Briefings in Bioinformatics,
Год журнала:
2023,
Номер
24(5)
Опубликована: Июнь 28, 2023
Most
life
activities
in
organisms
are
regulated
through
protein
complexes,
which
mainly
controlled
via
Protein-Protein
Interactions
(PPIs).
Discovering
new
interactions
between
proteins
and
revealing
their
biological
functions
of
great
significance
for
understanding
the
molecular
mechanisms
processes
identifying
potential
targets
drug
discovery.
Current
experimental
methods
only
capture
stable
interactions,
lead
to
limited
coverage.
In
addition,
expensive
cost
time
consuming
also
obvious
shortcomings.
recent
years,
various
computational
have
been
successfully
developed
predicting
PPIs
based
on
homology,
primary
sequences
or
gene
ontology
information.
Computational
efficiency
data
complexity
still
main
bottlenecks
algorithm
generalization.
this
study,
we
proposed
a
novel
framework,
HNSPPI,
predict
PPIs.
As
hybrid
supervised
learning
model,
HNSPPI
comprehensively
characterizes
intrinsic
relationship
two
by
integrating
amino
acid
sequence
information
connection
properties
PPI
network.
The
results
show
that
works
very
well
six
benchmark
datasets.
Moreover,
comparison
analysis
proved
our
model
significantly
outperforms
other
five
existing
algorithms.
Finally,
used
explore
SARS-CoV-2-Human
interaction
system
found
several
regulations.
summary,
is
promising
from
known
data.
Current Research in Biotechnology,
Год журнала:
2023,
Номер
7, С. 100164 - 100164
Опубликована: Ноя. 22, 2023
The
medicine
and
healthcare
sector
has
been
evolving
advancing
very
fast.
advancement
initiated
shaped
by
the
applications
of
data-driven,
robust,
efficient
machine
learning
(ML)
to
deep
(DL)
technologies.
ML
in
medical
is
developing
quickly,
causing
rapid
progress,
reshaping
medicine,
improving
clinician
patient
experiences.
technologies
evolved
into
data-hungry
DL
approaches,
which
are
more
robust
dealing
with
data.
This
article
reviews
some
critical
data-driven
aspects
intelligence
field.
In
this
direction,
illustrated
recent
progress
science
using
two
categories:
firstly,
development
data
uses
and,
secondly,
Chabot
particularly
on
ChatGPT.
Here,
we
discuss
ML,
DL,
transition
requirements
from
DL.
To
science,
illustrate
prospective
studies
image
data,
newly
interpretation
EMR
or
EHR,
big
personalized
dataset
shifts
artificial
(AI).
Simultaneously,
recently
developed
DL-enabled
ChatGPT
technology.
Finally,
summarize
broad
role
significant
challenges
for
implementing
healthcare.
overview
paradigm
shift
will
benefit
researchers
immensely.
Molecules,
Год журнала:
2024,
Номер
29(4), С. 903 - 903
Опубликована: Фев. 18, 2024
Drug
discovery
plays
a
critical
role
in
advancing
human
health
by
developing
new
medications
and
treatments
to
combat
diseases.
How
accelerate
the
pace
reduce
costs
of
drug
has
long
been
key
concern
for
pharmaceutical
industry.
Fortunately,
leveraging
advanced
algorithms,
computational
power
biological
big
data,
artificial
intelligence
(AI)
technology,
especially
machine
learning
(ML),
holds
promise
making
hunt
drugs
more
efficient.
Recently,
Transformer-based
models
that
have
achieved
revolutionary
breakthroughs
natural
language
processing
sparked
era
their
applications
discovery.
Herein,
we
introduce
latest
ML
discovery,
highlight
potential
models,
discuss
future
prospects
challenges
field.
ACS Catalysis,
Год журнала:
2023,
Номер
13(21), С. 14454 - 14469
Опубликована: Окт. 26, 2023
Emerging
computational
tools
promise
to
revolutionize
protein
engineering
for
biocatalytic
applications
and
accelerate
the
development
timelines
previously
needed
optimize
an
enzyme
its
more
efficient
variant.
For
over
a
decade,
benefits
of
predictive
algorithms
have
helped
scientists
engineers
navigate
complexity
functional
sequence
space.
More
recently,
spurred
by
dramatic
advances
in
underlying
tools,
faster,
cheaper,
accurate
identification,
characterization,
has
catapulted
terms
such
as
artificial
intelligence
machine
learning
must-have
vocabulary
field.
This
Perspective
aims
showcase
current
status
pharmaceutical
industry
also
discuss
celebrate
innovative
approaches
science
highlighting
their
potential
selected
recent
developments
offering
thoughts
on
future
opportunities
biocatalysis.
It
critically
assesses
technology's
limitations,
unanswered
questions,
unmet
challenges.
Critical Reviews in Food Science and Nutrition,
Год журнала:
2023,
Номер
65(4), С. 667 - 694
Опубликована: Ноя. 15, 2023
Even
though
plant
proteins
are
more
plentiful
and
affordable
than
animal
in
comparison,
direct
usage
of
plant-based
(PBPs)
is
still
limited
because
PBPs
fed
to
animals
as
feed
produce
animal-based
proteins.
Thus,
this
work
has
comprehensively
reviewed
the
effects
various
factors
such
pH,
temperature,
pressure,
ionic
strength
on
PBP
properties,
well
describes
protein
interactions,
extraction
methods
know
optimal
conditions
for
preparing
PBP-based
products
with
high
functional
properties
health
benefits.
According
cited
studies
current
work,
environmental
factors,
particularly
pH
significantly
affected
physicochemical
PBPs,
especially
solubility
was
76.0%
83.9%
at
=
2,
while
5.0
reduced
from
5.3%
9.6%,
emulsifying
ability
lowest
5.8
highest
8.0,
foaming
capacity
7.0.
Electrostatic
interactions
main
way
which
can
be
used
create
protein/polysaccharide
complexes
food
industrial
purposes.
The
yield
reached
up
86-95%
using
sustainable
efficient
routes,
including
enzymatic,
ultrasound-,
microwave-,
pulsed
electric
field-,
high-pressure-assisted
extraction.
Nondairy
alternative
products,
yogurt,
3D
printing
meat
analogs,
synthesis
nanoparticles,
bioplastics
packaging
films
best
available
PBPs-based
products.
Moreover,
those
that
contain
pigments
their
showed
good
bioactivities,
antioxidants,
antidiabetic,
antimicrobial.
Journal of Chemical Information and Modeling,
Год журнала:
2023,
Номер
63(15), С. 4505 - 4532
Опубликована: Июль 19, 2023
The
field
of
computational
chemistry
has
seen
a
significant
increase
in
the
integration
machine
learning
concepts
and
algorithms.
In
this
Perspective,
we
surveyed
179
open-source
software
projects,
with
corresponding
peer-reviewed
papers
published
within
last
5
years,
to
better
understand
topics
being
investigated
by
approaches.
For
each
project,
provide
short
description,
link
code,
accompanying
license
type,
whether
training
data
resulting
models
are
made
publicly
available.
Based
on
those
deposited
GitHub
repositories,
most
popular
employed
Python
libraries
identified.
We
hope
that
survey
will
serve
as
resource
learn
about
or
specific
architectures
thereof
identifying
accessible
codes
topic
basis.
To
end,
also
include
for
generating
fundamental
learning.
our
observations
considering
three
pillars
collaborative
work,
open
data,
source
(code),
models,
some
suggestions
community.
Computational and Structural Biotechnology Journal,
Год журнала:
2023,
Номер
21, С. 1487 - 1497
Опубликована: Янв. 1, 2023
One
of
the
key
features
intrinsically
disordered
regions
(IDRs)
is
their
ability
to
interact
with
a
broad
range
partner
molecules.
Multiple
types
interacting
IDRs
were
identified
including
molecular
recognition
fragments
(MoRFs),
short
linear
sequence
motifs
(SLiMs),
and
protein-,
nucleic
acids-
lipid-binding
regions.
Prediction
binding
in
protein
sequences
gaining
momentum
recent
years.
We
survey
38
predictors
that
target
interactions
diverse
set
partners,
such
as
peptides,
proteins,
RNA,
DNA
lipids.
offer
historical
perspective
highlight
events
fueled
efforts
develop
these
methods.
These
tools
rely
on
predictive
architectures
include
scoring
functions,
regular
expressions,
traditional
deep
machine
learning
meta-models.
Recent
focus
development
neural
network-based
extending
coverage
IDRs.
analyze
availability
methods
show
providing
implementations
webservers
results
much
higher
rates
citations/use.
also
make
several
recommendations
take
advantage
modern
network
architectures,
bundle
predictions
multiple
different
IDRs,
work
algorithms
model
structures
resulting
complexes.
Computational and Structural Biotechnology Journal,
Год журнала:
2023,
Номер
21, С. 1324 - 1348
Опубликована: Янв. 1, 2023
Proteins
mainly
perform
their
functions
by
interacting
with
other
proteins.
Protein–protein
interactions
underpin
various
biological
activities
such
as
metabolic
cycles,
signal
transduction,
and
immune
response.
However,
due
to
the
sheer
number
of
proteins,
experimental
methods
for
finding
non-interacting
protein
pairs
are
time-consuming
costly.
We
therefore
developed
ProtInteract
framework
predict
protein–protein
interaction.
comprises
two
components:
first,
a
novel
autoencoder
architecture
that
encodes
each
protein's
primary
structure
lower-dimensional
vector
while
preserving
its
underlying
sequence
attributes.
This
leads
faster
training
second
network,
deep
convolutional
neural
network
(CNN)
receives
encoded
proteins
predicts
interaction
under
three
different
scenarios.
In
scenario,
CNN
class
given
pair.
Each
indicates
ranges
confidence
scores
corresponding
probability
whether
predicted
occurs
or
not.
The
proposed
features
significantly
low
computational
complexity
relatively
fast
contributions
this
work
twofold.
First,
assimilates
into
pseudo-time
series.
Therefore,
we
leverage
nature
time
series
physicochemical
properties
encode
amino
acid
space.
approach
enables
extracting
highly
informative
attributes
reducing
complexity.
Second,
utilises
information
identify
based
on
configuration.
Our
results
suggest
performs
high
accuracy
efficiency
in
predicting
protein-protein
interactions.
Computational and Structural Biotechnology Journal,
Год журнала:
2024,
Номер
23, С. 1214 - 1225
Опубликована: Март 15, 2024
Rapid
advancements
in
protein
sequencing
technology
have
resulted
gaps
between
proteins
with
identified
sequences
and
those
mapped
structures.
Although
sequence-based
predictions
offer
insights,
they
can
be
incomplete
due
to
the
absence
of
structural
details.
Conversely,
structure-based
methods
face
challenges
respect
newly
sequenced
proteins.
The
AlphaFold
Multimer
has
remarkable
accuracy
predicting
structure
complexes.
However,
it
cannot
distinguish
whether
input
interact.
Nonetheless,
by
analyzing
information
models
predicted
Multimer,
we
propose
a
highly
accurate
method
for
interactions.
This
study
focuses
on
use
deep
neural
networks,
specifically
analyze
complex
structures
Multimer.
By
transforming
atomic
coordinates
utilizing
sophisticated
image-processing
techniques,
vital
3D
details
were
extracted
from
Recognizing
significance
evaluating
residue
distances
interactions,
this
leveraged
image
recognition
approaches
integrating
Densely
Connected
Convolutional
Networks
(DenseNet)
Deep
Residual
Network
(ResNet)
within
convolutional
networks
analysis.
When
benchmarked
against
leading
protein-protein
interaction
prediction
methods,
such
as
SpeedPPI,
D-script,
DeepTrio,
PEPPI,
our
proposed
method,
named
SpatialPPI,
exhibited
notable
efficacy,
emphasizing
promising
role
spatial
processing
advancing
realm
biology.
SpatialPPI
code
is
available
at:
https://github.com/ohuelab/SpatialPPI.