A census of P. longum's phytochemicals and their network pharmacological evaluation for identifying novel drug-like molecules against various diseases, with a special focus on neurological disorders

Abstract

Piper longum (P. longum, also called as long pepper) is one of the common culinary herbs that has been extensively used as a crucial constituent in various indigenous medicines, specifically in traditional Indian medicinal system known as Ayurveda. For exploring the comprehensive effect of its constituents in humans at proteomic and metabolic levels, we have reviewed all of its known phytochemicals and enquired about their regulatory potential against various protein targets by developing high-confidence tripartite networks consisting of phytochemical-protein target-disease association. We have also (i) studied immunomodulatory potency of this herb; (ii) developed subnetwork of human PPI regulated by its phytochemicals and could successfully associate its specific modules playing important role in diseases, and (iii) reported several novel drug targets. P10636 (microtubule-associated protein tau, that is involved in diseases like dementia etc.) was found to be the commonly screened target by about seventy percent of these phytochemicals. We report 20 drug-like phytochemicals in this herb, out of which 7 are found to be the potential regulators of 5 FDA approved drug targets. Multi-targeting capacity of 3 phytochemicals involved in neuroactive ligand receptor interaction pathway was further explored via molecular docking experiments. To investigate the molecular mechanism of P. longum's action against neurological disorders, we have developed a computational framework that can be easily extended to explore its healing potential against other diseases and can also be applied to scrutinize other indigenous herbs for drug-design studies.

Fig 1. The workflow of this study.

Fig 2. Hierarchical clustering of phytochemicals belonging to Piper longum.

Phytochemicals were clustered on the basis of atom-pair descriptors and Tanimoto coefficient using Chemmine tool. It can be easily seen that most of the phytochemicals belonging to benzodioxoles, fatty acyl and prenol lipids category were clustered together.

Fig 3

(A). A tripartite phytochemical—protein target—biochemical pathway (PC-PT-BP) network of the constituents of Piper longum. Top layer (blue) represents phytochemicals (159), middle layer (red) represents the potential protein targets (1109) and the third layer (green) represents association of protein targets with six pathway classes: metabolism, genetic information processing, environment information processing, cellular processes, organismal systems and human diseases. (B) Involvement of target proteins of Piper longum in various human pathway classes. The human pathway mapping of identified target proteins were distributed among 6 classes. Genetic information processing class includes pathways belonging to transcription, translation, replication & repair, folding, sorting and degradation. Environment information processing includes membrane transport, signal transduction, signaling molecules and their interactions. Cellular processes involve pathways of cell growth, cell death and transport (endocytosis, phagosome, lysosome etc.). Organismal system includes immune, endocrine, circulatory, digestive, excretory, nervous, sensory system. Human diseases and metabolism include pathways associated with diseases and metabolic system (carbohydrates, lipids, amino acids etc.) respectively. (C) Bioactives of Piper logum affecting the Human immune system. The first layer (blue) represents 106 phytochemicals, regulating 11 immune pathways (green) by targeting 131 proteins (red).

Fig 4

(A). Phytochemical—protein target—disease association (PC-PT-DA) network of Piper longum. Top layer (blue) represents phytochemicals (159), middle layer (red) represents their protein targets (1109) and the bottom layer (green) represents association of protein targets with 27 disease classes obtained from DisGeNET. Size of the nodes in third layer (green) is proportional to their degree value. (B) Piper longum protein targets and their involvement in 27 disease classes. Number of potential protein targets associated with various disease classes as obtained from DisGeNETv4.0. Maximum number of targets are related with neoplasm and nervous system, while muscular dystrophy and occupational diseases have least number of targets associated with them.

Fig 5. Potential FDA-approved targets from target dataset of Piper longum.

Middle layer (red) represents the 5 key protein targets, which are regulated by 77 phytochemicals of Piper longum, represented in the top layer (blue). Third layer (green) represents the mapping of these targets in 825 diseases.

Fig 6

(A). Protein-protein interaction subnetwork of Homo sapiens targeted by phytochemicals of P. longum. First neighbours of all the targets proteins were mapped into the human PPI as obtained from STRING having high confidence level (score ≥ 900). Green highlighted nodes in the network represent the location of the target proteins of P. longum. (B) Node degree distribution of the PPI subnetwork. The neighbourhood connectivity of each node is represented using node degree distribution graph, analysed using Cytoscape. Both axes of the graph are represented in the logarithmic scale.

Fig 7. Box and whisker plots showing ADMET properties distribution of the Piper longum’s phytochemicals.

The graph shows the plots of ADMET variables corresponding to octanal-water partition coefficient (log_P), water solubility (Water_sol.), skin permeability (Skin_perm.), blood-brain permeability (BBB_per), central nervous system permeability (CNS_perm.), total clearance (Total_clear.), maximum tolerated dose-humans (MTD_humans), oral rat acute toxicity (ORA_tox.), oral rat chronic toxicity (ORC_tox.), T. Pyriformis toxicity (T_pyri. tox), minnow toxicity (Minnow_tox), caco2 cell permeability (Caco2_per.) and intestinal absorption (Intestinal_abs.).

Fig 8. Neuroactive ligand receptor interaction pathway (path:hsa04080) obtained from KEGG database.

The mapped genes corresponding to CHRM, ADR, DRD, PTGER, CHRN and TRPV1 are marked in yellow.

Fig 9. Potential DPC-PTN network.

DPC (blue rhombus) stands for Drugabble PhytoChemicals from P. longum and PTN (red triangle) stands for Protein Targets selected from Neuroactive-ligand interaction pathway (path:hsa04080). 11 potential protein targets (covering 6 gene classes) for neurological disease and disorders from “path:hsa04080” are regulated by 14 potential drug-like phytochemicals of P. longum. Edge of the network represents the possible regulatory phytochemical partner for each protein target. Proteins are grouped according to their gene class. Three gene classes corresponding to adrenergic receptor (ADA), Muscarinic acetyl choline receptor (CHRM) and Dopamine receptor (DRD) constitute multiple proteins, while remaining three corresponding to Transient receptor potential cation channel subfamily V member 1 (TrpV1), Prostaglandin E receptor 1 (PTGER1) and Cholinergic receptor nicotinic alpha7 subunit (CHRNA7) are specific for a particular protein.

Fig 10. Hydrophobic interactions and hydrogen bonding between PL6 and its targets.

Two dimensional representation of interaction observed between PL6 and its interacting proteins listed in Table 5. (A) Ligplot⁺ analysis of the docked complex of PL6 and P14416: Dopamine receptor DRD2. (B) Ligplot⁺ analysis of the docked complex of PL6 and P53462: Dopamine receptor DRD3. (C) Ligplot⁺ analysis of the docked complex of PL6 and Q8NER1 (TrpV1). Protein residues involved in hydrophobic interactions are represented as arcs and hydrogen bonding with dashed lines.