Isolation, Virtual Screening, and Evaluation of Hazelnut-Derived Immunoactive Peptides for the Inhibition of SARS-CoV-2 Main Protease
Journal of Agricultural and Food Chemistry,
Год журнала:
2024,
Номер
72(20), С. 11561 - 11576
Опубликована: Май 13, 2024
The
aim
of
this
study
is
to
validate
the
activity
hazelnut
(Corylus
avellana
L.)-derived
immunoactive
peptides
inhibiting
main
protease
(Mpro)
SARS-CoV-2
and
further
unveil
their
interaction
mechanism
using
in
vitro
assays,
molecular
dynamics
(MD)
simulations,
binding
free
energy
calculations.
In
general,
enzymatic
hydrolysis
components,
especially
weight
<
3
kDa,
possess
good
immune
as
measured
by
proliferation
ability
mouse
splenic
lymphocytes
phagocytic
peritoneal
macrophages.
Over
866
unique
peptide
sequences
were
isolated,
purified,
then
identified
nanohigh-performance
liquid
chromatography/tandem
mass
spectrometry
(NANO-HPLC-MS/MS)
from
protein
hydrolysates,
but
Trp-Trp-Asn-Leu-Asn
(WWNLN)
Trp-Ala-Val-Leu-Lys
(WAVLK)
particular
are
found
increase
cell
viability
capacity
RAW264.7
macrophages
well
promote
secretion
cytokines
nitric
oxide
(NO),
tumor
necrosis
factor-α
(TNF-α),
interleukin-1β
(IL-1β).
Fluorescence
resonance
transfer
assay
elucidated
that
WWNLN
WAVLK
exhibit
excellent
inhibitory
potency
against
Mpro,
with
IC50
values
6.695
16.750
μM,
respectively.
Classical
all-atom
MD
simulations
show
hydrogen
bonds
play
a
pivotal
role
stabilizing
complex
conformation
protein–peptide
interaction.
Molecular
Mechanics/Generalized
Born
Surface
Area
(MM/GBSA)
calculation
indicates
has
lower
Mpro
than
WAVLK.
Furthermore,
adsorption,
distribution,
metabolism,
excretion,
toxicity
(ADMET)
predictions
illustrate
favorable
drug-likeness
pharmacokinetic
properties
compared
This
provides
new
understanding
immunomodulatory
hydrolysates
sheds
light
on
inhibitors
targeting
Mpro.
Язык: Английский
Large-scale Deep Learning Identifies the Antiviral Potential of PKI-179 and MTI-31 Against Coronaviruses
Antiviral Research,
Год журнала:
2024,
Номер
231, С. 106012 - 106012
Опубликована: Сен. 25, 2024
Язык: Английский
Hybrid intelligence for environmental pollution: biodegradability assessment of organic compounds through multimodal integration of graph attention networks and QSAR models
Environmental Science Processes & Impacts,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 1, 2025
Computational
methods
are
crucial
for
assessing
chemical
biodegradability,
given
their
significant
impact
on
both
environmental
and
human
health.
Organic
compounds
that
not
biodegradable
can
persist
in
the
environment,
contributing
to
pollution.
Our
novel
approach
leverages
graph
attention
networks
(GATs)
incorporates
node
edge
attributes
biodegradability
prediction.
Quantitative
Structure-Activity
Relationship
(QSAR)
models
using
two-dimensional
descriptors
alongside
weighted
average
stacking
approaches
were
employed
generate
ensemble
models.
The
GAT
demonstrated
a
stable
function
generally
higher
specificity
validation
set
compared
convolutional
network,
although
definitive
superiority
is
challenging
establish
owing
overlapping
standard
deviations.
However,
sensitivities
tended
decrease
with
potential
performance
overlap
interval
intersection.
Ensemble
learning
enhanced
several
metrics
individual
base
models,
combination
of
extreme
Gradient
Boosting
achieving
highest
precision
specificity.
Combining
random
forest
may
be
preferable
accurately
predicting
molecules,
whereas
suitable
prioritizing
correct
classification
nonbiodegradable
substances.
Important
descriptors,
such
as
SpMax1_Bh(m)
SAscore,
identified
at
least
two
QSAR
Despite
inherent
complexities,
ease
implementation
depends
factors
data
availability,
domain
knowledge.
Assessing
organic
essential
reducing
impact,
risks,
ensuring
regulatory
compliance,
promoting
sustainable
development,
supporting
effective
pollution
remediation.
It
assists
making
informed
decisions
about
use,
waste
management,
protection.
Язык: Английский
Discovery of novel VEGFR2 inhibitors against non-small cell lung cancer based on fingerprint-enhanced graph attention convolutional network
Journal of Translational Medicine,
Год журнала:
2024,
Номер
22(1)
Опубликована: Дек. 3, 2024
Despite
the
proven
inhibitory
effects
of
drugs
targeting
vascular
endothelial
growth
factor
receptor
2
(VEGFR2)
on
solid
tumors,
including
non-small
cell
lung
cancer
(NSCLC),
development
anti-NSCLC
solely
VEGFR2
still
faces
risks
such
as
off-target
and
limited
efficacy.
This
study
aims
to
develop
a
novel
fingerprint-enhanced
graph
attention
convolutional
network
(FnGATGCN)
model
for
predicting
activity
drugs.
Employing
multimodal
fusion
strategy,
integrates
feature
extraction
layer
that
comprises
molecular
fingerprint
extraction.
The
performance
evaluation
results
indicate
exhibits
high
accuracy
stability
in
activity.
Moreover,
we
explored
relationship
between
features
biological
through
visualization
analysis,
thus
improving
interpretability
approach.
Utilizing
this
model,
screened
ZINC
database
conducted
high-precision
docking,
leading
identification
11
potential
active
molecules.
Subsequently,
dynamics
simulations
free
energy
calculations
were
performed.
demonstrate
all
aforementioned
molecules
can
stably
bind
under
dynamic
conditions.
Among
short-listed
compounds,
top
six
exhibited
satisfactory
against
A549
cells.
Especially,
compound
Z-3
displayed
with
IC50
values
0.88
μM,
anti-proliferative
cells
4.23
±
0.45
μM.
approach
combines
advantages
target-based
phenotype-based
screening,
facilitating
rapid
efficient
candidate
compounds
dual
lines.
It
provides
new
insights
methods
Furthermore,
further
tests
revealed
Z1-Z3
Z6
manifested
relatively
strong
antiproliferative
activities
NCI-H23
NCI-H460,
low
toxicity
towards
GES-1.
hit
promising
candidates
inhibitors
NSCLC.
Язык: Английский