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Human blood plasma proteins modeling and binding
affinities with Δ9-tetrahydrocannabinol active metabolites:
In silico approach
Shravan B. Rathoda,*, Jinal C. Sonib, Priyanshu Vermac,#, Yogita Rawatd,#, Neha
Periwald, Pooja Arorac, Vikas Soodd , Mohmedyasin F. Mansurib
aDepartment of Chemistry, Smt. S. M. Panchal Science College, Talod, Gujarat, India
bDepartment of Microbiology, Smt. S. M. Panchal Science College, Talod, Gujarat, India
cDepartment of Zoology, Hansraj College, University of Delhi, New Delhi, India
dDepartment of Biochemistry, School of Chemical & Life Sciences, Jamia Hamdard, New
Delhi, India
#Equal contribution
*Corresponding author
Shravan B. Rathod: E-mail: shravanathorizon93@gmail.com
Phone: +91-8200040941
ORCID: https://orcid.org/0000-0002-1870-2508
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Abstract
Tetrahydrocannabinol (THC) is a key psychotropic constituent of cannabis
sativa. It is also known as Δ9-tetrahydrocannabinol (Δ9-THC). Previous study
suggested that owing to its high lipophilicity, it piles up in adipose tissue and it is
disseminated into blood stream for prolonged time. Research suggests that
numerous diseases such as multiple sclerosis, neurodegenerative disorders,
epilepsy, schizophrenia, osteoporosis, cancer, glaucoma and cardiovascular
disorders can be treated using this substance. However, apart from having
therapeutic potential, many studies have reported detrimental outcomes along
with addiction of Δ9-THC for short-term and long-term consumption. Thus, in
this study, we determined the binding affinities of Δ9-THC and its two active
metabolites, 11-Hydroxy-Δ9-tetrahydrocannabinol (11-OH-Δ9-THC) and
8beta,11-dihydroxy-Δ9-tetrahydrocannabinol (8β,11-diOH-Δ9-THC) with 401
human blood plasma proteins using molecular docking analysis. Results show
that Δ9-THC has greater binding potential with plasma proteins as compared to
other two metabolites. Overall, ADGRE5, ALB, APOA5, APOD, CP, PON1 and
PON3 proteins showed the highest binding affinities with three cannabis
metabolites.
Keywords: Tetrahydrocannabinol (THC), Psychoactive, Plasma proteins,
Molecular docking, Blind docking, Binding affinity
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Introduction
The consumption of cannabis in teenagers has surged significantly and, its
adverse effects on mental health and political debate on whether make it legalise
or not need urgent molecular level study of cannabis metabolites [1]. Even though
epidemiological studies reported the risk of overconsumption of cannabis during
adolescence leads to neuropsychiatric disorders in their later life, it still remains
widely used drug illicit [2–4]. Researchers have shown that administration of a
key ingredient, Δ9-tetrahydrocannabinol (Δ9-THC) of cannabis in animals during
their adolescence, induced biochemical and behavioural signs of psychosis and
depression later in life [5–10].
Interestingly, it was noted that the exposure of THC to female rats during
adolescence is linked with epigenetic modification of histone H3 in Prefrontal
cortex (PFC). Specifically, trimethylation at Lys9 position in N-terminus of
histone H3. This alternation in histone H3 causes impacts on genes which are
closely linked to the neuroplasticity and Endocannabinoid system (ECS)
mechanisms that ultimately leads to diseases. However, THC exposure to adults
results into minor effects on epigenome[1]. Thus, these findings suggest that the
risks of neuropsychiatric disorders, CNS alternation and the cognitive impairment
are escalated during adulthood due to the consumption of cannabis in adolescence
[11]. Another study revealed that Δ9-THC activates the presynaptic Cannabinoid
receptor type 1 (CB1) receptor which is key player in CNS development during
prenatal, postnatal and adolescence periods [12–14]. The CB1 is G protein-
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coupled receptors (GPCR) and found in glial cells and neurons and cells [15].
Another, Cannabinoid receptors type 2 (CB2) is also GPCR which is generally
located in hematopoietic cells and some specific regions of the brain and
peripheral cells have CB2 receptor. CB2 plays an important role in immunity
pathways and its activation leads to immunomodulatory and anti-inflammatory
response. CB1 pathways are activated through the stimulation of CB2 [16,17].
Further, Abey N. O. investigated the effects of Δ9-THC on blood chemistry and
organs cytoarchitecture and, it was observed significant decline in brain cognitive
function, brain total proteins, and nitric oxide. Additionally, statistically
significant difference were observed in various tissues and blood plasma [18].
Furthermore, computational and in vitro studies of Δ9-THC unveiled that this
constituent strongly binds to three Fatty acid-binding proteins (FABPs), FABP3,
FABP5 and FABP7 [19]. According to Schenk S. et al. analysis, they have
validated and reported that there are 1193 different proteins are present in human
blood plasma [20]. Literature review suggests that significant research has not
been yet done on the binding profile of Δ9-THC and its metabolites with human
blood plasma proteins. Hence, in this study, we determined the binding affinities
of Δ9-THC and its two active metabolites, 11-OH-Δ9-THC and 8β,11-diOH-Δ9-
THC with 401 plasma proteins using molecular docking approach. Fig. 1 shows
the chemical structures of these three compounds.
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Materials and methods
Protein modeling and ligand preparation
Initially, the database of human blood plasma proteins detected by immunoassay
was searched from The Human Protein Atlas
(https://www.proteinatlas.org/humanproteome/blood+protein/proteins+detected
+by+immunoassay) web portal. The total number of proteins were 419 on the
database. Many protein structures were available on the Protein data bank
(https://www.rcsb.org/) but large number of proteins had missing residues thus,
we employed newly developed neural network-based RoseTTAFold [21] protein
prediction tool (https://robetta.bakerlab.org/). This web tool has limitation that it
accepts only proteins which have residues range between 26 and 1201. Thus, out
of 419 plasma proteins, 18 proteins were excluded due to the lack of structure
prediction. These 401 predicted structures were downloaded and further used to
perform molecular docking analysis. The PDB structures of 401 human blood
plasma proteins are given in Supplementary file 1.
In case of ligands, we considered Δ9-tetrahydrocannabinol (Δ9-THC: PubChem
CID- 16078) and its two active metabolites, 11-Hydroxy-Δ9-
tetrahydrocannabinol (11-OH-Δ9-THC: PubChem CID- 37482) and 8,11-
dihydroxy-Δ9-tetrahydrocannabinol (8β,11-diOH-Δ9-THC: PubChem CID-
126961369). Firstly, 3D structures of these ligands were retrieved from the
PubChem Database (https://pubchem.ncbi.nlm.nih.gov/) in sdf format. Then, for
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the energy minimization, geometry optimization was performed on these ligands
using Auto Optimization Tool available in Avogadro software [22]. The
optimization was carried out by employing MMFF94s [23] force field and
Steepest descent [24] algorithm. Finally, the structures were saved as mol2 format
for further docking study.
Molecular docking
To determine the binding affinities of three THC metabolites with 401 human
blood plasma proteins, we utilized protein cavity-find guided Autodock Vina-
based blind docker, CB-DOCK [25] web server available at
http://clab.labshare.cn/cb-dock/php/. To calculate the binding affinity (Docking
score) of these three metabolites with 401 plasma proteins, protein as a PDB and
ligand as a mol2 format were uploaded to the server. We ran total 1203 (3*401)
jobs at the server. Initially, this tool searches top five binding cavities inside the
protein and orders them on the basis of cavity size. Then, it starts docking of
ligand with receptor and calculate the binding affinity for best pose inside each
cavity. Additionally, server provides details of cavity (volume, centre and size
coordinates) along with binding affinity (Vina score). Further, the interactions
analysis was carried out using Discovery Studio v.20.1 tool and UCSF Chimera
v.1.15 was used to make figures of top two complexes. The Electrostatic potential
(ESP) surface analysis of proteins was performed using PyMOL APBS
Electrostatics plugin.
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Results and discussion
Molecular docking is a powerful computational approach to make drug discovery
processes fast and less expensive. Molecular docking has many success stories
for the designing inhibitors against HIV-1, cancer and bacterial targets [26–29].
Hence, for the primary investigation of binding affinities of Δ9-THC and its two
active metabolites (11-OH-Δ9-THC and 8β,11-diOH-Δ9-THC) with human blood
plasma proteins, we used blind docking approach.
Docking results show that binding affinities of Δ9-THC, 11-OH-Δ9-THC and
8β,11-diOH-Δ9-THC with 401 human blood plasma proteins vary between -5.0
and -10.5 kcal/mol. However, Δ9-THC showed the greater binding affinities as
compared to other two metabolites (Fig. 2). This indicates that plasma proteins
have overall hydrophobic nature as they have highest binding affinity with Δ9-
THC. Δ9-THC is less polar (more hydrophobic) than 11-OH-Δ9-THC and 8β,11-
diOH-Δ9-THC (Fig. 1). The binding affinities of all these three metabolites are
given in Supplementary Table S1. Binding affinities of top 20 complexes are
shown in Table 1.
Additionally, we have carried out interaction analysis of top-two complexes of
each metabolite. In case of Δ9-THC, Apolipoprotein A5 (APOA5) &
Apolipoprotein D (APOD) have the large value of docking score, -10.3 & -10.1
kcal/mol respectively (Table 1). The structure and interaction plot of APOA5:Δ9-
THC and APOD:Δ9-THC complexes are illustrated in Fig. 3. It has been reported
that APOA5 plays vital role to metabolize triglyceride and triglyceride-rich
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lipoproteins. Current findings suggest the link between APOA5 and obesity [30].
Apolipoprotein D (APOD) is structurally different from the other apolipoproteins
and it is lipocalin family member. It is widely expressed in glial cells and neurons
of peripheral and central nervous system and carrier of tiny lipophilic candidates.
It is not only transporter but has vital role in modulating the oxidation condition
and stability of lipophilic molecules. Previous studies suggest that APOD was
upregulated in stroke, schizophrenia and Alzheimer’s disease conditions. Thus, it
has neuroprotective functions [31]. APOD forms barrel with eight antiparallel β-
strands and has α-helices at the side. It has around ~15 Å deep hydrophobic cavity
and entry diameter is also 15 Å (Fig. 3C) [32].
Fig. 3A & 3C represent the structure of APOA5:Δ9-THC and APOD:Δ9-THC
complexes. It can be seen from the Fig. 3B that in APOA5:Δ9-THC complex,
Val7, Trp10, Ala11, Gly26, Phe27, Leu56, Leu60, Leu96, Leu100 and Val103
residues have hydrophobic interactions such as π-alkyl and alkyl-alkyl. A single
hydrogen bond is formed by Asp63 and hydroxyl group of Δ9-THC. Whereas, in
APOD:Δ9-THC complex, Val, Phe, Leu, Tyr and Ile are the common residues
which have hydrophobic interaction with ligand (Fig. 3D). The residues from the
APOD interact with Δ9-THC are similar to the previously reported catalytic site
residues. However, the number of residues interacting with Δ9-THC in APOD:Δ9-
THC complex is slightly higher as compared to residues in APOA5:Δ9-THC
complex.
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Considering 11-OH-Δ9-THC metabolite, it can be observed (Table 1) that
Adhesion G protein-coupled receptor E5 (ADGRE5) and Ceruloplasmin (CP)
have better affinities around -9.5 kcal/mol in comparison with other proteins. The
ADGRE5 is the subclass of G-protein coupled receptors (GPCRs) that has
pharmacological significance [33]. It is also known as Cluster of differentiation
97 (CD97). This protein is broadly expressed muscle, immune, stem and
progenitor cells [34-36]. Additionally, recent study reported that ADGRE5/CD97
helps to suppress Nuclear factor kappa B (NF-κB) through upregulating the
Peroxisome proliferator- activated receptor gamma (PPAR-γ) in leukocytes [37].
Some studies also revealed that CD97 was found overexpressed in various
tumours and attunes tumorigenesis [38].
The Ceruloplasmin (CP) belongs to oxidase family and copper enzyme present in
serum [39]. In neurodegenerative diseases such as Parkinson's and Alzheimer's,
CP was found in Cerebrospinal fluid (CSF) with modified structure and state [40].
Like APOD, CP has also neuroprotective role [41]. In CP, six cupredoxin
domains are arranged side-by-side and form a cavity. CP has trinuclear copper
cluster and mononuclear copper centre with a 12-13 Å distance between them.
The trinuclear copper cluster is formed by three copper ions which are located in
three even domains. This cluster has also one type II (T2) and two type III (T3)
copper ions [42].
Complex structures of 11-OH-Δ9-THC with ADGRE5 and CP are given in Fig.
4A and C respectively. While the interactions maps are shown in Fig. 4B and D
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respectively. In ADGRE5 complex, none of the hydroxyl groups have polar
interactions with surrounding residues. All interactions are hydrophobic in nature.
Phe623 and Phe771 have π-π stacking interactions with aromatic ring in ligand.
Remaining interactions come from hydrophobic amino acids such as Phe597,
Leu686, Leu697, Val700, Leu757, Ile759 and Phe760 (Fig. 4B). However, 11-
OH-Δ9-THC has polar interactions with CP. Lys288 forms hydrogen bond with
hydroxyl group. Arg649 interacts with ligand through π-cation interaction.
Phe659, Leu664 and Tyr986 have hydrophobic (π-alkyl & alkyl-alkyl)
interactions (Fig. 4D). Additionally, Asn287 has unfavourable contact with
hydroxyl group of aromatic ring due to its donor nature and steric repulsion.
Finally, APOA5 and ADGRE5 were observed to have the large binding affinities
(~-9.0 kcal/mol) with third metabolite, 8β,11-diOH-Δ9-THC. The structures of
APOA5:8β,11-diOH-Δ9-THC and ADGRE5:8β,11-diOH-Δ9-THC complexes
are illustrated in Fig. 5A and 5C respectively whereas their interactions maps are
shown in Fig. 5B and 5D respectively. In these complexes, hydrophobic
interactions have been found dominant but both have polar interactions also. In
APOA5:8β,11-diOH-Δ9-THC, Lys48 and Asp63 are forming hydrogen bonds
with the hydroxyl groups of ligand. And, Trp10, Ala11, Leu14, Tyr30 and Leu96
interact through alkyl-alkyl hydrophobic interactions (Fig. 5B). Also, it has a
single unfavourable interaction with Asp63. But, in ADGRE5:8β,11-diOH-Δ9-
THC, the hydrogen bond is formed between Asn775 and aromatic hydroxyl group
and, carbon-hydrogen (C-H) polar hydrogen bond between Thr772 and
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methylene group carbon where hydroxyl group is attached (Fig. 5D). Two
aromatic residues, Phe623 and Phe771 show π-π stacking interactions with
aromatic ring of ligand. And, large contribution comes from hydrophobic
residues, Leu686, Leu697, Val700, Ile759 and Phe760 (Fig. 5D).
Furthermore, we have also carried out ESP analysis for APOA5, APOD,
ADGRE5 and CP to probe the ligand binding cavity electrostatic properties. Fig.
6A-D illustrate the hydrophobic nature of cavities in each four plasma proteins,
APOA5, APOD, ADGRE5 and CP respectively.
Conclusion
The principal constituent of cannabis, Δ9-THC has therapeutic significance as
well as some obscurity in adverse impacts on health. Owing to the lipophilic
nature of Δ9-THC, it can bind various human blood plasma proteins. Hence, we
investigated the bindings of Δ9-THC and its two active metabolites (11-OH-Δ9-
THC & 8β,11-diOH-Δ9-THC) with 401 human blood plasma proteins using blind
docking CB-DOCK tool. Further, we performed interactions analysis of top-two
complexes of each constituent with plasma proteins. Results suggest that Δ9-THC
showed higher tendency towards plasma protein binding as compared to its two
metabolites. The blood plasma proteins such as, ADGRE5, ALB, APOA5,
APOD, CP, PON1 and PON3 have suitable binding cavities to adapt Δ9-THC and
its active metabolites in comparison with remaining plasma proteins. Our study
is a primary investigation of binding affinity between Δ9-THC and plasma
proteins. However, our study provides input to further probe the structural,
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functional and dynamic impacts of these metabolites on blood plasma proteins
leading to adverse impacts of these cannabinoids on human health by performing
further studies.
Declaration of competing interest
No potential conflict of interest was reported by the author(s).
Acknowledgements
SBR is thankful to his Chemistry Department for providing computational
facilities and infrastructure.
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Table 1
Binding affinities of top 20 complexes of Δ9-THC and its two metabolites (Ligands) with
human blood plasma proteins.
Sr.
No.
Ligands
Δ9-THC
11-OH-Δ9-THC
8β,11-diOH-Δ9-THC
Protein
Binding
affinity
(kcal/mol)
Protein
Binding
affinity
(kcal/mol)
Protein
Binding
affinity
(kcal/mol)
1
APOA5
-10.3
ADGRE5
-9.6
APOA5
-9.3
2
APOD
-10.1
CP
-9.5
ADGRE5
-9.1
3
PON1
-9.5
ALB
-8.9
APOD
-9.1
4
CP
-9.3
APOA5
-8.9
CP
-9.1
5
ANGPTL3
-9.2
APOD
-8.9
PON3
-9.1
6
LEPR
-9.1
B2M
-8.9
VTN
-8.9
7
ADGRE5
-9
CETP
-8.9
ALB
-8.8
8
FGG
-8.9
VTN
-8.9
B2M
-8.8
9
AFM
-8.8
BPI
-8.8
BPI
-8.7
10
PLA2G2A
-8.8
AFP
-8.6
NAMPT
-8.7
11
APOF
-8.7
LBP
-8.6
SELE
-8.6
12
PDGFB
-8.7
LEPR
-8.6
FGG
-8.5
13
PON3
-8.7
NAMPT
-8.6
HPX
-8.5
14
ALB
-8.6
PLTP
-8.6
PON1
-8.5
15
BMP6
-8.6
IL1RL1
-8.5
BMP6
-8.4
16
C9
-8.6
OSM
-8.5
FABP3
-8.4
17
IL1RL1
-8.6
PON1
-8.5
HABP2
-8.4
18
SELE
-8.6
BMP7
-8.4
LBP
-8.4
19
VTN
-8.6
FABP3
-8.4
MMP9
-8.4
20
BPI
-8.5
FCRL5
-8.4
OSM
-8.4
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Graphical abstract
The sequences of the 401 human blood plasma proteins were retrieved from the UniProt
database and RoseTTAFold protein structure prediction tool was employed to predict the
structures of proteins. Next, three metabolites, Δ9-THC, 11-OH-Δ9-THC and 8β,11-diOH-Δ9-
THC were downloaded from the PubChem database and energy minimization of these three
metabolites were done using Avogadro molecular graphic tool. Finally, the molecular docking
of these three substances was performed with 401 (3*401= 1203) using blind docker CB-
DOCK tool and, interactions analysis of top two complexes of each metabolite was carried out.
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Fig. 1. (A-C) 2D structures of Δ9-THC, 11-OH-Δ9-THC and 8β,11-diOH-Δ9-THC respectively.
(D-F) 3D structures of Δ9-THC, 11-OH-Δ9-THC and 8β,11-diOH-Δ9-THC respectively. The
yellow spheres indicate the addition of new hydroxyl groups to the Δ9-THC.
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Fig. 2. The heatmap of binding affinity (Vina score in kcal/mol) of Δ9-THC and its two
metabolites with 401 human blood plasma proteins. Binding affinities correspond to color bar.
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Fig. 3. Complex structures and their 2D interactions. (A) APOA5:Δ9-THC complex. (B)
APOA5-Δ9-THC interactions. (C) APOD:Δ9-THC complex. (D) APOD:Δ9-THC interactions.
(APOA5: Apolipoprotein A5 & APOD: Apolipoprotein D). Orange-red circle shows the ligand
position.
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Fig. 4. Complex structures and their 2D interactions. (A) ADGRE5:11-OH-Δ9-THC complex.
(B) ADGRE5:11-OH-Δ9-THC interactions. (C) CP:11-OH-Δ9-THC complex. (D) CP:11-OH-
Δ9-THC interactions. (ADGRE5: Adhesion G protein-coupled receptor E5 & CP:
Ceruloplasmin). Orange-red circle shows the ligand position.
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Fig. 5. Complex structures and their 2D interactions. (A) APOA5:8,11-diOH-Δ9-THC
complex. (B) APOA5-8,11-diOH-Δ9-THC interactions. (C) ADGRE5:8,11-diOH-Δ9-THC
complex. (D) ADGRE5:8,11-diOH-Δ9-THC interactions. (APOA5: Apolipoprotein A5 &
ADGRE5: Adhesion G protein-coupled receptor E5). Orange-red circle shows the ligand
position.
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Fig. 6. Electrostatic potential surface (ESP) of four proteins. (A) Apolipoprotein A5 (APOA5).
(B) Apolipoprotein D (APOD). (C) Adhesion G protein-coupled receptor E5 (ADGRE5). (D)
Ceruloplasmin (CP). Ligand position is indicated and zoomed in black square box.
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