Journal of Biological Engineering,
Год журнала:
2023,
Номер
17(1)
Опубликована: Сен. 26, 2023
Mastitis
poses
a
major
threat
to
dairy
farms
globally;
it
results
in
reduced
milk
production,
increased
treatment
costs,
untimely
compromised
genetic
potential,
animal
deaths,
and
economic
losses.
Streptococcus
agalactiae
is
highly
virulent
bacteria
that
cause
mastitis.
The
administration
of
antibiotics
for
the
this
infection
not
advised
due
concerns
about
emergence
antibiotic
resistance
potential
adverse
effects
on
human
health.
Thus,
there
critical
need
identify
new
therapeutic
approaches
combat
One
promising
target
development
antibacterial
therapies
transmembrane
histidine
kinase
bacteria,
which
plays
key
role
signal
transduction
pathways,
secretion
systems,
virulence,
resistance.In
study,
we
aimed
novel
natural
compounds
can
inhibit
kinase.
To
achieve
goal,
conducted
virtual
screening
224,205
compounds,
selecting
top
ten
based
their
lowest
binding
energy
favorable
protein-ligand
interactions.
Furthermore,
molecular
docking
eight
selected
five
inhibitors
with
was
performed
evaluate
respect
top-screened
compounds.
We
also
analyzed
ADMET
properties
these
assess
drug-likeness.
two
(ZINC000085569031
ZINC000257435291)
(Tetracycline)
demonstrated
strong
affinity
were
subjected
dynamics
simulations
(100
ns),
free
landscape,
calculations
using
MM-PBSA
method.Our
suggest
have
serve
as
effective
be
utilized
veterinary
medicine
mastitis
after
further
validation
through
clinical
studies.
Nature Communications,
Год журнала:
2024,
Номер
15(1)
Опубликована: Март 27, 2024
Abstract
This
paper
presents
an
innovative
approach
for
predicting
the
relative
populations
of
protein
conformations
using
AlphaFold
2,
AI-powered
method
that
has
revolutionized
biology
by
enabling
accurate
prediction
structures.
While
2
shown
exceptional
accuracy
and
speed,
it
is
designed
to
predict
proteins’
ground
state
limited
in
its
ability
conformational
landscapes.
Here,
we
demonstrate
how
can
directly
different
subsampling
multiple
sequence
alignments.
We
tested
our
against
nuclear
magnetic
resonance
experiments
on
two
proteins
with
drastically
amounts
available
data,
Abl1
kinase
granulocyte-macrophage
colony-stimulating
factor,
predicted
changes
their
more
than
80%
accuracy.
Our
worked
best
when
used
qualitatively
effects
mutations
or
evolution
landscape
well-populated
states
proteins.
It
thus
offers
a
fast
cost-effective
way
at
even
single-point
mutation
resolution,
making
useful
tool
pharmacology,
analysis
experimental
results,
evolution.
Biomolecules,
Год журнала:
2024,
Номер
14(3), С. 339 - 339
Опубликована: Март 12, 2024
Recent
advancements
in
AI-driven
technologies,
particularly
protein
structure
prediction,
are
significantly
reshaping
the
landscape
of
drug
discovery
and
development.
This
review
focuses
on
question
how
these
technological
breakthroughs,
exemplified
by
AlphaFold2,
revolutionizing
our
understanding
function
changes
underlying
cancer
improve
approaches
to
counter
them.
By
enhancing
precision
speed
at
which
targets
identified
candidates
can
be
designed
optimized,
technologies
streamlining
entire
development
process.
We
explore
use
AlphaFold2
development,
scrutinizing
its
efficacy,
limitations,
potential
challenges.
also
compare
with
other
algorithms
like
ESMFold,
explaining
diverse
methodologies
employed
this
field
practical
effects
differences
for
application
specific
algorithms.
Additionally,
we
discuss
broader
applications
including
prediction
complex
structures
generative
design
novel
proteins.
Google
DeepMind
Technologies
Limited
(London,
United
Kingdom)
recently
released
its
new
version
of
the
biomolecular
structure
predictor
artificial
intelligence
(AI)
model
named
AlphaFold
3.
Superior
in
accuracy
and
more
powerful
than
predecessor
2,
this
innovation
has
astonished
world
with
capacity
speed.
It
takes
humans
years
to
determine
various
proteins
how
shape
works
receptors
but
3
predicts
same
seconds.
The
version's
utility
is
unimaginable
field
drug
discoveries,
vaccines,
enzymatic
processes,
determining
rate
effect
different
biological
processes.
uses
similar
machine
learning
deep
models
such
as
Gemini
(Google
Limited).
already
established
itself
a
turning
point
computational
biochemistry
development
along
receptor
modulation
development.
With
help
this,
researchers
will
gain
unparalleled
insights
into
structural
dynamics
their
interactions,
opening
up
avenues
for
scientists
doctors
exploit
benefit
patient.
integration
AI
like
3,
bolstered
by
rigorous
validation
against
high-standard
research
publications,
set
catalyze
further
innovations
offer
glimpse
future
biomedicine.
International Journal of Surgery,
Год журнала:
2024,
Номер
unknown
Опубликована: Март 19, 2024
Computer-aided
drug
design
(CADD)
is
a
technique
for
computing
ligand-receptor
interactions
and
involved
in
various
stages
of
development.
To
better
grasp
the
frontiers
hotspots
CADD,
we
conducted
review
analysis
through
bibliometrics.
The
emergence
of
Artificial
Intelligence
(AI)
in
drug
discovery
marks
a
pivotal
shift
pharmaceutical
research,
blending
sophisticated
computational
techniques
with
conventional
scientific
exploration
to
break
through
enduring
obstacles.
This
review
paper
elucidates
the
multifaceted
applications
AI
across
various
stages
development,
highlighting
significant
advancements
and
methodologies.
It
delves
into
AI's
instrumental
role
design,
polypharmacology,
chemical
synthesis,
repurposing,
prediction
properties
such
as
toxicity,
bioactivity,
physicochemical
characteristics.
Despite
promising
advancements,
also
addresses
challenges
limitations
encountered
field,
including
data
quality,
generalizability,
demands,
ethical
considerations.
By
offering
comprehensive
overview
discovery,
this
underscores
technology's
potential
significantly
enhance
while
acknowledging
hurdles
that
must
be
overcome
fully
realize
its
benefits.
Briefings in Bioinformatics,
Год журнала:
2024,
Номер
25(3)
Опубликована: Март 27, 2024
Abstract
In
this
review
article,
we
explore
the
transformative
impact
of
deep
learning
(DL)
on
structural
bioinformatics,
emphasizing
its
pivotal
role
in
a
scientific
revolution
driven
by
extensive
data,
accessible
toolkits
and
robust
computing
resources.
As
big
data
continue
to
advance,
DL
is
poised
become
an
integral
component
healthcare
biology,
revolutionizing
analytical
processes.
Our
comprehensive
provides
detailed
insights
into
DL,
featuring
specific
demonstrations
notable
applications
bioinformatics.
We
address
challenges
tailored
for
spotlight
recent
successes
bioinformatics
present
clear
exposition
DL—from
basic
shallow
neural
networks
advanced
models
such
as
convolution,
recurrent,
artificial
transformer
networks.
This
paper
discusses
emerging
use
understanding
biomolecular
structures,
anticipating
ongoing
developments
realm
The
AlphaFold
Protein
Structure
Database
(AFDB)
contains
more
than
214
million
predicted
protein
structures
composed
of
domains,
which
are
independently
folding
units
found
in
multiple
structural
and
functional
contexts.
Identifying
domains
can
enable
many
evolutionary
analyses
but
has
remained
challenging
because
the
sheer
scale
data.
Using
deep
learning
methods,
we
have
detected
classified
every
domain
AFDB,
producing
Encyclopedia
Domains.
We
nearly
365
over
100
be
by
sequence
covering
1
taxa.
Reassuringly,
77%
nonredundant
similar
to
known
superfamilies,
greatly
expanding
representation
their
space.
uncovered
10,000
new
interactions
between
superfamilies
thousands
folds
across
fold
space
continuum.
Expert Opinion on Drug Discovery,
Год журнала:
2023,
Номер
18(11), С. 1221 - 1230
Опубликована: Авг. 17, 2023
Macromolecular
X-ray
crystallography
and
cryo-EM
are
currently
the
primary
techniques
used
to
determine
three-dimensional
structures
of
proteins,
nucleic
acids,
viruses.
Structural
information
has
been
critical
drug
discovery
structural
bioinformatics.
The
integration
artificial
intelligence
(AI)
into
shown
great
promise
in
automating
accelerating
analysis
complex
data,
further
improving
efficiency
accuracy
structure
determination.
