Conotoxins: Classification, Prediction, and Future Directions in Bioinformatics
Rui Li,
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Junwen Yu,
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Dong-Xin Ye
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et al.
Toxins,
Journal Year:
2025,
Volume and Issue:
17(2), P. 78 - 78
Published: Feb. 9, 2025
Conotoxins,
a
diverse
family
of
disulfide-rich
peptides
derived
from
the
venom
Conus
species,
have
gained
prominence
in
biomedical
research
due
to
their
highly
specific
interactions
with
ion
channels,
receptors,
and
neurotransmitter
systems.
Their
pharmacological
properties
make
them
valuable
molecular
tools
promising
candidates
for
therapeutic
development.
However,
traditional
conotoxin
classification
functional
characterization
remain
labor-intensive,
necessitating
increasing
adoption
computational
approaches.
In
particular,
machine
learning
(ML)
techniques
facilitated
advancements
sequence-based
classification,
prediction,
de
novo
peptide
design.
This
review
explores
recent
progress
applying
ML
deep
(DL)
research,
comparing
key
databases,
feature
extraction
techniques,
models.
Additionally,
we
discuss
future
directions,
emphasizing
integration
multimodal
data
refinement
predictive
frameworks
enhance
discovery.
Language: Английский
PMPred-AE: a computational model for the detection and interpretation of pathological myopia based on artificial intelligence
Hongqi Zhang,
No information about this author
Muhammad Arif,
No information about this author
Maha A. Thafar
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et al.
Frontiers in Medicine,
Journal Year:
2025,
Volume and Issue:
12
Published: March 13, 2025
Introduction
Pathological
myopia
(PM)
is
a
serious
visual
impairment
that
may
lead
to
irreversible
damage
or
even
blindness.
Timely
diagnosis
and
effective
management
of
PM
are
great
significance.
Given
the
increasing
number
cases
worldwide,
there
an
urgent
need
develop
automated,
accurate,
highly
interpretable
diagnostic
technology.
Methods
We
proposed
computational
model
called
PMPred-AE
based
on
EfficientNetV2-L
with
attention
mechanism
optimization.
In
addition,
Gradient-weighted
class
activation
mapping
(Grad-CAM)
technology
was
used
provide
intuitive
interpretation
for
model’s
decision-making
process.
Results
The
experimental
results
demonstrated
achieved
excellent
performance
in
automatically
detecting
PM,
accuracies
98.50,
98.25,
97.25%
training,
validation,
test
datasets,
respectively.
can
focus
specific
areas
image
when
making
detection
decisions.
Discussion
developed
capable
reliably
providing
accurate
detection.
Grad-CAM
also
process
model.
This
approach
provides
healthcare
professionals
tool
AI
Language: Английский
StackAHTPs: An explainable antihypertensive peptides identifier based on heterogeneous features and stacked learning approach
IET Systems Biology,
Journal Year:
2025,
Volume and Issue:
19(1)
Published: Jan. 1, 2025
Abstract
Hypertension,
often
known
as
high
blood
pressure,
is
a
major
concern
to
millions
of
individuals
globally.
Recent
studies
have
demonstrated
the
significant
efficacy
naturally
derived
peptides
in
reducing
pressure.
Hypertension
one
risks
associated
with
cardiovascular
disorders
and
other
health
problems.
Naturally
sourced
bioactive
possessing
antihypertensive
properties
provide
considerable
potential
viable
substitutes
for
conventional
pharmaceutical
medications.
Currently,
thorough
examination
peptide
(AHTPs),
by
using
traditional
wet‐lab
methods
highly
expensive
labours.
Therefore,
in‐silico
approaches
especially
machine‐learning
(ML)
algorithms
are
favourable
due
saving
time
cost
discovery
AHTPs.
In
this
study,
novel
ML‐based
predictor,
called
StackAHTP
was
developed
predicting
accurate
AHTPs
from
sequence
only.
The
proposed
method,
utilise
two
types
feature
descriptors
Pseudo‐Amino
Acid
Composition
Dipeptide
encode
local
global
hidden
information
sequences.
Furthermore,
encoded
features
serially
merged
ranked
through
SHapley
Additive
explanations
(SHAP)
algorithm.
Then,
top
fed
into
three
different
ensemble
classifiers
(Bagging,
Boosting,
Stacking)
enhancing
prediction
performance
model.
StackAHTPs
method
achieved
superior
compare
ML
(AdaBoost,
XGBoost
Light
Gradient
Boosting
(LightGBM),
Bagging
Boosting)
on
10‐fold
cross
validation
independent
test.
experimental
outcomes
demonstrate
that
our
outperformed
existing
an
accuracy
92.25%
F1‐score
89.67%
test
non‐AHTPs.
authors
believe
research
will
remarkably
contribute
large‐scale
characterisation
accelerate
drug
process.
At
https://github.com/ali‐ghulam/StackAHTPs
you
may
find
datasets
used.
Language: Английский
Machine learning-based classification of viral membrane proteins
Grace-Mercure Bakanina Kissanga,
No information about this author
Sebu Aboma Temesgen,
No information about this author
Ahmad Basharat
No information about this author
et al.
Current Proteomics,
Journal Year:
2025,
Volume and Issue:
22(1), P. 100003 - 100003
Published: Feb. 1, 2025
Language: Английский
Integrating reduced amino acid with language models for prediction of protein thermostability
Qian Yan,
No information about this author
Yanrui Ding
No information about this author
Food Bioscience,
Journal Year:
2025,
Volume and Issue:
unknown, P. 106934 - 106934
Published: May 1, 2025
Language: Английский
EDS-Kcr: deep supervision based on large language model for identifying protein lysine crotonylation sites across multiple species
Hongqi Zhang,
No information about this author
Xinran Lin,
No information about this author
Yanting Wang
No information about this author
et al.
Briefings in Bioinformatics,
Journal Year:
2025,
Volume and Issue:
26(3)
Published: May 1, 2025
Abstract
With
the
rapid
advancement
of
proteomics,
post-translational
modifications,
particularly
lysine
crotonylation
(Kcr),
have
gained
significant
attention
in
basic
research,
drug
development,
and
disease
treatment.
However,
current
methods
for
identifying
these
modifications
are
often
complex,
costly,
time-consuming.
To
address
challenges,
we
proposed
EDS-Kcr,
a
novel
bioinformatics
tool
that
integrates
state-of-the-art
protein
language
model
ESM2
with
deep
supervision
to
improve
efficiency
accuracy
Kcr
site
prediction.
EDS-Kcr
demonstrated
outstanding
performance
across
various
species
datasets,
proving
its
applicability
wide
range
proteins,
including
those
from
humans,
plants,
animals,
microbes.
Compared
existing
prediction
models,
our
excelled
multiple
key
indicators,
showcasing
superior
predictive
power
robustness.
Furthermore,
enhanced
transparency
interpretability
through
visualization
techniques
mechanisms.
In
conclusion,
provides
an
efficient
reliable
suitable
diagnosis
development.
We
also
established
freely
accessible
web
server
at
http://eds-kcr.lin-group.cn/.
Language: Английский
Predicting cyclins based on key features and machine learning methods
Chengyan Wu,
No information about this author
Zhi‐Xue Xu,
No information about this author
Nan Li
No information about this author
et al.
Methods,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 1, 2024
Language: Английский
Empirical Comparison and Analysis of Artificial Intelligence-Based Methods for Identifying Phosphorylation Sites of SARS-CoV-2 Infection
Hongyan Lai,
No information about this author
Tao Zhu,
No information about this author
Sijia Xie
No information about this author
et al.
International Journal of Molecular Sciences,
Journal Year:
2024,
Volume and Issue:
25(24), P. 13674 - 13674
Published: Dec. 21, 2024
Severe
acute
respiratory
syndrome
coronavirus
2
(SARS-CoV-2)
is
a
member
of
the
large
family
with
high
infectivity
and
pathogenicity
primary
pathogen
causing
global
pandemic
disease
2019
(COVID-19).
Phosphorylation
major
type
protein
post-translational
modification
that
plays
an
essential
role
in
process
SARS-CoV-2–host
interactions.
The
precise
identification
phosphorylation
sites
host
cells
infected
SARS-CoV-2
will
be
great
importance
to
investigate
potential
antiviral
responses
mechanisms
exploit
novel
targets
for
therapeutic
development.
Numerous
computational
tools
have
been
developed
on
basis
phosphoproteomic
data
generated
by
mass
spectrometry-based
experimental
techniques,
which
can
accurately
ascertained
across
whole
SARS-CoV-2-infected
proteomes.
In
this
work,
we
comprehensively
reviewed
several
aspects
construction
strategies
availability
these
predictors,
including
benchmark
dataset
preparation,
feature
extraction
refinement
methods,
machine
learning
algorithms
deep
architectures,
model
evaluation
approaches
metrics,
publicly
available
web
servers
packages.
We
highlighted
compared
prediction
performance
each
tool
independent
serine/threonine
(S/T)
tyrosine
(Y)
datasets
discussed
overall
limitations
current
existing
predictors.
summary,
review
would
provide
pertinent
insights
into
exploitation
new
powerful
site
tools,
facilitate
localization
more
suitable
target
molecules
verification,
contribute
development
therapies.
Language: Английский