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PLoS Comput Biol. 2009 August; 5(8): e1000474. Published online 2009 August 21. doi: 10.1371/journal.pcbi.1000474. | PMCID: PMC2727484 |
Copyright Adams 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. A Mapping of Drug Space from the Viewpoint of Small Molecule
Metabolism James Corey Adams,#1 Michael J. Keiser,#2 Li Basuino,3 Henry F. Chambers,3 Deok-Sun Lee,4,5,6 Olaf G. Wiest,7 and Patricia C. Babbitt8,9,10* 1Graduate Program in Pharmaceutical Sciences and Pharmacogenomics,
University of California, San Francisco, California, United States of
America 2Graduate Program in Bioinformatics, University of California, San
Francisco, California, United States of America 3San Francisco General Hospital, University of California San Francisco,
San Francisco, California, United States of America 4Center for Complex Network Research and Departments of Physics, Biology,
and Computer Science, Northeastern University, Boston, Massachusetts, United
States of America 5Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston,
Massachusetts, United States of America 6Department of Natural Medical Sciences, Inha University, Incheon,
Korea 7Department of Chemistry and Biochemistry, University of Notre Dame, Notre
Dame, Indiana, United States of America 8Department of Bioengineering and Therapeutic Sciences, University of
California, San Francisco, California, United States of America 9Department of Pharmaceutical Chemistry, University of California, San
Francisco, California, United States of America 10California Institute for Quantitative Biosciences, University of
California, San Francisco, California, United States of America Philip E. Bourne, Editor University of California San Diego, United States of America Received April 9, 2009; Accepted July 16, 2009. Small molecule drugs target many core metabolic enzymes in humans and pathogens,
often mimicking endogenous ligands. The effects may be therapeutic or toxic, but
are frequently unexpected. A large-scale mapping of the intersection between
drugs and metabolism is needed to better guide drug discovery. To map the
intersection between drugs and metabolism, we have grouped drugs and metabolites
by their associated targets and enzymes using ligand-based set signatures
created to quantify their degree of similarity in chemical space. The results
reveal the chemical space that has been explored for metabolic targets, where
successful drugs have been found, and what novel territory remains. To aid other
researchers in their drug discovery efforts, we have created an online resource
of interactive maps linking drugs to metabolism. These maps predict the
“effect space” comprising likely target enzymes for each of
the 246 MDDR drug classes in humans. The online resource also provides
species-specific interactive drug-metabolism maps for each of the 385 model
organisms and pathogens in the BioCyc database collection. Chemical similarity
links between drugs and metabolites predict potential toxicity, suggest routes
of metabolism, and reveal drug polypharmacology. The metabolic maps enable
interactive navigation of the vast biological data on potential metabolic drug
targets and the drug chemistry currently available to prosecute those targets.
Thus, this work provides a large-scale approach to ligand-based prediction of
drug action in small molecule metabolism. All humans, plants, and animals use enzymes to metabolize food for energy, build
and maintain the body, and get rid of toxins. Drugs used to clear infections or
cure cancer often target enzymes in bacteria or cancer cells, but the drugs can
interfere with the proper function of human enzymes as well. Recent studies have
mapped drugs to enzymes and many other targets in humans and other organisms,
but have not focused on metabolism. In this study, we present a new method to
predict what enzymes drugs might affect based on the chemical similarity between
classes of drugs and the natural chemicals used by enzymes. We have applied the
method to 246 known drug classes and a collection of 385 organisms (including 65
National Institutes of Health Priority Pathogens) to create maps of potential
drug action in metabolism. We also show how the predicted connections can be
used to find new ways to kill pathogens and to avoid unintentionally interfering
with human enzymes. Drug developers have long mined small molecule metabolism for new drug targets and
chemical strategies for inhibition. The approach leverages the “chemical
similarity principle” [1] which states that similar molecules likely have
similar properties. Applied to small molecule metabolism, this principle has
motivated the search for enzyme inhibitors chemically similar to their endogenous
substrates. The approach has yielded many successes, including antimetabolites such
as the folate derivatives used in cancer therapy and the nucleoside analog pro-drugs
used for antiviral therapy. However, drug discovery efforts also frequently falter
due to unacceptable metabolic side-effect profiles or incomplete genomic information
for poorly characterized pathogens [2]– [4]. With the recent availability of large datasets of drugs and drug-like molecules,
computational profiling of small molecules has been performed to create global maps
of pharmacological activity. This in turn provides a larger context for evaluation
of metabolic targets. For example, Paolini et al. [5] identified 727 human
drug targets associated with ligands exhibiting potency at concentrations below 10
µM, thereby creating a polypharmacology interaction network organized by
the similarity between ligand binding profiles. Keiser et al. [6] organized known drug
targets into biologically sensible clusters based solely upon the bond topology of
65,000 biologically active ligands. The results revealed new and unexpected
pharmacological relationships, three of which involved GPCRs and their predicted
ligands that were subsequently confirmed in vitro. Cleves et al.
[7]
also rationalized several known drug side effects and drug-drug interactions based
upon three-dimensional modeling of 979 approved drugs. However, despite the clear
rationale and past successes in applying ligand-based approaches to drug discovery,
global mapping between drugs and small molecule metabolism, the goal of this study,
has been hindered by both methodological challenges and incomplete genomic
information. The relatively recent availability of metabolomes for numerous
organisms allows a fresh look on a large scale [8]– [13]. In this work, we link the chemistry of drugs to the chemistry of small molecule
metabolites to investigate the intersection between small molecule metabolism and
drugs. The Similarity Ensemble Approach (SEA) [6] was used to link
metabolic reactions and drug classes by their chemical similarity, measured by
comparing bond topology patterns between sets of molecules. Two types of molecule
sets are used in this work. The first comprises drug-like molecules known to act at
a specific protein target, and the second comprises the known substrates and
products of an enzymatic reaction. While this approach is complementary to target
and disease focused methods [5], [14]– [23], neither
protein structure nor sequence information is used in the comparisons. Thus, these
links provide an orthogonal view of metabolism based only upon the chemical
similarity between existing drug classes and endogenous metabolites. To provide the results in the context of metabolism, drug
“effect-space” maps were also created. For each of the 246 drug
classes investigated in this work, effect-space maps enable visualization of the
chemical similarities between drugs and metabolites painted onto human metabolic
pathways, allowing a unique assessment of potential drug action in humans. In
addition, to aid target discovery in pathogens, 385 species-specific effect-space
maps were created to show the predicted effect-space of currently marketed drugs,
painted onto metabolic pathways representing target reactions in model organisms and
pathogens. Examples of these maps are provided below and their applications in
predicting drug action, toxicity, and routes of metabolism are discussed. To enable
facile exploration of the drug-metabolite links established by this analysis,
interactive versions of both sets of maps are available at http://sea.docking.org/metabolism. Finally, using methicillin-resistant Staphylococcus aureus (MRSA), a
major pathogen causing both hospital- and community-acquired infections that is
resistant to at least one of the antibiotics most commonly used for treatment [24]– [28] as an example, we show
by retrospective analysis the use of species-specific maps for discovery and
evaluation of drug targets. This also illustrates how additional types of biological
information can be incorporated to enhance the value of these analyses. Drug-metabolite links reproduce known drug-target interactions To evaluate the chemical similarity between drug classes and metabolic reactions,
links between sets of metabolic ligands and sets of drugs were generated
according to SEA () [6]. The similarity metric consists of a descriptor,
represented by standard two-dimensional topological fingerprints, and a
similarity criterion, the Tanimoto coefficient (Tc). Expectation (E) values were
calculated for each set pair by comparing the raw scores to a background
distribution generated using sets of randomly selected molecules (see Methods for further details). To represent metabolic ligand sets, the MetaCyc
database, which includes enzymes from more than 900 different organisms
catalyzing over 6,000 reactions, was used [12]. The substrates and
products of each enzymatic reaction were combined to form a reaction set, each
of which was required to contain at least two unique compounds ( Datasets S1 and S2). Ubiquitous molecules called common carriers, which frequently play
critical roles in reaction chemistry but do not distinguish the function of a
specific enzyme, were removed, leaving a total of 5,056 reactions involving
4,998 unique compounds. To represent drugs, a subset of 246 targets of the MDL
Drug Data Report (MDDR) collection, which annotates ligands according to the
targets they modulate, was used ( Datasets S3 and S4) [30]. These sets contain 65,241 unique ligands with a
median and mean of 124 and 289 ligands per target, respectively. Overall, 246
drug versus 5,056 reaction set comparisons involving
1.39×10 9 pairwise comparisons were made. Although drugs and metabolites typically differ in their physiochemical
properties, significant and specific similarity links nonetheless emerged. Using
SEA at an expectation value cutoff of
E  = 1.0×10 −10,
a previously reported cutoff for significance [6], 54% (132
of 246) of drug sets link to an average of 43.7
(median = 10) or 0.9% of metabolic
reactions. None of the remaining 46% (114 of 246) of drug sets link
to any metabolic reaction sets. For instance, while the α-glucosidase
drug set links to the α-glucosidase reaction
(E = 1.00×10 −51),
the thrombin inhibitor drug set does not link significantly with any metabolic
reaction. The thrombin inhibitor drug set targets the serine protease thrombin,
which does not participate in small molecule metabolism, but rather plays a role
in the coagulation signaling cascade. Likewise, 40% (2,044 of 5,056)
of metabolic reactions hit an average of 2.8
(median = 2) or 1.1% of drug sets at
expectation value
E = 1.0×10 −10
or better. For instance, the metabolite set for retinal dehydrogenase reaction
set links, as expected, to the retinoid drugs at
E = 3.05×E −98,
but the valine decarboxylase reaction, which is not an MDDR drug target, does
not link significantly to any drug sets. These strikingly similar results
suggest both broad coverage (54% of drug sets and 40% of
metabolite sets) and specificity (single sets link to just 0.9% of
metabolite sets and 1.1% of drug sets, respectively). For full
results, see Dataset S5. To determine the utility of the method for recovery of known drug-target
interactions, it was hypothesized that chemical similarity between MetaCyc
reaction sets and corresponding MDDR drug sets could specifically recover the
known drug-target interactions. The 246 MDDR drug set targets include 62 enzymes
that could be mapped to MetaCyc via the Enzyme Commission (EC) number [31]
describing the overall reaction catalyzed [32]. The results
show that all 62 reaction sets for these targets link to at least one MDDR drug
set. The majority of best hits (42 out of 62) were found at expectation values
of E = 1.0×10 −10
or better ( Table 1). At expectation values better than
E = 1.0×10 −25,
61% (19 of 31) of best hits recover either the specific known target
or another enzyme in the same pathway. Examples of specific compounds linked by
this analysis are given in for a selected group of these best-scoring hits. | Table 1Metabolic enzyme targets and their best links to MDDR. |
Other links recovered off-pathway hits, which often reflect known
polypharmacology that is well-documented. For example, the glycinamide
ribonucleotide formyltransferase (GART) inhibitor drug set hits both the GART
reaction set
(E = 1.55×10 −82)
and the off-pathway but pharmacologically related antifolate target
dihydrofolate reductase (DHFR)
(E = 1.02×10 −134).
Other off-pathway hits reflect biological connections, or physical connections,
between targets. For example, the adenosine deaminase reaction set links to the
A 1 adenosine receptor agonist drug set
(E = 7.69×10 −159)
( Table 1) capturing the known interaction between A 1 adenosine
receptors and adenosine deaminase on the cell surface of smooth muscle cells
[33]. Considering only the stringent case of exact
matches based on EC numbers, a Mann-Whitney rank-sum test (also referred to as
the U-test) shows that the expectation values for links between reaction sets
and drug sets of known drug target enzymes were significantly better than the
expectation values for links to reaction sets of non-target enzymes, i.e., 62
known enzyme targets were recovered in a background of 4,920 non-target
“other” enzymes at a statistical significance of
P = 2.01×10 −6. In addition to recapitulating many known drug-target interactions, the links
identified by these comparisons also suggest new hypotheses about drug-target
interactions. One such new prediction involves the phospholipase A2 (PLA2)
inhibitor drug class. The substrates and products of PLA2 recapitulate its known
link to the PLA2 inhibitor drug set
(E = 9.82×10 −26),
however, the sterol esterase reaction returns an even better score against the
PLA2 inhibitor set
(E = 3.18×10 −44)
( Table 1). Although this predicted pharmacological relationship has, to our
knowledge, not been previously documented, the result is consistent with the
known biological relationship between PLA2 and sterol esterase. Both enzymes are
secreted by the pancreas and require phosphatidylcholine hydrolysis to
facilitate intestinal cholesterol uptake [34]. Thus, this link
suggests that therapeutic agents directed against PLA2 may also inhibit sterol
esterase, perhaps even more strongly than their intended target. Human drug “effect-space” maps detail interactions
between drug classes and enzyme targets To present links between small molecule metabolites and drugs in the context of
their known (and potential) metabolic targets, metabolic
“effect-space” maps for currently marketed drugs were
generated for each of the 246 drug classes investigated in this work. These maps
enable visualization of the chemical similarities between drugs and metabolites
painted onto human metabolic pathways, illustrating potential interactions
between an individual drug class and specific metabolic enzymes in humans.
Examples include the nucleoside reverse transcriptase, dihydrofolate reductase,
and thymidylate synthase inhibitors which target pyrimidine nucleotide
metabolism and biosynthesis of the essential coenzyme folate ( and Table 2). Using the canonical human metabolic pathways from HumanCyc [35], a
subset of the BioCyc [12] database collection, reactions in each
metabolic network have been colored according to their similarity to known drug
classes (). While Table 1 presents only the top link for each of 62 enzyme targets in MetaCyc
against the 246 MDDR drug classes, the networks in detail all significant hits for selected drug classes against the
pyrimidine and folate pathways. Interactive versions of these maps, one for each
of the 246 drug classes included in our analysis, are available online (see
below). | Table 2Links between selected drug classes and top ranked metabolic
reactions. |
It has previously been shown that chemical similarity between known drugs often
suggests novel drug-target interactions [5]– [7], [14]. Consistent with
these observations, effect-space maps such as those shown in can also be used to exploit chemical similarities between drugs and
metabolites to indicate potential routes of drug metabolism and toxicity [3], [11], [36], [37]. For
example, the nucleotide reverse transcriptase inhibitors (NRTIs) used in HIV
therapy are administered as pro-drugs. The effect-space map reflects this route
of NRTI metabolism leading to viral inhibition. The top three hits yielded by
the NRTI drug set queried against human metabolism – thymidine kinase
(E = 3.48×10 −26),
thymidylate kinase
(E = 7.48×10 −28),
and deoxythymidine diphosphate kinase
(E = 1.54×10 −24)
( reaction numbers 2, 3, and 4; additional results in Table 2) – successively phosphorylate the NRTI pro-drugs into the
pharmacologically active NRTI triphosphates [38], [39]. The
viral reverse transcriptase enzyme then incorporates the fully phosphorylated
NRTIs into the growing DNA strand, thereby terminating transcription of the
viral DNA. In this example, considerable toxicity mitigates the therapeutic
value of inhibiting viral DNA transcription since the phosphorylated NRTIs
directly inhibit human nucleotide kinases and mitochondrial DNA pol-γ.
They also may be incorporated by pol-γ into the growing human
mitochondrial DNA strand, and once incorporated are inefficiently excised by DNA
pol-γ exonuclease [40]. Thus, the effect-space map illustrates both
the route of metabolism and a mechanism of toxicity for NRTIs in humans. Drug effect-space maps also offer a broad glimpse of potential human metabolic
interactions predicting new “polypharmacology”. From the
ligand perspective, “drug polypharmacology” refers to a
single drug or drug class that hits multiple targets. For example, dihydrofolate
reductase (DHFR, reaction number 7 in ) uses NADPH to reduce 7,8-dihydrofolate to tetrahydrofolate. Antifolate
drugs inhibit DHFR, and, as expected, the DHFR drug set recovers the DHFR
reaction substrates and products as the top similarity hit in human metabolism
(E = 1.46×10 −82)
(, Table 2, ). However, at least 20 other reactions also use folate coenzymes in
human metabolism [41]– [43]. Accordingly, SEA
finds additional links between the DHFR drug set and established antifolate
targets outside the pyrimidine and folate biosynthesis pathways such as serine
hydroxymethyltransferase (SHMT,
E = 2.68×10 −44),
phosphoribosyl-aminoimidazole-carboxamide formyltransferase (AICAR
transformylase,
E = 2.21×10 −39),
and phosphoribosyl-glycinamide formyltransferase (GART,
E = 2.21×10 −39)
( Table 2). The effect-space maps in illustrate the results from Table 2 and in a single view, illustrating drug polypharmacology with respect to
critical metabolic pathways. Alternatively, from the target perspective, “target
polypharmacology” may refer to a single target being modulated by
multiple classes of drugs. For instance, thymidylate synthase (TS) is another
classic antifolate target that uses a folate coenzyme to methylate deoxyuridine
phosphate, generating deoxythymidine phosphate [44]– [47]. As
expected, the TS reaction links to known antifolate drug classes such as GART
inhibitors
(E = 4.76×10 −73)
and DHFR inhibitors
(E = 1.91×10 −48)
( Table 3 and ). However, TS is also effectively inhibited by uracil analogs such as
fluoropropynyl deoxyuridine, which is not a folate, but rather a pyrimidine
analog. Accordingly, the TS reaction also links to reverse transcriptase
inhibitors, which include fluoropropynyl deoxyuridine and additional pyrimidine
analogs such as azidothymidine (AZT)
(E = 5.68×10 −11)
(). The target polypharmacology of the thymidylate synthase enzyme is
mirrored by the drug polypharmacology of the thymidylate synthase inhibitors.
The TS inhibitors link not only to the reactions of deoxyribonucleotide
biosynthesis including thymidylate synthase
(E = 2.54×10 −75),
but also the GART
(E = 1.50×10 −60)
and DHFR
(E = 1.96×10 −123)
reactions ( and Table 2). Thus, SEA recapitulates the known polypharmacology of TS. Effect-space
maps illustrate and clarify these pharmacological relationships. | Table 3Links between selected metabolic reactions and top ranked drug
classes. |
Species-specific effect-space maps for pathogens and model organisms The great diversity of metabolic strategies, pathways, and enzymes present in
humans, model organisms, and pathogenic species presents both opportunities and
significant barriers to drug discovery. To address these issues,
species-specific effect-space maps were created for each of 385 organisms from
the BioCyc Database Collection. Target reactions existing in common and
differentially between each of these species and humans are shown in these
metabolic maps. As with the human effect-space maps, this set of maps is
available in interactive form online. To show how these maps may be used to
provide a context for drug discovery, MRSA is used as an example (). The global view of drugs and metabolism provided by this
species-specific map illustrates some of the daunting challenges to the
selection of tractable metabolic drug targets in this organism. As described for , each node in the MRSA network in represents one reaction set, the substrates and products of a single
metabolic reaction. Edges connect the reactions according to canonical BioCyc
MRSA pathways. Each reaction in the network has been colored according the
expectation value of the best link between the reaction set and any of the 246
MDDR drug sets. Lighter colored nodes have higher expectation values indicating
less drug-like reaction sets, while darker colored nodes indicate more drug-like
reaction sets. To provide therapeutic context, reactions that are also present
in human metabolism have been faded, indicating that drug sets targeting these
enzymes in MRSA may have the undesirable potential to inhibit the human enzymes
as well. As with the other organisms represented in our online maps, most
reactions in the MRSA subset have little chemical similarity to any MDDR drug
set. Although 74% of the 469 MRSA metabolic reactions have measurable
similarity to at least one MDDR drug set, only 36% of these links had
expectation values of
E = 1.0×10 −10
or better. Several complete pathways of diverse chemical classes, including
shikimic acid, phospholipid, peptidoglycan, teichoic acid, and molybdenum
cofactor biosynthesis, lack links to any drug set at all. Only 18 of the 469
MRSA metabolic reactions are already known to be drug targets in MDDR. Fourteen
of these are represented in (as diamonds), but all 18 of these also appear in humans. Enzymes that
catalyze these reactions in humans would likely be vulnerable to inhibitors
developed against these MRSA targets, putting those drugs at risk for toxicity. illustrates how additional information can be used to further filter
potential metabolic targets by painting additional biological or genomic data
onto a species-specific map. Since successful modulation of a target may not
alone be sufficient to kill a pathogen due to the presence of redundant pathways
for the formation of critical metabolites, integration of such additional
information into a metabolic map may provide added value in addressing the
multi-dimensional challenges of drug discovery. Flux balance analysis of
metabolic networks was used by several of the authors of this work to identify
essential enzymes and metabolites required for the formation of all necessary
biomass components for 13 strains of Staphylococcus
aureus including the methicillin-resistant N315 strain
(MRSA) [48]. Using these results, 39 essential reactions and
19 synthetic lethal reaction pairs could be mapped to our dataset (), highlighting those reactions for which inhibition is most likely to
result in the death of the organism. Several of these reactions have been
successfully targeted by currently marketed drugs, such as the previously
discussed antifolate targets DHFR
(E = 1.02×10 −134),
thymidylate synthase
(E = 2.54×10 −75),
and dihydrofolate synthase
(E = 1.35×10 −70).
This retrospective result illustrates the potential of such additional
information in enriching for targets and drug chemistry that have been proven
accessible. Other targets and pathways have not yet yielded successful drugs but
are under investigation in MRSA or other pathogens, such as the shikimate
pathway [49] in aromatic amino acid biosynthesis and the
histidine biosynthesis pathway [50]. The combination of the essentiality data with the drug space mapping emphasizes
the challenges to drug discovery against MRSA. Thus, while species-specific
antifolates do exist, many antifolates such as methotrexate used in cancer
therapy cause severe toxicity [43]. To avoid such toxicity, 14 of the 39
essential MRSA reactions that are also present in humans can be excluded from
further consideration as drug targets in MRSA. A compilation of all of the metabolic network maps generated in this study is
available at http://sea.docking.org/metabolism. These include interactive
versions of the human effect-space maps shown in , one for each of the 246 MDDR drug classes analyzed in this work, and
385 species-specific maps such as that shown in . The species-specific maps were generated from the BioCyc database
public collection, a compendium of 385 model organisms and pathogens whose
genomes have been sequenced and their metabolomes deciphered. Of these, 65 have
been designated as Priority Pathogens by the National Institute of Allergy and
Infectious Diseases (NIAID) and include Bacillus anthracis,
Brucella melitensis, Cryptosporidium
parvum, Salmonella, SARS, Toxoplasma
gondii, Vibrio cholerae, and Yersinia pestis
[51].
Browse and similarity search tools are also provided, allowing exploration of
the metabolic reaction sets and current drug classes used in this work, as well
as comparison to user-defined custom ligand sets. These interactive tools enable
facile exploration between the vast biological data on potential metabolic drug
targets in these organisms and the drug chemistry currently available to
prosecute those targets. A key product of this study is the construction of drug-metabolite correspondence
maps that provide both a global view and a more contextual picture of predicted drug
action in human metabolism than has been previously available. Several aspects of
these maps deserve particular emphasis. First, despite the differences in
physiochemical properties of most drugs and small molecule metabolites, numerous
links arise between drugs and metabolism. Viewed in the context of metabolic
networks, the pharmacological relationships predicted by these links can be readily
interpreted in a way that is biologically sensible. Moreover, as shown by both the
drug effect space maps and species-specific maps, our retrospective analyses confirm
that biologically and pharmacologically significant connections can be recovered,
capturing known polypharmacology and revealing the relevant chemotypes previously
explored in drug development. The metabolome-wide exploratory tools provided with
these map sets also enable a new way to interrogate the links between drugs and
metabolism that will likely be useful for prediction of new targets and to indicate
routes of drug metabolism and toxicity. Further, by integrating biological
information such as essentiality and synthetic lethal analyses with the metabolic
context, our approach allows users to focus evaluation of potential targets around
specific types of data simply by painting the results on to metabolic maps. With respect to the coverage of drug links across small molecule metabolism that this
study provides, we note that the SEA method relies solely upon the chemical
similarity of ligands to establish links between drug sets and reaction sets. Based
on these links, and the biologically sensible connections shown in the results, we
infer that a particular drug class may act on a certain target. However, drugs may
also act against an enzyme active site without resembling the endogenous substrate,
or by allosteric regulation at an entirely different site. The SEA method, as
applied here to the substrates and products of metabolic reactions, does not capture
these additional drug-target links. Other viable strategies are available for
targeting metabolic enzyme active sites that use principles unrelated to the
ligand-drug similarities that are the focus of our approach [52]– [55]. For
instance, Tondi et al. designed novel inhibitors of thymidylate synthase that
complemented the three dimensional structure of the active site. Five high-scoring
compounds selected for testing were dissimilar to the substrate but bound
competitively with it [55]. While many classical kinase inhibitors interact
directly with the ATP binding site, imatinib (tradename Gleevec) represents a new
generation of allosteric protein kinase inhibitors that alter the kinase
conformation to prevent ATP binding. Other allosteric kinase inhibitors prevent the
protein substrate from loading [52]. While a quantitative determination of the proportion of drug-target links that cannot
be accessed by our approach is beyond the scope of this study, we can provide a
rough estimate for the frequency of such cases based on the results reported in
Table 1. Of the 62 known enzyme targets in MetaCyc, 42 (68%) the
substrate/product metabolite sets show significant chemical similarity to at least
one MDDR drug set, establishing a reasonable first pass estimate for the percentage
of current enzyme targets accessible to this approach. Furthermore, 40%
(2,044 of 5,056) of all MetaCyc reaction sets linked at
E = 1.0×10 −10 or
better to MDDR, with each reaction linking to an average of just 2.8 MDDR drug sets.
These results indicate broad and specific coverage of metabolism, and suggest that
numerous additional enzyme targets may be accessible by the method presented here. Conclusion Using the SEA method, we have shown that comparison between ligand sets
representing MDDR drug classes and ligand sets representing the substrates and
products of metabolic reactions yields statistically significant links between
known drugs and enzyme targets. Because the method is based on chemical
similarity and requires only information from these molecule sets rather than
the sequence, structure or physiochemistry of the targets, this ligand-based
approach is independent from, and complementary to, protein structure and
sequence based methods. Our results also suggest the potential of this method
for predicting previously unknown interactions between drug classes and
metabolic targets, recovering routes of metabolism and toxicity in humans, and
identifying potential drug targets (as well as challenges for target discovery)
in emerging pathogens. Thus, by mapping the chemical diversity of drugs to small
molecule metabolism using ligand topology, this work establishes a computational
framework for ligand-based prediction of drug class action, metabolism, and
toxicity. Compound sets All compounds, both drugs and metabolites, are represented using Daylight
SMILES strings [29]. Sets comprised of isomers with unique
compound names were retained, even though stereochemistry was later removed
as part of the molecule fingerprinting process. Ligand sets Reaction sets were extracted from the 8.15.2007 release of MetaCyc based upon
the substrates and products annotated to each reaction. Two filters were
applied. First, the ten most common metabolites based on the number of
occurrences in the MetaCyc metabolic network were removed: water, ATP, ADP,
NAD, pyrophosphate, NADH, carbon dioxide, AMP, glutamate, and pyruvate.
Second, each reaction set was required to include at least two unique
compounds, as indicated by a MetaCyc or a MDDR unique compound id. Drug sets Drug sets were extracted from the MDDR, a compilation of about 169,000
drug-like ligands in 688 activity classes, each targeting a specific enzyme
(designated by the Enzyme Commission (E.C.) number). The subset of this
database for which mappings between enzymes and the MDDR drug classes were
available was used. These mappings were based on a previous study that maps
E.C. numbers, GPCRs, ion channels and nuclear receptors to MDDR activity
classes [32]. Only sets containing five or more
ligands were used. Salts and fragments were removed, ligand protonation was
normalized and duplicate molecules were removed. Of the 688 targets in the
MDDR, 97 were excluded as having too few ligands (<5), and another
345 targets were excluded because their definitions did not describe a
molecular target, e.g., drugs associated only with an annotation such as
“Anticancer” were not used. The remaining 246 enzyme
targets were together associated with a total of 65,241 unique ligands, with
a median and mean of 124 and 289 drug ligands per target. For further
details, see Keiser et al. [6]. Set comparisons All pairs of ligands between any two sets were compared using a pair-wise
similarity metric, which consists of a descriptor and a similarity
criterion. For the similarity descriptor, standard two-dimensional
topological fingerprints were computed using the Scitegic ECFP4 fingerprint
[56]. The similarity criterion was the widely used
Tanimoto coefficient (Tc) [57]. For set comparisons, all pair-wise Tcs
between elements across sets were calculated, and those scoring above a
threshold were summed, giving a raw score relating the two sets. The
Tanimoto coefficient threshold of 0.32 was determined according to a
previously published method based upon fit to an extreme value distribution
[6]. A model for random similarity similar to that
used by BLAST [58] was used to generate expectation values
(E) which are used to describe the strengths of relationships discovered
using this protocol [6]. All scores reported here are based upon
the background distribution and cutoff scores generated using the drug sets
extracted from the MDDR collection. For further details, see Keiser et al.
[6]. Network visualization was performed in
Cytoscape 2.6.2 [59] using the γ-files hierarchical
layout algorithm. MRSA essentiality and synthetic lethal analysis Essentiality and synthetic lethal data generated as described earlier [48].
Briefly, the metabolic network was reconstructed from the genome to include
all reactions that have an active flux The essentiality of a given enzyme
was then assessed by the effect of the removal of that enzyme on biomass
production. Similarly, synthetic lethal pairs can be identified by
systematic pairwise deletion of enzymes and recalculation of biomass
production in an ideally rich medium. Dataset S1 MetaCyc reaction sets (0.47 MB TXT) Dataset S2 SMILES describing the molecular strucutre of MetaCyc reaction substrates and
products (0.25 MB TXT) Dataset S3 MDDR drug sets (0.51 MB TXT) Dataset S4 SMILES describing the molecular structure of MDDR ligands. (4.45 MB TXT) Dataset S5 E-values for links between MDDR drug sets and MetaCyc reaction sets (3.12 MB CSV) We thank Elsevier MDL for the MDDR and Scitegic for PipelinePilot. 1. Johnson M, Lajiness M, Maggiora G. Molecular similarity: a basis for designing drug screening
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