Supplementary Material on Drug-Target Network Muhammed A. Yıldırım, Kwang-Il Goh, Michael E. Cusick, Albert-László Barabási & Marc Vidal

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1 Supplementary Material on Drug-Target Network Muhammed A. Yıldırım, Kwang-Il Goh, Michael E. Cusick, Albert-László Barabási & Marc Vidal Contents I. Chemical Similarity between Drugs Targeting the Same Protein II. III. IV. Construction of the Drug Target Network Component Size Distributions of DN and TPN Topological features of DN and TPN V. Essentiality of Drug Targets VI. VII. VIII. IX. Drug Targets in the Human Protein-Protein Interaction Network Topological Features of Drug Targets in the Human PPI Network Human Disease Network Properties of Drug Targets in the Human Disease Network X. Expression Profiles of Drug Targets and Disease Genes XI. Supplementary References

2 I. Chemical Similarity between Drugs Targeting the Same Protein Large numbers of drugs target common proteins, e.g. the Histamine H Receptor (HRH) (targeted by 5 drugs), the Muscarinic Cholinergic Receptor (CHRM) (targeted by 48 drugs), the αa Adrenergic Receptor (ADRAA) (targeted by 42 drugs), and the Dopamine Receptor D 2 (DRD2) (targeted by 4 drugs). All of these drugs are chemically different, but they might show chemical similarities due to a common ancestry. The DrugBank website provides a chemical search tool that can be used to find drugs with similar structures to a submitted query chemical. We developed a web robot to utilize this tool, and we built networks for the drugs targeting the same protein. An exact match gives a score of 2, so we set a cut-off of 5 to draw an edge between two proteins (Supplementary Fig. a). Proteins targeted by many drugs tend not to be connected to any other drug in the chemical similarity network showing that their chemical structure is unique. However, there are also drugs connected to each other exhibiting a cliquelike nature. For instance, drugs targeting HRH have 5 such cliques composed of 36 drugs (Supplementary Fig. b), drugs targeting ADRAA have 2 cliques composed of 4 drugs (Supplementary Fig. c), drugs targeting DRD2 have 3 cliques composed of 22 drugs (Supplementary Fig. d), and drugs targeting CHRM have 2 cliques composed of 2 drugs (Supplementary Fig. e). II. Construction of the Drug Target Network DrugBank combines detailed drug (i.e. chemical, pharmacological and pharmaceutical) data with comprehensive drug target (i.e. sequence, structure, and pathway) information. We downloaded the drug and drug target information from DrugBank Database as of March We used the SwissProt ID to discriminate only drugs targeting human proteins. There are 89 FDA approved drugs and 88 experimental drugs complying with these criteria. The FDA-approved drugs target 394 human proteins, and experimental drugs target 73 human proteins. The total number of drug target proteins reported in Drugbank is,. By using the drug target associations we generated the bipartite graph shown in Fig. 2. We used Pajek ( to visualize the network. In this map, circular nodes represent the drugs and the rectangular boxes the drug target proteins. The area of the nodes is proportional to the degree in the network. The coloring for the drug nodes was done following the Anatomical Therapeutic Chemical (ATC) classification code provided 2

3 by DrugBank. ATC codes are controlled by the World Health Organization Centre for Drug Statistics Methodology. Drugs are classified into groups at five different levels, and we used the first level (the main category) to color the nodes. For drugs with more than one classification, we applied majority voting among different codes. Target proteins were colored according to their cellular component profiles, by filtering the Gene Ontology information to map the components to Membrane, Cytoplasm, Organelles, Nucleus, Secreted and Not Available (including other cellular components and unknown proteins). We considered proteins residing in organellar membranes as membrane proteins. The length of the edges in this network was varied to make the graph layout optimally viewable. Next, we generated the drug network (DN) and the target protein network (TPN) projections of the DTN. In the DN, nodes represent drugs and the connections are made when two drugs share at least one target protein (Supplementary Fig. 2). In the protein centric TPN, protein nodes are connected if they are both targeted by at least one drug simultaneously (Fig. 3). The edge thickness between two proteins in the TPN is proportional to the number of drugs targeting these proteins together. The color scheme for the DN and TPN is the same as the DTN. Layouts of all networks were generated by a simple force-directed algorithm, followed by a local manual rearrangement for visual clarity, while leaving the overall layout of the network unperturbed. III. Component Size Distributions of DN and TPN The DN and TPN exhibit different component size distributions compared to, control randomized networks obtain by randomizing the drug target protein associations while keeping both the number of proteins that a drug targets and the number of drugs that a protein is targeted fixed (Supplementary Fig. 3a,b). The giant component of the DN contains 53% of all drugs, whereas the giant component of the TPN contains 3% of all drug target proteins. According to random graph theory, as the density of edges in a graph increases a giant component forms whose size scales extensively and generally the proportion of nodes in the giant component is around 8-9%. Our randomized networks are no exception to this generalization; however, our observed network giant component sizes are much smaller than the random expectation. A similar pattern is observed after the inclusion of experimental drugs and their targets (Supplementary Fig. 3c,d). The pattern is reversed for experimental drugs for which the giant 3

4 component size is larger than the random case (Supplementary Fig. 3e,f). Generally, the sizes of the second and third largest components are larger than the random control in all graphs. For a given drug category, the number of drugs in the giant component and the number of distinct components that the drugs in this category are given in the following table: Drug Category Total Number of Drugs Number of Drugs in the Giant Component Number of Components that the category is present Anti-Infectives 6 4 Antineoplastics Antiparasitic 7 2 Blood 39 3 Cardiovascular Dermatological 25 8 Genito-Urinary Hormones 2 9 Metabolism Musculoskeletal 54 3 Nervous System Respiratory Sensory Organs Various Tyrosine kinase inhibitors generally function through targeting many proteins at once. Especially after the success of Imatinib, many kinase inhibitors were developed and are currently in the approval pipeline. These drugs might be responsible for the polypharmacology and more random associations observed in the experimental drugs. To test this effect, we excluded all the tyrosine kinase inhibitors, i.e. a drug that has at least one tyrosine kinase target, from the giant component analysis. The giant component sizes for the drugs and the targets were 66 and 596 respectively. After 4 randomizations of the drug and target associations, the expected giant component sizes for the experimental drugs and their targets were 599 ± 2 and 55 ±. The effect of polypharmacology results in a larger than expected giant component size. Upon excluding the tyrosine kinase inhibitors, the giant component size of the drug network decreased to 529 which is smaller than the expected giant component size of the randomized networks (549 ± 2). However, the observed giant component size of the target network (472) remains larger 4

5 than the expected giant component size (424 ± 9). Hence, we cannot conclude that the tyrosine kinase inhibitors are the major contributors to the increased polypharmacology. IV. Topological features of DN and TPN A scale-free degree distribution, where degree is the number of connections per node, implies a preferential attachment model of network growth, particularly when new network nodes are added one at a time, as in the growth of the drug target network. Both the DN and TPN exhibit an apparent scale-free like degree (k) distribution (Supplementary Fig. 4a-d), with most drugs (proteins) linked to only a few other drugs (proteins), while a few drugs (proteins) represent hubs that are connected to a large number of distinct drugs (proteins). The exponents of the observed scale-free distributions are small compared to other biological networks. A small exponent indicates a strong preferential attachment model for network growth 2. These results imply that most new connections are made through a handful of existing nodes, especially through highly targeted proteins. Upon inclusion of experimental drugs the TPN degree distribution still shows an apparent scale-free distribution, although the exponent value decreases from.59 ±.7 to.5 ±.. This indicates that experimental drugs introduce more random associations between the targeted proteins. There is no significant change in the exponent of the DN after the addition of experimental drugs. Both the DN and TPN display modular structure. A module in the DN consists of group of related drugs that target more than one common target, while a module in the TPN corresponds to one or more drugs targeting all or most proteins in the module. In the TPN modules appear as complete cliques, where all proteins in the clique are fully connected to the other members of the clique (Fig. 3). The average clustering coefficient measures the modularity or cliquishness of a network 3, 4. To test for the clustering coefficient, we generated randomized versions of the DN and the TPN by randomizing the connections between drugs and their targets in the bipartite DTN while keeping the degree distributions constant. Then we projected randomized DN and TPN from this randomized DTN. In the DN 59 out of 8 drugs have clustering coefficient, and the average clustering coefficient (.839 ±.) is many times larger than the average of clustering coefficients (. ±.4) of 4 randomized networks (Supplementary Fig. 4e). Similarly, the average clustering coefficient of the TPN (.67 ±.25) is an order of magnitude larger than for randomized networks (.45 ±.6). Higher 5

6 values of clustering coefficient mostly comes from drugs targeting three or more proteins or proteins targeted by three or more drugs at once. Upon inclusion of experimental drugs the average clustering coefficient of the TPN increased from.67 ±.25 to.783 ±. (Supplementary Fig. 4e), which is again higher than the average clustering coefficient of randomized networks (.2 ±.4), hinting at two trends: (i) there are many experimental drugs targeting more than two new target proteins and (ii) many experimental drugs introduce new connections between old targets. V. Essentiality of Drug Targets To predict the essentiality of a human gene, we used the phenotype information of the corresponding mouse orthologs. A human gene was defined as essential if a knock-out of its mouse ortholog confers lethality. We obtained the human-mouse orthology and mouse phenotype data from Mouse Genome Informatics 5 on January 3, 26. We considered the classes of embryonic/prenatal lethality and postnatal lethality as lethal phenotypes, and the rest of phenotypes as non-lethal ones. There were,267 mouse-lethal human orthologs, of which 77 are approved drug targets and of which 49 are targets of all drug targets (including both approved and experimental drugs). The fraction of essential proteins for approved targets, and all targets are shown. Approved Targets All Targets All Proteins Essential 77 (9%) 49 (5%),267 Non-Essential 45 (37%) 287 (28%),8 Unknown 72 (44%) 575 (57%) Cancer drugs might selectively act to terminate cancer cells by targeting essential gene products. To test this, we looked at the proportion of essential proteins among the targets of approved oncology drugs. Out of 6 protein targets of oncology drugs 7 were essential (28%), which is only slightly higher than the ratio of the essential proteins in targets of the all approved drugs (9%). 6

7 VI. Drug Targets in the Human Protein-Protein Interaction Network We looked at the distribution of drug targets in the human protein-protein interaction (PPI) map 8,9. By starting from a drug target or any protein in the network (for the randomized control) we looked at the fraction of drug targets for each distance while applying the breadth-first search algorithm. The distance is defined as smallest number of edges between pairs of nodes. Rather than being random, we saw an enhancement in distances and 2, the fraction of drug targets being larger at distance 2 than fraction of drug targets at distance (Supplementary Fig. 5a,b). If there were a naïve clustering of the drug targets, we would expect to see a monotonic decrease of fractions with the distance, as observed for the drug targets in the disease gene network (Fig. 5c). Hence the distance 2 enhancement is an inherent feature of the drug targets in the PPI. This apparent increase in distances and 2 also relates to the families of druggable proteins. Having an increase in distance 2 means that the drugs share a protein interactor rather than interacting directly between themselves. For instance, G protein coupled receptors (GPCR) show a much sharper peak around distance 2, which can be explained by the fact that GPCRs interact with G proteins and rarely interact with each other (Supplementary Fig. 5c). But if we look at kinases, the peak around distance is higher but the peak around distance 2 is still prominent (Supplementary Fig. 5d). This shows that some kinases interact with each other, but others share a protein interactor rather than directly interacting with each other. Another interesting feature of kinases is the decrease after distance 3 for both the kinase group and the random control. This result indicates that kinases are central in the network and they usually can be reached from any node within 3 interactions. We also measured the number of shared neighbors for two proteins if the two proteins are targeted simultaneously by a drug. We saw that for both targets of approved drugs and targets of all drugs there is a significant increase for the shared neighbors, as suggested by distance 2 enhancement in the fraction measure. The number of shared neighbors for drug targets is given in the table below: Approved Targets All Targets Observed 9 33 Expected ± 3 96 ± P-Value

8 VII. Topological Features of Drug Targets in the Human PPI Network Aside from the degree of a node in the Human PPI (Fig. 4c), one can also measure average Betweenness (number of shortest paths passing through the node), average Closeness (inverse of the average length of the shortest paths calculated for all pairs starting from a particular node) and average Clustering Coefficient (see Methods) of the nodes. We used Pajek to calculate all these topological features for the human PPI network. The degree of a node in the human PPI network correlates well with essentiality. In order to discriminate the effect of essential proteins that would inflate the average degree of the drug targets, we divided each protein group into essential and non-essential sets. All essential components of a protein group have significantly higher average degree than the corresponding non-essential part (Supplementary Fig. 5e). However, drug targets have higher average degree compared to the network average if we compare essential and non-essential parts separately. Hence, our conclusion about the degree of drug targets would not be influenced by the essential proteins in the group. This degree difference might be due to investigational bias towards known drug target proteins, thus yielding more interactors for them, especially in the literature curated dataset (PPI information obtained from methods like Y2H is unbiased 8 ; however, Y2H is known to show fewer interactions for membrane proteins, and drug targets are frequently membrane proteins). Using the degree information, we can also heuristically calculate the expected ratio of the drug targets, or druggable genome. First, we binned the data according to their degree, i.e. we assigned certain range of degrees to a single bin. Initially each bin was the degree of the protein itself. However, it is well known that protein interaction networks are scale-free, so logarithmic binning was the second choice for different logarithmic bases. The ratio of the druggable genome was estimated by: where ratio = i Pr ( bin ) i n i i * n bin is the i th bin, ( ) i bin to the number of all target proteins, and i Pr bin i is the ratio of number of target proteins belonging to the i th ni is the number of proteins, regardless of being a target or not, in the i th bin. Linear binning gives a fraction of %. By increasing the base of the logarithm, this ratio increased from % for the base.25 to 2% for the base 2. Larger 8

9 logarithms bin large chunks of data together, so we believe that -2% is a reasonable interval, which corresponds to 3, to 6, proteins. This estimate agrees well with the earlier estimates of disease-modifying genes which was around 3,, genes. Betweenness quantifies the expected information flow from a node. Betweenness of the drug targets is larger than the randomized case (Supplementary Fig. 5f). This shows that drugs preferentially target proteins that are more central to the cell in terms of decision flow. As drugs change the phenotypic state of the cell, it is not surprising to see that drug targets have higher average betweenness. Closeness also measures centrality and the behavior of different protein classes is similar to their behavior for the betweenness measure (Supplementary Fig. 5g). The average clustering coefficient profile shows a different behavior than the other two measures. The average clustering coefficient of the disease related proteins is higher than the other protein classes (Supplementary Fig. 5h). Targets of all drugs (both approved and experimental) has significantly lower average clustering coefficient. This might arise because most drug targets are preferentially distance 2 away for this class of proteins (Supplementary Fig. 5b) and this is indicative of fewer inter-neighbor connections. We also looked at the topological features of two protein families that are prominent druggable targets: GPCRs and protein kinases. We obtained the list of GPCRs from the Molecular Class-Specific Information System (MCSIS) project website ( and list of kinases from Gene Ontology (GO) database. We found 285 GPCR proteins (excluding the taste receptors) and 464 protein kinases. Interestingly GPCRs have very low degree compared to the other proteins, whereas kinases show large connectivity (Supplementary Fig. 5e). This trend persists for the betweenness (Supplementary Fig. 5f). VIII. Human Disease Network The human disease network (HDN) is reported elsewhere 6. We briefly describe the dataset and procedure to build this network. The data was obtained from the Morbid Map (MM) of the Online Mendelian Inheritance in Man (OMIM) 7, which has the most up-to-date disorder-gene associations. The MM was downloaded as of 2 December 25. Each entry of the MM contains information about the name of the disorder, associated gene symbols, OMIM ID, and the chromosomal location. The strong associations, i.e. at least one mutation in the gene is proven to be causative to the disorder, have the (3) label, and there were 2,929 disorder terms with this 9

10 tag. We merged these disorders into,284 distinct disorders first automatically and then by manual validation. A broader classification into 22 disorder classes was done manually, including 55 disorders assigned to the multiple class for disorders with multiple clinical features, and 3 disorders assigned to the unknown class. The map in Fig. 5a was obtained by combining disease nodes (circles) to gene nodes (rectangles). Coloring of the nodes represents the disease classes. In the map, genes that are associated with only a single disease are omitted for clarity and aesthetics. IX. Properties of Drug Targets in the Human Disease Network We looked at the average number of genes associated with each disease and the average number of disorders associated with each gene. When one of the genes associated with a disorder is also a drug target (for both approved and experimental targets) the average number of genes associated with that disease is higher than the random case (Supplementary Fig. 6a). For the genes that are also approved drug targets the average number of associated disorders was higher than random, whereas this average was slightly lower for experimental drug targets (Supplementary Fig. 6b). These results indicate that experimental drug targets are more peripheral in the disease gene network. From these observations we would expect the average degree of target of approved drugs to be highest among all gene classes (Fig. 5b), but we see an opposite trend. This is largely due to approved drug targets being peripheral at the disease gene network as well. For instance cancer genes that are targeted by approved drugs are peripheral at the cancer disease cluster where most of the higher degree genes reside in the center (Fig. 5b). For different categories of genes, the ratio of the diseases associated with targets of approved drugs change drastically. However, the average degree of targets of drugs approved after 996 increases from 3.58 ±.47 to 4.7 ±.52. Note that the degree in the human disease gene network is correlated with the number of diseases the gene is involved with, and the number of other genes associated with the same diseases. This increase could indicate investigational bias towards diseases with few or no effective indicated drugs, hence producing larger numbers of associated disease genes.

11 X. Expression Profiles of Drug Targets and Disease Genes We used microarray data available for 36 normal human tissues 2. There is expression information for 293 approved drug targets, 88 of all (approved plus experimental) drug targets. First we looked at the co-expression correlations between the proteins that are targeted by the same drug (Supplementary Fig. 7a). We see a significantly more co-expression for the targets of approved drugs (Kolmogorov Smirnov test, P < -9 ). To quantify how well at least one of the targets of the drugs is co-expressed with corresponding disease-causing genes, we took the maximum of the Pearson Correlation Coefficients (PCC) obtained from the drug-disease pairs, where at least one drug target protein and one corresponding disease gene product were in the expression dataset. The distribution has a second peak around (Supplementary Fig. 7b), corresponding to a drug targeting exactly the disease-causing gene product. Other than this peak, drug target disease gene pairs show less correlation than random. For the random set two groups of genes with the same number of disease genes and drug targets were selected randomly for each drug disease pair. The neurological, respiratory, psychiatric and endocrine disease classes show more co-expression; in contrast ophthamological, gastrointestinal and immunological disease classes show less (Supplementary Fig. 7c).

12 XI. Supplementary References. Newman, M. Scientific collaboration networks. I. Network construction and fundamental results. Phys. Rev. E 64, 63 (2). 2. Albert, R. & Barabasi, A.L. Statistical mechanics of complex networks. Reviews of Modern Physics 74, (22). 3. Ravasz, E., Somera, A., Mongru, M., Oltvai, Z. & Barabási, A.-L. Hierarchical organization of modularity in metabolic networks. Science 297, (22). 4. Watts, D. & Strogatz, S. Collective dynamics of 'small-world' networks. Nature 393, (998). 5. Eppig, J.T. et al. The Mouse Genome Database (MGD): from genes to mice--a community resource for mouse biology. Nucleic Acids Res. 33, D (25). 6. Goh, K.I. et al. The human disease network. Proc. Natl. Acad. Sci. USA 4, (27). 7. Hamosh, A., Scott, A.F., Amberger, J.S., Bocchini, C.A. & McKusick, V.A. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33, D54-57 (25). 8. Rual, J.-F. et al. Toward a proteome-scale map of the human protein-protein interaction network. Nature 437, (25). 9. Stelzl, U. et al. A human protein-protein interaction network: A resource for annotating the proteome. Cell 22, (25).. Jeong, H., Mason, S.P., Barabasi, A.L. & Oltvai, Z.N. Lethality and centrality in protein networks. Nature 4, 4-42 (2).. Hopkins, A.L. & Groom, C.R. The druggable genome. Nat. Rev. Drug Discov., (22). 2. Ge, X. et al. Interpreting expression profiles of cancers by genome-wide survey of breadth of expression in normal tissues. Genomics 86, 27-4 (25). 2

13 a) b) Drugs Targeting HRH Brompheniramine Dexbrompheniramine Chlorpheniramine Chlorpheniramine Similarity Score = 2 Chlorpheniramine Carbinoxamine Similarity Score = 9 Tripelennamine Chlorpheniramine Carbinoxamine Doxylamine Chlorpheniramine Dexbrompheniramine Similarity Score = 8 Chlorpheniramine Tropicamide Similarity Score = 7 Bromodiphenhydramine Diphenhydramine Orphenadrine Chlorpheniramine Chloroquine Chlorpheniramine Orphenadrine Similarity Score = 6 Similarity Score = 5 c) Metaraminol Drugs Targeting ADRAA Epinephrine Pseudoephedrine Maprotiline Thioridazine Trifluoperazine Methoxamine Amphetamine Flupenthixol Midodrine Risperidone Nefazodone Phenylephrine Phenylpropanolamine Clemastine Diphenylpyraline Desipramine Maprotiline Mequitazine Olopatadine Cetirizine Propiomazine Triprolidine Trimeprazine Methdilazine Azatadine Hydroxyzine Benzquinamide Promethazine Promazine Epinastine Prochlorperazine Levocabastine Nedocromil Emedastine Cyclizine Doxepin OlanzapineDesloratadine Trazodone Risperidone Azelastine Buclizine Meclizine Cinnarizine Astemizole Pemirolast Terfenadine Fexofenadine Dimenhydrinate Histamine Phosphate Clozapine Ketotifen Fumarate Loratadine Ziprasidone Thiethylperazine Promethazine Promazine Benzphetamine Phenoxybenzamine Droperidol Domperidone Acetophenazine Aripiprazole Cabergoline Fluphenazine Flupenthixol Ziprasidone Perphenazine Thioridazine Prochlorperazine Trifluoperazine Clozapine Cinnarizine Buspirone Apomorphine Levodopa Haloperidol Risperidone Perphenazine Ziprasidone Nefazodone Alfuzosin Terazosin Prazosin Thiethylperazine Pergolide Bromocriptine Tamsulosin Labetalol Ergotamine Guanethidine Nilutamide Tolazoline Amiodarone Betanidine Carvedilol Dapiprazole Doxazosin Epinastine Guanadrel Sulfate d) Amantadine Mesoridazine Quetiapine Loxapine Olanzapine Metoclopramide Norgestrel Nicergoline Oxymetazoline Propiomazine Drugs Targeting DRD2 Propiomazine Chlorprothixene Triflupromazine Promethazine Minaprine Promazine Chlorpromazine Sulpiride Pimozide Pramipexole Remoxipride Ropinirole e) Drugs Targeting CHRM Dicyclomine Trospium Methscopolamine Anisotropine Methylbromide Clidinium Oxybutynin Cyclopentolate Tridihexethyl Scopolamine Propantheline Oxyphenonium Atropine Homatropine Methylbromide Methantheline Hyoscyamine Ipratropium Metixene Biperiden Benztropine Oxyphencyclimine Glycopyrrolate Diphenidol Ethopropazine Propiomazine Trihexyphenidyl Cyclizine Triflupromazine Pilocarpine Metoclopramide Bethanechol Carbachol Desipramine Promazine Olanzapine Buclizine Promethazine Cycrimine Procyclidine DoxylamineCarbinoxamine Flavoxate Cevimeline Tolterodine Solifenacin succinate Benzquinamide Succinylcholine Pirenzepine Cryptenamine Supplementary Figure. Chemical similarity of drugs targeting same protein. a) Drugs with different chemical similarity scores for the input drug Chlorpheniramine. b-e) Chemical similarity networks for drugs targeting the same protein. Two drugs are connected if the similarity score is at least 5. A blue edge corresponds to similarity score of 5 or 6, orange edge corresponds to similarity score of 7 or 8, a red edge is similarity score of 9 or 2. Shaded regions are regions of high similarity showing a clique like nature. b) Chemical similarity network for drugs targeting HRH. c) Chemical similarity network for drugs targeting ADRAA. d) Chemical similarity network for drugs targeting DRD2. e) Chemical similarity network for drugs targeting CHRM.

14 METABOLISM BLOOD CARDIOVASCULAR DERMATOLOGICAL GENITO-URINARY HORMONES ANTI-INFECTIVES ANTINEOPLASTIC MUSCULOSKELETAL NERVOUS SYSTEM ANTIPARASITIC RESPIRATORY SENSORY ORGANS VARIOUS Supplementary Figure 2. The Drug Network (DN). In the DN each node corresponds to a distinct chemical entity, and each node is colored based on the anatomical therapeutic chemical class it belongs to. The names of the 4 drug classes are shown on the right. The size of each node is proportional to the number of proteins targeted by the corresponding drug. The link thickness is proportional to the number of proteins shared by the drugs it connects. The nodes are colored according to their Anatomical Therapeutic Chemical Classification.

15 Average Number of Components... a) b) App Drugs (DN) Random Approved Targets (TPN) Random Average Number of Components Average Number of Components All Drugs Random c) d) e) f) Experimental Drugs Random Component Size All Targets Random Experimental Targets Random Component Size Supplementary Figure 3. Component Size Distributions of SN and TPN. a) for the drug network (DN), b) for the target protein network (TPN), c) for the DN after addition of the experimental drugs, d) for the TPN after addition of targets of the experimental drugs, e) for the DN consisting only of the experimental drugs, and f) for the TPN consisting only of targets of the experimental drugs.

16 a) Drug Network (DN) b) Target Protein Network (TPN) k.22±.5 k.59±.7 5 c) DN + Experimental Drugs d) TPN + Targets of Experimental Drugs.3±.6.5±. k k e) Average Clustering Coefficient Observed DN.8 TPN DN TPN Random DN+EXP TPN+EXP TPN+EXP DN+EXP Supplementary Figure 4. Degree Distributions and Clustering Coefficients of DN and TPN. a) of the drug network (DN), b) of the target protein network (TPN), c) of the DN after addition of the experimental drugs, d) of the TPN after addition of targets of the experimental drugs. e) Clustering coefficient of the networks compared to the random case.

17 a) b) Fraction Approved Targets All Fraction All Targets All c) Distance GPCRs All d) Distance Kinases All Fraction.2 Fraction...5 e) Distance All Non Essential Essential f) Distance All Non Essential Essential g) Average Degree All Proteins Target Proteins (TP) TP + EXP Disease Kinases GPCRs h) Average Betweenness.2. All Proteins Target Proteins (TP) TP + EXP Disease Kinases GPCRs Average Closeness All Proteins All Non Essential Essential Target Proteins (TP) TP + EXP Disease Kinases GPCRs Average Clustering Coefficient All Proteins All Non Essential Essential Target Proteins (TP) TP + EXP Disease Kinases GPCRs Supplementary Figure 5. Drug Targets and Some Druggable Families in the Human Protein Protein Interaction Network. a) Fraction of targets of approved drugs while applying a breadth-first search starting from either a target protein or a random protein in human PPI network. b) Doing the same for GPCRs. c) Doing the same for kinases. d) Doing the same for targets of all drugs (approved and experimental). e) Average degree of several classes of proteins in the human PPI network. f) Average betweenness of several classes of proteins in the human PPI network. g) Average closeness of several classes of proteins in the human PPI network. h) Average clustering coefficient of several classes of proteins in the human PPI network.

18 a) b) Average Number of Associated Genes Disorders (286) Disease with App Targets (239) Disease with Exp Targets (234) Average Number of Associated Disorders 2.5 Disease genes (777) Approved Targets (66) Experimental Targets (2) c) 25 2 With Drug Targets No Drug Targets Count 5 5 Endocrine Hematological Cardiovascular Psychiatric Unclassified Connective_tissue_disorder Renal Immunological Nutritional Metabolic Bone Ear,Nose,Throat Neurological Developmental Disease Categories Ophthamological Respiratory Cancer Muscular Skeletal multiple Gastrointestinal Dermatological Supplementary Figure 6. Disease and Gene Statistics in Human Disease Network. a) Average number of genes for all diseases in the data set compared to diseases which are associated with genes that are also approved (App) targets or experimental (Exp) targets. Numbers in the parenthesis show how many drugs are in each category. b) Average number of disorders associated with each disease compared to the average number of diseases for the genes that are also approved drug targets or experimental drug targets. Numbers in the parenthesis show how many genes are in each category. c) Count of disorders with genes that are also approved drug targets for different disease categories. Disease categories are ranked according to the ratio of number of disorders associated with approved drug targets to all disorders in that category (except the Nutritional disease class where there are only four disorders).

19 a) Frequency.2.5. Approved Target Proteins All Target Proteins All Proteins b) Frequency Drug Targets vs Disease Genes Random.5.5 c) Average Max PCC Expression PCC Neurological(74) Respiratory(36) Unclassified(7) Psychiatric(44) Endocrine(5) Metabolic(27) Nutritional(7) Cardiovascular(42) Cancer() Hematological(44) Renal(6) Bone(26) Dermatological(4) Disease Categories Average Max PCC Connective_tissue_disorder(54) multiple(7) Muscular(5) Ophthamological(36) Gastrointestinal() Immunological(42) Supplementary Figure 7. Expression Profile Correlations. a) Frequency of expression correlations for proteins that are targeted by approved (or approved plus experimental) drugs simultaneously compared with all calculated correlations. b) Frequency of average maximum PCC between drug targets and corresponding disease genes. c) Ranked average max PCC for various disease categories. Numbers in the parenthesis show how many such drugdisease pairs were calculated in each disease category.