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Future Foods 9 (2024) 100354
Available online 23 April 2024
2666-8335/© 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-
nc-nd/4.0/).
Pharmacoinformatics and cellular studies of algal peptides as functional
molecules to modulate type-2 diabetes markers
Rudy Kurniawan
a
,
b
, Nurpudji Astuti Taslim
c
,
*
, Hardinsyah Hardinsyah
d
, Andi Yasmin Syauki
c
,
Irfan Idris
e
, Andi Makbul Aman
f
, Happy Kurnia Permatasari
g
, Elvan Wiyarta
h
, Reggie Surya
i
,
Nelly Mayulu
j
, Purnawan Pontana Putra
k
, Raymond Rubianto Tjandrawinata
l
, Trina
Ekawati Tallei
m
, Bonglee Kim
n
, Apollinaire Tsopmo
o
, Fahrul Nurkolis
p
a
Graduate from School of Medicine, Faculty of Medicine, Hasanuddin University, Makassar, Indonesia
b
Diabetes Connection Care, Eka Hospital Bumi Serpong Damai, Tangerang 15321, Indonesia
c
Division of Clinical Nutrition, Department of Nutrition, Faculty of Medicine, Hasanuddin University, Makassar 90245, Indonesia
d
Division of Applied Nutrition, Faculty of Human Ecology, Department of Community Nutrition, IPB University, Bogor, Indonesia
e
Department of Physiology, Faculty of Medicine, Hasanuddin University, Makassar, Indonesia
f
Department of Internal Medicine, Faculty of Medicine, Hasanuddin University, Makassar, Makassar, Indonesia
g
Department of Biochemistry and Biomolecular, Faculty of Medicine, University of Brawijaya, Malang 65145, Indonesia
h
Department of Neurology, Faculty of Medicine, Universitas Indonesia-Dr. Cipto Mangunkusumo National Hospital, Jakarta 10430, Indonesia
i
Department of Food Technology, Faculty of Engineering, Bina Nusantara University, Jakarta 11480, Indonesia
j
Department of Nutrition, Faculty of Health Science, Muhammadiyah Manado University, Manado 95249, Indonesia
k
Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Andalas, Padang 25163, Indonesia
l
Department of Biotechnology, Faculty of Biotechnology, Atma Jaya Catholic University of Indonesia, Jakarta 12930, Indonesia
m
Department of Biology, Faculty of Mathematics and Natural Sciences, Sam Ratulangi University, Manado 95115, Indonesia
n
Department of Pathology, College of Korean Medicine, Kyung Hee University, Kyungheedae-Ro 26, Dong-Daemun-Gu, Seoul 05254, South Korea
o
Food Science and Nutrition Program, Department of Chemistry, Carleton University, 1125 Colonel by Drive, Ottawa ON K1S 5B6, Canada
p
Department of Biological Sciences, Faculty of Sciences and Technology, State Islamic University of Sunan Kalijaga (UIN Sunan Kalijaga), Yogyakarta 55281, Indonesia
ARTICLE INFO
Keywords:
Marine peptide
Diabetes
Metabolic syndrome
Algae
Functional food
MAPK8
ABSTRACT
Novel dietary strategies are urgently needed to address the limitations of current management and treatment
options of Type-2 Diabetes (T2D). Marine algae-derived peptides (MAP) represent a promising avenue, although,
their potential remains mostly underexplored. This study employs pharmacoinformatics and in vitro methods to
evaluate the antidiabetic properties of MAP and provide new insights their mechanisms to mitigate the preva-
lence of T2D. Through a systematic search and predictive modeling, peptides were identied and assessed for
bioactivity, toxicity, and drug-likeness. Furthermore, molecular docking simulations with protein targets related
to T2D identied binding sites that be used to optimize the activity of MAP. The structure-activity relationship
prole of MAP reveals 13 candidates with probable activity (Pa) scores >0.4, indicative of insulin promoter. The
peptide FDGIP (P13;Phe-Asp-Gly-Ile-Pro) from Caulerpa lentillifera had the best in silico assessment value
compared to 50 other peptides and its activity was conrmed by in vitro data (e.g.EC
50
60.4 and 57.9 for
α
-amylase and
α
-glucosidase inhibitions). Interestingly, in 3T3-L1 cells, P13 exhibited inhibitory activities
against transcription factors and hormones (MAPK8-JNK1/PPARGC1A/Ghrelin/GLP-1/CPT-1) that can regulate
blood sugar and decrease as anti-diabetes. P13 then appears to be a peptide with antidiabetic action that may be
used in the formulation foods to manage T2D.
1. Introduction
Type-2 Diabetes (T2D) is still increasing globaly, and remains a
signicant cause of illness and death (Khan et al., 2020). T2D is caused
by complex interactions between that involve insulin resistance and
β-cell malfunction, resulting in long-term high blood sugar levels
* Corresponding author.
E-mail address: pudji_taslim@yahoo.com (N.A. Taslim).
Contents lists available at ScienceDirect
Future Foods
journal homepage: www.elsevier.com/locate/fufo
https://doi.org/10.1016/j.fufo.2024.100354
Received 13 March 2024; Received in revised form 15 April 2024; Accepted 22 April 2024
Future Foods 9 (2024) 100354
2
(Galicia-Garcia et al., 2020). This metabolic abnormality is linked con-
ditions such as cardiovascular diseases, nephropathy, neuropathy, and
retinopathy, which have a major inuence on both quality of life and
healthcare expenses (Donath et al., 2019).
The current therapy approaches for T2D aim to enhance the body’s
response to insulin, increase the production of insulin, and regulate the
way glucose is processed in the body (Galicia-Garcia et al., 2020).
Although there are many pharmacological medications, including met-
formin, sulfonylureas, and insulin, available for therapy, these in-
terventions have often proved to be limited in inadequacy owing to
factors such as secondary failure, unpleasant side effects, and patient
non-compliance (Donath et al., 2019; Galicia-Garcia et al., 2020).
Therefore, research to nd further the mechanism behind T2D, to nd
novel approaches and novel properties that provide wider metabolic
advantages, fewer adverse effects, and enhanced patient compliance is
important.
Marine algae are is recognized as a source of diversity and unique
bioactive chemicals that can have multifunctional properties (Menaa
et al., 2021). Many functions of the marine compounds however have
not fully been investigated. Promising compounds in marine algae that
may be further be investigated belong the lipids, polysaccharides, ca-
rotenoids, phenolics, phycobiliproteins, or peptides (Cheung et al.,
2015; Menaa et al., 2021). Marine-derived peptides specically, have
gained considerable interest because of their structural diversity which
can allow them to regulate metabolisms, including those that are
benecial tto diabetes, obesity, and the heart diseases (Cheung et al.,
2015). The efcacy of peptides, such semaglutide, highlights the ther-
apeutic possibilities of peptides in the treatment of metabolic diseases,
including T2DM (Selvarajan and Subramanian, 2023; Shestakova et al.,
2021).
Although the importance of peptides generated from marine algae in
regulating metabolic pathways is acknowledged, there is a lack of
thorough research examining their potential in T2D. In order to ll this
knowledge gap, the present study utilizes a combination of pharma-
coinformatics, chemistry-based and cellular methods to thoroughly
examine the mechanism of algal peptides. The ultimate goal is to design
functional foods that may effectively control T2D. This project involves
doing a virtual search of scientic databases to nd potentially bene-
cial peptides derived from marine algae. Evaluate their therapeutic
potential utilizing network pharmacology, molecular docking, dynamic
simulation approaches, and a cell culture model that is relevant for
metabolic diseases, like T2D. This provides new knowledge about how
these peptides work and their potential as functional dietary ingredients
for managing diabetes.
This study is the rst to extensively investigate the use of peptides
generated from marine algae as novel agents for managing T2D. The
primary aim of this study is to identify and characterize bioactive pep-
tides from marine algae with potential antidiabetic properties, and to
investigate their mechanisms of action through pharmacoinformatics
approaches and in vitro evaluations. This research hypothesizes that
certain marine algae-derived peptides can modulate key pathways
involved in glucose metabolism, insulin sensitivity, and β-cell function,
thereby exerting a therapeutic effect on T2D. By validating this hy-
pothesis, the study seeks to provide a scientic foundation for the
development of functional foods enriched with marine algae-derived
peptides, offering a novel and natural strategy for T2D management
and prevention.
2. Methods
2.1. Search strategy and selection criteria of algae peptides
References for this study were identied through PubMed searches
for articles published up to January 2024 using the terms ’Algae’ or
’Seaweed’ and ’Peptide’ or ‘Protein’. Additionally, relevant articles in
this topic area were found through searches in Google Scholar and the
Scopus Collection. Only articles published in English and the references
cited within them were reviewed.
2.2. Prediction of bioactive peptide activities, toxicity analysis, and drug-
likeness
Marine peptides from systematic literature were analyzed for po-
tential bioactivity using the WAY2DRUG PASS prediction tool (http
://www.pharmaexpert.ru/passonline/predict.php, accessed on 26
February 2024) for metabolic syndrome treatment, which specically
targets the insulin promoter through structure-activity relationship
(SAR) analysis to compare input compounds with known compounds
that show specic potency (Druzhilovskiy et al., 2017). The probability
of being active (Pa) value represents the output prediction score ob-
tained from the database, which shows the potency of the compound
being tested. A Pa value >0.4 indicates that the compound is predicted
to have high potential, for example, as an antidiabetic agent, because of
its similarity to compounds in the database. Drug similarity character-
istics were determined for each ligand based on Lipinski’s Rule 5 (Ro5),
which was analyzed using the Protox II database (https://tox-new.ch
arite.de/protox_II/index.php?site=compound_input, accessed on 26
February 2024) and the ADMETLab 2.0 database (https://admetmesh.
scbdd.com/service/evaluation/index, accessed on 26 February 2024)
using the SMILES notation of each compound as input (Banerjee et al.,
2018; Dong et al., 2018; Norinder and Bergstr¨
om, 2006). The SMILES
notation for each compound was obtained from PubChem (htt
ps://pubchem.ncbi.nlm.nih.gov, accessed on 26 February 2024),
Chemdraw and ChEBI.
2.3. Protein target identication and analysis for T2D
Target analysis of marine peptides was carried out using the
SuperPred target analysis tool (https://prediction.charite.de/, accessed
26 February 2024) by entering the SMILES notation for each peptide and
the cut-off score for SuperPred Target for the model’s probability and
accuracy was set at 80 % (range from 0 to 100 %) (Dunkel et al., 2008;
Gallo et al., 2022). Genes and proteins associated with metabolic syn-
drome were taken from the Open Targets database (http://www.
opentargets.org/, accessed on 26 February 2024). The disease-related
targets and targets of marine peptides were then mapped using a Venn
diagram to determine the intersection of the corresponding targets.
Target annotation of marine peptides was carried out using the DAVID
web server (https://david.ncifcrf.gov/, accessed on 26 February 2024)
with a focus on biological processes and Kyoto Encyclopedia of Genes
and Genomes (KEGG) pathways (Gfeller et al., 2014).
2.4. Network pharmacology analysis
Analysis of interactions between target proteins obtained from ma-
rine peptides and their relationship to metabolic syndrome was carried
out using the STRING Database (Search Tool for Retrieval of Interacting
Genes/Proteins) Version 12.0 (Asadzadeh et al., 2023). The input con-
sists of target proteins derived from marine peptides along with in-
tersections of proteins related to metabolic syndrome carried out using
the STRING Database (Search Tool for Retrieval of Genes/Proteins,
including the insulin promoter receptor which are known to be closely
related to the incidence of metabolic syndrome. In the analysis using the
STRING Database, the organism was set as Homo sapiens (human), and to
ensure strong interactions a high condence score threshold of 0.9 was
applied for this analysis. The resulting analysis data is presented in TSV
format from the STRING database and downloaded to be processed for
advanced analysis using CytoScape Version 3.10.1 for in-depth investi-
gation of network analysis which also allows exploration of key network
parameters such as degree, betweenness centrality, and closeness cen-
trality between receptors.
R. Kurniawan et al.
Future Foods 9 (2024) 100354
3
2.5. Molecular docking simulation
The target protein was chosen based on its potential as shown from
the results of Network Pharmacology (NP), followed by Molecular
Docking. One peptide was selected based on the potential yield of Mo-
lecular Docking followed by Molecular Dynamic at one receptor with
high centrality.
The protein-ligand docking was conducted using cavity-detection-
guided Blind Docking (CB-Dock2) server which integrates cavity
detection, docking, and homologous template tting (Liu et al., 2022;
Yang et al., 2022). The CB-Dock2 method automatically identies
binding sites, calculates their center and size, customizes the docking
box size according to the query ligands, and performs molecular docking
with AutoDock Vina.
The enzymes or proteins (with PDB ID) used were MAPK8 (3ELJ),
PPARG (8BF1), CPT-1 (1NDB), GLP-1 (4ZGM), GHRL (6ZYF) Human
Pancreatic
α
-Amylase (2QV4), and Human Pancreatic
α
-Glucosidase
(3L4Y). Water molecules and other heteroatoms were deleted from the
uploaded protein structures before docking by the CB-Dock2 Server by
default. All receptor or target proteins .pdb format from RSCB Protein
Data Bank (https://www.rcsb.org; accessed on 27 February 2024); Li-
gands were obtained from PubChem in .sdf form (https://pubchem.ncbi.
nlm.nih.gov; accessed on 27 February 2024), and compounds not found
in PubChem were visualized using 22.2.0 ChemDraw MacBook Version.
2.6. Molecular dynamic simulation
The protein used in this Molecular Dynamic simulation (MDs) study
included Jnk1 complexed with a bis-anilino-pyrrolopyrimidine inhibitor
(PDB ID: 3ELJ) (Chamberlain et al., 2009). Preparation of the protein
and algal peptide P13 (Peptida FDGIP (Phe-Asp-Gly-Ile-Pro) was chosen
for MDs since the peptide showed the best docking score to the overall
receptors compared to other peptides. This preparation involved the use
of dockprep in Chimera software (Pettersen et al., 2004), while sequence
gaps were addressed using VMD and Alphafold software (Humphrey
et al., 1996) (Jumper et al., 2021). Input le preparation was conducted
using CHARMM-GUI (Lee et al., 2020). Ionization was achieved by
adding NaCl at a concentration of 0.15 M. The simulations employed
Periodic Boundary Conditions with Particle-Mesh Ewald and Fast
Fourier Transform. The force elds applied were Amber19sb for the
protein, General Amber Force Field 2 (GAFF2) for the P13 (Tian et al.,
2020), and OPC for the water model (Izadi et al., 2014). Hydrogen mass
repartitioning was performed to redistribute mass from heavy atoms
connected to hydrogen into the bonded hydrogens, enabling simulation
at a speed of 4 fs (Gao et al., 2021). Minimization was carried out using a
constraint algorithm with 50,000 steps. The equilibration process
involved the NVT ensemble with Nos´
e–Hoover thermostat for a total of
125,000 steps (Evans and Holian, 1985), followed by production sim-
ulations with the NPT ensemble using Parrinello-Rahman pressure
coupling at a temperature of 310 K (Parrinello and Rahman, 1981). All
simulations were conducted using Gromacs 2022.2 software, with a
total simulation time of 100 ns (Abraham et al., 2015) (Putra et al.,
2024). Free energy calculations using the Molecular Mechanics Poisson
Boltzmann Surface Area (MMPBSA) approach are conducted utilizing
gmx_MMPBSA (Vald´
es-Tresanco et al., 2021)
2.7. In vitro study assessments
2.7.1. Preparation of P13 FDGIP peptide sample
The synthetic peptide derivative of C. lentillifera FDGIP (Phe-Asp-
Gly-Ile-Pro; pentapeptide), which exhibited the best docking value, was
obtained from the Biochemistry and Biomolecular Laboratory, Brawi-
jaya University, Indonesia. The peptide was synthesized using a Solid
Phase Peptide Synthesizer aided by Microwave with an internal tem-
perature sensor. The synthesis commenced from the C-terminal amino
acid (AA) as the rst synthesized residue. HD-Pro-2-CITrt-Resin (for the
synthesis of FDGIP, FP-5), each swollen in 6 mL of DMF for 1 hour, and
the preparation of the target peptide was conducted according to the
manufacturer’s instructions and referred to a similar research protocol
that has been well-established with a purity level and Average Local
Condence reaching 97 % (Joel et al., 2018).
2.7.2. Assessment of
α
-glucosidase and
α
-amylase inhibition
Two different inhibitory activity tests on the peptide P13 (FDGIP)
were performed as per methodologies described in previous literature,
with acarbose, as a positive controls (Nurkolis et al., 2023b). In the
α
-glucosidase inhibition experiment, a phosphate buffer solution with a
volume of 50 mL (pH 6.9) containing the enzyme
α
-glucosidase (1.52
UI/mL) was prepared. Maltose and sucrose solutions were added to the
mixture, and then samples (P13) were added at various concentrations
(ranging from 25, 50, 75, 100, and 125 µg/mL). Each sample was then
mixed and incubated at 37 ◦C for 20 min. The enzyme was later inac-
tivated by heating the tubes at 100 ◦C for 2 min. For the
α
-amylase in-
hibition experiment, diluted P13 samples were incubated at ve
different concentrations ranging from 25 to 125 µg/mL along with 0.006
M sodium chloride (NaCl), sodium phosphate buffer (Na
2
HPO
4
, pH 6.9),
and 0.5 mg/mL of pig/porcine pancreatic amylase. Afterward, at room
temperature 25 ◦C incubate each mixture of combined 500 µL of 1 %
starch solution for ten minutes incubation. Following this, 3,5-dinitro
salicylic acid (CAS 609–99–4) was added to complete the reaction,
and the mixture was incubated at 100 ◦C for 5 min. After cooling at 22
◦C, the absorbance of each sample P13 was measured at 540 nm after
dilution with distilled water.
2.7.3. Antioxidant activity against ABTS
Antioxidant activity was assessed via the scavenging of 2,2
′
-azino-bis
(3-ethylbenzothiazoline-6-sulfonic acid) or diammonium salt radical
cations (ABTS) radicals according to published procedures (Hayes et al.,
2023; Sabrina et al., 2022). Potassium persulfate (K
2
S
2
O
8,
2.4 mM) and
ABTS (7 mM) were mixed in a 1:1 ratio, protected from light with
aluminum foil, and allowed to react at 22 ◦C for 14 h in the dark. The
mixture was diluted to obtain a working solution with absorption of
0.706 at 734 nm (e.g., 1 mL of stock solution plus 60 mL of EtOH). A new
working solution was prepared for each test. The P13 sample was stored
at 25, 50, 75, 100, and 125
μ
g/mL concentrations, to be diluted with
ABTS working solution (1 mL), and absorbance was measured after 7
min at 734 nm.
The inhibition of ABTS is expressed as a percentage (%), and is
determined according to the formula:
Inhibition Activity (%) = [A0−A1
A0]×100% (1)
where A0 is the blank absorption, and A1 represents the standard or
sample absorption.
2.7.4. Cell culture and cell viability of 3T3-L1 cells
The broblast 3T3-L1 cells were purchased from American Type
Culture Collection (ATCC CL-173) via Brawijaya University. Culture and
differentiation were based on the modication proposed by Zhang et al.
and Jeong and Park (Jeong and Park, 2020; Zhang et al., 2019), pre-
adipocyte 3T3-L1 cells were initially seeded in 6-well plates at a density
of 1 ×10
5
cells per well and then cultured in Dulbecco’s Modied Eagle
Medium (DMEM) with 10 % FBS at 37 ◦C for 24 h in a 5 % carbon di-
oxide atmosphere. On the second day, adipocyte differentiation was
performed by treating 3T3-L1 cells with a differentiation medium con-
sisting of DMEM with 10 % FBS, mixed with 0.5 mM 3-isobutyl-1-me-
thylxanthine (IBMX), 1
μ
M dexamethasone, and 10
μ
g/ mL. insulin.
This process took three days. On Day 5, the original medium was
removed and a new insulin medium, consisting of DMEM with 10 % FBS
and 10 ug/mL insulin, was used to maintain adipocyte characteristics
with an incubation period of three days. It was then possible to assess the
R. Kurniawan et al.
Future Foods 9 (2024) 100354
4
effect of the P13 (0.25, 0.5, 1 mM) and simvastatin (1.5, 3, 6 uM) using
tests carried out in triplicate in a differentiation medium.
The MTT reduction assay was used to assess the viability of 3T3-L1
preadipocyte cells (American Type Culture Collection, Manassas, VA,
USA). This initially involved culturing cells in Dulbecco’s modied Ea-
gle’s medium (DMEM) containing 10 % FBS (fetal bovine serum) at a
density of 5 ×10
3
cells per well in a 96-well plate at 37 ◦C for 24 h in 5 %
carbon dioxide. The atmosphere before treatment with different extract
concentrations along with simvastatin for 72 h. After incubation, each
well received 100
μ
L of MTT solution (5 mg/mL), before a further four
hours of incubation at 37 ◦C. Then 100
μ
L of DMSO was added to
dissolve MTT-formazan crystals in viable cells. before measuring the
absorbance at 540 nm. The percentage of cell viability was then calcu-
lated based on data from the treatment wells for viable cells compared to
the results of the control wells.
2.7.4.1. Assessment of MAPK8, PPARG, GHRL, CPT-1 and GLP1 expres-
sions on the preadipocyte 3T3-L1 cells. The analysis of MAPK8 or JNK1,
PPARG, Human GHRL(Ghrelin), CHPT1 or CPT1, and GLP1 expressions
was carried out in accordance with the manufacturer’s protocol
(Elabscience® Elabscience Biotechnology Co., Ltd; Wuhan, China) and
established research experimental guidelines/protocol (Nurkolis et al.,
2023a) with modication. To detect MAPK8, PPARG, CPT1, and GLP1,
the polyvinylidene diuoride membrane was treated with a blocking
solution consisting of 5 % dry skimmed milk in a buffer consisting of Tris
with Tween (T-TBS) saline buffer. This was done to prevent the mem-
brane from absorbing any detection reagents. This buffer has a con-
centration of 0.1 % Tween 20 and contains 20 mmol/L Tris–HCl, 0.138
mol/L Sodium chloride (NaCl; Sigma-Aldrich, Darmstadt, Germany),
and has a pH of 7.4. The membrane was treated with a blocking solution
consisting of 5 % albumin (specically bovine serum albumin or BSA) in
T-TBS to identify phosphorylated MAPK8, PPARG, GHRL CPT1, and
GLP1. This was done so that phosphorylated protein could be detected.
To assess the expression of MAPK8, PPARG, CPT1, and GLP1, a special
methodology was followed. The process included exposing the cell
membrane to primary antibodies, followed by secondary antibodies
associated with peroxidase. Primary and secondary antibodies were
diluted in a solution containing 5 % bovine serum albumin (BSA) in a
T-TBS solution. This comprehensive antibody-based technique was
adopted to gain insight into MAPK8, PPARG, CPT1, and GLP1 expression
while ensuring precision through antibody dilution and appropriate
incubation conditions. To complete the information, the experimental
process involved seeding 5000 MCF-7 cells into each well using 100
μ
L/well. These cells were treated with P13 with different concentration
gradients of 0, 25, 50, 75, 100, and 125
μ
g/mL within a 24-hour incu-
bation time. Next, the data obtained were analyzed to ascertain the
percentage value relative to the control group (a group consisting of cells
that were not given any treatment or 0
μ
g/mL of P13). This percentage
(%) value assessment is facilitated through optical density (OD) mea-
surements performed using spectrophotometers (SmartSpec Plus from
Bio-Rad Laboratories. Inc, California, Amerika) at 665 nm and 620 nm
wavelengths.
2.8. Data analytics and management
Statistical analysis of data was carried out using the MacBook version
of GraphPad Prism Premium 10 software (GraphPad Software, Inc.; San
Diego, CA, USA). The Shapiro-Wilk test is performed to evaluate the
distribution of data. If the data were normally distributed (signicance
<0.05), a One-Way ANOVA test was performed to test the average
difference between treatment groups. Otherwise, the Kruskal-Wallis test
will be performed. The 50 % lethal value (Lethal Concentration 50 or
LC
50
) of ABTS and DPPH,
α
-glucosidase, and
α
-amylase inhibition ac-
tivities was analyzed using the statistical analysis package GraphPad
Premium’s non-linear regression (log(inhibitor) vs. normalized response
– variable slope’ while to see the signicance value (95 %CI) of miR-21/
132 expression through Two-Way ANOVA test.
3. Results
3.1. Marine algae peptides (MAP) observe from literature study
The provided table 1 presents a comprehensive prole of algae
peptides extracted from various species, along with their respective se-
quences, chemical information (SMILES Canonical), ChEBI IDs or Pub-
Chem CIDs, and corresponding references. Each peptide entry
encompasses essential details, including its source species, peptide
sequence, chemical structure represented by Simplied Molecular Input
Line Entry System (SMILES) notation or canonical SMILES, unique
identiers such as ChEBI IDs or PubChem CIDs, and relevant literature
references. The peptides listed demonstrate a diverse range of sequences
and structures derived from different algae species.
3.2. Predicted LD
50
values, toxicity classication, drug-likeness, and
network pharmacology of MAP
This Table 2 presents the structure-activity relationship (SAR) prole
of insulin promoters derived from bioactive peptides extracted from
marine algae (MAP). The MAPs are evaluated based on their biological
activity of Pa score, predicted LD
50
values, toxicity classication, and
drug-likeness according to the Lipinski Rule, Pzer Rule, and GSK
criteria. Thirteen peptides P8, P9, P14, P30, P35, P36, P46, P47, P48,
and P50 demonstrate Pa scores greater than 0.4, suggesting their po-
tential to promote insulin secretion effectively. Peptides P9 and P36
exhibit the highest Pa scores of 0.77 and 0.88, respectively, indicating
strong insulin-promoting activity. On the other note, the predicted LD
50
values for the peptides range from 300 to 6800 mg/kg, indicating
varying levels of toxicity. Peptides with lower LD
50
values may pose
higher toxicity risks. According to toxicity classication, peptides are
categorized into toxicity classes ranging from 3 to 6, with higher classes
indicating lower toxicity. Peptides P46, P47, and P48 are classied as
potentially toxic based on toxicity analysis (table 2), suggesting poten-
tial safety concerns and not being of interest to further study inclusion
criteria. In terms of drug-likeness, most peptides adhere to the Lipinski
Rule, Pzer Rule, and GSK criteria, indicating their potential suitability
as drug candidates. So the peptides that were continued for molecular
docking studies were only P8, P9, P11, P13, P14, P30, P35, P36, P37,
P50.
To nd out the central receptors that play a role in diabetes signaling,
especially T2D, a network pharmacology analysis was carried out and in
the analysis of targets related to T2D disease and targets of MAP mapped
on the Venn diagram of Fig. 1 (1A), it was found that the intersection of
the appropriate targets of peptides and T2D was 301 genes- proteins.
Based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) anal-
ysis of protein-protein interactions, it is conrmed that the target of
these peptides is the T2D signaling pathway and insulin resistance
(Fig. 1B). Further analysis of interactions between target proteins ob-
tained from MAP and their relationship with T2D yielded several
possible signals in the management of T2D as presented in Fig. 1C and
Table 3. This Table 3 provides an overview of the top one protein-
protein interaction (PPI) network analyses, including key parameters
such as degree, betweenness centrality, closeness centrality, overall
score, and associated pathways. The analysis focuses on three critical
proteins: MAPK8, PPARGC1A, and GHRL, along with their respective
network characteristics and pathway associations. The analysis high-
lights the central roles of MAPK8, PPARGC1A, and GHRL in the protein-
protein interaction network, underscoring their potential signicance in
mediating biological processes and signaling pathways associated with
metabolic regulation and disease pathogenesis, particularly in diabetes
and related complications.
R. Kurniawan et al.
Future Foods 9 (2024) 100354
5
Table 1
Prole of algae peptide from the systematic literature study.
Pepides
No
Species Peptides SMILES Canonical ChEBI ID or
PubCHem CID
References
P1 Chlorella vulgaris VECYGPNRPQF (Val-Glu-Cys-Tyr-Gly-
Pro-Asn-Arg-Pro-Gln-Phe)
CC(C)C(C(=O)NC(CCC(=O)O)C(=O)NC(CS)C(=O)NC
(CC1=CC=C(C=C1)O)C(=O)NCC(=O)N2CCCC2C(=O)NC
(CC(=O)N)C(=O)NC(CCCN=C(N)N)C(=O)N3CCCC3C(=O)
NC(CCC(=O)N)C(=O)NC(CC4=CC=CC=C4)C(=O)O)N
CID: 102481159 (Sheih et al.,
2009)
P2 Navicula incerta PGWNQWFL (Pro-Gly-Trp-Asn-Gln-
Trp-Phe-Leu)
O=C(NCC(N[C@@H](CC1=CNC2=CC=CC=C12)C(N
[C@@H](CC(N)=O)C(N[C@@H](CCC(N)=O)C(N[C@@H]
(CC3=CNC4=CC=CC=C34)C(N[C@@H]
(CC5=CC=CC=C5)C(N[C@@H](CC(C)C)C(O)=O)=O)=
O)=O)=O)=O)=O)[C@H]6NCCC6
Chemdraw (Kang et al.,
2012)
P3 Navicula incerta VEVLPPAEL (Val-Glu-Val-Leu-Pro-Pro-
Ala-Glu-Leu)
N[C@@H](C(C)C)C(N[C@@H](CCC(O)=O)C(N[C@@H](C
(C)C)C(N[C@@H](CC(C)C)C(N1[C@@H](CCC1)C(N2
[C@@H](CCC2)C(N[C@@H](C)C(N[C@@H](CCC(O)=O)C
(N[C@@H](CC(C)C)C=O)=O)=O)=O)=O)=O)=O)=O)=O
Chemdraw (Kang et al.,
2012)
P4 Chlorella
ellipsoidea
LNGDVW (Leu-Asn-Gly-Asp-Val-Trp) CC(C)CC(C(=O)NC(CC(=O)N)C(=O)NCC(=O)NC(CC(=O)
O)C(=O)NC(C(C)C)C(=O)NC(CC1=CNC2=CC=CC=C21)C
(=O)O)N
CID: 71480488 (Ko et al.,
2012b)
P5 Chlorella
ellipsoidea
VEGY (Val–Glu–Gly–Tyr) N[C@@H](C(C)C)C(N[C@@H](CCC(O)=O)C(NCC(N
[C@@H](CC1=CC=C(C=C1)O)C(C)=O)=O)=O)=O
Chemdraw (Ko et al.,
2012a)
P6 Undaria
pinnatida
YH (Tyr-His) N[C@@H](Cc1ccc(O)cc1)C(=O)N[C@@H](Cc1c[nH]cn1)C
(O)=O
CHEBI:73695 (Suetsuna et al.,
2004)
P7 Undaria
pinnatida
KY (Lys-Tyr) NCCCC[C@H](N)C(=O)N[C@@H](Cc1ccc(O)cc1)C(O)=O CHEBI:73608 (Suetsuna et al.,
2004)
P8 Undaria
pinnatida
FY (Phe-Tyr) N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccc(O)cc1)C
(O)=O
CHEBI:73637 (Suetsuna et al.,
2004)
P9 Undaria
pinnatida
IY (Ile-Tyr) CC[C@H](C)[C@H](N)C(=O)N[C@@H](Cc1ccc(O)cc1)C
(O)=O
CHEBI:74326 (Suetsuna et al.,
2004)
P10 Nannochloropsis
oculata
GMNNLTP (Gly-Met-Asn-Asn-Leu-Thr-
Pro)
NCC(N[C@@H](CCSC)C(N[C@@H](CC(N)=O)C(N
[C@@H](CC(N)=O)C(N[C@@H](CC(C)C)C(N[C@@H]
([C@H](O)C)C(N1[C@@H](CCC1)C(O)=O)=O)=O)=O)=
O)=O)=O
Chemdraw (Samarakoon
et al., 2013)
P11 Nannochloropsis
oculata
LEQ (Leu-Glu-Gln) CC(C)CC(C(=O)NC(CCC(=O)O)C(=O)NC(CCC(=O)N)C
(=O)O)N
CID: 145456411 (Samarakoon
et al., 2013)
P12 Palmaria palmata IRLIIVLMPILMA (Ile-Arg-Leu-Ile-Ile-
Val-Leu-Met-Pro-Ile-Leu-Met-Ala)
CCC(C)C(C(=O)NC(CCCN=C(N)N)C(=O)NC(CC(C)C)C(=O)
NC(C(C)CC)C(=O)NC(C(C)CC)C(=O)NC(C(C)C)C(=O)NC
(CC(C)C)C(=O)NC(CCSC)C(=O)N1CCCC1C(=O)NC(C(C)
CC)C(=O)NC(CC(C)C)C(=O)NC(CCSC)C(=O)NC(C)C(=O)
O)N
CID: 134827470 (Fitzgerald
et al., 2012)
P13 Caulerpa
lentillifera
FDGIP (Phe-Asp-Gly-Ile-Pro) N[C@@H](CC1=CC=CC=C1)C(N[C@@H](CC(O)=O)C(N
[C@@H](CC(O)=O)C(N2[C@@H](CCC2)C(O)=O)=O)=
O)=O
Chemdraw (Joel et al.,
2018)
P14 Caulerpa
lentillifera
AIDPVRA (Ala-Ile-Asp-Val-Arg-Ala) N[C@@H](C)C(N[C@@H]([C@@H](C)CC)C(N[C@@H]
(CC(O)=O)C(N[C@@H](C(C)C)C(N[C@@H](C(C)C)C(N
[C@@H](C)C(O)=O)=O)=O)=O)=O)=O
Chemdraw (Joel et al.,
2018)
P15 Caulerpa racemosa ELWKTF (Glu-Leu-Trp-Lys-Thr-Phe) N[C@@H](CCC(O)=O)C(N[C@@H](CC(C)C)C(N[C@@H]
(CC1=CNC2=CC=CC=C12)C(N[C@@H](CCCCN)C(N
[C@@H]([C@H](O)C)C(N[C@@H](CC3=CC=CC=C3)C
(O)=O)=O)=O)=O)=O)=O
Chemdraw (Nurkolis et al.,
2022)
P16 Gracilariopsis
chorda
IDHY (Ile-Asp-His-Tyr) N[C@@H]([C@@H](C)CC)C(N[C@@H](CC(O)=O)C(N
[C@@H](CC1=CNC=N1)C(N[C@@H](CC2=CC=C(C=C2)
O)C(O)=O)=O)=O)=O
Chemdraw (Mune Mune
et al., 2023)
P17 Gracilariopsis
chorda
LVVER (Leu-Val-Val-Glu-Arg) N[C@@H](CC(C)C)C(N[C@@H](C(C)C)C(N[C@@H](C(C)
C)C(N[C@@H](CCC(O)=O)C(N[C@@H](CCCNC(N)=N)C
(O)=O)=O)=O)=O)=O
Chemdraw (Mune Mune
et al., 2023)
P18 Mazzaella japonica DFGVPGHEP (Asp-Phe-Gly-Val-Pro-
Gly-His-Glu-Pro)
N[C@@H](CC(O)=O)C(N[C@@H](CC1=CC=CC=C1)C
(NCC(N[C@@H](C(C)C)C(N2[C@@H](CCC2)C(NCC(N
[C@@H](CC3=CNC=N3)C(N[C@@H](CCC(O)=O)C(N4
[C@@H](CCC4)C(O)=O)=O)=O)=O)=O)=O)=O)=O)=O
Chemdraw (Kumagai et al.,
2020)
P19 Mazzaella japonica YGDPDHY (Tyr-Gly-Asp-Pro-Asp-His-
Tyr)
N[C@@H](CC1=CC=C(C=C1)O)C(NCC(N[C@@H](CC
(O)=O)C(N2[C@@H](CCC2)C(N[C@@H](CC(O)=O)C(N
[C@@H](CC3=CNC=N3)C(N[C@@H](CC4=CC=C(C=C4)
O)C(O)=O)=O)=O)=O)=O)=O)=O
Chemdraw (Kumagai et al.,
2020)
P20 Mazzaella japonica TIMPHPR (Thr-Ile-Met-Pro-His-Pro-
Arg)
N[C@@H]([C@H](O)C)C(N[C@@H]([C@@H](C)CC)C(N
[C@@H](CCSC)C(N1[C@@H](CCC1)C(N[C@@H]
(CC2=CNC=N2)C(N3[C@@H](CCC3)C(N[C@@H](CCCNC
(N)=N)C(O)=O)=O)=O)=O)=O)=O)=O
Chemdraw (Kumagai et al.,
2020)
P21 Mazzaella japonica YRD (Tyr-Arg-Asp) O=C(N[C@@H](CC(O)=O)C(O)=O)[C@@H](NC(=O)
[C@@H](N)CC1=CC=C(O)C=C1)CCCN=C(N)N
CHEBI:164955 (Kumagai et al.,
2020)
P22 Mazzaella japonica VSEGLD (Val-Ser-Glu-Gly-Leu-Asp) N[C@@H](C(C)C)C(N[C@@H](CO)C(N[C@@H](CCC(O)=
O)C(NCC(N[C@@H](CC(C)C)C(N[C@@H](CC(O)=O)C
(O)=O)=O)=O)=O)=O)=O
Chemdraw (Kumagai et al.,
2020)
P23 Mazzaella japonica GGPAT (Gly-Gly-Pro-Ala-Thr) NCC(NCC(N1[C@@H](CCC1)C(N[C@@H](C)C(N[C@@H]
([C@H](O)C)C(O)=O)=O)=O)=O)=O
Chemdraw (Kumagai et al.,
2020)
(continued on next page)
R. Kurniawan et al.
Future Foods 9 (2024) 100354
6
Table 1 (continued )
Pepides
No
Species Peptides SMILES Canonical ChEBI ID or
PubCHem CID
References
P24 Mazzaella japonica SSNDYPI (Ser-Ser-Asn-Asp-Tyr-Pro-Ile) N[C@@H](CO)C(N[C@@H](CO)C(N[C@@H](CC(N)=O)C
(N[C@@H](CC(O)=O)C(N[C@@H](CC1=CC=C(C=C1)O)C
(N2[C@@H](CCC2)C(N[C@@H]([C@@H](C)CC)C(O)=
O)=O)=O)=O)=O)=O)=O
Chemdraw (Kumagai et al.,
2020)
P25 Mazzaella japonica CPYDWV (Cys-Pro-Tyr-Asp-Trp-Val) N[C@@H](CS)C(N1[C@@H](CCC1)C(N[C@@H]
(CC2=CC=C(C=C2)O)C(N[C@@H](CC(O)=O)C(N[C@@H]
(CC3=CNC4=CC=CC=C34)C(N[C@@H](C(C)C)C(O)=O)=
O)=O)=O)=O)=O
Chemdraw (Kumagai et al.,
2020)
P26 Mazzaella japonica NLGN (Asn-Leu-Gly-Asn) N[C@@H](CC(N)=O)C(N[C@@H](CC(C)C)C(NCC(N
[C@@H](CC(N)=O)C(O)=O)=O)=O)=O
Chemdraw (Kumagai et al.,
2020)
P27 Palmaria palmata SDITRPGGNM (Ser-Asp-Ile-Thr-Arg-
Pro-Gly-Gly-Asn-Met)
N[C@@H](CO)C(N[C@@H](CC(O)=O)C(N[C@@H]
([C@@H](C)CC)C(N[C@@H]([C@H](O)C)C(N[C@@H]
(CCCNC(N)=N)C(N1[C@@H](CCC1)C(NCC(NCC(N
[C@@H](CC(N)=O)C(N[C@@H](CCSC)C(O)=O)=O)=O)=
O)=O)=O)=O)=O)=O)=O
Chemdraw (Lafarga et al.,
2020)
P28 Palmaria palmata LRY (Leu-Arg-Tyr) O=C(N[C@@H](CCCN=C(N)N)C(=O)N[C@@H]
(CC1=CC=C(O)C=C1)C(O)=O)[C@@H](N)CC(C)C
CHEBI:159001 (Echave et al.,
2021)
P29 Porphyra spp. GGSK (Gly-Gly-Ser-Lys) NCC(NCC(N[C@@H](CO)C(N[C@@H](CCCCN)C(O)=O)=
O)=O)=O
Chemdraw (Admassu et al.,
2018)
P30 Porphyra spp. ELS (Glu-Leu-Ser) O=C(N[C@@H](CO)C(O)=O)[C@@H](NC(=O)[C@@H]
(N)CCC(O)=O)CC(C)C
CHEBI:163045 (Admassu et al.,
2018)
P31 Pyropia haitanensis QTDDNHSNVLWAGFSR (Gln-Thr-Asp-
Asp-Asn-His-Ser-Asn-Val-Leu-Trp-Ala-
Gly-Phe-Ser-Arg)
N[C@@H](CCC(N)=O)C(N[C@@H]([C@H](O)C)C(N
[C@@H](CC(O)=O)C(N[C@@H](CC(O)=O)C(N[C@@H]
(CC(N)=O)C(N[C@@H](CC1=CNC=N1)C(N[C@@H](CO)C
(N[C@@H](CC(N)=O)C(N[C@@H](C(C)C)C(N[C@@H]
(CC(C)C)C(N[C@@H](CC2=CNC3=CC=CC=C23)C(N
[C@@H](C)C(NCC(N[C@@H](CC4=CC=CC=C4)C(N
[C@@H](CO)C(N[C@@H](CCCNC(N)=N)C(O)=O)=O)=
O)=O)=O)=O)=O)=O)=O)=O)=O)=O)=O)=O)=O)=O
Chemdraw (Mao et al.,
2017)
P32 Enteromorpha
clathrata
PAFG (Pro-Ala-Phe-Gly) O=C(N[C@@H](C)C(N[C@@H](CC1=CC=CC=C1)C(NCC
(O)=O)=O)=O)[C@H]2NCCC2
Chemdraw (Pan et al.,
2016)
P33 Bangia fusco-
purpurea
ALLAGDPSVLEDR (Ala-Leu-Leu-Ala-
Gly-Asp-Pro-Ser-Val-Leu-Glu-Asp-Arg)
N[C@@H](C)C(N[C@@H](CC(C)C)C(N[C@@H](CC(C)C)C
(N[C@@H](C)C(NCC(N[C@@H](CC(O)=O)C(N1[C@@H]
(CCC1)C(N[C@@H](CO)C(N[C@@H](C(C)C)C(N[C@@H]
(CC(C)C)C(N[C@@H](CCC(O)=O)C(N[C@@H](CC(O)=O)
C(N[C@@H](CCCNC(N)=N)C(O)=O)=O)=O)=O)=O)=
O)=O)=O)=O)=O)=O)=O)=O
Chemdraw (Wu et al.,
2017)
P34 Bangia fusco-
purpurea
VVGGTGPVDEWGIGAR (Val-Val-Gly-
Gly-Thr-Gly-Pro-Val-Asp-Glu-Trp-Gly-
Ile-Gly-Ala-Arg)
N[C@@H](C(C)C)C(N[C@@H](C(C)C)C(NCC(NCC(N
[C@@H]([C@H](O)C)C(NCC(N1[C@@H](CCC1)C(N
[C@@H](C(C)C)C(N[C@@H](CC(O)=O)C(N[C@@H](CCC
(O)=O)C(N[C@@H](CC2=CNC3=CC=CC=C23)C(NCC(N
[C@@H]([C@@H](C)CC)C(NCC(N[C@@H](C)C(N
[C@@H](CCCNC(N)=N)C(O)=O)=O)=O)=O)=O)=O)=
O)=O)=O)=O)=O)=O)=O)=O)=O)=O
Chemdraw (Wu et al.,
2017)
P35 Ulva rigida AFL (Ala-Phe-Leu) O=C(N[C@@H](CC(C)C)C(O)=O)[C@@H](NC(=O)
[C@@H](N)C)CC1=CC=CC=C1
CHEBI:158423 (Paiva et al.,
2016)
P36 Ulva rigida IP (Ile-Pro) CC[C@H](C)[C@H](N)C(=O)N1CCC[C@H]1C(O)=O CHEBI:74076 (Paiva et al.,
2016)
P37 Gracilariopsis
lemaneiformis
QVEY (Gln-Val-Glu-Tyr) N[C@@H](CCC(N)=O)C(N[C@@H](C(C)C)C(N[C@@H]
(CCC(O)=O)C(N[C@@H](CC1=CC=C(C=C1)O)C(O)=O)=
O)=O)=O
Chemdraw (Cao et al.,
2017)
P38 Gracilariopsis
lemaneiformis
SFYYGK (Ser-Phe-Tyr-Tyr-Gly-Lys) N[C@@H](CO)C(N[C@@H](CC1=CC=CC=C1)C(N
[C@@H](CC2=CC=C(C=C2)O)C(N[C@@H](CC3=CC=C
(C=C3)O)C(NCC(N[C@@H](CCCCN)C(O)=O)=O)=O)=
O)=O)=O
Chemdraw (Su et al., 2022)
P39 Gracilariopsis
lemaneiformis
RLVPVPY (Arg-Leu-Val-Pro-Val-Pro-
Tyr)
N[C@@H](CCCNC(N)=N)C(N[C@@H](CC(C)C)C(N
[C@@H](C(C)C)C(N1[C@@H](CCC1)C(N[C@@H](C(C)C)
C(N2[C@@H](CCC2)C(N[C@@H](CC3=CC=C(C=C3)O)C
(O)=O)=O)=O)=O)=O)=O)=O
Chemdraw (Su et al., 2022)
P40 Gracilariopsis
lemaneiformis
YIGNNPAKG (Tyr-Ile-Gly-Asn-Asn-Pro-
Ala-Lys-Gly)
N[C@@H](CC1=CC=C(C=C1)O)C(N[C@@H]([C@@H](C)
CC)C(NCC(N[C@@H](CC(N)=O)C(N[C@@H](CC(N)=O)C
(N2[C@@H](CCC2)C(N[C@@H](C)C(N[C@@H](CCCCN)C
(NCC(O)=O)=O)=O)=O)=O)=O)=O)=O)=O
Chemdraw (Su et al., 2022)
P41 Gracilariopsis
lemaneiformis
FQINMCILR (Phe-Gln-Ile-Asn-Met-Cys-
Ile-Leu-Arg)
N[C@@H](CC1=CC=CC=C1)C(N[C@@H](CCC(N)=O)C(N
[C@@H]([C@@H](C)CC)C(N[C@@H](CC(N)=O)C(N
[C@@H](CCSC)C(N[C@@H](CS)C(N[C@@H]([C@@H](C)
CC)C(N[C@@H](CC(C)C)C(N[C@@H](CCCNC(N)=N)C
(O)=O)=O)=O)=O)=O)=O)=O)=O)=O
Chemdraw (Deng et al.,
2018)
P42 Gracilariopsis
lemaneiformis
TGAPCR (Thr-Gly-Ala-Pro-Cys-Arg) N[C@@H]([C@H](O)C)C(NCC(N[C@@H](C)C(N1[C@@H]
(CCC1)C(N[C@@H](CS)C(N[C@@H](CCCNC(N)=N)C(O)=
O)=O)=O)=O)=O)=O
Chemdraw (Deng et al.,
2018)
P43 Ulva intestinalis FGMPLDR (Phe-Gly-Met-Pro-Leu-Asp-
Arg)
N[C@@H](CC1=CC=CC=C1)C(NCC(N[C@@H](CCSC)C
(N2[C@@H](CCC2)C(N[C@@H](CC(C)C)C(N[C@@H](CC
(O)=O)C(N[C@@H](CCCNC(N)=N)C(O)=O)=O)=O)=O)=
O)=O)=O
Chemdraw (Sun et al.,
2019)
(continued on next page)
R. Kurniawan et al.
Future Foods 9 (2024) 100354
7
3.3. Molecular docking study in T2D markers
Table 4 presents the binding energy values (ΔG; kcal/mol) obtained
from molecular docking simulations, which are being studied for their
potential as inhibitors for Type-2 Diabetes Mellitus (T2DM) therapeutic
targets using marine peptides. The binding energies indicate the
attraction of marine peptides to many important receptors involved in
T2DM, such as MAPK8 (JNK1), PPARGC1A, Ghrelin, GLP-1, CPT-1,
α
-Amylase, and
α
-Glucosidase. Peptide P13 had the highest favorable
binding energy of -8.8 kcal/mol for MAPK8 (JNK1), indicating a
considerable potential for interaction. This signicantly exceeded the
binding energies of the control drugs, Pioglitazone (-7.5 kcal/mol) and
Metformin (-4.4 kcal/mol). Similarly, P13 exhibited a higher binding
afnity with PPARGC1A, as shown by a binding energy of -8.3 kcal/mol,
surpassing that of the control samples.
The interactions between the ghrelin receptor and peptides have
shown encouraging outcomes, particularly with P9, which had a binding
energy of -7.5 kcal/mol. By contrast, the control substances Pioglitazone
and Metformin exhibited binding energies of -6.5 kcal/mol and -5.4
kcal/mol, respectively. Within the GLP-1 receptor context, peptide P37
had a binding energy of -7.3 kcal/mol, while the control drugs Piogli-
tazone and Metformin displayed binding energies of -7.0 kcal/mol and
-4.5 kcal/mol, respectively. Regarding CPT-1, peptide P13 exhibited
remarkable binding afnity at -9.6 kcal/mol, which was notably greater
than the binding afnities of the control substances Pioglitazone (-8.3
kcal/mol) and Metformin (-5.4 kcal/mol). Peptide P13 exhibited sig-
nicant binding energies of -8.7 kcal/mol with
α
-Amylase and
α
-Glucosidase, indicating its potential as a versatile treatment agent for
Type 2 Diabetes Mellitus (T2DM). The binding energy values of this
peptide were much more favorable compared to Pioglitazone (-7.8 kcal/
mol and -8.2 kcal/mol) and Metformin (-5.5 kcal/mol for both en-
zymes). This suggests that the peptide has a higher potential for inhi-
bition. Overall, the molecular docking simulations highlight the
substantial inhibitory capacity of marine peptides against important
targets associated with Type 2 Diabetes Mellitus (T2DM). The results
emphasize the potential of marine peptides as a novel approach in the
treatment of type 2 diabetes mellitus (T2DM). Peptide P13, in particular,
shows a wide-ranging ability to inhibit various targets.
3.4. Molecular dynamics simulations in most central receptor (MAPK8/
JNK1)
In this study, we conducted a detailed analysis of molecular dy-
namics simulations (Fig. 2) to explore the behavior of two molecular
complexes: Jnk1 protein with Algal Peptides, and Semaglutide peptide
with Algal Peptides (Fig. 2A). Our primary aim was to evaluate the
stability and dynamics of these complexes using Root-mean-square de-
viation (RMSD) as a measure. The RMSD analysis provided valuable
insights into the dynamics of both complexes (Handayani et al., 2023).
For the Jnk1 complex with Algal Peptides, we observed consistently
stable movements of the peptides throughout the simulation. This sta-
bility was evident from the consistently low RMSD values, indicating
minimal deviation of the peptide atoms from their initial positions.
Specically, the RMSD uctuated between 0.25–1.48 nm from 0 to 17
ns, followed by stable behavior at 0.6–0.7 nm from 18 to 100 ns. During
the analysis of the Jnk1-Algal Peptides complex, the RMSF values offer
valuable insights into the dynamic behavior of individual amino acids
(Fig. 2B). Notably, Valine 399 displays the highest degree of uctuation,
Table 1 (continued )
Pepides
No
Species Peptides SMILES Canonical ChEBI ID or
PubCHem CID
References
P44 Ulva intestinalis MELVLR (Met-Glu-Leu-Val-Leu-Arg) N[C@@H](CCSC)C(N[C@@H](CCC(O)=O)C(N[C@@H]
(CC(C)C)C(N[C@@H](C(C)C)C(N[C@@H](CC(C)C)C(N
[C@@H](CCCNC(N)=N)C(O)=O)=O)=O)=O)=O)=O
Chemdraw (Sun et al.,
2019)
P45 Isochrysis galbana YMGLDLK (Tyr-Met-Gly-Leu-Asp-Leu-
Lys)
N[C@@H](CC1=CC=C(C=C1)O)C(N[C@@H](CCSC)C
(NCC(N[C@@H](CC(C)C)C(N[C@@H](CC(O)=O)C(N
[C@@H](CC(C)C)C(N[C@@H](CCCCN)C(O)=O)=O)=O)=
O)=O)=O)=O
Chemdraw (Wu et al.,
2015)
P46 Chlorella
sorokiniana
WV (Trp–Val) CC(C)[C@H](NC(=O)[C@@H](N)Cc1c[nH]c2ccccc12)C
(O)=O
CHEBI:74877 (Lin et al.,
2018)
P47 Chlorella
sorokiniana
VW (Val–Trp) C(=O)([C@@H](N)C(C)C)N[C@H](C(=O)O)
CC1=CNC2=C1C=CC=C2
CHEBI:92809 (Lin et al.,
2018)
P48 Chlorella
sorokiniana
IW (Ile–Trp) CC[C@H](C)[C@H](N)C(=O)N[C@@H](Cc1c[nH]
c2ccccc12)C(O)=O
CHEBI:74080 (Lin et al.,
2018)
P49 Chlorella vulgaris VHW (Val-His-Trp) CC(C)C(C(=O)NC(CC1=CN=CN1)C(=O)NC
(CC2=CNC3=CC=CC=C32)C(=O)O)N
CHEBI:166230 (Xie et al.,
2018)
P50 Chlorella vulgaris TTW (Thr-Thr-Trp) CC(C(C(=O)NC(C(C)O)C(=O)NC
(CC1=CNC2=CC=CC=C21)C(=O)O)N)O
CHEBI:164275 (Xie et al.,
2018)
Table 2
SAR prole of insulin promoters (Pa score >0.4) from bioactive peptides of marine algae.
Peptides Pa Score* Toxicity Model Computation Analysis** Drug-Likeness**
Insulin Promotor Predicted LD
50
(mg/kg) Toxicity Class Lipinski Rule Pzer Rule GSK
P8 0.64 6800 6 Accepted Accepted Accepted
P9 0.77 2400 5 Accepted Accepted Accepted
P11 0.43 3000 5 Rejected Accepted Accepted
P13 0.59 500 4 Rejected Accepted Rejected
P14 0.64 2000 4 Rejected Accepted Rejected
P30 0.53 3000 5 Accepted Accepted Accepted
P35 0.60 2287 5 Accepted Accepted Accepted
P36 0.88 3000 5 Accepted Accepted Accepted
P37 0.46 2400 5 Rejected Accepted Rejected
P46 0.52 300 3 Accepted Accepted Accepted
P47 0.62 300 3 Accepted Accepted Accepted
P48 0.67 300 3 Accepted Accepted Accepted
P50 0.46 800 4 Accepted Accepted Rejected
R. Kurniawan et al.
Future Foods 9 (2024) 100354
8
indicating substantial conformational changes or exibility throughout
the simulation. This suggests that Valine 399 may play a signicant role
in crucial interactions or structural adaptations within the complex. The
interaction between the Jnk1 protein complex and Algal Peptides, as
well as between Semaglutide peptide and Algal Peptides, was investi-
gated (Fig. 2C). Analysis of the solvent-accessible surface area (SASA) of
both complexes revealed signicant differences. The Jnk1 complex with
Algal Peptides exhibited a larger surface area, ranging from 238 to 249
Fig. 1. Network Pharmacology MAP against T2D. (1A) Venn diagram showing shared target MAP and genes associated with T2D. (1B) Annotation of gene metabolic
biological processes KEGG for MAP targets. (1C) Protein-protein interaction (PPI) of MAP target in T2D.
R. Kurniawan et al.
Future Foods 9 (2024) 100354
9
nm
2
.
Table 5 offers insights into hydrogen bond occupancy between donor
and acceptor molecules in two distinct conditions: the Jnk1 complex
(PDB ID: 3ELJ), both interacting with Algal peptides (ALG). Within the
Jnk1 complex, a predominant hydrogen bond is observed between
LYS55-Side (donor) and ALG428-Side (acceptor), with an occupancy
rate of 66.27 %. Additionally, noteworthy bonds include SER34-Main
with ALG428-Side (15.87 %), and ALG428-Side with ASN114-Side
(9.88 %). Overall, these ndings underscore differing interaction pat-
terns between the two complexes and the Algal peptides, with the Jnk1
complex demonstrating more dominant and signicant hydrogen bond
interaction.
Negative delta energies for van der Waals and electrostatic in-
teractions (-32.24 ±0.54 and -16.74 ±0.78, respectively) suggest their
favorable contributions to system stability, while the positive delta en-
ergy for electrostatic Poisson-Boltzmann energy (42.94 ±6.31) implies
less favorable interactions due to solvent effects (Table 6). Polar energy
contributes negatively (-4.01 ±0.03), indicating stabilizing polar in-
teractions. The negative delta energy for GGAS (-48.99 ±0.95) and
positive delta energy for GSOLV (38.93 ±6.31) reect the balance be-
tween solute-solvent interactions and the energy required for solute
transfer. Overall, the total delta energy (-10.06 ±6.38) provides a
comprehensive assessment of system stability and energetics, crucial for
understanding biomolecular behavior and applications in structural
biology and drug design.
Table 3
Results of the top one protein-protein interaction (PPI) network analyses.
Name Degree Betweenness
Centrality
Closeness
Centrality
Overall
Score
Pathway
MAPK8 5 0.25 0.77 6.02 LEFR/JAK/STAT3 signaling pathway, Signal transduction, AGE-RAGE signaling pathway in
diabetic complications, T2D, insulin signaling pathway, TNF signaling pathway
PPARGC1A 5 0.55 0.70 6.25 CPT1 beta-oxidation pathway in adipocytokine Signaling Pathway, AGE-RAGE signaling pathway
in diabetic complications, T2D, insulin signaling pathway.
GHRL 5 0.58 0.64 6.22 AGE-RAGE signaling pathway in diabetic complications, T2D, insulin signaling pathway
Table 4
Binding energy (ΔG; Kcal/mol) of molecular docking parameter of marine peptides.
Compounds and Control as Ligands MAPK8 (JNK1) PPARGC1A Ghrelin GLP-1 CPT-1
α
-Amylase
α
-Glucosidase
Control Pioglitazone -7.5 -6.9 -6.5 -7.0 -8.3 -7.8 -8.2
Control Metformin -4.4 -4.9 -5.4 -4.5 -5.4 -5.5 -5.5
P8 -7.9 -7.6 -7.2 -7.2 -9.4 -8.6 -7.3
P9 -7.1 -7.0 -7.5 -6.3 -8.1 -7.2 -7.3
P11 -6.7 -6.7 -6.6 -6.5 -8.1 -7.0 -6.6
P13 -8.8 -8.3 -6.9 -7.1 -9.6 -8.7 -8.7
P14 -7.6 -7.7 -6.5 -6.4 -8.2 -8.4 -7.1
P30 -7.1 -5.9 -5.9 -5.6 -8.2 -6.5 -6.9
P35 -8.7 -7.4 -6.4 -6.4 -9.0 -7.0 -7.6
P36 -6.1 -6.0 -7.2 -5.4 -6.4 -6.2 -6.5
P37 -8.2 -7.6 -6.5 -7.3 -9.6 -7.4 -7.7
P50 -9.4 -7.8 -6.4 -7.0 -8.7 -7.7 -7.7
Fig. 2. A. Illustrates the Root-mean-square deviation (RMSD) for Jnk1 MAPK8 (PDB ID: 3ELJ) complexed with P13 Peptide, indicating stable movements of P13
Peptide during the simulation; Fig. 2B. The Root Mean Square Fluctuation (RMSF) for Jnk1 or MAPK8 complexed with P13 Peptide, highlighting the amino acids that
undergo uctuation during the simulation; Fig. 2C. Solvent-accessible surface area (SASA) Jnk1 complexed and P13 Peptide, Area value is in the range 238–249 nm
2
.
Table 5
Depicts the hydrogen occupancy observed during the simulation between Algal
peptides (ALG) with MAPK8/Jnk1 complexed.
PDB ID Donor Acceptor Occupancy
3ELJ LYS55-Side ALG428-Side 66.27 %
SER34-Main ALG428-Side 15.87 %
ALG428-Side ASN114-Side 9.88 %
SER34-Side ALG428-Side 6.59 %
ARG69-Side ALG428-Side 5.19 %
ASN114-Side ALG428-Side 1.10 %
ALG428-Side ASP169-Side 1.10 %
ALA36-Main ALG428-Side 1.10 %
MET111-Main ALG428-Side 1.00 %
R. Kurniawan et al.
Future Foods 9 (2024) 100354
10
3.5. Scavenging of ABTS radicals by P13 Peptide
The data presented in Fig. 3 suggests a comparative analysis of the
ABTS inhibition activity between P13 and Trolox, serving as the control.
The EC
50
value of P13 was found to be 53.49
μ
g/mL, while the EC
50
value of Trolox, the control substance, was slightly higher at 53.98
μ
g/
mL. The lower EC
50
value of P13 indicates that it exhibits a more potent
ABTS inhibition activity compared to Trolox. This suggests that P13 has
a greater ability to scavenge ABTS radicals and thus possesses stronger
antioxidant properties.
3.6. Inhibition of carbohydrates hydrolytic enzymes by P13 Peptide
From Fig. 4, it can be inferred that the
α
-amylase inhibition activity
of P13 is more potent than Acarbose and Metformin controls, as evi-
denced by the lower EC
50
value of P13 (60.38
μ
g/mL <62.81
μ
g/mL;
and 60.38
μ
g/mL <69.88
μ
g/mL). Similar ndings were discovered on
the
α
-glucosidase inhibition activity of P13, which also showed stronger
inhibitions compared to Acarbose and Metformin (57.85
μ
g/mL <59.35
μ
g/mL; and 57.85
μ
g/mL <66.35
μ
g/mL). These results suggest that
P13 possesses stronger in vitro antidiabetic properties than the con-
ventional drugs Acarbose and Metformin in terms of inhibiting both
sugar digestive enzymes.
3.7. Effect of P13 on cellular expression of kinases and hormones relevant
to T2D
Data obtained on 3T3-L1 cells revealed signicant modulations
across various key proteins implicated in the pathology of T2D (Fig. 5).
Notably, the expressions of MAPK8/JNK1, PPARGC1A, Ghrelin, GLP-1,
and CPT-1, which play crucial roles in T2D pathogenesis, were assessed
in response to P13 treatment. The results demonstrated a noteworthy
reduction in the expression levels of these T2D-related proteins
following P13 treatment when compared to the control group, which did
not receive any treatment (p <0.0001; Fig. 5). This observation suggests
that P13 exerts a substantial inhibitory effect on the expression of
proteins associated with increased risks of T2D, indicating its potential
therapeutic relevance in managing the disease. Furthermore, the protein
expression modulations induced by P13 closely mirrored those elicited
by Metformin, a well-established antidiabetic medication. The similarity
in the protein expression patterns between P13-treated cells and
Metformin-treated cells underscores their comparable antidiabetic
efcacy.
3.8. Effect of P13 peptide on suppression of 3T3-L1 cell line
Fig. 6 presents compelling insights into the impact of varying con-
centrations of P13 and Metformin on the viability of 3T3-L1 cells,
serving as a model for the adipogenesis process. At a concentration of 25
μ
g/mL, P13 exhibited a remarkable reduction in the viability of 3T3-L1
cells when contrasted with both the control placebo and Metformin-
treated cells (p <0.0001). This signicant decrease underscores the
potent anti-adipogenic activity of P13, suggesting its efcacy in
impeding the adipogenic process at lower concentrations. Upon esca-
lating the concentrations of P13 to 50
μ
g/mL and 75
μ
g/mL, a similar
pattern of reduced cell viability was observed compared to the control
group, indicating a sustained anti-adipogenic effect of P13 across a
range of concentrations. However, it is noteworthy that the viability-
lowering effect of P13 at these higher concentrations was signicantly
less pronounced when compared to Metformin-treated cells. Intrigu-
ingly, as the concentrations of P13 were further increased to 100
μ
g/mL
and 125
μ
g/mL, a signicant anti-adipogenesis effect was still evident
compared to the control group. Remarkably, at these elevated concen-
trations, the anti-adipogenic efcacy of P13 mirrored that of Metformin,
suggesting comparable effects on inhibiting the adipogenic process.
These ndings underscore the dose-dependent nature of P13
′
s anti-
adipogenic activity, with higher concentrations demonstrating
enhanced efcacy in suppressing adipogenesis.
4. Discussions
The comprehensive analysis of molecular dynamics simulations
Table 6
Free energy calculation using MMPBSA.
PDB ID Delta Energy Component (Kcal/mol)
Van der Waals Electrostatic Energy (EEL) Electrostatic Poisson Boltzmann (EPB) Energy Polar GGAS GSOLV Total
3elj -32.24 ±0.54 -16.74±0.78 42.94±6.31 -4.01±0.03 -48.99±0.95 38.93±6.31 -10.06±6.38
Fig. 3. Antioxidants capability of P13 Peptide via ABTS Inhibition Activity Assay.
R. Kurniawan et al.
Future Foods 9 (2024) 100354
11
presented in this study sheds light on the intricate behaviour and
functional attributes of two distinct molecular complexes: the Jnk1
protein with Algal Peptides, and the Semaglutide peptide with Algal
Peptides. Through the evaluation of various parameters including Root-
mean-square deviation (RMSD), Root-mean-square uctuation (RMSF),
solvent-accessible surface area (SASA), hydrogen bond occupancy, and
energy contributions, a deeper understanding of the stability, dynamics,
and interaction proles of these complexes was achieved.
The RMSD analysis revealed intriguing insights into the structural
stability of the Jnk1 complex with Algal Peptides, exhibiting consis-
tently low deviation values throughout the simulation period. This sta-
bility, characterized by minimal uctuation in the peptide atoms’
positions, underscores the robust nature of these interactions. Despite
more than two decades of research, the intricacy of the JNK pathway
remains bafing, since several protein partners or isoforms of JNKs
manifest a wide range of effects. Numerous nuclear transcription factors,
cytoplasmic proteins involved in vesicular transport or cytoskeleton
control, cell membrane receptors, and mitochondrial proteins are ex-
amples of these JNK substrates (Zeke et al., 2016). Moreover, the RMSF
analysis identied specic amino acids, notably Valine 399, exhibiting
signicant conformational exibility, indicative of their pivotal roles in
mediating dynamic interactions within the complex environment. In
addition to being crucial for protein stability and function, folded pro-
teins’ signicant side-chain exibility may also be involved in regulating
routes of energetic connection between allosteric sites (Friedland et al.,
2008).
Hydrogen bond occupancy data further elucidated the intricate
intermolecular interactions between the Jnk1 complex and Algal Pep-
tides, highlighting distinct binding patterns crucial for complex stability
and functionality. The binding of both peptides to the
α
-glucosidase and
α
-amylase required hydrogen bond interactions (Ibrahim et al., 2018).
The predominance of specic hydrogen bonds underscores the selective
nature of molecular recognition and binding events, providing valuable
insights for rational drug design and optimization strategies. Further-
more, the energetic contributions from van der Waals, electrostatic,
polar, and solvent-related interactions offer crucial insights into the
thermodynamic stability and solvation dynamics of the molecular
complexes. The negative delta energies associated with van der Waals
and polar interactions signify favorable contributions to system stability,
whereas the positive delta energies for electrostatic Poisson-Boltzmann
energy and solvation-related terms suggest solvent effects inuencing
complex energetics (Lu and Chen, 2020).
Importantly, the biological relevance of the studied complexes ex-
tends beyond their structural and dynamic properties, as evidenced by
Fig. 4. Antidiabetic potential of P13 Peptide via Inhibition of carbohydrates hydrolytic enzymes; (1A) EC
50
α
-amylase inhibition; (1B) EC
50
α
-glucosidase inhibition.
Fig. 5. Suppression of kinases and hormones relevant to T2D by P13 Peptide. Signicant (p <0.05 or p <0.0001). Not signicant (p >0.05)
R. Kurniawan et al.
Future Foods 9 (2024) 100354
12
their pharmacological activities. The evaluation of ABTS inhibition,
α
-glucosidase, and
α
-amylase inhibition activities highlights the poten-
tial therapeutic applications of the investigated peptides, particularly in
combating oxidative stress and managing diabetes mellitus. The supe-
rior inhibitory efcacy of P13 compared to conventional drugs un-
derscores its promising pharmacological prole and therapeutic
potential in addressing metabolic disorders. In cellular and animal
models, food-derived antioxidant peptides activate endogenous antiox-
idant defense mechanisms while also lowering the generation of reactive
oxygen species (Zhu et al., 2022). A comparison of the ABTS inhibitory
activity between P13 and Trolox, the control, is suggested by the results
shown in Fig. 3. P13 was shown to have an EC
50
value of 53.49
μ
g/mL,
whereas the reference drug, Trolox, had an EC
50
value of 53.98
μ
g/mL.
In comparison to Trolox, P13 has a more effective ABTS inhibitory ac-
tion, as evidenced by its lower EC
50
value. This implies that P13 has
enhanced antioxidant qualities and is better able to scavenge ABTS
radicals. It is noteworthy that previous studies have only depicted
antioxidative peptides from algae protein hydrolysate (Sheih et al.,
2009). Peptides, or protein hydrolysates, are intriguing additives that
can improve the nutritional, functional, and organoleptic qualities of
food in addition to serving as a natural substitute for articial antioxi-
dants (Zhu et al., 2022).
It has been determined that food-derived peptides are able to inhibit
α
-amylase and
α
-glucosidase activities (Li et al., 2022). However, the
remaining challenge is to nd those peptides with high antidiabetic
properties. Using Acarbose and Metformin as controls, data from this
study suggests that P13
′
s
α
-amylase inhibitory action is more effective,
as shown by P13
′
s lower EC
50
value (60.38
μ
g/mL <62.81
μ
g/mL and
60.38
μ
g/mL <69.88
μ
g/mL). Comparing P13
′
s
α
-glucosidase inhibitory
activity to Acarbose and Metformin, similar results were found (57.85
μ
g/mL <59.35
μ
g/mL and 57.85
μ
g/mL <66.35
μ
g/mL), indicating
better antidiabetic qualities. These ndings indicate that P13 inhibits
both
α
-amylase and
α
-glucosidase activities more potently than the
traditional medications Metformin and Acarbose. As highlighted by
(Leong and Chang, 2024), peptides and proteins from algae are a
prospective source of bioactive components. Their bioactivities are
inuenced by the presence of hydrophobic and/or aromatic amino
acids, which potentially inuence the anti-diabetic properties of P13.
Furthermore, peptides with low molecular weights often have higher
bioactivities.
The broblast 3T3-L1 data of cells treated with P13 showed the
modulation of key protein expressions associated with Type 2 Diabetes
Mellitus (T2D) which further emphasizes the potential therapeutic
relevance of this peptide in mitigating the pathological mechanisms
underlying the disease. The reduced expression levels of most of the
quantied T2D-related proteins by P13 mirrored those of established
antidiabetic medication Metformin used a control. The decrease
expression of c-Jun N-terminal kinase 1(JNK1) family by P13 is prom-
ising Obesity-associated chronic inammation triggers c-Jun N-terminal
kinases (JNK) family and related pathways, which reduce insulin target
cell sensitivity, inhibit insulin signalling, and affect glucose and lipid
metabolism (Feng et al., 2020). The decreased expression of
MAPK8/JNK1 by P13 can lead to reduced risk of T2D-related metabolic
syndrome (Fig. 7). Another noticeable change is the suppression of
expression of PPARGC1A by P13 Peptide (Fig. 7). The synthesis of ATP
and mitochondrial activity are prerequisites for insulin release in
pancreatic islets. The master regulator of mitochondrial genes, peroxi-
some proliferator-activated receptor gamma coactivator-1 alpha (pro-
tein PGC-1
α
; gene PPARGC1A), is a transcriptional coactivator. In T2D
patients’ muscle, its expression is reduced and linked to poor oxidative
phosphorylation. In islets from type 2 diabetes patients, there was a 90
% (p <0.005) decrease in PPARGC1A mRNA expression, which was
linked with a decrease in insulin production. After siRNA was used to
downregulate PPARGC1A expression in human islets, insulin secretion
was 41 % (p ≤0. 01) lower (Ling et al., 2008). These ndings high-
lighted that PPARGC1A is important in human islet insulin secretion,
which is strongly correlated with the incidence of T2D and P13 peptide
is potentially acts as their novel inhibitor.
The expressions of Ghrelin, GLP-1, and CPT-1 were also reduced by
P13. Additionally, data point to ghrelin possibly having a considerably
Fig. 6. Cell Viability of 3T3-L1 cell line by P13 treatments. Signicant (p <0.05 or p <0.0001). Not signicant (p >0.05).
R. Kurniawan et al.
Future Foods 9 (2024) 100354
13
larger function in glucose metabolism and energy balance. According to
certain literature data, ghrelin signalling has a physiological role in
controlling body fat and energy storage. Through its effect on the
pancreatic islets of Langerhans, ghrelin also appears to be crucial in
modulating glucose metabolism, making it a viable therapeutic target to
explore in studies of antidiabetic molecules (Sovetkina et al., 2020).
GLP-1, an incretin hormone, plays a pivotal role in diabetes manage-
ment by normalizing fasting plasma glucose levels, inhibiting pancreatic
glucagon secretion, inducing satiety, slowing gastric emptying, and
inducing weight loss. Its therapeutic use, particularly through GLP-1
receptor agonists, is recommended by the American Diabetes Associa-
tion for diabetic patients with atherosclerotic cardiovascular disease or
chronic kidney disease after uncontrolled metformin treatment, despite
concerns regarding adverse effects such as cancer, systematic compli-
cations, and hypoglycemia, especially in combined treatments and
certain individuals (Ja’arah et al., 2021). Therefore, lowered GLP-1
expressions will have similar effects as GLP-1 receptor agonists, which
is benecial in T2D conditions. Meanwhile, Carnitine palmitoyl-
transferase 1 (CPT-1) plays a crucial role in diabetes through fatty acid
beta-oxidation. It facilitates the transport of long-chain fatty acids into
the mitochondria, where beta-oxidation occurs, producing energy
(Schlaepfer and Joshi, 2020). Therefore, lowered CPT-1 expression will
suppress energy production while also maintaining balanced blood
glucose and energy levels.
The overall possible mechanism of P13 in lowering markers of T2D is
summarized in Fig. 7.. In conclusion, the integrative approach employed
in this study, combining structural, dynamic, energetic, and pharma-
cological analyses, provides a holistic understanding of the molecular
complexes under investigation and underscores their potential as ther-
apeutic agents in addressing diverse biomedical challenges. The insights
gained from this study pave the way for future research endeavors aimed
at harnessing the therapeutic potential of bioactive peptides derived
from algal sources for the development of novel pharmacological
interventions.
5. Conclusions
In a comprehensive and systematic manner, potential marine algal
peptides with inhibitory activity against diabetes-related receptors at
the molecular level were identied through an in silico pharma-
coinformatics approach. One potential peptide with the best overall
receptor inhibition activity is FDGIP (P13; Phe-Asp-Gly-Ile-Pro), as
demonstrated by network pharmacology, molecular docking, and mo-
lecular dynamics simulation, along with a predicted safe toxicity prole
for consumption. Interestingly, in vitro conrmation studies concluded
that apart from scavenging free radicals or acting as an antioxidant
agent, P13 peptide also exhibits inhibition activity against MAPK8-
JNK1/PPARGC1A/Ghrelin/GLP-1/CPT-1, accompanied by signicant
suppression of 3T3-L1 preadipocyte cell line compared to the metfor-
min. Moreover, P13 showed satisfactory inhibition against sugar meta-
bolic enzymes (
α
-Amylase and
α
-Glucosidase). Therefore, P13 emerges
as a peptide with anti-diabetic activity suitable for incorporation into
the development of functional foods to combat T2D, with further studies
warranted in vivo and human clinical trials based on the dosages derived
from this study.
CRediT authorship contribution statement
Rudy Kurniawan: Writing – original draft, Resources, Project
administration, Methodology, Investigation, Formal analysis, Data
Fig. 7. Possible-Biomechanism of peptide P13 (FDGIP) in controlling markers of T2D Related-Metabolic Syndrome. Created with BioRender.com Premium License by
Fahrul Nurkolis.
R. Kurniawan et al.
Future Foods 9 (2024) 100354
14
curation, Conceptualization. Nurpudji Astuti Taslim: Writing – review
& editing, Writing – original draft, Validation, Supervision, Project
administration, Methodology, Investigation, Conceptualization. Har-
dinsyah Hardinsyah: Writing – review & editing, Supervision,
Conceptualization. Andi Yasmin Syauki: Writing – review & editing.
Irfan Idris: Writing – review & editing. Andi Makbul Aman: Writing –
review & editing. Happy Kurnia Permatasari: Writing – original draft,
Validation, Supervision, Formal analysis. Elvan Wiyarta: Writing –
original draft, Formal analysis, Data curation. Reggie Surya: Writing –
review & editing. Nelly Mayulu: Writing – review & editing, Validation.
Purnawan Pontana Putra: Writing – original draft, Software. Ray-
mond Rubianto Tjandrawinata: Writing – review & editing, Valida-
tion, Supervision. Trina Ekawati Tallei: Writing – review & editing,
Validation, Supervision, Methodology. Bonglee Kim: Writing – review
& editing, Validation, Supervision. Apollinaire Tsopmo: Writing – re-
view & editing, Writing – original draft, Supervision. Fahrul Nurkolis:
Writing – review & editing, Writing – original draft, Visualization, Su-
pervision, Software, Resources, Project administration, Methodology,
Investigation, Formal analysis, Data curation, Conceptualization.
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Data availability
Data will be made available on request.
Ethical statement - studies in humans and animals
The study in this article does not involve human subjects and nor is it
research involving animals.
Funding
This research received no external funding.
Data availability statement
The datasets presented in this study can be requested from the cor-
responding author or FN.
Acknowledgments
The authors would like to greet and thank all contributors who have
provided input and motivation in conducting the research reported in
this article.
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