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Chikodili, I.M., Chioma, I.I., Chinwendu, N.M., IfedibaluChukwu, E.I. In-silico study for African plants with possible beta-cell regeneration effect through inhibition of DYRK1A. Sciences of Phytochemistry 2022, 1(1), 13-28.
Chikodili, IM, Chioma, II, Chinwendu, NM, IfedibaluChukwu, EI. In-silico study for African plants with possible beta-cell regeneration effect through inhibition of DYRK1A. Sciences of Phytochemistry. 2022; 1(1):13-28.
Igbokwe Mariagoretti Chikodili, Ibe Ifeoma Chioma, Nnorom Miriam Chinwendu, Ejiofor InnocentMary IfedibaluChukwu. 2022. "In-silico study for African plants with possible beta-cell regeneration effect through inhibition of DYRK1A" Sciences of Phytochemistry 1, no. 1:13-28.
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Home / Sciences of Phytochemistry / Volume 1 Issue 1 / 10.58920/sciphy01010013
Research Article
by Igbokwe Mariagoretti Chikodili, Ibe Ifeoma Chioma, Nnorom Miriam Chinwendu, Ejiofor InnocentMary IfedibaluChukwu ★
Academic editor: James H. Zothantluanga
Sciences of Phytochemistry 1(1): 13-28 (2022); https://doi.org/10.58920/sciphy01010013
This article is licensed under the Creative Commons Attribution (CC BY) 4.0 International License.
Abstract: The continuous destruction of normal insulin-producing pancreatic beta-cells is a contributing factor in all common forms of diabetes, due to insufficient production of insulin, especially in type 1 diabetes. There are attempts at beta-cells transplantation, but the cost and availability of donors pose a great challenge to the process. Dual-Specificity Tyrosine Phosphorylation-Regulated Kinase A (DYRK1A) plays a crucial role in beta-cells destruction. Our research targets to identify plants that can be utilized as a possible alternative approach to beta-cell replacement through a pharmacologically induced regeneration of new beta-cells in-silico. The 3D structure DYRK1A and 6511 phytochemicals were obtained from the Protein Data Bank and the African Natural Products Database respectively. They were duly prepared for molecular docking simulations (MDS). MDS was implemented, after validation of docking protocols, in AutoDock-Vina®, with virtual screening scripts. Phytocompounds with good binding affinities for DYRK1A were selected as frontrunners. The compounds were screened for toxicity, Lipinski’s rule confirmation with Data Warrior software followed by kinase inhibitory bioactivity prediction with the Molinspiration Chemoinformatics web tool. Twelve phytocompounds were found to be predictably highly active in-silico against DYRK1A with good drug-like property based on Lipinski’s rule, non-mutagenic, non-tumorigenic, no reproductive effect, and non-irritant, with high predicted bioactivity. In-silico active phytocompounds against DYRK1A with their plant sources and physicochemical parameters were identified. Further studies will be carried out in-vitro and in-vivo to validate the results of this study using plants containing the identified phytocompounds.
Keywords: Beta-cellsRegenerationPhytocompoundsDYRK1AVirtual screeningDiabetes
Diabetes is a life-threatening global health issue as a
result of its high
incidence [1], associated disability, and
mortality [2]. The pancreatic beta-cell deficit is a significant part of the pathophysiological
mechanism [3]. Beta-cells considerable damage leads to long-lasting endocrine insufficiency with
the possibility of a permanent diabetic state. On the bright side, pancreatic beta-cell regeneration
is a promising pharmacological strategy for recovering Beta-cells. In adults, it is known
that the endocrine pancreas has a regulated ability for self-regeneration [4]. Consequently, approaches
for stimulating beta-cell restoration have insightful inferences for the
treatment and management of diabetes, particularly for type 1 diabetes and late-type 2 diabetes with considerable
beta-cell loss.
Two possible approaches exist through which pancreatic beta-cells can be
regenerated. The first approach is by preventing beta-cell loss precisely
through the inhibition of beta-cell apoptosis and dedifferentiation. The second
approach is to stimulate new endogenous regeneration and exogenous
supplementation. For about a century, researchers have attempted pancreatic
beta-cells regeneration. Under specific physiological environments, such as pregnancy,
obesity, and conditions of insulin resistance, the adaption of islet and
improved beta-cell mass take place in animal models and humans [5-8]. Contemporary advances in new
technologies have offered additional substantiation on the generation of beta-cells.
Single-cell RNA sequencing available data have revealed that human islets comprise four
discrete subtypes of beta-cells [9] and probably transitional phases [10]. These suggest that
beta-cells can acclimatize and undergo transdifferentiation or neogenesis.
Physiological restoration research can make available data on the development
of medication targeted toward beta-cell regeneration. Several approaches have
been reported to be utilized in the promotion of beta-cells regeneration. The
strategies include pancreatectomy, partial duct ligation, and chemical-induced
massive beta-cell loss [11-15]. Molecular routes that cause multiplications in
the mass of beta-cells have been comprehensively explored. Only a small portion
of the materials studied have been found to have clinical, pre-clinical, or
clinical potential as medicines. Thousands of materials have been studied, and
hundreds are effective in the course of beta-cell restoration.
The CMGC (CDK, MAPK, CDC-like kinases, GSK3 kinase)
family of eukaryotic protein kinases has been demonstrated to play crucial
roles in neurodegenerative illnesses [16, 17], cell death, and tumorigenesis. Dual-specificity
tyrosine phosphorylation-regulated kinase A (DYRK1A) is a member of this family [18, 19]. A regulator of regeneration
pathways essential to human insulin-producing pancreatic -cells has recently
been discovered as DYRK1A [20-23]. Numerous studies have explored the development of DYRK1A
inhibitor scaffolds, given the involvement of DYRK1A in these diseases [17-20,
22-24]. Several DYRK1A inhibitors from natural sources like harmine and small molecules have been identified and
characterized [22, 25-48]. Harmine and its analogs (-carbolines) are the most
often researched DYRK1A inhibitors, and they continue to be among the most
effective and readily available inhibitor families that can be taken orally
[17, 49]. The presence of harmine in the hallucinogenic infusion of ayahuasca
and its affinity for serotonin, tryptamine, and other receptors in the central
nervous system, in addition to its kinase inhibitory activity, have led to the
hypothesis that harmine is a hallucinogen [50, 51]. Harmine and its analogs had been reported to block the DYRK1A-mediated
phosphorylation of tau proteins in the CNS [52]. They also showed anti-proliferative action,
including inhibition of topoisomerase I [53, 54], inhibition of CDKs [55],
activation of cell apoptosis [56], and DNA intercalation [57].
This research aims to determine druggable
enzyme/target/receptor that is vital in the pathogenesis of beta-cell apoptosis,
identify phytocompounds with high binding affinity against the identified
target using molecular docking simulation, and determine the drug-likeness of promising phytocompounds based on
Lipinski’s rule, determine the toxicity of the phytocompounds in-silico, undertake bioactivity
prediction of the phytocompounds on Molinspiration platform and identify the
plant sources of the frontrunner compounds.
Personal computer, African
Natural Compounds Database, PubChem (http://Pubchem.ncbi.nlm.nih.gov) [58],
Linux operating system (Ubuntu desktop 18.04), Protein data bank
(https://www.rcsb.org/) [59], DataWarrior software [60], PyMol
software [61],
AutoDockTools-1.5.6 software [62], Autodockvina 1.1.2 software [63], on Ubuntu operating
system, Molinspiration Chemoinformatics web tool
(https://www.molinspiration.com/cgi-bin/properties) [64].
Literature was mined to identify the target/receptor for
possible induction of beta-cell regeneration. This was done to check the
importance of the target/receptors in the onset and pathophysiology of beta-cell
destruction. This gives more information about the receptor, functions,
properties, and its druggability.
After the identification of several targets/receptors, literature mining, and analysis
of the target/receptor, Dual-specificity tyrosine phosphorylation-regulated
kinase A in 3D format was obtained from Protein Data Bank (PDB) with the respective PDB code;
6UWY. The initial preparation of the PDB file to select the required chains, and delete multiple ligands was done
with PyMol software. The PyMol software was employed to gain insight into
the ligands binding to the receptors. The receptor was prepared for molecular
docking simulations with the AutoDockTool. In the preparation, polar hydrogens and
Kollman’s charges were added to the receptors and they were saved in the pdbqt file format. The pdbqt file format is the
structural format required for the protein and ligand for carrying out molecular docking simulation.
The electrostatic grid boxes and the 3-dimensional affinity of different sizes and centers, as
indicated in Table 1 below, were created around the active
site of the protein.
Table 1 Grid box parameters used for
the molecular docking simulations
|
6UWY |
|
Centres |
Sizes |
|
X |
-59.224 |
10 |
Y |
-24.052 |
8 |
Z |
24.659 |
12 |
A total number of 6511 isolated phytocompounds were
obtained from the African Natural Products Database
(African-compounds.org) [65, 66] in
the
3D-structure data file format. The phytocompounds were loaded onto the DataWarrior software. Molecular properties such as
molecular weight, hydrogen bond donor, hydrogen bond acceptor, partition coefficient (log P),
and topological polar surface area
(TPSA) were determined. Lipinski's rule of five violations was noted. The
phytocompounds were also screened for toxicity (mutagenicity, carcinogenicity,
tumorigenicity, and reproductive effect).
Phytocompounds following Lipinski’s rule of 5 with no toxicity in-silico were prepared for the molecular docking simulation. Reference ligands were
identified from the literature including the compound co-crystallized with the receptor/protein on the
PDB database. In
preparation of the ligands for molecular docking simulation, all rotatable
bonds, Torsions, and Geistegers charges were assigned and saved in the pdbqt file
format.
To validate the molecular docking
simulations protocol for the 6UWY (DYRK1A) protein, the PDB structure of this
protein in complex with a reference inhibitor was reproduced in-silico. The deletion of the reference
compound from the protein was done with the PyMol software. Polar hydrogen, Kollman charges, grid box sizes, and centers at a grid space of
1.0 Å were determined with the AutoDockTools-1.5.6 [62, 63]. The protein was saved in the pdbqt file format. The reference
compound was prepared for molecular docking simulation with the AutoDockTools-1.5.6. All
rotatable bonds, including torsions, were permitted to remain rotatable. Then,
output was produced with the pdbqt file extension. Molecular docking simulation
of the protein and reference compound was implemented locally with
the AutoDockVina® [63] on a Linux
platform using the centers and sizes with a virtual screening shell script.
Docked conformations were visualized in the PyMol-1.4.1
software and the
binding poses of the
co-crystal inhibitors were compared with the re-docked co-crystal structures of the
reference compound.
The phytocompounds were prepared in batches for molecular
docking simulations using virtual screening scripts against the
dual-specificity tyrosine phosphorylation-regulated kinase A. Following the
validation of docking methods, four replicates of Molecular Docking Simulations
were performed on a Linux platform using AutoDockVina® and related tools. To
determine the leading phytocompounds, binding free energy values (kcal/mol SD)
were ranked.
The online Molinspiration web tool version 2011.06 (www.molinspiration.com) was supplied SMILES notations of the
leading phytocompounds to forecast the bioactivity scores for kinase inhibition.
The predicted percentage of absorption (% ab) of the
frontrunner phytocompounds was calculated with the method reported by Zhao et al. (2002) [67]. The following formula was
used: %ab =
109 – (0.345 x TPSA).
The drug-likeness assessment of the 6511 phytocompounds
based on Lipinski’s rule of five was done to screen out phytocompounds with
violations of the rules. After the screening, a total number of 3814
phytocompounds had no violation of Lipinski’s rule, while 2697 phytocompounds
violated the rules. Toxicity assessment on the 3814 phytocompounds that
did not violate Lipinski's rule was carried out with
DataWarrior in to identify phytocompounds that might be
mutagenic, tumorigenic, irritating, or have reproductive consequences. A total
number of 1897 phytocompounds were found to have none of the listed toxicities in-silico. Total polar surface area
(TPSA) was also analyzed for all the phytocompounds.
The docking protocol validation was done to ensure in-silico reproducibility of the
experimental protein-ligand interactions obtained from the protein data bank. The results
obtained from the docking validations are presented below in Figures
1 and 2.
Figure 1 represents the structural conformation and superimposition of the docked
ligand (blue) and co-crystallized ligand (green) in the Dual-specificity
tyrosine phosphorylation-regulated kinase A binding site. Figure 2A shows the 2D representation of the
co-crystallized ligand-protein interaction, while Figure 2B shows the 2D representation of the docked
ligand-protein interaction. Comparative analysis of the docked ligand and
co-crystallized ligand-protein interaction reveals a 90.9% match.
Figure
1
Superimposed view of DYKR1A reference compound in blue and docked reference
compound in green
Figure 2 2D representation of the (A) co-crystallized
ligand-protein interaction and (B) the docked ligand-protein interaction
The molecular docking of the phytocompounds was performed
on DYKR1A to identify phytocompounds with better in-silico inhibitory activity
against DYKR1A than the reference compounds. The reference compounds are listed
in the
last three rows of Table 2. The docking was also performed to study the
phytocompounds-proteins interaction pattern at the binding sites of these
proteins. Phytocompounds with better binding affinities/energies than the
reference compounds as can be observed from the mean binding affinity, are
presented in Table 2.
Table 2 Phytocompounds with better
binding energy values on DYRK1A than reference compounds
S/N |
Compound Name |
Mean binding affinity |
Molecular Weight |
cLogP |
Hydrogen Acceptor |
Hydrogen Donor |
TPSA |
1 |
Lanuginosine |
-11.3 ± 0 |
305.29 |
3.46 |
5 |
0 |
57.65 |
2 |
4-Beta,8-alpha-dihydroxy-6-alpha-vanilloyloxydauc-9-ene |
-11.23 ± 0.06 |
400.51 |
3.25 |
5 |
1 |
72.83 |
3 |
Aegyptinone A |
-10.87 ± 0.06 |
310.39 |
1.29 |
3 |
0 |
57.20 |
4 |
Sigmoidin A |
-10.70 ± 0.17 |
424.49 |
5.86 |
6 |
4 |
107.22 |
5 |
Penilactone |
-10.70 ± 0.00 |
304.3 |
1.67 |
6 |
1 |
89.90 |
6 |
Altertoxin I |
-10.60 ± 0.00 |
352.34 |
2.36 |
6 |
4 |
115.06 |
7 |
Sigmoidin B |
-10.50 ± 0.00 |
356.37 |
3.83 |
6 |
4 |
107.22 |
8 |
6,7-Dehydro-19-beta-hydroxyschizozygin |
-10.50 ± 0.00 |
337.4 |
0.53 |
5 |
1 |
43.21 |
9 |
Ungeremine |
-10.40 ± 0.00 |
265.27 |
3.42 |
4 |
1 |
43.62 |
10 |
Anastatin B |
-10.40 ± 0.00 |
378.34 |
3.58 |
7 |
4 |
120.36 |
11 |
Latrunculin B |
-10.40 ± 0.00 |
357.56 |
4.49 |
4 |
2 |
83.86 |
12 |
Scalarolide |
-10.40 ± 0.00 |
386.57 |
4.51 |
3 |
1 |
46.53 |
13 |
Feselol |
-10.40 ± 0.00 |
386.53 |
3.61 |
4 |
1 |
55.76 |
14 |
Assafoetidnol A |
-10.40 ± 0.00 |
398.5 |
3.15 |
5 |
2 |
75.99 |
15 |
Chamanetin |
-10.40 ± 0.00 |
364.4 |
3.80 |
5 |
3 |
86.99 |
16 |
Neoclerodan-5,10-en-19,6beta,20,12-diolide |
-10.40 ± 0.00 |
315.48 |
1.96 |
2 |
0 |
40.13 |
17 |
Chrysophanol- isophyscion bianthrone |
-10.37 ± 0.06 |
508.53 |
4.63 |
7 |
4 |
124.29 |
18 |
3-Taraxasterol |
-10.30 ± 0.00 |
430.76 |
9.48 |
1 |
1 |
20.23 |
19 |
Helioscopinolide C |
-10.30 ± 0.00 |
330.42 |
2.43 |
4 |
1 |
63.60 |
20 |
3beta-hydroxyisopimaric acid |
-10.30 ± 0.00 |
317.45 |
1.40 |
3 |
1 |
60.36 |
21 |
Taraxasterol |
-10.23 ± 0.06 |
424.71 |
7.00 |
1 |
1 |
20.23 |
22 |
3beta-hydroxymansumbin-13(17)-en-16-one |
-10.20 ± 0.00 |
332.53 |
4.53 |
2 |
1 |
37.30 |
23 |
Dihydrofumariline |
-10.20 ± 0.00 |
354.38 |
1.15 |
6 |
2 |
61.59 |
24 |
12alpha-acetoxy-24,25-epoxy-24-hydroxy-20,24-dimethylscalarane |
-10.17 ± 0.35 |
460.7 |
5.86 |
4 |
1 |
55.76 |
25 |
3,4,18-cyclopropa-12-hydroxy-ent-abiet-7-en-16,14-olide |
-10.13 ± 0.06 |
316.44 |
2.70 |
3 |
1 |
46.53 |
26 |
13-Hydroxyfeselol |
-10.13 ± 0.06 |
400.51 |
3.53 |
5 |
2 |
75.99 |
27 |
Stemmin C |
-10.10 ± 0.00 |
332.48 |
3.40 |
3 |
2 |
57.53 |
28 |
Helioscopinolide A |
-10.10 ± 0.00 |
318.46 |
3.07 |
3 |
1 |
46.53 |
29 |
Foetidin |
-10.10 ± 0.17 |
381.49 |
5.47 |
4 |
2 |
51.83 |
30 |
2,11-didehydro-2- dehydroxylycorine |
-10.10 ± 0.00 |
274.34 |
0.05 |
4 |
2 |
43.13 |
31 |
Voucapane |
-10.10 ± 0.00 |
286.46 |
5.48 |
1 |
0 |
13.14 |
32 |
Trachyloban-19-oic Acid |
-10.10 ± 0.00 |
299.43 |
1.42 |
2 |
0 |
40.13 |
33 |
Abyssinin II |
-10.10 ± 0.10 |
370.4 |
4.11 |
6 |
3 |
96.22 |
34 |
(-)-Semiglabrin |
-10.10 ± 0.00 |
392.41 |
4.24 |
6 |
0 |
71.06 |
35 |
Taraxerone |
-10.10 ± 0.61 |
426.73 |
7.59 |
1 |
0 |
17.07 |
36 |
Pratorinine |
-10.07 ± 0.06 |
267.28 |
2.63 |
4 |
1 |
49.77 |
37 |
Ergosterol |
-10.07 ± 0.92 |
396.66 |
6.87 |
1 |
1 |
20.23 |
38 |
Solanidin |
-10.07 ± 0.06 |
400.67 |
3.2 |
2 |
2 |
24.67 |
39 |
Calotroproceryl acetate B |
-10.00 ± 0.00 |
466.75 |
7.66 |
2 |
0 |
26.30 |
40 |
Botryorhodine B |
-10.00 ± 0.00 |
314.29 |
3.45 |
6 |
2 |
93.06 |
41 |
Asteriscunolide A |
-10.00 ± 0.00 |
250.34 |
2.93 |
3 |
0 |
43.37 |
42 |
Diazo derivative of Inuloxin A |
-10.00 ± 0.00 |
264.36 |
3.61 |
3 |
0 |
35.53 |
43 |
Thymelol |
-10.00 ± 0.00 |
354.31 |
1.87 |
7 |
1 |
91.29 |
44 |
Polyanthin |
-10.00 ± 0.69 |
424.54 |
4.92 |
5 |
0 |
61.83 |
45 |
Samarcandin |
-10.00 ± 0.44 |
400.51 |
3.76 |
5 |
2 |
75.99 |
46 |
8alpha-isobutanoyloxy-5-Alpha-Hydroxy-2-
Oxo-11,13-dehydroguaia-1(10), 3-dien-6alpha,12-Olide |
-10.00 ± 0.00 |
334.41 |
1.85 |
5 |
1 |
72.83 |
47 |
Aloenin acetal |
-10.00 ± 0.00 |
436.41 |
0.33 |
10 |
3 |
133.14 |
48 |
Retroisosenine |
-10.00 ± 0.00 |
336.41 |
-0.99 |
6 |
1 |
66.27 |
49 |
Ent-trachyloban-18- oic Acid |
-10.00 ± 0.00 |
301.45 |
1.69 |
2 |
0 |
40.13 |
50 |
Trachylobane |
-10.00 ± 0.00 |
274.49 |
5.48 |
0 |
0 |
0.00 |
51 |
Lanceolatin B |
-10.00 ± 0.00 |
262.26 |
3.82 |
3 |
0 |
39.44 |
52 |
12-Hydroxy-8,12-Abietadiene-3,11,14-Trione |
-10.00 ± 0.00 |
329.42 |
1.05 |
4 |
0 |
74.27 |
53 |
Hosloppone |
-10.00 ± 0.00 |
300.44 |
4.41 |
2 |
2 |
40.46 |
54 |
Abyssinone II |
-10.00 ± 0.00 |
324.38 |
4.52 |
4 |
2 |
66.76 |
55 |
Lanceolatin A |
-9.97 ± 0.40 |
336.39 |
4.21 |
4 |
1 |
55.76 |
56 |
Postratol |
-9.97 ± 0.06 |
460.61 |
8.57 |
4 |
2 |
66.76 |
57 |
Erythroxyl-4(17),15(16)-Dien-3-One |
-9.97 ± 0.06 |
270.41 |
4.54 |
1 |
0 |
17.07 |
58 |
3-O-Benzoylhosloppone |
-9.97 ± 0.12 |
420.55 |
4.76 |
4 |
1 |
63.60 |
59 |
7-Keto-8alpha-hydroxy-deepoxysarcophine |
-9.93 ± 0.06 |
332.44 |
3.49 |
4 |
1 |
63.60 |
60 |
3-[6-(3-Methyl-But-2-enyl)-1H-Indolyl]-6-(3-methyl-but-2-enyl)-1H-Indole |
-9.93 ± 0.06 |
368.52 |
7.25 |
2 |
1 |
20.72 |
61 |
(6Z)-Cladiellin (cladiella-6Z,11(17)-dien-3-Ol) |
-9.90 ± 0.00 |
306.49 |
4.64 |
2 |
1 |
29.46 |
62 |
Hippacine |
-9.90 ± 0.00 |
251.24 |
2.78 |
4 |
2 |
62.46 |
63 |
1,2-Dehydrobeninine |
-9.90 ± 0.00 |
327.45 |
-0.34 |
4 |
2 |
34.93 |
64 |
Sipholenol J |
-9.90 ± 0.00 |
462.67 |
4.13 |
5 |
3 |
86.99 |
65 |
Wtmannin |
-9.90 ± 0.00 |
428.44 |
1.62 |
8 |
0 |
109.11 |
66 |
Gummosin |
-9.90 ± 0.00 |
384.51 |
3.58 |
4 |
1 |
55.76 |
67 |
Badrakemin |
-9.90 ± 0.35 |
382.54 |
4.98 |
3 |
2 |
38.69 |
68 |
(-)-Samarcandone |
-9.90 ± 0.00 |
398.5 |
3.78 |
5 |
2 |
72.83 |
69 |
Totaradiol |
-9.90 ± 0.00 |
302.46 |
4.52 |
2 |
2 |
40.46 |
70 |
Abietatriene |
-9.90 ± 0.00 |
268.44 |
5.55 |
0 |
0 |
0.00 |
71 |
6,7-Dehydroroyleanon |
-9.90 ± 0.00 |
313.42 |
1.48 |
3 |
0 |
57.2 |
72 |
5-OH-3-methylnaphtho[2-3-C]Furan-4,9-dione |
-9.90 ± 0.00 |
232.23 |
1.40 |
4 |
1 |
67.51 |
73 |
3'-Prenylnaringenin |
-9.90 ± 0.00 |
338.36 |
4.36 |
5 |
3 |
86.99 |
74 |
Lysicamine |
-9.9 ± 0. |
291.31 |
3.28 |
4 |
0 |
48.42 |
75 |
5-Deoxyabyssinin II |
-9.87 ± 0.15 |
354.4 |
4.45 |
5 |
2 |
75.99 |
76 |
Ekeberin A |
-9.87 ± 0.06 |
456.71 |
6.19 |
3 |
0 |
35.53 |
77 |
Aegyptinone B |
-9.83 ± 0.06 |
327.4 |
1 |
4 |
1 |
77.43 |
78 |
Pratorimine |
-9.8 ± 0 |
265.27 |
3.06 |
4 |
1 |
51.46 |
79 |
Anhydroverlotorin |
-9.8 ± 0 |
250.34 |
3.09 |
3 |
0 |
43.37 |
80 |
Nagilactone F |
-9.8 ± 0 |
316.4 |
2.2 |
4 |
0 |
52.60 |
81 |
Totarolone |
-9.8 ± 0 |
300.44 |
4.66 |
2 |
1 |
37.30 |
82 |
Voucapan-5-ol |
-9.8 ± 0 |
300.44 |
4.38 |
2 |
1 |
33.37 |
83 |
Coladonin |
-9.8 ± 0.82 |
384.51 |
3.93 |
4 |
1 |
55.76 |
84 |
Anhydrolycorine |
-9.8 ± 0.17 |
251.28 |
2.98 |
3 |
0 |
21.70 |
85 |
8-C-P-Hydroxybenzylluteolin |
-9.8 ± 0.69 |
392.36 |
3.56 |
7 |
5 |
124.29 |
|
4-(7-Methoxy-1-methyl-9H-beta-carbolin-9-Yl)butanamide |
-9.80 ± 0.00 |
297.36 |
1.97 |
5 |
2 |
70.15 |
|
(1Z)-1-(3-Ethyl-5-hydroxy-2(3H)-benzothiazolylidene)-2-propanone
(INDY) |
-7.50 ± 0.00 |
235.31 |
2.01 |
3 |
1 |
42.23 |
|
Gnf4877 |
-7.28 ± 0.10 |
494.53 |
2.51 |
10 |
4 |
143.57 |
Results of the bioactivity prediction of the 85
phytocompounds with better binding affinities than the reference compounds are
presented in Table 3. The phytocompounds were
screened for kinase inhibitory activity because the protein of interest (DYRK1A) is a kinase. Twelve
phytocompounds were found to possess kinase inhibitory activity based on the scores.
Some of the phytocompounds have better inhibitory scores than the reference
compounds as can be observed in Table 3.
Tables
3
Bioactivity scores of DYRK1A active phytochemicals with their plant sources
S/N |
Phytocompounds |
Kinase
inhibitory score |
Plant
sources |
1 |
Lysicamine |
0.42 |
Annickia
kummeriae |
2 |
Lanuginosine |
0.40 |
Magnolia
grandiflora |
3 |
Pratorinine |
0.40 |
Crinum
americanum |
4 |
Hippacine |
0.40 |
Crinum
bulbispermum |
5 |
Pratorimine |
0.40 |
Crinum
americanum |
6 |
4-(7-methoxy-1-methyl-9-H-beta-carbolin-9-yl)-butanamide |
0.37 |
|
7 |
3-[6-(3-methyl-but-2-enyl)-1-H-indolyl]-6-(3-methyl-but-2-enyl)-1-H-indole |
0.32 |
Monodora
angolensis |
8 |
8-C-p-hydroxybenzylluteolin |
0.27 |
Thymus
hirtus |
9 |
GNF4877 |
0.25 |
|
10 |
3'-Prenylnaringenin |
0.21 |
Erythrina
abyssinica |
11 |
Lanceolatin B |
0.15 |
Tephrosia
purpurea |
12 |
Lanceolatin A |
0.10 |
Tephrosia
purpurea |
13 |
Aegyptinone B |
0.02 |
Zhumeria majdae |
14 |
(-)-Semiglabrin |
0.00 |
Tephrosia
purpurea |
15 |
(1Z)-1-(3-Ethyl-5-hydroxy-2-(3H)-benzothiazolylidene)-2-propanone
(INDY) |
-0.47 |
|
The results of the predicted percentage absorption of the
frontrunner phytocompounds with that of the reference compounds are presented
in Table 4. The prediction is based on
the TPSA values.
Table
4 Predicted
percentage of absorption
Compounds |
TPSA |
%Ab |
3-[6-(3-methyl-but-2-enyl)-1-H-indolyl]-6-(3-methyl-but-2-enyl)-1-H-indole |
20.72 |
101.85 |
Lanceolatin B |
39.44 |
95.39 |
(1Z)-1-(3-Ethyl-5-hydroxy-2(3H)-benzothiazolylidene)-2-propanone
(INDY) |
42.23 |
94.43 |
Lysicamine |
48.42 |
92.30 |
Pratorinine |
49.77 |
91.83 |
Pratorimine |
51.46 |
91.25 |
Lanceolatin A |
55.76 |
89.76 |
Lanuginosine |
57.65 |
89.11 |
Hippacine |
62.46 |
87.45 |
4-(7-methoxy-1-methyl-9-H-beta-carbolin-9-yl)-butanamide |
70.15 |
84.80 |
(-)-Semiglabrin |
71.06 |
84.48 |
Aegyptinone B |
77.43 |
82.29 |
3'-Prenylnaringenin |
86.99 |
78.99 |
8-C-p-hydroxybenzylluteolin |
124.29 |
66.12 |
GNF4877 |
143.57 |
59.47 |
Figure
3
Structures of the frontrunner phytocompounds and reference compounds
The study was set out to determine the binding affinities of
phytocompounds from the African natural product database to DYRK1A compared to
the reference compounds INDY, 4-(7-methoxy-1-methyl-9H-beta-carbolin-9-yl)
butanamide and GNF4877 with in-silico molecular docking simulation. The process of developing new
drugs has proven to be difficult due to the enormous expense of drug discovery
and development, and the time needed. Modern research now relies heavily on
computer-aided drug design, also known as the "in-silico" approach to drug discovery and design. Drug
discovery and development are sped up by computer-aided drug design elements
including molecular docking, molecular dynamics, QSAR, ADMET tool, and their
accurate predictions.
On the other hand, medicines and medicinal substances
have historically been derived from nature, primarily plants. The majority of
medicines on the market today are either isolated or created from isolates
derived from natural sources. Based on their use in conventional medical
procedures, the majority of these currently utilized medications are made from
natural sources [68]. To date, more novel compounds are being
isolated from plants [69] and deposited in chemical databases. There are also
general biological databases and specialized databases on which thousands of
proteins are deposited to aid scientific research [70].
In this work, we retrieved 6511 phytocompounds from the
African natural database that were purported to be isolated from African
plants. Lipinski's rule of five was utilized to determine the drug-likeness of
these phytocompounds. Pharmaceutical chemists frequently utilize Lipinski's
rule of five to forecast the oral bioavailability of possible lead or
therapeutic compounds during drug design and development. Lipinski's "rule
of five" states that a candidate molecule is more likely to be orally
active if it meets the following criteria: a) has a molecular weight below 500;
b) has an estimated octanol/water partition coefficient (Log P is less than 5);
c) has fewer than five hydrogen bond donors; and d) has fewer than ten hydrogen
bond acceptors [71-73].
The DataWarrior software uses a precomputed collection of
structural pieces that, when found in the structures under study, trigger
toxicity alerts. All compounds from the Registry of Toxic Effects of Chemical
Substances (RTECS) database [74] that are acknowledged to be active in a
certain toxicity class were thoroughly destroyed to compile these fragment
lists. During the shredding, compounds
were first severed, with each rotating link leading to a set of core fragments.
These, in turn, were utilized to reconstitute all possible significant
substructures of the original molecule. The frequency of any fragment
(core and created fragments) within all chemicals in that toxicity class was
then determined using a substructure search process. Additionally, it
identified these fragment frequencies in more than 3000 traded medications'
structural data. Any fragment was viewed as a risk factor if it frequently
occurs as the substructure of dangerous chemicals but never or very
occasionally in the traded pharmaceuticals. This assumption was made based on
the notion that sold drugs are primarily free of toxic effects. A total of 1897
phytocompounds did not exhibit any in-silico
mutagenicity, tumorigenic, irritant, or reproductive impacts based on this
explained fragments search. No fragments or fragments known to have any of the
toxicities listed in the Registry of Toxic Effects of Chemical Substances were
present in these phytocompounds.
From the molecular docking result, 85 phytocompounds were
obtained with better binding affinity than the reference compounds, as shown in
Table 2. Lower binding affinity
suggests better ligand binding. The importance of binding affinity values is
determined by the most significant magnitude negative value, representing the
most favorable conformation of the complex formed when the ligand involved
efficiently binds with the protein's active site. As observed, the mean binding
affinity scores are in negative values. This is because protein-ligand binding
only occurs spontaneously when the free energy change is negative, and the
difference in ∆G levels of complexed and unbound free states is proportional to
the stability of the protein-ligand interaction. Both protein folding and
protein-ligand binding occur when ∆G is low in the system [75, 76]. Hence, negative ∆G scores
indicate the stability of the resulting complexes with the receptor molecules, which is an
essential characteristic of efficacious drugs [77].
From the molinspiration bioactivity prediction, twelve
compounds were found to be very active kinase inhibitors. Based on the
prediction, two of the three reference compounds used were also very active
kinase inhibitors. One of the reference compounds was predicted to be a
moderately active kinase inhibitor. In molinspiration, biological activity is
measured by a bioactivity score that is categorized as active (0.00 to 0.5),
moderately active (0.00 to -0.5), and inactive (less than -0.5) [64].
The calculated percentage absorption (%ABS) of the
frontrunner phytocompounds ranged between 66.12% and 101.85%, indicating that
these phytocompounds have good permeability in the cellular membrane. The
percentage absorption was calculated from the topological polar surface area
(TPSA). The frontrunner phytocompounds exhibited computational TPSA values
between 20.72 and 124.29 Å2 and have good intestinal absorption. As
a guide, orally active drugs transported by the transcellular route should not
exceed a PSA of about 120 Å2 [78,79]. Similarly, for good brain
penetration of CNS drugs, this number should even be tailored to PSA<100Å2
[79] or even smaller, <60–70 Å2
[78].
Finally, observation of the frontrunner phytocompounds'
structures compared with reference compounds, as presented in Figure 3, reveals some structural
activity relationships that might be necessary for the inhibition of DYRK1A.
The frontrunner compounds are composed of phenolics and alkaloids. From the 2D
structure of the PDB reference compound presented in Figure 2, it can be observed that nitrogen, oxygen, and hydrogen atoms (which are all components of the
frontrunner phytocompounds) are necessary for the protein-ligand interactions. Previous in-vitro research has shown that some natural products such
as
alkaloids and polyphenolic compounds act as inhibitors of DYRK1A. Epigallocatechin gallate, a major
catechin component of green tea, when tested in a panel of 28 kinases structurally and
functionally related to DYRK1A, showed selective inhibitory activity against
DYRK1A (IC50 330 nM [ATP] = 100 μM) [80]. Acaninol B, which was isolated
from the Leguminosae plant Acacia
nilotica [81], exhibited only moderate action against DYRK1A (IC50 19 M
[ATP] = 15 M) [82]. The already known CDK inhibitor flavopiridol was discovered
to be a powerful DYRK1A inhibitor (IC50 0.3 M) [85] after being screened
against a panel of five kinases using a set of natural and synthetic flavonoids
and flavonoidal alkaloids. A strong DYRK1A inhibitor with an IC50 of 19 nM,
staurosporine is an indolecarbazole derived from Streptomyces staurosporeus
[86]. However, it is highly nonselective toward other kinases [87,88]. The
L-rhamnulose-modified staurosporine analog was similarly significantly
effective against DYRK1A (IC50 4 nM) [88]. Alkaloid acrifoline has a very
strong DYRK1A inhibitory effect (IC50 = 0.075 M). Atalaphyllidine and
chlorospermine B are both moderately effective DYRK1A inhibitors [89]. Recent
studies have demonstrated the potency of two granulatimide analogs as DYRK1A
inhibitors, with IC50 values of 0.26 and 0.09 M, respectively [90,91].
The results of in-silico studies translate well during in-vitro or in-vivo studies depending on the computational study design. In
synthetic chemistry, the bioavailability, toxicity, and potential bioactivity
of compound libraries are often studied with in-silico techniques before synthesis [92]. Similarly, we have prepared the study design of
the present in-silico study to
maximize the chance of obtaining fruitful results in bioassays. Molecular
docking is one of the fastest and most reliable in-silico techniques to study the binding affinity, binding pose,
and molecular interactions between a ligand and a protein [93]. Molecular
docking studies are sometimes combined with in-vivo
studies to study the molecular interaction and binding affinity of compounds
with studied biomarkers [94, 95]. To tackle the severity of the ongoing coronavirus
disease 2019 pandemic, cancer, and other infectious diseases, several
researchers have designed their in-silico
studies to maximize the chance of obtaining fruitful results in bioassays [96-104]. This
discussion highlights the robustness of in-silico
studies in drug discovery and development, corroborates and validates the study
design used in the present in-silico
study, and justifies that the African natural product database
harbors promising phytocompounds as DYRK1A inhibitors.
Small-scale suppression of the
DYRK1A molecules can offer a remedy for the pharmaceutical intervention of
beta-cell regeneration in diabetes since options for treating beta-cell regeneration are a significant
unmet therapeutic need. However, due to the conventional function of DYRK1A in
controlling multiple signaling pathways vital to neuronal development and
functions, caution should be used while trying to modulate it so that its
activity is reduced to that which is typically seen in healthy individuals. These current in-silico tests' findings imply that
3-[6- (3-methyl-but-2-enyl) -1H-indolyl] -6-(3-methyl-but-2-enyl) -1H-indole, Lanceolatin B, Lysicamine, Pratorinine, Pratorimine, Lanceolatin A, Lanuginosine, Hippacine, (-)-Semiglabrin, Aegyptinone B, 3'-Prenylnaringenin and
8-C-p-hydroxybenzylluteolin are candidate ligands for activating beta-cells
regeneration. The phytocompounds exhibit good intestine absorption, according
to computational analyses of drug-likeness, TPSA, and % absorption. Finally, these
phytocompounds have been recognized by the in-silico
analysis as prospective novel medication candidates. Validating this in-silico work requires further thorough
research using different models, such as in-vivo
assays using the phytocompounds or extracts containing the phytocompounds.
Not applicable
We express our gratitude to the Principal
Investigator, Drug Design and Informatics Group (DDIG) laboratory, for granting
us free access to the facility. We appreciate the contribution of the CURIES
research team of the Faculty of Pharmaceutical Sciences, Nnamdi Azikiwe
University, Awka to this ongoing research.
The authors declare no conflicting interests.
IMC: Study design and experiments; ICI: Figures and
critical review; NMC: Figures and critical review; EII: Study design,
experiments, and supervision
Chikodili, I.M., Chioma, I.I., Chinwendu, N.M., IfedibaluChukwu, E.I. In-silico study for African plants with possible beta-cell regeneration effect through inhibition of DYRK1A. Sciences of Phytochemistry 2022, 1(1), 13-28.
Chikodili, IM, Chioma, II, Chinwendu, NM, IfedibaluChukwu, EI. In-silico study for African plants with possible beta-cell regeneration effect through inhibition of DYRK1A. Sciences of Phytochemistry. 2022; 1(1):13-28.
Igbokwe Mariagoretti Chikodili, Ibe Ifeoma Chioma, Nnorom Miriam Chinwendu, Ejiofor InnocentMary IfedibaluChukwu. 2022. "In-silico study for African plants with possible beta-cell regeneration effect through inhibition of DYRK1A" Sciences of Phytochemistry 1, no. 1:13-28.
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