GESSE: Predicting Drug Side Effects from DrugâTarget Relationships

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GESSE: Predicting Drug Side Effects From Drug-Target Relationships Violeta I Perez Nueno, Michel Souchet, Arnaud S. Karaboga, and David W Ritchie J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.5b00120 • Publication Date (Web): 07 Aug 2015 Downloaded from http://pubs.acs.org on August 14, 2015

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Journal of Chemical Information and Modeling

GESSE: Predicting Drug Side Effects From DrugTarget Relationships Violeta I. Pérez-Nueno 1,*, Michel Souchet 1, Arnaud S. Karaboga 1 and David W. Ritchie 2

1

Harmonic Pharma, Espace Transfert, 615 rue du Jardin Botanique, 54600

Villers-les-Nancy, France Tel: +33-354 958 604 Fax: +33-383 593 046. E-mail: [email protected], [email protected], [email protected] 2

INRIA Nancy – Grand Est, Equipe Capsid, 615 rue du Jardin Botanique, 54600

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ABSTRACT The in silico prediction of unwanted side effects (SEs) caused by the promiscuous behavior of drugs and their targets is highly relevant to the pharmaceutical industry. Considerable effort is now being put into the computational and experimental screening of several suspected off-target proteins in the hope that SEs might be identified early, before the cost associated with developing a drug candidate rises steeply. Following this need, we present a new method called GESSE to predict potential SEs of drugs from their physico-chemical properties (3D shape plus chemistry) and to target protein data extracted from predicted drug-target relationships. The GESSE approach uses a canonical correlation analysis of the full drug-target and drug-SE matrices, and it then calculates a probability that each drug in the resulting drug-target matrix will have a given SE using a Bayesian discriminant analysis (DA) technique. The performance of GESSE is quantified using retrospective (external database) analysis and literature examples, using area under the ROC curve (AUC), “top hit rates”, misclassification rates, and a Chi-square independence test. Overall, the robust and very promising retrospective statistics obtained and the many SE predictions that have experimental corroboration demonstrate that GESSE can successfully predict potential drug−SE profiles of candidate drug compounds from their predicted drug-target relationships.

KEYWORDS Drug side-effects (SEs), Term Clusters (TCs), Gaussian Ensemble Screening (GES), GES ligand-target relationships, Canonical Correlation Analysis (CCA), Discriminant Analysis (DA).


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INTRODUCTION The in silico prediction of unwanted side effects (SEs) caused by the promiscuous behavior of drugs and their targets is highly relevant to the pharmaceutical industry. A particular challenge is to achieve an appropriate balance between drug efficacy and possible adverse effects as early as possible in order to reduce the possibility that safety issues might appear during clinical trials, or even after a drug has reached the market. Considerable effort is now being put into the computational and experimental screening of suspected off-target proteins in the hope that SEs might be identified early, before the cost associated with developing a drug candidate rises steeply. In this regard, Bowes et al.1 described rational strategies and methodologies for in vitro pharmacological profiling at four major pharmaceutical companies (AstraZeneca, GlaxoSmithKline, Novartis and Pfizer). They shared their knowledge and experience of the use of existing screening technologies to detect offtarget interactions of compounds, as well as to define a minimum panel of targets that should be considered. These companies have been screening compounds for up to 10 years and have generated robust data to create this panel. Here, the term “robust” is understood to mean that all assays (binding or functional) included in the early profiling panel produce reliable and reproducible results, and that they have predictive value for safety. According to Bowes et al., although in vitro pharmacological profiling can be used to predict SEs, detecting drug SEs experimentally remains challenging and costly1,2. Therefore, in silico prediction of SEs early in the drug discovery process promises to complement and speed up (or even perhaps to avoid) the long and expensive process of in vitro safety profiling.

Regarding in silico methods, several computational approaches have been developed recently to identify possible SEs and to use SEs to predict drug-target relationships. They can be classified into pathway-based approaches and chemical-based approaches. Pathway-based approaches deal with molecular network information such as the proteins targeted by a given drug, gene-disease-drug connections, drug-drug interactions, and clinically known SEs combined with known drug-disease relationships. They build different pharmacological networks and then train models on them in order to predict adverse drug reactions for unknown drug-SE associations. For example, Campillos et al.3 used phenotypic SE similarities to infer whether two drugs share a target, and therefore to be able to find new targets for known drugs from drugs with similar SEs. Lee et al.4 proposed a process-drug-SE network for automatically discovering the relationship between biological processes and SEs using a co-occurrence based multi-level network. Cheng et al.5 developed a drug-SE similarity inferencing method to predict drug-target interactions (DTIs) from a known DTI network of approved drugs and target proteins. Yang and Agarwal6



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proposed a drug repositioning approach based on associations between diseases and SEs. They built naïve Bayes models using SEs as features in order to predict indications for diseases. Scheiber et al.7, Xie et al.8, Wallach et al.9, and Takarabe et al.10,11 also proposed ways to link drug SEs and biological pathways. Scheiber et al. compared biological pathways affected by toxic compounds and those affected by non-toxic compounds. Xie et al. and Wallach et al. predicted potential SEs by docking drugs into protein binding pockets similar to those of their primary targets, and they then mapped the proteins with the best docking scores to known biological pathways. Takarabe et al. defined pharmacological similarity for all possible drugs using the US Food and Drug Administration’s (FDA’s) adverse event reporting system (AERS) and they developed a method to predict unknown drug-target interactions on a large scale by relating the pharmacological similarities of drugs to the genomic sequence similarities of target proteins.

On the other hand, compound-based approaches relate the chemical structures of drug molecules with drug SEs. The basic idea is that similar ligands are likely to interact with similar proteins, so that predictions may be made by comparing drug chemical structures, protein sequences, and known drug–protein interactions. For example, Scheiber et al.12 developed a method that associates chemical substructures with SEs. Simon et al.13 related drugprotein interaction profiles (binding free energies computed from docking) with SE profiles compiled from the literature using canonical correlation analysis (CCA) followed by linear discriminant analysis (LDA). Yamanishi et al.14 proposed a method to predict the pharmacological effects of drugs using their chemical structures in order to interpret drug-target interactions, as well as a method to predict potential SE profiles of drug candidate molecules by correlating sets of chemical substructures and SEs using Kernel regression models15,16. In a similar way, Pauwels et al.17, Mizutani et al.18 and Atias and Sharan19 predicted potential SE profiles by correlating sets of chemical substructures and SEs using CCA.

Several databases are available for studying relationships between drugs, targets, biological processes, and SEs. For example, Lamb et al. developed a connectivity map approach20 to combine and analyze a drug-gene-disease network from large scale experimental gene expression responses to drugs, data from the SIDER side effects resource21 (which associates drugs with their observed adverse drug reactions), PharmGKB22 (which provides drug-disease associations), and the KEGG database23 (which maps proteins to biological pathways). Concerning terminology, the majority of in silico approaches use gene ontology (GO) terms24 to describe biological processes,


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and Medical Subject Headings (MeSH) or Unified Medical Language System (UMLS) vocabularies25 to describe diseases.

Unfortunately, biology and chemistry are often considered separately, and this can lead to incomplete models that do not provide a unified view of SEs. Most in silico methods for predicting SEs focus either on the use of information about only protein targets or only drug chemical structures. However, when predicting drug SEs, it intuitively seems more desirable to consider both target protein and chemical structure information simultaneously. In this regard, Duran-Frigola and Aloy analyzed both approaches by investigating the molecular bases of over 1,600 SEs26. They gave mechanistic explanations for most SEs and they emphasized the need to combine biology and chemistry to capture complex phenomena not covered in the molecular biology view. Yamanishi et al.15 developed an integrated framework to predict potential SEs of drugs from their chemical structures and target protein information on a large scale. With a view to joining biology and chemistry, Mizutani et al.27 used the FDA Adverse Event Reporting System (FAERS), which provides a valuable resource for pharmaco-epidemiology (the study of the uses and the effects of drugs in human populations). They used a biclustering approach to calculate the relationships between drugs and adverse reactions from a large FAERS data set, and they demonstrated a systematic way to find cases where different drug administration regimes resulted in similar adverse reactions, and where the same drug could cause different reactions in different patients.

Here, we also aim to integrate the chemical space of drug structures and the biological space of drug target proteins. We previously introduced the “Gaussian Ensemble Screening” (GES) approach to quantitatively predict polypharmacological relationships between drug classes rapidly and reliably28, and the GES “computational polypharmacology fingerprint” (CPF) for encoding drug promiscuity information29. In this paper, we present a new method, called GESSE, to predict potential SEs of drugs from their physico-chemical properties (i.e. 3D shape plus chemistry) and target protein data extracted from predicted GES drug-target relationships.

To our knowledge, no other computational method has been reported for predicting drug SEs by associating SEs with predicted drug-target relationships using each drug's physico-chemical properties. The two most similar previous approaches are those of Pauwels et al.17, who predicted drug SEs by associating SEs with the presence of certain chemical substructures, and Simon et al.13, who predicted drug SEs by associating SEs with predicted drug-target interactions, according to calculated binding free energies.



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To demonstrate the usefulness of our approach, we predicted the SEs for a set of DrugBank30 drugs for which drug-target relationships had been calculated by GES for 777 targets. The performance of the approach was quantified using retrospective analysis and literature examples. Overall, the robust and very promising retrospective statistics obtained and the many SEs predictions having experimental corroboration demonstrate that GESSE can successfully predict drug−SE profiles of candidate drug compounds from predicted drug-target relationships.

METHODS SEs profile matrix. To build a SE profile matrix, or “SE matrix”, we used our NetworkDB relational database31 containing 554 DrugBank drugs linked to 1077 SEs. This database integrates data about drugs and their targets from several data sources including DrugBank, UniProt, KEGG, and GO, together with their related SEs compiled from SIDER v221. Terms describing individual side effects reported in SIDER are clustered into 112 Term Clusters (TCs) using an expert-validated32 semantic similarity measure derived from MedDRA33. In fact, two SE matrices were built which we call “NetworkDB_TC” and “NetworkDB_SE” for brevity. In NetworkDB_TC, each drug is represented by a binary profile whose 112 elements encode the presence or absence of a TC, thus giving a SE profile matrix of 554x112 elements. Similarly, in NetworkDB_SE each drug is represented by a vector of 1077 elements encoding the presence or absence of an individual SE for each drug, giving a matrix of 554x1077 elements.

GES drug–target relationship matrix. In the GES approach, a “ligand set” is defined as a cluster of high-affinity ligands that bind to a specific target. The main novelty of GES is to represent such a cluster as a Gaussian distribution with respect to a selected center molecule (CM)28. Using spherical harmonic (SH) surface shapes, it is straightforward to calculate the CM of a ligand set. However, because Gaussian functions require a distance coordinate rather than a similarity score, we calculate the normalized SH distance (0.0 ≤ x ≤ 1.0) between the CM and each cluster member using the assumption that it is valid to let Distance = 1 – Similarity28. We extracted from the DrugBank database 6,353 drug entries which are in clinical trials or are on the market, and the SH shape-plus-chemistry representations of these drugs were calculated using the Harmonic Pharma


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Chemistry Coefficient (HPCC)34. These drugs were grouped into 778 ligand sets according to the targets to which they bind. Ten conformations for each molecule were computed, and HPCC representations were calculated for each conformation. We then used the CAST clustering algorithm35 to cluster the members of each ligand set using a PARAFIT Tanimoto similarity score of 0.65, as described previously28. This gave 777 ligand set shape clusters. Thus all ligand sets remained unsplit, except for one that had substantially different drug scaffolds. We then calculated the CM for each cluster, to give our simplified CM representation of each ligand set. A matrix of HPCC similarity scores between the 554 drugs of NetworkDB and the CMs of the ligand sets of the 777 target ligand sets were calculated using the Gaussian overlap score as described previously28,29. The results from the all-versus-all comparisons were recorded as a matrix of GES p-values, which we call the GES ligandtarget relationship matrix, or more simply the “GES matrix”.

Canonical Correlation Analysis (CCA) Canonical Correlation Analysis (CCA) allows two sets of variables to be studied, and to extract a new set of “canonical variables” which are as far as possible correlated with each set and orthogonal to each other36. CCA has been used previously to predict drug-SE profiles using different input matrices, such as chemical substructure profiles17,19 or binding free energies calculated from ligand-protein docking13. Our objective here is to extract a group of canonical variables that capture common features from our two sets of variables: one containing information about drug-target relationships (the GES matrix) and one containing drug side effects (the SE matrix). In order to try to predict SEs as reliably as possible, we explore three variations of CCA, namely ordinary canonical correlation analysis (OCCA), sparse canonical correlation analysis (SCCA) and regularized canonical correlation analysis (RCCA).

Ordinary canonical correlation analysis (OCCA) Ordinary canonical correlation analysis (OCCA) aims to find linear combinations of the variables in a vector of features, x, that correlate maximally with linear combinations of the variables in some other feature vector, y. These linear combinations are the so-called canonical variables, u and v, such that u = α x and v = β y, with weight vectors α = (α1,..., αp) and β = (β1,..., βq). For our specific case with n drugs, we consider a n × p matrix, X, containing p target relationships variables and a n × q matrix, Y, containing q SE variables. Thus each drug is represented by a drug-target relationship vector x = (x1 , ..., xp )T and a side-effect feature vector y = (y1 , ..., yq )T T

. If we now consider two linear combinations of drug-target relationships and drug SEs for the kth drug, uk = α xk



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T

and vk = β yk, then the vectors uk and vk are the canonical components of xk and yk, respectively. The goal of ordinary CCA is to find optimal weight vectors α and β (or canonical coefficients) by maximizing the correlation between the canonical variable pairs, (uk, vk), which is also known as the canonical correlation:

ρ =corr(u, v) =

∑ n

n i=1

α T xi ⋅ β T yi 2

n

∑ (α x ) ∑ (β y ) where ∑ u = 0,∑ v = 0 i=1

n

n

i=1 i

i=1 i

T

i

T

i=1

i

2

,

Equation 1

The residuals of pairs of variables (uk, vk) are analysed progressively in order to find the weights which maximize the correlation. This process then continues until a "significance" cutoff is reached or the maximum number of pairs (which equals the smaller of q and p) has been found. As each pair of canonical variables is calculated from the residuals of the preceding pair(s), the resulting canonical variables are orthogonal. Because the change in the canonical correlation decreases with the number of variables pairs, it is important to choose a good dimension (number of variables) for good predictive performance. OCCA is performed in GESSE using the R package PMA [Erreur ! Source du renvoi introuvable.].

Sparse canonical correlation analysis (SCCA) One drawback of OCCA is that it can be difficult to interpret the results when there are many non-zero elements in the weight vectors α and β . Consequently, sparse canonical correlation analysis (SCCA) aims to change small weights into zeros for easier interpretation. More specifically, given the above X and Y matrices, SCCA maximises the product u’X’Yv subject to constraints on u and v (penalty_x and penalty_y). These penalty terms are parameters to control the sparsity and are restricted to the ranges 0 Contributes mechanism GO:0046961 hsa05012+513 05012 Parkinson's disease ATP synthase activity, 05010 Alzheimer's disease to ATPase Contributes GO:0046933 to ATP binding GO:0005524

Synthesis and activity Dopamine beta-hydroxylase Uridine kinase interconversion of UMP kinase activity GO:0004849 Metabolic pathways Pyrimidine metabolism Nucleoside diphosphate nucleotide di- and GO:0033862 hsa01100+51727 hsa00240+51727 kinase activity GO:0004550 triphosphates REACT_21330

Metabolism of xenobiotics by cytochrome P450 Aldosterone-regulated hsa00980+3290 Synthesis of bile acids and sodium reabsorption Metabolic pathways bile salts via hsa04960+3290 hsa01100+3290 7alpha-hydroxycholesterol Glucocorticoid biosynthesis H01111 Cortisone reductase Bile acid binding REACT_11041 Steroid REACT_11036 11-beta-hydroxysteroid Carboxylic acid binding deficiency (CRD)hormone GO:0032052 Oxidoreductase activity, biosynthesis dehydrogenase (NADP+) GO:0031406 Metabolism of xenobiotics acting on NAD(P)H, quinone Ketosteroid monooxygenase Alditol:NADP+ Steroid of hormone Synthesis bile acids and hsa00140+3290 activity GO:0070524 Trans-1,2-dihydrobenzene-1,2-diol by cytochrome P450 Synthesis as of bile acids and or similar Phenanthrene activitycompound GO:0047086 1-oxidoreductase activity biosynthesis bile salts via dehydrogenase hsa00980+1646 bile 9,10-monooxygenase salts via activity acceptor GO:0016655 GO:0004032 hsa00140+1646 27-hydroxycholesterol GO:0047115 24-hydroxycholesterol activity GO:0018636 REACT_11048 REACT_11053

Cannabinoid receptor 2

Smoothened homolog

D-alanyl-D-alanine carboxypeptidase dacC

Glycine receptor subunit alpha-1

(TC092) Tinnitus DB01263

Nuclear receptor ROR-beta

DB00961

Tryptophan 2,3-dioxygenase

!!!!!!!!!!!!!!!!!!!!!!!!!!!

01502 Vancomycin resistance

H01005 Dopamine Catecholamine biosynthesis beta-hydroxylase deficiency Tyrosine metabolism REACT_15551 pathways Copper ionMetabolic binding Catalytic activity hsa00350+1621 hsa01100+1621 GO:0005507 GO:0003824

(TC089) Gastritis DB00764

!

Prothrombin Sphingomyelin Tyrosine-protein Prolyl 4-hydroxylase phosphodiesterase phosphatase non-receptor subunit alpha-1 type 4

Glutamate [NMDA] receptor subunit epsilon-1

E)#

Synthesis and Metabolic pathways Cytidylate kinase activityinterconversion of hsa01100+51727 nucleotide di- and GO:0004127 UMP kinase activity triphosphates REACT_21330 GO:0033862 Pyrimidine metabolism hsa00240+51727 Uridine kinase activity GO:0004849 Nucleoside diphosphate kinase activity GO:0004550

Transcription factor binding Steroid hormone receptor Circadian rhythm - mammal Nuclear Receptor GO:0008134 Zinc ion binding activity GO:0003707 hsa04710+6096 transcription pathway Sequence-specific DNA GO:0008270 REACT_15525 binding transcription factor Sequence-specific DNA Ligand-activated activity GO:0003700 binding GO:0043565 sequence-specific DNA binding RNA polymerase II transcription factor activity GO:0004879

UMP-CMP kinase

Nuclear receptor ROR-beta Metabolism ofPhenanthrene xenobiotics Alditol:NADP+ 9,10-monooxygenase cytochrome P450 Carboxylicbyacid binding 1-oxidoreductase Synthesis of bileGO:0031406 acidshsa00980+1646 and activity GO:0018636 activity GO:0004032 bile salts via 27-hydroxycholesterol REACT_11048 Ketosteroid monooxygenase activity GO:0047086

ATP binding GO:0005524

Synthesis of bile acids and bile salts via 7alpha-hydroxycholesterol REACT_11041

(TC045) Tendonitis

Synthesis of bile acids and bile salts via 24-hydroxycholesterol REACT_11053 Steroid hormone biosynthesis hsa00140+1646

C-terminus binding MetalProtein ion binding Chromatin binding GO:0008022 GO:0046872 mTOR signaling pathway GO:0003682 Regulation of autophagy hsa04150+5562 hsa04140+5562 Histone serine kinase activity GO:0035174

DB01238

DB00990 DB00399

AMP-activated protein kinase activity GO:0004679 [acetyl-CoA carboxylase] kinase activity GO:0050405

Aldo-keto reductase family 1 member C2

Adipocytokine signaling pathway hsa04920+5562

Dual 3',5'-cyclic-AMP and -GMP phosphodiesterase 11A DNA

(TC020) Blood calcium decreased

Trans-1,2-dihydrobenzene-1,2-diol dehydrogenase activity GO:0047115 Oxidoreductase activity, acting on NAD(P)H, quinone or similar compound as acceptor GO:0016655 Bile acid binding GO:0032052

05410 Hypertrophic cardiomyopathy (HCM)

Sphingomyelin phosphodiesterase DB00687

DB00839

5'-AMP-activated protein kinase catalytic subunit alpha-1

DNA topoisomerase 4 subunit A Tyrosine-protein phosphatase non-receptor type 4

DB01124

Protein kinase activity GO:0004672

DB00672 Smoothened homolog (TC052) Hyperkeratosis

3-phosphoinositide-dependent protein kinase 1

DB01120

Insulin signaling pathway hsa04910+5562

Prothrombin

DB00443

Tau-protein kinase activity GO:0050321

Receptor-type tyrosine-protein phosphatase epsilon

Protein binding GO:0005515 05204 Chemical carcinogenesis

04932 Non-alcoholic fatty liver disease (NAFLD) Regulation of Rheb GTPase activity by AMPK REACT_21393 Regulation of AMPK activity via LKB1 REACT_21285

(TC016) Hypothermia

(TC094) Porphyria

50S ribosomal protein L4

Kinase binding GO:0019900 Hypertrophic cardiomyopathy (HCM) [hydroxymethylglutaryl-CoA cAMP-dependent protein reductase (NADPH)] kinase hsa05410+5562 kinase activity GO:0004691 activity GO:0047322

DB01124

Glycine receptor subunit alpha-1 DNA topoisomerase 4 subunit B

Corticosteroid 11-beta-dehydrogenase isozyme 1

Guanylate cyclase soluble subunit alpha-2 (TC007) Candidiasis

DB00741

Sphingomyelin phosphodiesterase

(TC081) Bursitis

Cytochrome P450-cam

DB01097

DB00394

Protein binding GO:0005515

Prolyl 4-hydroxylase subunit alpha-1


5'-AMP-activated protein Calcium-transporting kinase catalytic subunit ATPase type 2C member 1 alpha-1

Heme binding GO:0020037

(TC028) Furuncle

Tyrosine-protein phosphatase non-receptor type 4

DB00547

Metal ion binding GO:0046872 Non-membrane spanning protein tyrosine phosphatase activity GO:0004726protein binding Cytoskeletal GO:0008092

L-ascorbic acid binding GO:0031418 Eosinophil peroxidase

Receptor-type tyrosine-protein phosphatase epsilon

Farnesyl pyrophosphate synthetase 4-hydroxyphenylpyruvate dioxygenase Calcium/calmodulin-dependent 3',5'-cyclic nucleotide Calcium/calmodulin-dependent phosphodiesterase 1B 3',5'-cyclic nucleotide phosphodiesterase 1A

Transmembrane receptor Protein homodimerization protein tyrosineactivity GO:0042803 phosphatase activity GO:0005001

Dopamine beta-hydroxylase

Peroxidase activity Asthma hsa05310+8288 H01094 Eosinophil GO:0004601 05310 Asthma peroxidase deficiency Catalytic activity GO:0003824 Tyrosine metabolism hsa00350+1621 (TC106) Gastroenteritis viral

Catecholamine biosynthesis REACT_15551 Metabolic pathways hsa01100+1621 H01005 Dopamine Copper ion binding beta-hydroxylase GO:0005507 deficiency

!!!!!!!!!!!!!!!!!!!!!!!!!!!!

!

DB00672

!

Legend Targets SEs Drugs Pathways Molecular4functions Disease8related4genes

!

Fig. (9). A) Network of the highest top five scored correlated targets-drug-side effects in the extracted 10 CCs. The highest positive weighted proteins (blue rectangles), the high scoring drugs (green diamonds) for both targets and SEs, and the highest positive weighted side effects (red triangles) are connected if they appear in the same canonical component (CC). B) Network of drug-targeted proteins and side effects in the extracted 10 CCs



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with an edge-weighted spring embedded layout where the length of the edges in the network reflects the CCA α weights when connecting targets and SEs and CCA u CC score when connecting drugs and SEs. The associated pathways to the drugs causing the side effects (yellow circles), the disease-related genes (pink hexagons) and the related molecular functions (orange circles) are also shown. We consider that a high scoring drug perturbed a pathway if the latter was related to at least one of the high weighted target proteins in RCCA. Zoomed subnetwork for easier visibility for CC1 (C), CC2 (D) and CC5 (E). High-resolution network graphs are available in the Supporting Information.


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A)#

B)# PROBABLE FATTY ACID Leukocyte elastase SYNTHASE FAS (FATTY ACID Carbamoyl-phosphate cAMP-specific 3',5'-cyclic Prostaglandin reductase 2 synthase Serine hydroxymethyltransferase, SYNTHETASE) Vitamin K-dependent protein S Gamma-aminobutyric-acid phosphodiesterase 4A [ammonia], mitochondrial cytosolic Lactoylglutathione lyase receptor subunit beta-2 Vitamin K-dependent protein C

Acyl chain remodelling of PC, PE, Glycerophospholipid metabolism GnRH signaling pathway hsa04912 PG, PI, PS (REACT_120829, Glutamatergic synapse hsa04724 Pancreatic secretion hsa04972 Calcium ion binding GO:0005509 Ras signaling pathway hsa04014 hsa00564 Long-term depression hsa04730 Synthesis of PA REACT_120906 121369, 121324, 120722, 121384) hsa05145 alpha-Linolenic acid metabolism Toxoplasmosis hsa00592 Vascular smooth muscle contraction MAPK signaling pathway hsa04010 Fcmetabolism epsilon RI signaling hsa04270 Ether lipid hsa00565pathway Phospholipase A2 activity hsa04664 GO:0004623 VEGF signaling pathway hsa04370 Fat digestion and absorption LinoleicArachidonic acid metabolism hsa00591 hsa04975 acid metabolism Vascularhsa00590 smooth muscle contraction hsa04270

Mast/stem cell growth factor receptor Myeloperoxidase NADPH oxidase organizer 1 Epidermal growth factor receptor

115069

Angiotensin-converting enzyme Pyruvate-ferredoxin oxidoreductase NADH-ubiquinone oxidoreductase chain 1

DB00540 DB01242

D(1B) dopamine receptor DB00851

DB01084

Glucocorticoid receptor

DB00859 DB00321

5-hydroxytryptamine 2B receptor

5-dioxygenase 1

DB00557

DB00780

Dihydropteroate synthase 2

DB00548

DB00478

DB01119 DB01586

DB00806 50S ribosomal protein L4

Potassium transporter DB00458

DB01159 DB00270 Poly [ADP-ribose] polymerase 1 DB01080

Acetyl-CoA acetyltransferase, mitochondrial 50S ribosomal protein L22

Chromodomain-helicase-DNA-binding protein 1 Tubulin beta chain Penicillin-binding protein 3 23S rRNA

DB01153 DB01127

DB00559 DB00669

cassette transporter MethionineATP-binding aminopeptidase 2 sub-family C member 8

DB00408

Retinal rod rhodopsin-sensitive B-Raf proto-oncogene cGMP 3',5'-cyclic serine/threonine-protein phosphodiesterasekinase subunit gamma

DB00433

DB00718 DB00216 DB00927 DB00357

D(2) dopamine receptor

DB01099

(TC084) Cough

DB01214

Alpha-N-acetylglucosaminidase

Metabolic pathways hsa01100

Glutathione reductase,ATP-sensitive inward rectifier mitochondrial potassium channel 8

DB00521

DB01627

DB00819 DB00924 Vascular endothelial growth factor receptor 1

DB00314 Estrogen receptor beta

DB00831

Tumor necrosis factor ligand Sodium/calcium exchanger superfamily member 11 1

MecA PBP2' (penicillin binding protein 2')

DB00393

DB00582 (TC077) Chest pain

(TC101) Chondrodystrophy

DB00811

Plasminogen

DB01057

DB06782

(TC112) Cervical dysplasia DB00961 Sodium channel protein type 4 subunit alpha

Sodium-dependent noradrenaline transporter

Aminopeptidase N Penicillin-binding protein 2x

Elastin

Muscarinic acetylcholine receptor M5 DB01023 Serine/threonine-protein kinase mTOR

DNA

DB00809

3-phosphoinositide-dependent protein kinase 1

(TC028) Furuncle DB00454 Glutamate [NMDA] receptor DB00299 subunit 3A

Glycosyltransferase GtfA

DB00779

Substance-P receptor

DB00312 DB01238 DB00238

Fibroblast growth factor receptor 2

Cadherin-5

DB00816

Cytochrome P450-cam 5-hydroxytryptamine 2A receptor (TC024) Oropharyngeal pain

DB01241

Aldo-keto reductase family 1 member C2

130362

dehydrogenase/Delta DB00843 5-->4-isomerase type I

DB01254

Prostaglandin E2 receptor, EP1 subtype

DB03225

(TC051) Thrombocytopenia

DB00425 DB01165 DB00346 DB01137

DB00252

DB00465 DB01016

DB00518

DB00281

Nuclear factor NF-kappa-B p105 subunit

Histamine H3 receptor Isoleucyl-tRNA synthetase (TC082) Ovarian cyst DB00499

DB00398

(TC063) Testicular mass DB01161

DB01258

Transcription factor AP-1

DB00690


DB00916DB00829 DB00150


(TC061) Malabsorption

Tryptophan metabolism hsa00380

Sodium-dependent noradrenaline transporter

Substance-P receptor DNA topoisomerase 4 subunit B

Proteoglycans in cancer ko05205

Tryptophan 2,3-dioxygenase activity GO:0004833

DB00215 DB00210

Tryptophan catabolism REACT_916 Metal ion binding GO:0046872

DB00481 DB00454

DNA topoisomerase 4 subunit A

DB00346

Heme binding GO:0020037 Oxygen binding GO:0019825 Heparin-binding growth factor 2

Amino acid binding GO:0016597 Implications for cancer and immune biology OMIM 191070

bacterial outer membrane DB01110

Mitogen-activated protein kinase 1 3-phosphoinositide-dependent DNA gyrase subunit A protein kinase 1

Eosinophil cationic protein

(TC042) Penile pain

Gamma-aminobutyric acid type B receptor, subunit 1

Hedgehog signaling pathway Pathways in cancer ko05200 ko04340

DB01009 DB00962

DB01069

DB01057DB00737

DB00194

DB00674

signaling REACT_111184 ATP binding GO:0005524 PI-3K and PI3K cascade (REACT_21270, 976 )

ATP-binding cassette sub-family G member 2 Serine/threonine-protein kinase mTOR (TC086) Tuberculosis

(TC052) Hyperkeratosis DB00831

DB01132 DB01258

Receptor tyrosine-protein kinase erbB-2

DB00756

(TC003) Peptic ulcer Prostaglandin E2 receptor, EP2 subtype

DNA-directed RNA polymerase subunit beta' Apoptosis regulator Bcl-2

DB00918 DB00802

DB00659

DB00250

DB00797

(TC059) Sciatica

P2Y purinoceptor 12 DB00737 Glutathione S-transferase P

(TC013) Pyrexia

Potassium-transporting ATPase alpha chain 1potential Transient receptor Hypoxanthine-guanine cation channel subfamily V phosphoribosyltransferase Calcium ions 3 member

DB01229 (TC064) Nausea DB00210 DB01039

DB00399

DB00214 DB01214 DB00187 DB00808

DB00447

FRS2-mediated cascade regulation REACT_21247 Negative of FGFR

Prolactin receptor

(TC045) Tendonitis DB01018 DB00794 (TC005) Tachycardia DB00474

DB00796

DB01048

DB01097 DB00537 DB01059

DB01044

(TC037) Dizziness

DB01079

70S ribosome Calcium/calmodulin-dependent 3',5'-cyclic nucleotide Heparin-binding growth factor 2 phosphodiesterase 1B

Regulation of actin Ras and Rap1 signaling pathways cytoskeleton hsa04810 (hsa04014, hsa04015 Prostate cancer hsa05215

Orphan nuclear receptor PXR

(TC033) Musculoskeletal discomfort (TC077) Chest pain

(TC078) Coagulopathy (TC015) Myalgia Copper

Hedgehog signaling Basal cell carcinoma ko05217 Basal cell carcinoma H00039

101449 Platelet glycoprotein IX Transient receptor potential cationE2 channel subfamily A Prostaglandin receptor EP4 member 1 subtype Transient receptor potential cation channel subfamily M member 8 Coagulation Factor XII Multidrug resistance protein 1

Lacrimo-auriculo-dento-digital Fibroblast growth factor binding syndromeGO:0017134 (LADD) H00642 MAPK signaling pathway hsa04010 DNA-directed RNA polymerase beta chain

DB01079 DB00484

Toll-like receptor 8

Isoleucyl-tRNA synthetase

Ferriprotoporphyrin IX Voltage-dependent T-type calcium channel subunit alpha-1G D-alanyl-D-alanine endopeptidase

DB00983

DB00839

ATP-sensitive inward rectifier potassium channel 11

DB00281

(TC063) Testicular mass DB00263

DB01264 (TC110) Hypotension (TC046) Pseudomembranous colitis DB00259 (TC027) Herpes simplex DB06698(TC076) Asthma

4-aminobutyrate aminotransferase, mitochondrial

Ceramide glucosyltransferase Toll-like receptor 8

(TC010) Dry skin

DB00282

Calcium/calmodulin-dependent 3',5'-cyclic nucleotide phosphodiesterase 1A

DB00753 DB00696 (TC106) Gastroenteritis viral DB00809

(TC081) Bursitis DB00228

Mucin-2 Interleukin-5

Phospholipase C-mediated cascade REACT_21310 Heparin binding GO:0008201

Metabolic pathways hsa01100

DB00582

ATP-binding cassette transporter DB00672 (TC022) Skin hyperpigmentation sub-family C member 8 DB01254

(TC028) Furuncle DB00145 (TC084) Cough

Smoothened homolog DB00623

DB00532

Alpha-1B adrenergic receptor

DB01120

DB01008

DB01156

Protein binding GO:0005515

Estrogen-related Cytochrome P450 17A1 receptor gamma DB00993 DB00779

(TC100) Erythema multiforme

DB01132

DB00887

DB01267

(TC049) Night blindness DB00573

(TC106) Gastroenteritis viral

Copper Ornithine aminotransferase, DB00978 mitochondrial DB00484

3-phosphoshikimate 1-carboxyvinyltransferase

DB00481

Constitutive PI3K/AKT Signaling in Cancer REACT_147727

Fibroblast growth factor receptor 2

(TC037) Dizziness

(TC032) Abortion spontaneous

Ornithine aminotransferase, mitochondrial

Arginine and proline metabolism hsa00330 Biosynthesis of antibiotics hsa01130 Secondary hyperammonemia H01400 Pyridoxal phosphate binding GO:0030170 Ornithinaemia H00189 Amino acid synthesis and interconversion (transamination) REACT_238

Potassium voltage-gated channel Antithrombin-III subfamily E member 1

DB01124

DB01067

DB00531 3 beta-hydroxysteroid Tubulin beta-1 chain

Apoptosis regulator ATP-binding cassette Bcl-2 sub-family Orphan nuclear receptor PXR G member DNA-directed RNA DNA-directed RNA2polymerase polymerase subunit beta' beta chain

DB00312

DB00264

DNA polymerase subunit alpha B

163831

FGFR2bEndocytosis and FGFR2chsa04144 ligand binding and activation (REACT_9416, 9413)

(TC101) Chondrodystrophy C-C chemokine receptor typeDB00745 5

DB00937 DB00822

DB01072 DB00795

DB00745 DB00659

DB00195

DB00275

(TC086) Tuberculosis

Retinoic acid receptor responder protein 1

DB00833

DB00280 DB00594 DB00264

DB00447

Serum paraoxonase/arylesterase DB00900 Smoothened homolog 1 Tyrosine-protein C-C chemokine receptor type 5 phosphatase Xanthine dehydrogenase/oxidase non-receptor type 1 DB00263 DB00911 DB00696

DB00887 Dihydroorotase

DB00214

72 kDa type IV collagenase

DB01032 DB01120

Sodium-dependent dopamine transporter

DB01221

Tryptophan 2,3-dioxygenaseprotein 1C Penicillin-binding

DB00297

Niemann-Pick C1-like protein 1

DB00787

DB00937 DB00292

DB00187

DB01032

DB00203 DB00705

SHC-mediated cascade REACT_21374 Gastric cancer H00018 Protein tyrosine kinase activity GO:0004713 DB00811

DB00499

DB00813

(TC027) Herpes simplex

DB00784 DB00369 Cystic fibrosis transmembrane DB00501 Dihydropteroate synthetase conductance regulator

Dihydropterate synthase

(TC032) Abortion spontaneous DB00721

DB00668

Fibroblast growth factor-activated receptor activity H00458 GO:0005007 Craniosynostosis

DB01023

DB01221 130362

Glutamate [NMDA] receptor subunit 3A

Pathways in cancer hsa05200 PIP3 activates AKT signaling REACT_75829

MecA PBP2' (penicillin binding protein 2')

DB00314

Translocator protein

DB00787

Tyrosine-protein phosphatase non-receptor type 1

Tumor necrosis factor

DB00641 Aluminum

DB00310

DB00292

DB00784

DB00150

Sodium-dependent serotonin transporter

DB00754

(TC067) Wound DB01067

DB00900

cGMP-dependent 3',5'-cyclic phosphodiesterase Opioid receptor, sigma 1

Plasminogen

DB00393

Sodium-dependent serotonin transporter

DB00252 (TC100) Erythema multiforme

DB00369

(TC042) Penile pain

Nuclear factor NF-kappa-B p105 subunit

D-Ala-D-Ala moiety of Sodium channel protein type 2 NAM/NAG peptide subunits of subunit alpha peptidoglycan

Signaling by FGFR mutants and Activated point mutants of FGFR2 amplification FGFR2 REACT_120863 mutants (REACT_121398, 121255)

3 beta-hydroxysteroid dehydrogenase/Delta 5-->4-isomerase type I

Elastin Dihydropteroate synthetase

Protein homodimerization activity GO:0042803

DB00521

Dihydroorotase

30S ribosomal protein S10 DB01097 DB00843

(TC082) Ovarian cyst

Guanylate cyclase solubleDB01039 subunit alpha-2

DB00698

DB00916 Guanylate cyclase soluble subunit alpha-2

DB00374 DB00251 DB00841 Sodium channel protein type 10 subunit alpha

DB00839

5-hydroxytryptamine 2A receptor

DB01035

DB00196 DB00222

Xanthine dehydrogenase/oxidase ATP-sensitive inward rectifier potassium channel 8

DB06151

Neuronal acetylcholine receptor subunit alpha-10

DB01264

ATP-sensitive inward rectifier potassium channel 11

Gamma-hydroxybutyrate receptor

DB00228

(TC112) Cervical dysplasia

178246 DB04930

Dipeptidase 1

10A Cell division protein kinase 2

Dihydroorotate dehydrogenase, mitochondrial

(TC045) Tendonitis

DB00808 DB00623

DB00816

DB01016

Muscarinic acetylcholine receptor M5

DB00790 Schistosome Calcium (Ca 2+) Channel

PotassiumcAMP voltage-gated channel and cAMP-inhibited cGMP subfamily KQT member 1 3',5'-cyclic phosphodiesterase DB01324 DB00972 DB00560

DB00422

DB06700

Nuclear receptor 0B1

Short/branched chain specific Thioredoxin reductase acyl-CoA dehydrogenase, mitochondrial

Cannabinoid receptor 1 Pencillin BindingCarnitine Protein 3, P. O-palmitoyltransferase aeruginosa I, liver isoform

Sialidase-2 DB01143 Group IIE secretory phospholipase A2 2-amino-4-hydroxy-6-hydroxymethyldihydropteridine pyrophosphokinase


DB00993

DB00215 DB00477

Oxygen-insensitive NADPH nitroreductase Peroxisome proliferator-activated receptor gamma

DB00979

Neuraminidase

DNA topoisomerase DB008024 subunit B DB00259 (TC076) Asthma DNA topoisomerase 4 subunit A DB00850 DB01033 DB00822 DB00753

DB00308

Gamma-aminobutyric acid type B receptor, subunit 1 DB01156 (TC051) Thrombocytopenia

Gamma-aminobutyric acid receptor subunit theta Prostaglandin E2 receptor, EP3 subtype

Neuronal acetylcholine receptor subunit beta-4

(TC005) Tachycardia DB01230

DB00631

DB00303

DB00943

DB00920 (TC067) Wound

DB00951 DB00436 DB00855 DB00829

DB00672

DB00480

DB01268

DB00246

3-phosphoshikimate 1-carboxyvinyltransferase

Histone deacetylase 9

Cytochrome P450 51A1 DB00797

DB00474 DB00986 DB06698 DB01018 (TC052) Hyperkeratosis (TC110) Hypotension DB00145

(TC046) Pseudomembranous (TC022) Skin colitis hyperpigmentation

Prolactin receptor

DB00609 DB00381

DB00243 DB00496

Xanthine oxidase activity GO:0004855 Purine catabolism REACT_2086 Caffeine metabolism hsa00232

Drug metabolism - other enzymes hsa00983 Xanthinuria H00192 Xanthine dehydrogenase activity GO:0004854 DB00862

DB01008

Geranylgeranyl pyrophosphate synthetase

DB00432 DB00250

30S ribosomal protein S10

266760

DB00465 Cytochrome P450-cam

(TC064) Nausea

Nitric oxide synthase, inducible DB01167

DB00794 DB00602

Vascular endothelial growth Indoleamine 2,3-dioxygenase factor

DB01149 DB00412

6-phosphogluconate DNA polymerase I dehydrogenase, decarboxylating

DB01004

DB00331

DB04485 DB00847

DB00339

DB01128

Free fatty acid receptor 1 Estrogen sulfotransferase

Adenosine A2b receptor

(TC081) Bursitis

2 iron, 2 sulfur cluster binding Electron carrier activity GO:0009055 GO:0051537

DB00395 DB01148

Catechol O-methyltransferase

(TC079) Decreased appetite

IronPurine ion binding GO:0005506 metabolism hsa00230

DB00246 DB00496 DB00308

Canalicular multispecific organic anion transporter 1

Cadherin-5

DB01047

Aldo-keto reductase family 1 Peroxidase/catalase memberTC1

(TC013) Pyrexia DB00720 Nuclear receptor 0B1 DB00639

DB00243

Enoyl-[acyl-carrier-protein] reductase [NADH]

DB00489

DB00716

Long-chain-fatty-acid--CoA ligase 3

GO:0050660 Oxidoreductase activity, acting on the aldehydePeroxisome or oxo grouphsa04146 of donors GO:0016903

DB00381

bacterial outer membrane

Mitogen-activated protein kinase DB00643 1

Molybdopterin cofactor binding UDP-N-acetylmuramate GO:0043546 GO:0008762 dehydrogenase Flavin adenineactivity dinucleotide binding

DB00609

Aldehyde dehydrogenase, mitochondrial

DB00600

DB01173

Transcription factor AP-1

Phospholipase DihydrofolateA2 reductase Phosphate

DB00952

DB05246 (TC033) Musculoskeletal discomfort

DB00918

Estrogen sulfotransferase Tumor necrosis factor ligand superfamily member 11 Long-chain-fatty-acid--CoA ligase 3


DB00283

DB01189 (TC015) Myalgia (TC078) Coagulopathy DB01393

Sodium/calcium exchanger 1 Estrogen receptor beta Indoleamine 2,3-dioxygenase Vascular endothelial growth factor

DB00690 Trivalent metal cations

Integrin beta-3

DB00553 Toll-like receptor 9

DB01110 DNA gyrase subunit A

DB01158 DB01115

DB00632 DB01222 DB00333 DB00442 DB00199

Glutathione reductase, Free fatty acid receptor 1mitochondrial Group IIE secretory phospholipase A2

DB00884 DB00697 DB00608 DB00399

Adenosine A3 receptor Chloride channel protein ClC-Ka Integrin alpha-IIb

Prostaglandin F2-alpha receptor

Cytochrome P450 17A1

DB00748

Adrenodoxin, mitochondrial D(3) dopamine receptor Farnesyl pyrophosphate Cytochrome P450 51 Receptor-type tyrosine-protein synthetase phosphatase epsilon

Inhibitor of nuclear factor RAF proto-oncogene kappa-B kinase S100-A12 subunit beta Protein serine/threonine-protein kinase

DB00513

DB00434

Nuclear Receptor transcription kinase deficiency (GKD) [Homo Sapiens] 46,XYGlycerol disorders ofpathway sex development H00552 REACT_15525.4 (Disorders of gonadal development) H00607

DB00451

DB00344 DB00726 DB01233

DB00435 DB01577 DB00191

DB01319

Keratin, type I cytoskeletal 12 Fatty acid synthase Tubulin alpha chain

Retinal cone rhodopsin-sensitive Potassium voltage-gated channel cGMP 3',5'-cyclic subfamily H member phosphodiesterase subunit 7 Penicillin-binding protein 2B gamma Potassium voltage-gated channel subfamily H member 6 Protein S100-A13

Sodium channel protein type 5 subunit alpha

DB01114

DB00418

Type-1 angiotensin II receptor Gonadotropin-releasing hormone II receptor

Glutamate receptor, ionotropic kainate 1 11B1, Cytochrome P450 mitochondrial Vitamin K epoxide reductase Tyrosine-protein phosphatase complex subunit 1 Cholecystokinin type A receptor non-receptor type 4

Synaptic vesicular Probable arabinosyltransferase C amine transporter Probable arabinosyltransferase B Probable arabinosyltransferase A Dihydropteroate synthase 1 Procollagen-lysine,2-oxoglutarate

Calcium/calmodulin-dependent 3',5'-cyclic nucleotide phosphodiesterase 1B Calcium/calmodulin-dependent 3',5'-cyclic nucleotide phosphodiesterase 1A

DB00559 P2Y purinoceptor 12 DB00196 DB00669DB00222

Synaptic vesicle glycoprotein 2A

70S ribosome Serum paraoxonase/arylesterase 1DB00451 Penicillin-binding protein 1C

DB00862

Toll-like receptor 9

DB06700 DB00679 DB01224 DB01122

Thyroid hormone receptor beta-13 ADP/ATP translocase

4-aminobutyrate aminotransferase, mitochondrial

DB04930 Estrogen-related receptor gamma

DB00543

ADP/ATP translocase 2

DB01220 FolC bifunctional protein DB01100 [Includes: Folylpolyglutamate DB00549 synthase DB00673

DB00619

DB00665

DB00395 266760 DB01267

DB01035

DB01291

Sodium-dependent dopamine Adenosine A2b receptor transporter

DB00818 DB00218

[Pyruvate dehydrogenase [lipoamide]] kinase isozyme 1, mitochondrial ADP/ATP translocase Thyroid hormone1 receptor alpha

Dihydroorotate dehydrogenase, Methionine aminopeptidase 2 mitochondrial

Macrophage metalloelastase Potassium voltage-gated channel subfamily A member 1 D1 dopamine receptor-interacting protein calcyon

Troponin C, skeletal muscle

Hepatitis C Brite 05160 Sphingolipid signaling pathway ko04071 FoxO signaling pathway ko04068 Thyroid hormone signaling pathway ko04919

Dipeptidyl peptidase 4

DB01148

Canalicular multispecific organic anion transporter 1

Ergosterol, Candida albicans

DB01149

Aldehyde dehydrogenase, mitochondrial

DB00244 DB00788 DB00712 DB00909 DB00482

Folic acid synthesis protein FOL1 Dihydropteroate synthase 2 Synaptic vesicular amine transporter Probable arabinosyltransferase B

DB00698 DB00790

DB00345 DB00261 DB00741

DB06151

Tubulin beta-1 chain

5-hydroxytryptamine 2B receptor

178246

Macrothrombocytopenia, autosomal dominant, TUBB1-related OMIM 613112 Pathogenic Escherichia coli infection hsa05130

Peroxidase/catalase T

Kinesins REACT_25201

Enoyl-[acyl-carrier-protein]

MHC class II antigen presentation REACT_121399

reductase Nitric oxide[NADH] synthase, inducible Aldo-keto reductase Oxygen-insensitive NADPH family 1 member C1 nitroreductase

Phagosome hsa04145

Catechol O-methyltransferase

Hedgehog 'off' state REACT_267634 Recycling pathway of L1 REACT_22365 Separation of Sister

Integrin beta-3 Dihydrofolate reductase Chloride channel protein ClC-Ka Phospholipase A2 Adenosine A3 receptor Integrin alpha-IIb

Tubulin alpha chain

Procollagen-lysine,2-oxoglutarate Dihydropteroate Probable arabinosyltransferase C synthase 5-dioxygenase 1 1

Chromatids REACT_150471 Recruitment of NuMA to mitotic REACT_15510 Gapcentrosomes junction hsa04540 Orphan Prefoldin mediated transfer of transporters REACT_267716 Pathogenic Escherichia coli substrate Translocation of GLUT4 to to the Structural constituent infectionof Brite 05130 CCT/TriC REACT_16936 Mitotic Prometaphase REACT_682 plasma membrane REACT_147867 cytoskeleton GO:0005200 GTP binding GO:0005525

Translocator protein Prostaglandin E2 receptor, EP3 subtype cGMP-dependent 3',5'-cyclic phosphodiesterase

Thioredoxin reductase

DNAreceptor polymerase I 2+) Neuronal acetylcholine Schistosome Calcium (Ca 1 subunitDipeptidase alpha-10 Channel 6-phosphogluconate Short/branched chain specific Gamma-aminobutyric acid Opioid receptor, dehydrogenase, decarboxylating acyl-CoA dehydrogenase, Histone deacetylase 9 sigma 1 receptor subunit theta mitochondrial

Post-chaperonin tubulin folding pathway REACT_16967 Resolution of Sister Chromatid Cohesion REACT_150425 Microtubule-dependent trafficking of connexons from Golgi to the plasma membrane REACT_11039 GTPase activity GO:000392

Glucocorticoid receptor D(1B) dopamine receptor

Non-small cell lung cancer ko05223 Endometrial cancer ko05213 Non-small cell lung cancer Brite AMPK signaling pathway ko04152 Endometrial cancer Brite 05213 05223 Insulin signaling pathway ko04910 Neurotrophin signaling pathway Prostate cancer ko05215 T cell receptor signaling pathway Fc epsilon RIko04722 signaling pathway PI3K-Akt signaling pathway ko04660 ko04664 PPAR signaling pathway ko03320 Choline ko04151 metabolism in cancer Prostate cancer Brite 05215 Proteoglycans ko05231 in cancer Brite 05205

Vitamin K epoxide reductase complex subunit 1-like protein 1

ProbableKeratin, arabinosyltransferase A 12 type I cytoskeletal Fatty acid synthase

DB01627

Potassium transporter

Hepatitis C ko05160 mTOR signaling pathway ko04150 Toxoplasmosis Brite 05145

High affinity cAMP-specific and DNAfactor polymerase beta Coagulation IX IBMX-insensitive 3',5'-cyclic phosphodiesterase 8A

Phosphate

DB01189

Toxoplasmosis ko05145 Focal adhesion ko04510

IBMX-insensitive 3',5'-cyclic phosphodiesterase 8B

Trivalent metal cations Metalloproteinase Fibroblast growth factor 4

phosphatidylinositol-3-phosphate Choline metabolism in cancer Brite Aldosterone-regulated sodium protein kinase activity GO:0004676 05231 ko04960 reabsorption

Vitamin K-dependent protein Z BDNF/NT-3 growth factors receptor Potassium voltage-gated channel Sphingomyelin phosphodiesterase subfamily KQT member 2 High affinity cAMP-specific and

DB01047 DB00412

DB00608 DB00697 DB00884

High affinity interleukin-8 receptor A 7 CDP-diacylglycerol--inositol Penicillin-binding protein 3-phosphatidyltransferase

DB00641DB01167 DB00813

2-amino-4-hydroxy-6-hydroxymethyldihydropteridine pyrophosphokinase

Beta-3 adrenergic receptor Beta-lactamase SHV-1 precursor Aromatic-L-amino-acid DNA topoisomerase I, ligase A 5-hydroxytryptamine 1FD-alanine--D-alanine receptor Alanine racemase decarboxylase mitochondrial Cholesterol side-chain cleavage Beta-lactamase TEM enzyme, mitochondrial

C)#

2-amino-4-hydroxy-6-hydroxymethyldihydropteridine Metabolic pathways ec01100 Folate biosynthesis ec00790 diphosphokinase activity GO:0003848

DB00451 DB00696DB00214 DB00454 DB01214

DB00802

DB00669 DB00918DB00222 DB00559

DB00474

DB00196

DB00252 DB00250 DB01018

DB01067130362

(TC052) Hyperkeratosis

Amino acid synthesis and interconversion (transamination) Biosynthesis of antibiotics hsa01130 REACT_238 46,XY disorders of sex development (Disorders of gonadal development) H00607

Secondary hyperammonemia H01400 DB00623 DB00862 (TC022) Skin hyperpigmentation

178246

DB00215

Ornithinaemia H00189

DB00698

Nuclear Receptor transcription pathway [Homo Sapiens] REACT_15525.4

DB01035DB00809

DB00843 DB00790 DB00187

DB01156

DB00263 Glycerol kinase deficiency (GKD) H00552

Pyridoxal phosphate binding GO:0030170

DB04930 DB01039

DB06151

DB00753 DB00210

DB00808

DB01132 DB00659

DB00737

Arginine and proline metabolism hsa00330

DB00499

DB00794 DB01079 DB01258

Nuclear receptor 0B1

DB00481

(TC042) Penile pain

DB00346 (TC086) Tuberculosis

DB00314

Metabolic pathways hsa01100 Ornithine aminotransferase, mitochondrial

DB00281

DB06698 DB01221

DB00696

DB01057 DB00831

DB00839

(TC028) Furuncle

DB00228

DB01254 DB00623

(TC077) Chest pain

DB00779

DB00887

DB00784

DB01267

DB00797

DB00745 DB00993 DB01023

DB01120 DB00900

DB00802

(TC084) Cough

DB00292

(TC045) Tendonitis

DB00811

DB01120

DB00937

DB00228 DB00263 DB00259

DB00809

130362

DB06698

(TC067) Wound DB00243 DB00308

DB00484

DB00145

DB00521

DB01221

DB00784

(TC112) Cervical dysplasia DB01264

DB00609

(TC084) Cough

DB00150 DB00447 DB00474

DB00787

DB00465 DB00246

(TC063) Testicular mass

DB00381

DB00145 (TC028) Furuncle


DB00900

DB00393

DB00816

(TC106) Gastroenteritis viral

DB00937

DB00787

DB00672 DB00582

DB00916 DB00499 DB00264

DB01156

DB00822 DB01008

DB00496 DB00811

DB00659

DB01254 DB01097 DB00312 DB01039


DB00737

DB00252

DB00259

(TC022) Skin hyperpigmentation

DB00839

DB01032

DB01097 DB00264 DB01148DB00412 266760 DB01047

DB00395 DB01149

DB00484

DB00187

DB01018

DB01016

DB00312 DB00369

DB00753

DB00808

DB01067 DB00794 DB01023 DB00250 DB00314

DB00369

DB00993

DB00900 DB00312

DB00281

DB00745 DB00481

DB00499 DB00787

DB00210 DB01079

DB00784 DB00937 DB00369 DB00264

DB01189 DB00813 DB01057

DB00797

DB01627

DB01110

DB00263

DB01132 DB01258 DB00831 DB00737 DB00346

DB01167 DB00829 DB00582

(TC106) Gastroenteritis viral DB00916 DB00484

DB01264

DB00150

DB00641 Hedgehog 'off' state REACT_267634 GTPase activity GO:000392

(TC086) Tuberculosis

Structural constituent of cytoskeleton GO:0005200 Pathogenic Escherichia coli infection Brite 05130


Mitotic Prometaphase REACT_682

Gap junction hsa04540

DB00187

DB01057

Post-chaperonin tubulin folding pathway REACT_16967

DB00215 DB00808 DB00659

Phagosome hsa04145 Kinesins REACT_25201

DB00250 DB00794 DB01067

Recycling pathway of L1 REACT_22365

Translocation of GLUT4 to the plasma membrane REACT_147867

DB00447


Tubulin beta-1 chain MHC class II antigen presentation REACT_121399

Microtubule-dependent trafficking of connexons from Golgi to the plasma membrane REACT_11039

(TC022) Skin hyperpigmentation

DB00187 DB01039


Prefoldin mediated transfer of substrate to CCT/TriC REACT_16936

DB01254

DB00393 DB00809


DB00887

DB00454 Fat digestion and absorption hsa04975

Recruitment of NuMA to mitotic centrosomes REACT_15510

DB00484

GTP binding GO:0005525

DB00499

Ether lipid metabolism hsa00565 (TC037) Dizziness

alpha-Linolenic acid metabolism hsa00592

Macrothrombocytopenia, autosomal dominant, TUBB1-related OMIM 613112

Separation of Sister Chromatids REACT_150471

DB01254

Resolution of Sister Chromatid Cohesion REACT_150425

Pathogenic Escherichia coli infection hsa05130 Orphan transporters REACT_267716

DB00808 DB01018 Phospholipase A2 activity GO:0004623

DB01264

DB00753

DB00292

DB01023

(TC106) Gastroenteritis viral DB00312

DB00214 DB00696 VEGF signaling pathway hsa04370

DB00887

DB01032

DB01067

Linoleic acid metabolism hsa00591

DB01016

DB00937 DB00784


Long-term depression hsa04730

DB00264 DB00787

Group IIE secretory phospholipase A2 DB00753

Arachidonic acid metabolism hsa00590 Synthesis of PA REACT_120906

DB00659

DB00369

DB00451 DB00918 DB00196

Glycerophospholipid metabolism hsa00564

DB00559

Vascular smooth muscle contraction hsa04270

DB00862 DB00690 DB00839 DB01097

266760

DB01214

DB00259

DB00697 DB06700 DB00816

DB01008

DB00252

DB00412 DB01047 DB01149

Calcium ion binding GO:0005509

DB00395DB00145

Ras signaling pathway hsa04014

DB00822

GnRH signaling pathway hsa04912

Glutamatergic synapse hsa04724

Vascular smooth muscle contraction Fc epsilon RI signaling pathway hsa04270Metabolic pathways hsa01100 hsa04664 Tubulin alpha chain

Legend

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Targets SEs Drugs Pathways Molecular4functions Disease8related4genes


DB00884 DB01221

DB01148

DB00802DB00222 DB00215

Toxoplasmosis hsa05145

Acyl chain remodelling of PC, PE, PG, PI, PS (REACT_120829, 121369, 121324, 120722, 121384)

DB00608 DB00779

DB00292DB00669

DB00474 MAPK signaling pathway hsa04010

DB00399 (TC064) Nausea (TC051) Thrombocytopenia


Pancreatic secretion hsa04972


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Fig. (10). A) Network of the extracted twenty targets and drugs (with highest positive weights and highest canonical score, respectively) for the selected highest correlated CCs (which correspond to SEs). Highest positive weight targets and highest scoring drugs for targets are connected to the SE corresponding to the high correlated CC. Target proteins are represented as blue rectangles, SEs as red triangles, and drugs as green diamonds. B) Network for the top ten proteins with highest positive weights shown in purple rectangles with dark blue highlighted edges. The top high scoring drugs are shown in fuchsia diamonds. The target related pathways are shown as yellow circles, the disease-related genes as pink hexagons and the related molecular functions as orange circles. The length of the edges in the network reflects the CCA α weights when connecting targets and SEs and CCA u CC score when connecting drugs and SEs. C) Zoomed sub-network for easier visibility for some examples of the highest positive weighted targets Nuclear receptor 0B1 (top left), Ornithine aminotransferase (top right), Group IIE phospholipase A2 (bottom left) and Tubulin beta-1 chain (bottom right). High-resolution network graphs are available in the Supporting Information.


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For Table of Contents Use Only

GESSE: Predicting Drug Side Effects From Drug-Target Relationships

Li : Estrogen

D gi (x)

gj (x) σi

CM

x"xi"

xi

Myalgia

σj

CM

x"xj"

Violeta I. Pérez-Nueno, Michel Souchet, Arnaud S. Karaboga and David W. Ritchie

Tachycardia

Lj : Androgen

xj

Prediction

Hepatitis

Sij = 0.57

GES : Relating drugs with targets Chemical Space Biological Space

…

Pulmonary edema

SE : Side - effects Phenotypic Space

GESSE: Predicting Drug Side Effects From Drug-Target Relationships


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