Biophysical Journal,
Год журнала:
2024,
Номер
123(17), С. 2790 - 2806
Опубликована: Фев. 1, 2024
De
novo
peptide
design
is
a
new
frontier
that
has
broad
application
potential
in
the
biological
and
biomedical
fields.
Most
existing
models
for
de
are
largely
based
on
sequence
homology
can
be
restricted
evolutionarily
derived
protein
sequences
lack
physicochemical
context
essential
folding.
Generative
machine
learning
promising
way
to
synthesize
theoretical
data
on,
but
unique
from,
observable
universe.
In
this
study,
we
created
tested
custom
generative
adversarial
network
intended
fold
into
β-hairpin
secondary
structure.
This
deep
neural
model
designed
establish
preliminary
foundation
of
approach
conformational
properties
20
canonical
amino
acids,
example,
hydrophobicity
residue
volume,
using
extant
structure-specific
from
PDB.
The
beta
robustly
distinguishes
structures
β
hairpin
α
helix
intrinsically
disordered
peptides
with
an
accuracy
up
96%
generates
artificial
minimum
identities
around
31%
50%
when
compared
against
current
NCBI
PDB
nonredundant
databases,
respectively.
These
results
highlight
specifically
anchored
by
property
features
acids
expand
sequence-to-structure
landscape
proteins
beyond
evolutionary
limits.
Journal of Pharmaceutical Analysis,
Год журнала:
2024,
Номер
15(1), С. 101046 - 101046
Опубликована: Июль 18, 2024
Natural
antimicrobial
peptides
(AMPs)
are
promising
candidates
for
the
development
of
a
new
generation
antimicrobials
to
combat
antibiotic-resistant
pathogens.
They
have
found
extensive
applications
in
fields
medicine,
food,
and
agriculture.
However,
efficiently
screening
AMPs
from
natural
sources
poses
several
challenges,
including
low
efficiency
high
antibiotic
resistance.
This
review
focuses
on
action
mechanisms
AMPs,
both
through
membrane
non-membrane
routes.
We
thoroughly
examine
various
highly
efficient
AMP
methods,
whole-bacterial
adsorption
binding,
cell
chromatography
(CMC),
phospholipid
membrane-mediated
capillary
electrophoresis
(CE),
colorimetric
assays,
thin
layer
(TLC),
fluorescence-based
screening,
genetic
sequencing-based
analysis,
computational
mining
databases,
virtual
methods.
Additionally,
we
discuss
potential
developmental
enhancing
discovery.
provides
comprehensive
framework
identifying
within
complex
product
systems.
International Journal of Molecular Sciences,
Год журнала:
2024,
Номер
25(3), С. 1391 - 1391
Опубликована: Янв. 23, 2024
Bioactive
peptides,
specific
protein
fragments
with
positive
health
effects,
are
gaining
traction
in
drug
development
for
advantages
like
enhanced
penetration,
low
toxicity,
and
rapid
clearance.
This
comprehensive
review
navigates
the
intricate
landscape
of
peptide
science,
covering
discovery
to
functional
characterization.
Beginning
a
peptidomic
exploration
natural
sources,
emphasizes
search
novel
peptides.
Extraction
approaches,
including
enzymatic
hydrolysis,
microbial
fermentation,
specialized
methods
disulfide-linked
extensively
covered.
Mass
spectrometric
analysis
techniques
data
acquisition
identification,
such
as
liquid
chromatography,
capillary
electrophoresis,
untargeted
analysis,
bioinformatics,
thoroughly
outlined.
The
bioactivity
incorporates
various
methodologies,
from
vitro
assays
silico
techniques,
advanced
approaches
phage
display
cell-based
assays.
also
discusses
structure–activity
relationship
context
antimicrobial
peptides
(AMPs),
ACE-inhibitory
(ACEs),
antioxidative
(AOPs).
Concluding
key
findings
future
research
directions,
this
interdisciplinary
serves
reference,
offering
holistic
understanding
their
potential
therapeutic
applications.
ACM Computing Surveys,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 21, 2025
Deep
learning
tools,
especially
deep
generative
models
(DGMs),
provide
opportunities
to
accelerate
and
simplify
the
design
of
drugs.
As
drug
candidates,
peptides
are
superior
other
biomolecules
because
they
combine
potency,
selectivity,
low
toxicity.
This
review
examines
fundamental
aspects
current
DGMs
for
designing
therapeutic
peptide
sequences.
First,
relevant
databases
in
this
field
introduced.
Next,
situation
data
representation
where
it
can
be
optimized
discussed.
Then,
after
introducing
basic
principles
variants
diverse
DGM
algorithms,
applications
these
methods
optimize
stated.
Finally,
we
present
several
challenges
devising
a
powerful
model
that
meet
requirements
different
biological
properties
peptides,
as
well
future
research
directions
address
challenges.
Frontiers in Bioinformatics,
Год журнала:
2023,
Номер
3
Опубликована: Июль 13, 2023
Antimicrobial
peptides
(AMPs)
are
components
of
natural
immunity
against
invading
pathogens.
They
polymers
that
fold
into
a
variety
three-dimensional
structures,
enabling
their
function,
with
an
underlying
sequence
is
best
represented
in
non-flat
space.
The
structural
data
AMPs
exhibits
non-Euclidean
characteristics,
which
means
certain
properties,
e.g.,
differential
manifolds,
common
system
coordinates,
vector
space
structure,
or
translation-equivariance,
along
basic
operations
like
convolution,
not
distinctly
established.
Geometric
deep
learning
(GDL)
refers
to
category
machine
methods
utilize
neural
models
process
and
analyze
settings,
such
as
graphs
manifolds.
This
emerging
field
seeks
expand
the
use
structured
these
domains.
review
provides
detailed
summary
latest
developments
designing
predicting
utilizing
GDL
techniques
also
discusses
both
current
research
gaps
future
directions
field.
Journal of Chemical Information and Modeling,
Год журнала:
2024,
Номер
64(13), С. 4941 - 4957
Опубликована: Июнь 14, 2024
Anticancer
peptides
(ACPs)
play
a
vital
role
in
selectively
targeting
and
eliminating
cancer
cells.
Evaluating
comparing
predictions
from
various
machine
learning
(ML)
deep
(DL)
techniques
is
challenging
but
crucial
for
anticancer
drug
research.
We
conducted
comprehensive
analysis
of
15
ML
10
DL
models,
including
the
models
released
after
2022,
found
that
support
vector
machines
(SVMs)
with
feature
combination
selection
significantly
enhance
overall
performance.
especially
convolutional
neural
networks
(CNNs)
light
gradient
boosting
(LGBM)
based
approaches,
demonstrate
improved
characterization.
Assessment
using
new
test
data
set
(ACP10)
identifies
ACPred,
MLACP
2.0,
AI4ACP,
mACPred,
AntiCP2.0_AAC
as
successive
optimal
predictors,
showcasing
robust
Our
review
underscores
current
prediction
tool
limitations
advocates
an
omnidirectional
ACP
framework
to
propel
ongoing
Heliyon,
Год журнала:
2024,
Номер
10(22), С. e40265 - e40265
Опубликована: Ноя. 1, 2024
Due
to
the
spread
of
antibiotic
resistance,
global
attention
is
focused
on
its
inhibition
and
expansion
effective
medicinal
compounds.
The
novel
functional
properties
peptides
have
opened
up
new
horizons
in
personalized
medicine.
With
artificial
intelligence
methods
combined
with
therapeutic
peptide
products,
pharmaceuticals
biotechnology
advance
drug
development
rapidly
reduce
costs.
Short-chain
inhibit
a
wide
range
pathogens
great
potential
for
targeting
diseases.
To
address
challenges
synthesis
sustainability,
methods,
namely
machine
learning,
must
be
integrated
into
their
production.
Learning
can
use
complicated
computations
select
active
toxic
compounds
metabolic
activity.
Through
this
comprehensive
review,
we
investigated
method
as
tool
finding
peptide-based
drugs
providing
more
accurate
analysis
through
introduction
predictable
databases
selection
development.
Signal Transduction and Targeted Therapy,
Год журнала:
2025,
Номер
10(1)
Опубликована: Март 5, 2025
The
successful
approval
of
peptide-based
drugs
can
be
attributed
to
a
collaborative
effort
across
multiple
disciplines.
integration
novel
drug
design
and
synthesis
techniques,
display
library
technology,
delivery
systems,
bioengineering
advancements,
artificial
intelligence
have
significantly
expedited
the
development
groundbreaking
drugs,
effectively
addressing
obstacles
associated
with
their
character,
such
as
rapid
clearance
degradation,
necessitating
subcutaneous
injection
leading
increasing
patient
discomfort,
ultimately
advancing
translational
research
efforts.
Peptides
are
presently
employed
in
management
diagnosis
diverse
array
medical
conditions,
diabetes
mellitus,
weight
loss,
oncology,
rare
diseases,
additionally
garnering
interest
facilitating
targeted
platforms
advancement
vaccines.
This
paper
provides
an
overview
present
market
clinical
trial
progress
therapeutics,
platforms,
It
examines
key
areas
through
literature
analysis
emphasizes
structural
modification
principles
well
recent
advancements
screening,
design,
technologies.
accelerated
including
peptide-drug
complexes,
new
vaccines,
innovative
diagnostic
reagents,
has
potential
promote
era
precise
customization
disease
therapeutic
schedule.
