Conceived and designed the experiments: JCA MJK OGW PCB. Performed the experiments: JCA MJK LB. Analyzed the data: JCA OGW. Contributed reagents/materials/analysis tools: MJK D-SL HFC. Wrote the paper: JCA PCB.
The authors have declared that no competing interests exist.
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”
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.
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)
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
Finally, using methicillin-resistant
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 (
SEA compares groups of ligands based upon bond topology. Example ligand sets include the thymidylate synthase reaction set, composed of the reaction substrates and products, and the nucleotide reverse transcriptase inhibitor (NRTI) drug set, which includes known inhibitors of the nucleoside reverse transcriptase enzyme. Fingerprints representing the bond topology of each molecule are generated. Raw scores between sets are calculated based upon Tanimoto coefficients between fingerprints for all molecule pairs. Finally, the raw scores are compared to a background distribution to determine the expectation value (E) representing the chemical similarity between sets. See
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
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
Enzyme Target |
EC# | Best Hit MDDR Drug Set | Best Hit E-value |
Adenosylmethionine decarboxylase | 4.1.1.50 | S-Adenosyl-L-Homocysteine Hydrolase Inhibitor | 2.71E-216 |
Adenosine deaminase | 3.5.4.4 | Adenosine (A1) Agonist | 7.69E-159 |
Dihydrofolate reductase | 1.5.1.3 | Glycinamide Ribonucleotide Formyltransferase Inhibitor | 1.02E-134 |
Catechol O-methyltransferase | 2.1.1.6 | S-Adenosyl-L-Homocysteine Hydrolase Inhibitor | 4.67E-127 |
Purine-nucleoside phosphorylase | 2.4.2.1 | Adenosine (A1) Agonist | 8.35E-105 |
Ribose-phosphate pyrophosphokinase | 2.7.6.1 | S-Adenosyl-L-Homocysteine Hydrolase Inhibitor | 4.33E-91 |
3′,5′-cyclic-nucleotide phosphodiesterase | 3.1.4.17 | S-Adenosyl-L-Homocysteine Hydrolase Inhibitor | 1.23E-77 |
Guanylate cyclase | 4.6.1.2 | Purine Nucleoside Phosphorylase Inhibitor | 2.68E-60 |
RNA-directed DNA polymerase | 2.7.7.49 | S-Adenosyl-L-Homocysteine Hydrolase Inhibitor | 1.06E-52 |
Sterol esterase | 3.1.1.13 | Phospholipase A2 Inhibitor | 3.18E-44 |
Ribonucleoside-diphosphate reductase | 1.17.4.1 | S-Adenosyl-L-Homocysteine Hydrolase Inhibitor | 2.47E-38 |
Diaminopimelate epimerase | 5.1.1.7 | Nitric Oxide Synthase Inhibitor | 2.43E-24 |
Membrane dipeptidase | 3.4.13.19 | Nitric Oxide Synthase Inhibitor | 2.81E-23 |
Sterol O-acyltransferase | 2.3.1.26 | Adenosine (A2) Agonist | 4.95E-22 |
Hydroxymethylglutaryl-CoA reductase (NADPH) | 1.1.1.34 | Adenosine (A2) Agonist | 4.95E-22 |
IMP dehydrogenase | 1.1.1.205 | Adenosine (A1) Agonist | 8.98E-17 |
ATP-citrate (pro-S-)-lyase | 4.1.3.8 | Adenosine (A2) Agonist | 1.83E-15 |
Glutamate–cysteine ligase | 6.3.2.2 | Nitric Oxide Synthase Inhibitor | 2.71E-11 |
Dopamine-beta-monooxygenase | 1.14.17.1 | Adrenergic (beta1) Agonist | 3.81E-11 |
Nucleoside-diphosphate kinase | 2.7.4.6 | P2T Purinoreceptor Antagonist | 2.76E-10 |
Exact matches (the enzyme is the canonical target of the best MDDR hit) are shown in
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 A1 adenosine receptor agonist drug set (E = 7.69×10−159) (
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) (
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 (
Each node represents one reaction set – the substrates and products of a single human metabolic reaction. Edges connect the reactions in the canonical pathway as annotated in HumanCyc
1 | Dihydrofolate reductase (DHFR) | 1.96E-123 |
2 | Methyltetrahydrofolate-corrinoid-iron-sulfur protein methyltransferase | 3.58E-102 |
3 | Methionyl-tRNA formyltransferase | 1.97E-99 |
4 | Methylenetetrahydrofolate reductase | 2.67E-86 |
5 | Thymidylate synthase (TS) | 2.54E-75 |
6 | Formate-tetrahydrofolate ligase | 1.44E-74 |
7 | Dihydrofolate synthetase | 1.35E-70 |
8 | Aminomethyltransferase | 7.13E-63 |
9 | 5-methyltetrahydrofolate-homocysteine S-methyltransferase | 2.80E-62 |
10 | Phosphoribosylaminoimidazolecarboxamide (AICAR) formyltransferase | 1.50E-60 |
11 | Phosphoribosylglycinamide formyltransferase (GART) | 1.50E-60 |
1 | Dihydrofolate reductase (DHFR) | 1.46E-82 |
2 | Methyltetrahydrofolate-corrinoid-iron-sulfur protein methyltransferase | 2.84E-75 |
3 | Methylenetetrahydrofolate reductase | 6.01E-73 |
4 | Methionyl-tRNA formyltransferase | 7.00E-66 |
5 | Aminomethyltransferase | 6.90E-55 |
6 | Formate-tetrahydrofolate ligase | 6.15E-49 |
7 | Thymidylate synthase (TS) | 1.91E-48 |
8 | 5-methyltetrahydrofolate-homocysteine S-methyltransferase | 2.60E-45 |
9 | 3-methyl-2-oxobutanoate hydroxymethyltransferase | 2.68E-44 |
10 | Glycine decarboxylase | 2.68E-44 |
11 | Glycine hydroxymethyltransferase (SHMT) | 2.68E-44 |
12 | Dihydrofolate synthetase | 9.65E-42 |
13 | Phosphoribosylaminoimidazolecarboxamide (AICAR) formyltransferase | 2.21E-39 |
14 | Phosphoribosylglycinamide formyltransferase (GART) | 2.21E-39 |
1 | Thymidylate kinase | 7.48E-28 |
2 | Thymidine kinase | 3.48E-26 |
3 | Deoxythymidine diphosphate kinase | 1.54E-24 |
4 | Ribonucleoside-triphosphate reductase | 2.88E-14 |
5 | Deoxyuridine triphosphate pyrophosphatase | 5.60E-12 |
6 | Deoxyuridine kinase | 1.14E-11 |
7 | Deoxyuridine diphosphate kinase | 1.45E-11 |
8 | Thymidylate synthase (TS) | 5.68E-11 |
It has previously been shown that chemical similarity between known drugs often suggests novel drug-target interactions
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
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
1 | Thymidylate synthase inhibitor (TS) | 2.54E-75 |
2 | Glycinamide ribonucleotide formyltransferase inhibitor (GART) | 4.76E-73 |
3 | Thymidine kinase inhibitor (TK) | 1.18E-62 |
4 | Dihydrofolate reductase inhibitor (DHFR) | 1.91E-48 |
5 | Folylpolyglutamate synthetase inhibitor | 2.27E-31 |
6 | Nucleoside reverse transcriptase inhibitor (NRTI) | 5.68E-11 |
1 | Glycinamide Ribonucleotide Formyltransferase Inhibitor | 1.02E-134 |
2 | Thymidylate Synthetase Inhibitor | 1.96E-123 |
3 | Dihydrofolate Reductase Inhibitor | 1.46E-82 |
4 | Folylpolyglutamate Synthetase Inhibitor | 3.15E-62 |
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 (
Canonical pathway representation of methicillin-resistant
As described for
Canonical pathway representation of methicillin-resistant
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
A compilation of all of the metabolic network maps generated in this study is available at
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
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
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.
All compounds, both drugs and metabolites, are represented using Daylight SMILES strings
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 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
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
Essentiality and synthetic lethal data generated as described earlier
MetaCyc reaction sets
(0.47 MB TXT)
SMILES describing the molecular strucutre of MetaCyc reaction substrates and products
(0.25 MB TXT)
MDDR drug sets
(0.51 MB TXT)
SMILES describing the molecular structure of MDDR ligands.
(4.45 MB TXT)
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.