Chemogenomics knowledgebase and systems pharmacology for hallucinogen target identification-Salvinorin A as a case study

Abstract

Drug abuse is a serious problem worldwide. Recently, hallucinogens have been reported as a potential preventative and auxiliary therapy for substance abuse. However, the use of hallucinogens as a drug abuse treatment has potential risks, as the fundamental mechanisms of hallucinogens are not clear. So far, no scientific database is available for the mechanism research of hallucinogens. We constructed a hallucinogen-specific chemogenomics database by collecting chemicals, protein targets and pathways closely related to hallucinogens. This information, together with our established computational chemogenomics tools, such as TargetHunter and HTDocking, provided a one-step solution for the mechanism study of hallucinogens. We chose salvinorin A, a potent hallucinogen extracted from the plant Salvia divinorum, as an example to demonstrate the usability of our platform. With the help of HTDocking program, we predicted four novel targets for salvinorin A, including muscarinic acetylcholine receptor 2, cannabinoid receptor 1, cannabinoid receptor 2 and dopamine receptor 2. We looked into the interactions between salvinorin A and the predicted targets. The binding modes, pose and docking scores indicate that salvinorin A may interact with some of these predicted targets. Overall, our database enriched the information of systems pharmacological analysis, target identification and drug discovery for hallucinogens.

Keywords: Chemogenomics database; Drug abuse; Hallucinogen; Salvinorin A; Systems pharmacology; Target identification.

Figure 1. Representative ligands of opioid receptors

Salvinorin A is a non-nitrogenous opioid receptor agonist that shares little structural similarity with other ligands of opioid receptors.

Figure 2. Schematic diagram of HTDocking, TargetHunter, and 3D homology modeling

(A) When a structure of small molecule is submitted to the HTDocking website, and the HTDocking program will automatically calculate the binding affinity values of this molecule against a list of protein targets. The predicted targets will be ranked by docking scores and can be retrieved through the website. (B) A structure of small molecule is submitted to the TargetHunter website together with a structure similarity threshold, and the TargetHunter program will automatically search similar compounds above that similarity based on the molecular fingerprint. The targets of this compound are predicted as the annotated targets of its similar compounds. The results will be ranked by similarity scores. TargetHunter will provide information including structures of similar compounds as well as their targets, bioassays and references. (C) The sequences of to be modeled proteins were retrieved from UniProt. Then a template search for each sequence was performed from the Protein Data Bank (PDB) according to the sequence identities. Sequence alignments of the protein to be modeled and the templates were done by using the Modeller 9.12 software. Alignments were adjusted according to the literature. Finally, the homology models were built.

Figure 3. Summary of targets and pathways for hallucinogens

(A) 144 hallucinogens related targets were summarized according to 46 hallucinogenic compounds. (B) Hallucinogens in different list of schedule drugs according to the CSA (Controlled Substances Act) and their corresponding targets. These hallucinogens were classified by different schedule lists with distinct colors. The blue and orange bars indicated the Schedule I and unscheduled hallucinogens, respectively. The red, green, and purple and cyan bars denoted hallucinogens in Schedule II, III, IV, and V, respectively (C) Hallucinogens in different uses and their targets. These hallucinogens were classified by different uses with distinct colors. The blue and orange bars indicated the approved and unclassified hallucinogens, respectively. The red, green, and purple and cyan bars denoted hallucinogens in experiment use, experiment use and being illicit, being withdrawn, being illicit and withdrawn, respectively. (D) Hallucinogens related pathways. Hallucinogens and their targets were plotted according to the pathways the targets involved.

Figure 4. Systems pharmacological analysis of salvinorin A and other known hallucinogens

(A) Abbreviations, 5HT=5-hydroxytryptamine receptor, ACM=Muscarinic acetylcholine receptor, ADA1A=Alpha-1A adrenergic receptor, AOFA=Amine oxidase [flavin-containing] A, AOFB=Amine oxidase [flavin-containing] B, CNR=Cannabinoid receptor, OPRK=Kappa opioid receptor, DRD2=Dopamine receptor 2, CP2B6=Cytochrome P450 2B6, CP2D6=Cytochrome P450 2D6, NCF2=Neutrophil cytosol factor 2, RAC1=Ras-related C3 botulinum toxin substrate 1, SPRE=Sepiapterin reductase, SUIS=Sucrase-isomaltase (B) Three similar compounds of salvinorin A were chosen according to the results from TargetHunter. OPRK is a known target for all of them. Other four targets including CB1, CB2, ACM2 and DRD2 were predicted according to TargetHunter and HTDocking results.

Figure 5. Interactions between salvinorin A and predicted targets

(A) The 3D homology model of CB1. The pocket was emphasized in pink. The important residues within pocket were highlighted with yellow. (B) The interactions between salvinorin A and CB1. The purple dash lines represent the π-π interactions between Phe57 and the furan moiety of the salvinorin A. The docking score of salvinorin A on CB1 is 9.60. (C) The co-crystal structure of ACM2 (PDB entry: 3UON, resolution: 3.00 Å), binding affinity K_i = 0.2nM. (D) The docking results between salvinorin A and ACM2. Salvinorin A have a similar position and pose with QNB, the original ligand of ACM2 within the binding pocket of ACM2. The docking score of salvinorin A on ACM2 is 8.32.

Figure 6. Interactions between salvinorin A and predicted targets

(A) The 3D homology model of CB2. The pocket was emphasized in purple. The important residues within the pocket were highlighted with green. (B) The interactions between salvinorin A and CB2. The docking score of salvinorin A on CB2 is 8.11. (C) The 3D homology model of DRD2. The pocket was emphasized in pink. The important residues within the pocket were highlighted with green. (D) The interactions between salvinorin A and DRD2. The docking score of salvinorin A on DRD2 is 7.76.

Figure 6. Interactions between salvinorin A and predicted targets

(A) The 3D homology model of CB2. The pocket was emphasized in purple. The important residues within the pocket were highlighted with green. (B) The interactions between salvinorin A and CB2. The docking score of salvinorin A on CB2 is 8.11. (C) The 3D homology model of DRD2. The pocket was emphasized in pink. The important residues within the pocket were highlighted with green. (D) The interactions between salvinorin A and DRD2. The docking score of salvinorin A on DRD2 is 7.76.