only a limited range of allthe possible targets bind to the generic matrices and true
targets not present in this subset cannot be detected. A
similar approach, termed affinity-based protein profiling
(ABPP), uses chemically reactive enzyme-specific probe
molecules to capture e.g. kinases [14] or proteases [35] and
purify them via a biotin tag [36]. While the ABPP metho-dology follows the same downstream concepts as the
generic compound matrices, it offers the advantage to
potentially being able to differentiate between active and
inactive enzymes. Taken together, these methods are broad-range highly multiplexed assays but they are subjected to a
strong bias as they focus on a predefined subproteome.
2.5 The application of quantitative proteomics
Stable isotope-labeling techniques, e.g. iTRAQ [37], TMT
[38], or SILAC [39], find a natural application in chemical
proteomics to render the competition experiment we
mentioned previously more precise. For instance, one drug
Figure 2. Competition with a free compound in a second
experiment sheds light on the nonspecific binders. In compar-
ison with the original pulldown (left), proteins have the oppor-
tunity to bind to the free compound in the competition pulldown
(right). As a result, direct high-affinity binders and their inter-
actors are found in much reduced abundance in the purified
sample. A comparison of spectral counts – or truly quantitative
measurements – identifies such reductions readily. Abundant
low-affinity proteins binding to the immobilized compound do
not find sufficient free compound copies to significantly reduce
their presence in the purified sample. These are identified by
essentially constant spectral counts.
Figure 3. Drug pulldowns retrieve protein complexes. (A) A drug
can bind to isolated proteins (a–c) but it frequently binds to a
protein (f) that is part of a protein complex (d–g). (B) The pull-
down experiment will identify the direct drug–protein interac-
tions as well as the indirect protein interactions through the
complexes (d, e, g). Without a priori knowledge on the binding
mechanism, it is essentially impossible to distinguish direct from
indirect interactions from such data. When available, informa-
tion on direct – binary – interactions between proteins might
delineate complexes but not indicate which complex member
binds to the drug.
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pulldown can be performed in biological duplicates in twoiTRAQ 4-plex channels with the corresponding two
competition pulldowns occupying the other two channels.
With such an experimental design, the less accurate spectral
counts are replaced by relative quantitative measures. The
selection of proteins that directly interact with the
compounds is then rather straightforward, particularlywhen combined with appropriate statistical models [40, 41].
In principle, a similar multiplexing approach could improve
the comparison of a drug pulldown with the corresponding
cell line core proteome as discussed above. In this situation,however, the highly complex core proteome would mask a
significant portion of the pulled down proteins and this
option should be disregarded.
In subproteome-focused applications, it is advantageousto perform competition pulldowns with increasing amounts
of the free compound, and to combine the pulldowns in asingle iTRAQ or TMT experiment [13, 34] to obtain
dose–response curves. Via an innovative protocol, Sharma
et al. were able to determine the dissociation constant and
the IC50 of gefitinib, an EGFR kinase inhibitor in clinical
use for lung cancer [42]. Namely, comparing a first experi-ment with a subsequent pulldown performed on the
supernatant of the first one, they determined the immobi-
lized gefitinib dissociation constant. In parallel, they
performed competition experiments with different concen-trations of the free compound to obtain the gefitinib IC50,
which combined with the immobilized gefitinib dissociationconstant gave the free compound dissociation constant.
Experiments were performed using SILAC 3-plex.
3 Computational methods
We introduce several computational techniques that are
useful in analyzing drug target lists, starting with rathersimple methods that ignore important aspects of chemical
proteomic data, and expanding with more sophisticated
algorithms that integrate additional domain-specific knowl-
edge. For the sake of concision, we often refer to the iden-
tification of relevant biological pathways as a model problem
but, unless otherwise specified, the methods apply to otherreference biological data sets as well. Moreover, we exem-
plify several methods with the target profile analysis of the
tyrosine kinase inhibitors imatinib, dasatinib, bosutinib, and
bafetinib that are in clinical use or in development as ther-apeutic agents, e.g. against CML and other malignant
diseases. They provide convenient illustrations of common
difficulties.
3.1 Classical mapping and enrichment methods
Lists of drug protein targets can be difficult to interpret
directly, especially if they comprise more than a few familiar
entities. The classical bioinformatic solution to relate
protein lists with existing knowledge is to first map thoseproteins onto descriptions of biological functions and then
to look for significant associations by means of statistical
tests (Fig. 4A). A standard example is to search for biological
pathways that are likely to be modulated by a compound.
There exist databases that describe each pathway with a
graphical representation and a list of involved proteins, e.g.KEGG [43] or NCI-PID [44]. The analysis is performed
ignoring the graphical structure by comparing the number
of protein targets present in a pathway with the total
number of proteins in this pathway and in the humangenome. A proportion of targets found in a pathway that is
larger than what is expected by chance indicates potential
pathway regulation (Fig. 4A). All the significant hits
obtained in a pathway database search are reported with anindication of statistical significance, e.g. a p-value. This
procedure is named as enrichment analysis .Depending on the research project, several databases can
be considered for enrichment analysis (Table 2). While
pathway databases are frequently employed in drug target
analysis, they can be complemented by gene ontology (GO)biological process (BP) descriptions [45], which document a
hierarchy of biological functions from metabolism to
signaling with some disease-related processes included as
well. As GO is not only a catalogue of sets of proteins
associated with a biological process, but it comes with ahierarchy, i.e. it is an ontology , enrichment analyses can be
performed at various levels of details. For instance, the GO
consortium has proposed slimmed ontologies that retain
rather general functions and certain tools propose their own
definitions of GO levels, e.g. DAVID [46]. If the interest is to
discover an unknown mechanism of binding, it is possibleto perform enrichment analyses on the target protein
domains or to use the GO molecular function ontology
(GO MF). More generally, any database containing sets of
proteins that each share any specific characteristic can beused to perform enrichment analyses [47]. Table 2 contains
a list of databases and tools that can be applied for this
purpose.
3.2 The use of target affinities and secondary
interactors
Chemical proteomics usually delivers lists of drug targets
with a notion of ‘‘weight,’’ which can be a direct measure of the target affinity [13, 42], or a rough indicator provided by
the spectral count or a related quantity (protein sequence
coverage, log-transformations, etc.) [26]. Such weights
inform on the importance of the targets and they obviously
have the potential to improve enrichment analyses
performed otherwise with all targets considered equal. Interms of effect strength, high-affinity targets are the best
candidates to be the mediators of biological response regu-
lation. Medium-affinity targets that are abundant might also
play an important role, provided the drug is present at
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sufficient concentration. Spectral counts reflect a mixture
of target affinity and abundance and, although they are
less precise than affinity estimates, they therefore repre-
sent a convenient ad hoc indication of target importance.
In the case of available affinity estimates, more precisetarget weights can be determined. An obvious choice is
the affinity estimate itself, but it is also worth investigat-
ing the product of the affinity and a protein abundance
estimate.As indicated above, drug pulldowns retrieve protein
complexes – completely or partially – and target lists can
contain proteins that do not interact with the drug directly.
One can argue that the important units of molecular func-
tion are in fact the protein complexes, and to have several
members of a complex in the target list should not perturbthe bioinformatic analysis excessively. Indeed, secondary
interactors might even facilitate the analysis since knowl-
edge present in databases is incomplete, and it is possible
that association with a pathway or with any relevant biolo-
gical concept exists for a secondary interactor, but not for thecorresponding drug target. Working with kinase inhibitors,
where nonkinases are likely secondary interactors after
nonspecific binder filtering, we reduced nonkinase weights
in the analysis by a factor of 0.25 [26, 48].Enrichment analysis on the basis of a weighted protein
target list, eventually containing secondary interactor
weights, cannot be performed with standard tools because
the hypergeometric test would no longer be valid. It must be
substituted with another test that models random associa-
tion-weighted scores and, in general, there is no theoretical
Figure 4. General principle of enrichment analysis and its
extensions. (A) Drug protein targets are mapped to sets found in
a database, such as pathways or GO terms. Each pathway
(P1–P6) is composed of a certain number of proteins (small
circles). Determining whether a given pathway is significantly hit
by the target list requires a statistical null-model, i.e. a model
that allows us to compute the probability to find a given number
of targets in a pathway by random chance. Conceptually, the
situation is an experiment where from an urn containing N balls
in total, R of which are red, n balls are drawn randomly and we
want to compute the probability that they contain r red balls. The
urn is the set of all the proteins found in all the pathways of the
database (N 535 in the figure). The R red balls are the targets
(five in the figure, unmapped targets are ignored). The n drawn
balls are the proteins found in a given pathway (9 for P3) and r is
the number of targets in this pathway (3). The probability to find
r targets in a pathway of size n by chance is given by the
hypergeometric probability density P (r |n ,R ,N ), and summing
over all the possible values k Zr gives the pathway p -value.
When this p -value is below a chosen cutoff, say 1%, the pathway
is considered significantly enriched in the protein targets. This
method is equivalent to Fisher’s exact test, and with the numbers
above we find P (r |n ,R ,N )50.0841 and p o0.0946, which is not
significant. (B) With weights w i associated with drug targets, the
score s of a pathway P is the sum of the weights of the proteins
found the pathway (in case a multiplicative score is preferred,
logarithms can be summed). To estimate the distribution of
scores observed by random chance, a large number of subsets P 0of the database proteins with the same size as P are generated.
For each, the weights of the targets in P 0 are summed, and a
histogram of the null-distribution is obtained. The histogram can
be used as such or a theoretical distribution fit, e.g. Gamma, and
s p -value is estimated. (C) Individual pathway p -value thresholds
are adapted to control the FDR among the set of pathways
selected as significant. (D) In TopGO analysis [50], terms of a GO
that are found significant (lower red node) are excluded from
subsequent calculations, whereas nodes containing targets butnot significant (indicated with a ‘‘-’’) contribute to their parents
(upwards arrows). (E) Double filtering pathway selection by
requiring a target to be present in the pathway and coherent
downstream regulation measured in a complementary experi-
ment such as expression proteomics. Here, inhibiting the red
proteins with a drug should increase the expression of the
green protein. (F) Principle of local enrichment, i.e. as imple-
mented in the functional cloud method [58]. A group of adjacent
proteins in the interactome are found that share a common
functional annotation a. Such subnetworks can be scored and
those which obtain a score higher than what is expected by
chance represent biological functions that are likely to be
modulated by the drug.
3
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statistical distribution to do this. However, nonparametricmethods such as permutation tests provide a convenient
solution to model the null-distribution and they can work
with virtually any weighted scoring scheme (Fig. 3B).
3.3 Multiple testing
As a drug target list is searched against a database of path-ways, or any other option mentioned above, the comparison
of the target list with each individual pathway yields a
p-value. To impose a maximum false-positive rate (FPR) a as
a threshold to individual p-values does not provide a
convenient way of controlling the false-positive rate in the
final selection of significant pathways. The p-values areobtained from a null-model that ignores the multiple
selections and, in particular, the number of true positives
present in the database. The most stringent solution is to
obtain individual p-values smaller than a/N , where N is thenumber of pathways described in the database. This solu-
tion is named the Bonferroni correction and it ensures that
the probability to have one or more false positives among
the selected pathways is not more than a. The problem with
this approach (and related less strict ones such as the Sidak
correction) is that sensitivity is clearly reduced, and itignores the number of selected pathways completely,
considering the total database size only.
A more appropriate and widely used solution is to control
the false discovery rate (FDR), which is defined as the rate of
false positives among the selection of significant pathways[49]. FDR is a much more natural concept that is related to
the selection size, which is readily understandable for the
user of a tool. There exist methods to automatically adjust p-
value thresholds on individual pathways such that a prere-quired FDR threshold is met (Fig. 4C) and many common
tools such as DAVID [46] offer this option.
3.4 The use of structures in the enrichment
Different improvements over the enrichment analysis
methods have been proposed to increase specificity. In most
cases, these methods try to make a better use of the struc-
ture of the reference data described in the database. A firstexample is provided by the GO, which can be compared with
drug targets at different levels of details. Full detail analyses
might return very specific and not so relevant annotations as
significant hits. High-level analyses, with all the detailed GOterms mapped onto a few generic ones, might hide inter-
esting differences at higher levels of details. Several authors
have proposed methods to combine both options. An
interesting example is TopGO [50], where detailed GO terms
not found to be significant participate in the analysis of
more generic terms, whereas detailed significant terms areexcluded from further analysis (Fig. 4D). DAVID proposes
only an alternative which is to use the most detailed GO
terms and to group them a posteriori to reduce the output
complexity.
Table 1. Major sources of nonspecific binders and their solutions
Source Solution method Limitations Refs.a)
Binds to the beads
or to the linker
Negative control with blocked beads
Frequent hitter in previous pulldowns Might eliminate correct targets
Better linker (with hydrophilic spacers) [83]
Low-affinity
abundant
proteins
Frequent hitter in previous pulldowns Might eliminate correct targets
Negative control with another compound Must ascertain there is no shared target; works
better with a close chemical structure which is
not always available with no shared target
[84, 85]
Subtract core pr oteome Will n ot remove medium-abun dant no
nspecific binders completely; no chance to
find abundant targets
[23, 86, 87]
Pulldown enrichment versus
core proteome
Will not remove medium-abundant nonspecific
binders completely
[8]
Competition with free compound [13, 31]
Public database of frequent hitters Might eliminate correct targets [88]
Indirect
interactions
Protein family, e.g. kinases only for
kinase inhibitors
Exclude the possibility of unexpected targets [8, 89]
Binary protein interaction from
databases
Reduces possibilities but does not indicate the
direct binders clearly
[53]
Binding domains, predicted
protein interactions
Reduces possibilities but does not indicate the
direct binders clearly; potential higher rate of
false-positive binary interactions compared with
measured binary interactions
[33]
a) Relevant references to either illustrate the successful application of the solution method or some of its limitations.
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As a second example of exploiting structures in the
reference data, it is possible to filter pathway hits by inte-
grating expression proteomics or gene microarray data,where drug-treated cells are profiled. Pathways truly
modulated by a drug should contain upstream targets and
exhibit downstream regulation (Fig. 4E). There also exist
algorithms developed for gene microarray data that score theglobal coherence of multiple hits on pathways, taking their
topology into account [51]. Weighted target lists can be
submitted to such programs.
3.5 Integrating protein interactions – A first systems
biology method
To a certain extent, definitions of pathways and biological
processes are arbitrary and, for sure, many relationships
between proteins and genes are not known [52]. Large
amounts of human protein–protein physical interactions
(PPI) have been collected and stored in public repositoriessuch as IntAct, MINT, BioGRID, HPRD, and InnateDB
[53–57]. The interactome, i.e. the network of all the PPIs,
constitutes a valuable complementary approach to consider
drug targets in a broader context. It has been shown thatproteins sharing physical interactions often share a function
and, consequently, proximity in the interactome helps in
improving the specificity of enrichment analysis. This can
be explained by the frequent participation of proteins in
several functions or pathways depending on associations
with other proteins. Therefore, if a protein A can associatewith B for a certain function or with C for another one,
to find A and B in a drug pulldown indicates that the first
function is modulated and not the second one. This
further underlines the potential interest of secondary
Table 2. Selection of useful resources
Name Typea) Description and references
Classical enrichment analysis
DAVID W Generic, simple, and rich tool (pathways, GO, domains, etc.) [46]
GO W The European Bioinformatics Institute website provides the
GOs [45] with toolsPathway databases D,W K EGG [ 43], N CI-PID [44], BioCar ta (www.biocarta.com), NetPath [90],
and WikiPathways [91] are commonly used databases whose
websites provide mapping tools
Biological pathways
PPI databases D,W MINT [54], IntAct [53], DIP [92], HPRD [57], BioGRID [55], and
InnateDB [56] are common repositories of PPIs
STRING, STITCH D, W STRING is an interaction database that complements PPI data
with interactions inferred from text mining, coevolution, and
simultaneous presence in pathways [93]; STITCH is a related
project that compiles drug–protein interactions [94]
Diseases
OMIM D, W Database of gene–disease associations [95]
Tumors D, W COSMIC [96] and Oncomine (www.oncomine.org) compile
genetic defects found in tumors
Drug databases
Compounds D, W DrugBank integrates information on drugs and their targets [97],
SIDER compiles drug side effects [98], SMPDB provides drug
metabolic pathways [99], MMsINC describes a vast collection
of compounds [100], and ChEBI [101] lists active compounds
TCM database D, W A comprehensive database of active molecules in TCM [74]
Tools
Network display and
analysis tools
T Cytoscape is the most widely used tool with many plug-ins to perform
network analyses and extensions [102], BiologicalNetworks is an
alternative system also offering a rich set of functions [103]
R T R (www.r-project.org) is a data analysis environment and programming
language that provides a comprehensive set of packages to analyze
networks, perform enrichment analysis, etc., via its bioinformatics
extension (www.bioconductor.org)
MeV T A generic tool to perform a multitude of gene expression profile analyses,
also applicable to proteomic data [104]
SwissDock W Ligand–small molecule binding tool to identify true interactors [105]
a) D, database; W, web site with query tools; T, stand alone tool.
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binders to increase precision with regard to the context of
drug action.
A first approach to improve enrichment analyses consists
in finding interactome subnetworks that contain the drugtargets and are enriched for an annotated biological function
[58] (Fig. 4F). We found this method very useful in analyz-
ing the bafetinib target profile, which featured 33 kinases
that yielded a clear CML-relevant association with apoptosis.
As a comparison, direct GO enrichment analysis only yiel-
ded three significant (1%) biological processes, none of which was directly cancer related. KEGG pathway enrich-
ment did not find any significant hit at the 1% significance
level.
3.6 More global analysis of the impact on the
protein interaction network
So far, we have presented methods where the target profile
was analyzed through its mapping onto existing biologicalconcepts to predict the impact of drug treatment. We already
exploited the interactome as a mean of embedding target
profiles in a broader and more neutral context. Here, we go
one step farther by trying to expand the target list with
functionally associated proteins. In this procedure, only the
topology of the interactome matters and existing functional
annotations are ignored.
In principle, adjacency within the interactome oftenimplies a related function due to the common participation
to a complex or a pathway. Directly expanding the target list
with immediate network neighbors is a first option
(Fig. 5A). We occasionally obtained satisfying results, but
this approach usually does not perform well either because
more distant relevant neighbors are missed or too manyadditional proteins are added, resulting in dilution of the
fingerprint of relevant biological functions (Fig. 5B). As an
example of this difficulty, bafetinib profile with direct
interactions increased from 33 initial proteins to 831. GOenrichment analysis yielded 676 1%-significant biological
processes [58], a massive dilution of information.
In general, PPI networks have a small-world or scale-free
topology [59], which means that their organization resem-
bles air traffic routes: every airport is at a distance of one or
two flights from a large hub that connects it to another hub,which is close to the final destination. Therefore, adding
more than one layer of neighbors frequently encounters
highly connected proteins and too many proteins are
included (Fig. 5A). Obviously, the solution to this problem is
A D
B
C
Figure 5. Target profile expansion. (A) Adjacent proteins (1) of a target (red) are likely to share a function. By adding one additional layer of
neighbors (2), functional specificity is often lost because of highly connected nodes. (B) To limit an explosion of the expanded target
profile, proteins that are linked to two targets at least (1) are added only. In a relaxed version, it is possible to consider proteins with one
link to a target only, provided they are directly linked to another such protein (2). (C) Diffusion over a small example network. The two
rectangle nodes indicated by gray arrows represent targets with identical weights. We selected the top 10 scores on the network after
diffusion and we observe synergistic effects that do not select nodes on the basis of their distance to the targets only. (D) We used
bosutinib target profile [26] and selected its 5% expanded profile [48] to illustrate the application of diffusion methods. Triangles are
targets, diffusion score are indicated by a color-scale (red, strong), immune system-related proteins are shown with large nodes and their
names are followed in brackets by the numbers of interactions with other immune system proteins found within the subnetwork. This
subnetwork suggests a high risk of immunosuppressive side effects.
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to constrain the expansion procedure such that proteins areadded only when sufficient evidence of a potential associa-
tion with the targets exists. Proteins with direct PPIs with at
least two targets constitute safe expansions (Fig. 5B) and
they usually increase signal to noise in the functional
analysis [16], i.e. submitting the expanded list to enrichment
analysis yields more relevant hits without generating addi-tional false positives, or even reducing false positives
through FDR control.
There are cases where small target lists cannot be
augmented sufficiently this way and to consider a secondlayer of PPI is necessary. Usually, the list of added proteins
explodes with a second layer and all the target spectrum
specificity is lost. It can be done by imposing that such
proteins have at least one PPI with a target and one together(Fig. 5B) but this is not a complete secondary layer of PPI
and in many cases it is not even sufficient to control theexplosion of the expanded target list.
One elegant solution to the above limitations involves the
notion of diffusion over a network. The concept is rather
natural: starting from a set of seed proteins, in our case thedrug targets, their ‘‘influence’’ diffuses over the network to
give a score to all the other proteins (Fig. 5C). The interest
of this method is that the global network topology is
exploited and synergies between close protein targets confer
increased scores to linked proteins. Distant-related proteinsthat are connected to drug targets through specific paths,
which do not contain highly connected proteins, can bescored relatively high (Fig. 5C). In a neutral context, where it
is not known which protein interaction is crucial for a
treatment or a disease, diffusion methods provide efficient
methods that capture a notion of functional proximity thatfollows the biological intuition. The actual computation of
the diffusion and the weights can be implemented as the
asymptotic distribution of a random walk or first passage
times [48, 60, 61], or more precisely controlled throughdiffusion kernels [60]. In every case, affinity weights can be
used to adjust the importance of the seed proteins. Inter-
estingly, World Wide Web HTML document hyperlinks
constitute a network that has also a small-world topology
and random walk methods are at the heart of widely used
web search engines.On the basis of the weights determined by the diffusion
method, it is possible to select either the top K proteins or,
through many repeated diffusions using random target
spectra, to determine a score threshold [48] and thus obtaina subnetwork. Figure 5D shows the 5% significant bosutinib
subnetwork, which revealed a strong association with
immune system pathways [48]. This immunosuppressive
risk illustrates a plausible application of this method to the
prediction of side effects, since dasatinib, a related kinase
inhibitor in clinical use, has been documented previously tocause such effects [62] and several proteins in the bosutinib
subnetwork, such as LYN [62], BTK [63], TBK1 [64], and SYK
[65, 66], are known to cause immunosuppression upon
inactivation.
3.7 Toward drug efficacy predictions
The next step in the interpretation of chemical proteomic
target profiles consists in relating target profiles to diseases
and patient genetic backgrounds. The main tool to compute
this relationship is an intuitive notion of similarity over the
interactome. Through diffusion methods, we can computethe influence of a drug profile, i.e. we compute a drug
treatment model. We can do the same starting with the
genes causing a disease and thus obtain a disease model.
Comparing the two models, we can estimate drug treatmentefficiencies for a certain disease [48] (Fig. 6A). This method
has its origin in the work of researchers who tried to relate
phenotypes (diseases, patient records, etc.) with genetic
information (genes causing a disease, genetic defects, etc.)through the exploitation of PPI networks. Such studies can
predict unknown new important players in certain pathol-ogies [60, 67, 68], suggest new drug targets, or even and
closely related to our topic suggest candidate molecules to
treat a disease [69].
Direct KEGG pathway enrichment analysis of the imati-nib target profile yielded only two 5%-significant hits, none
of which was CML relevant. Expanding the imatinib target
lists through direct PPIs generated a list of 295 proteins.
When submitted to KEGG enrichment analysis, this exten-ded protein list returned 30 5%-significant hits with apop-
tosis at rank 4. For comparison, the network diffusion
methods combined with drug efficacy scores returned CML
as the top hit [48]. Furthermore, we were able to show that
reasonable estimates of treatment efficacy can be obtained
through drug efficacy scores when comparing four BCR-ABL kinase inhibitors. We also showed that modifying the
disease model to introduce the constitutive activation of the
LYN kinase observed in certain imatinib-resistant patients,
the computation could determine an increased score for the
second-generation compounds dasatininb, bosutinib, andnilotinib, especially designed to target LYN in addition to
BCR-ABL. The imatinib score remained essentially constant
thereby illustrating the potential to segregate patients with
these methods.
We compared the target profiles of all the four inhibitors
with a list of diseases and proposed plausible additionalindications for each compound (Fig. 6B). For instance, lung
cancer was highly ranked for dasatinib and actual efficacy
was shown in another study [18]. Noonan syndrome was
ranked first for bosutinib, which makes sense since severalcancer-associated genes (KRAS, PTPN11, SOS1, RAF1) are
involved [70] and kinase inhibitor treatments are currently
considered for this syndrome. These examples illustrate
how target profiles can be analyzed to predict new drug
indications.
There is a lot of evidence that distinct pathologies mightshare molecular mechanisms [71–73] and, looking forward,
this further increases the opportunities for drug repurpos-
ing: drugs can be related to diseases and, through disease
associations, proposed as therapeutic agents for new disea-
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ses (Fig. 6C). Drug treatment models can also be applied to
compare drugs and potentially assign documented sideeffects or areas of applications to new compounds (Fig. 6C).
3.8 Data sets not from chemical proteomics
One important aspect discussed is the difficulty of inter-preting complex and potentially large drug profiles in a
systems biology perspective. Other fields of research related
to human health deal with similar situations. For instance,TCM research has accumulated a lot of information on
many active molecules present in a TCM drug and their
target proteins [74]. A TCM drug action can be analyzed with
the concepts presented here [75–77]. In fact, these were
pioneered by TCM researchers [78–80]. A similar consid-
eration can be made regarding nutritional science, wherethrough the ingestion of food multiple molecular changes
can be induced in human gourmets, suggesting that nutri-
genomics could benefit from such systems-wide impact
analyses as it is collecting evidence on the impact of specificdiets on human proteins [81, 82].
4 Perspective and concluding remarks
We have presented a brief overview of chemical proteomicmethods and introduced various bioinformatic methods to
analyze drug target profiles. The simplest methods
performed enrichment analyses, where sets of proteins, e.g.
biological pathways, are compared with drug profiles to
detect over-representation of the targets in those sets. Then,modeling the specifics of chemical proteomics better, we
introduced methods that take into account estimations of
drug–protein affinities. Finally, expanding further, we
presented methods that implement systems-wide analysesby embedding the drug profiles in global models of cell
biology such as an interactome.The high degree of interconnectivity among the entities
involved in biological processes is a natural and strong
argument to investigate the positive and negative conse-
quences of drug treatment from the point of view of systemsbiology. We have provided examples of kinase inhibitors that
have a broad spectrum of targets, e.g. bafetinib with 33
kinases, and whose target profiles cannot be analyzed
successfully without the contribution of the human inter-actome data. Furthermore, we have showed how informa-
tion on the molecular causes of diseases can be correlated
with the consequences of drug treatment over the inter-
actome to obtain reasonable predictions of side effects,
additional indications, and match with individual patient
genetic background (Fig. 6A and B). More generally, themeasurement of target profiles provides an elegant way to
compare drugs with each other on the basis of their action
on pathways or the interactome (Fig. 6C) and, in combina-
tion with similar comparisons realized with diseases, itmakes it possible to build a complex set of relationships that
allow transferring information from one well-characterized
compound to a new molecule or to postulate side effects.
It is well known that patients must be segregated to
improve treatment efficacy, limit their cost, and reduce new
compound attrition rates. The spectacular improvementsachieved in DNA-sequencing technologies open avenues in
obtaining very detailed information on patient genomes. We
believe that, at the other end of the spectrum, chemical
proteomics and the systems biology analysis of its data has a
Figure 6. Scoring drug treatment efficacy. (A) On the basis of a
diffusion method and drug targets with their affinities, a drug
treatment model is built. Similarly, genes or proteins causing a
disease can be used to build a disease model, eventually inte-
grating patient-specific information such as genetic abnormal-
ities. At the intersection of the two models, a drug efficacy score
can be computed, e.g. multiplying the two diffusion scores given
by each model to individual proteins and summing over the
proteins in the intersection. (B) Two obvious applications of
treatment efficacy scores. Drugs can be compared for a chosen
disease or patient to predict an adequate treatment, i.e. to
implement personalized medicine. Conversely, a chosen
compound can be compared with disease models to find new
indications for this compound (‘‘drug repurposing’’). (C) Drugs
cannot only be compared with diseases but relationships
between drugs can be exploited to transfer knowledge available
for some compounds to a new compound and, identically,
shared molecular bases of distinct pathologies can be exploited
to predict side effects and repurpose drugs.
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fundamental role to play in order to link patient specificdigital models to accurate models of drug action.
The authors thank Professor Shao Li and Professor Jing Zhao
for their help on TCM applications.
The authors have declared no conflict of interest.
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