Mapping drug-target interactions and synergy in multi-molecular therapeutics for pressure-overload cardiac hypertrophy

Advancements in systems biology have resulted in the development of network pharmacology, leading to a paradigm shift from “one-target, one-drug” to “target-network, multi-component therapeutics”. We employ a chimeric approach involving in-vivo assays, gene expression analysis, cheminformatics, and network biology to deduce the regulatory actions of a multi-constituent Ayurvedic concoction, Amalaki Rasayana (AR) in animal models for its effect in pressure-overload cardiac hypertrophy. The proteomics analysis of in-vivo assays for Aorta Constricted and Biologically Aged rat models identify proteins expressed under each condition. Network analysis mapping protein–protein interactions and synergistic actions of AR using multi-component networks reveal drug targets such as ACADM, COX4I1, COX6B1, HBB, MYH14, and SLC25A4, as potential pharmacological co-targets for cardiac hypertrophy. Further, five out of eighteen AR constituents potentially target these proteins. We propose a distinct prospective strategy for the discovery of network pharmacological therapies and repositioning of existing drug molecules for treating pressure-overload cardiac hypertrophy.


I.
Drug Repositioning for Cardiac Hypertrophy

• Molecular Docking: Binding complexes
The similar structure-function theory described in the above sections relies on the binding of ligands (drugs and metabolites) to their respective receptors (proteins/drugtargets). For a drug to exert similar or identical action of its structurally similar metabolite or drug, should bind to the same site on the receptor protein/ drug target 1 .
The combination of the drug pharmacophore and the target binding site residues is referred to as a biophore 2 . Therefore, we used molecular docking to identify the similar biophores for determining the drug molecules, which can be repositioned for use in cardiovascular diseases. The set of important target proteins from the topological analysis of the protein-protein interaction network were used in this study.
We performed blind docking for the metabolites and their structurally similar drugs by incorporating the entire protein in the docking grid, using AutoDock Vina 3 . The docked complexes were analyzed for similarity (recurring residues) of their respective biophores and further, investigated for known interactions and biological activity of the protein targets used for molecular docking. Such information is crucial for ascertaining the utility of metabolites of AR as potential alternates to currently used therapeutic drugs targeted against identical protein targets. Conversely, the drugs similar to metabolites can also be repositioned for their combinatorial use in the treatment of cardiac hypertrophy. The list of metabolites, their similar drugs and associated target proteins used for molecular docking are given in Table 1 of the main manuscript. The biophores obtained for each of the protein-drug and their corresponding proteinmetabolite complexes had similar binding pockets except for HBB-Cholic acid and HBB-Sebacic acid complexes.
The drug-receptor and metabolite-receptor complexes obtained through a blind docking protocol are given in Figures. S2-S8. The complete biophore fingerprint of recurring and non-recurring residues for each of the protein complexes are enlisted in (Table S8).

Gallic and Ellagic acid in AR:
Supplementary Figure S1: Gallic and Ellagic acid in AR. a) The HPTLC chromatogram shows the peaks identified as Gallic acid and Ellagic acid found in the AR concoction. b) The table shows the concentration of Gallic and Ellagic acid in AR as a weight by weight percentage.

ACADM-Guanylic Acid-FAD:
Supplementary Figure S2: Biophore Fingerprints for ACADM: ACADM protein was docked with Flavin Adenine Dinucleotide (FAD) and guanylic acid (AR metabolite). The docking of the two ligands with ACADM resulted in identical biophoric signatures. The ligands interact with 13 identical amino acid residues of ACADM. The common residues have been encircled in the two complexes for comprehension. This suggests the common biological effects of the two ligands (FAD and guanylic acid).

5.
HBB-Gallic Acid-2,6-Dicarboxy Napthalene: Supplementary Figure S5: Biophore Fingerprints for HBB with Gallic Acid: HBB protein was docked with 2,6-Dicarboxy naphthalene and gallic acid (AR metabolite). The docking of the two ligands with HBB resulted in identical biophoric signatures. The ligands interact with 7 identical amino acid residues of HBB. The common residues have been encircled in the two complexes for comprehension. This suggests the common biological effects of the two ligands.  Guanylic Acid As per the ADMET predictions of the DrugBank database, guanylic acid has the probability of 71% of absorbance from intestines and a 95% probability of crossing the blood brain barrier. 4 2.

Supplementary
Cholic Acid Cholic Acid is an approved drug and orally administered for the treatment of bile acid synthesis disorders and peroxisomal disorders. This suggests it is absorbed in the plasma. 5 3. L-Methionine The supplementation of L-methionine increases the plasma absorbance of other metabolites. Legend for extra files (Dataset S1) 1. Supplementary Dataset S1: In-vivo data, Similarity search, Proteins lists and PPI interactions. The dataset tabulates the lists of proteins with their expression profiles from in-vivo experiments for Aorta Constricted and Biologically Aged rat models. Additionally, the dataset S1 enlists L1 (AC) and L2 (BA), targets associated with metabolites and their similar drugs (L3) and the intersection of L1, L2 and L3 proteins as common proteins (L4). It also gives information on the similar drugs associated with all the 18 AR metabolites and targets corresponding to them. The metabolites associated with common proteins (L4), their similar drugs and other related details such as Tc score, the status of drugs (approved, experimental, investigational) etc. are also found. The dataset S1 also shows of the network connections of the L4 proteins which were further analyzed for various network topologies.