Cellular and Molecular Life Sciences,
Год журнала:
2023,
Номер
80(4)
Опубликована: Март 23, 2023
ABCG46
of
the
legume
Medicago
truncatula
is
an
ABC-type
transporter
responsible
for
highly
selective
translocation
phenylpropanoids,
4-coumarate,
and
liquiritigenin,
over
plasma
membrane.
To
investigate
molecular
determinants
observed
substrate
selectivity,
we
applied
a
combination
phylogenetic
biochemical
analyses,
AlphaFold2
structure
prediction,
dynamics
simulations,
mutagenesis.
We
discovered
unusually
narrow
transient
access
path
to
central
cavity
MtABCG46
that
constitutes
initial
filter
phenylpropanoids
through
lipid
bilayer.
Furthermore,
identified
remote
residue
F562
as
pivotal
maintaining
stability
this
filter.
The
determination
individual
amino
acids
impact
transport
specialized
metabolites
may
provide
new
opportunities
associated
with
ABCGs
being
interest,
in
many
biological
scenarios.
Signal Transduction and Targeted Therapy,
Год журнала:
2023,
Номер
8(1)
Опубликована: Март 14, 2023
Abstract
AlphaFold2
(AF2)
is
an
artificial
intelligence
(AI)
system
developed
by
DeepMind
that
can
predict
three-dimensional
(3D)
structures
of
proteins
from
amino
acid
sequences
with
atomic-level
accuracy.
Protein
structure
prediction
one
the
most
challenging
problems
in
computational
biology
and
chemistry,
has
puzzled
scientists
for
50
years.
The
advent
AF2
presents
unprecedented
progress
protein
attracted
much
attention.
Subsequent
release
more
than
200
million
predicted
further
aroused
great
enthusiasm
science
community,
especially
fields
medicine.
thought
to
have
a
significant
impact
on
structural
research
areas
need
information,
such
as
drug
discovery,
design,
function,
et
al.
Though
time
not
long
since
was
developed,
there
are
already
quite
few
application
studies
medicine,
many
them
having
preliminarily
proved
potential
AF2.
To
better
understand
promote
its
applications,
we
will
this
article
summarize
principle
architecture
well
recipe
success,
particularly
focus
reviewing
applications
Limitations
current
also
be
discussed.
PLoS ONE,
Год журнала:
2023,
Номер
18(3), С. e0282689 - e0282689
Опубликована: Март 16, 2023
AlphaFold
changed
the
field
of
structural
biology
by
achieving
three-dimensional
(3D)
structure
prediction
from
protein
sequence
at
experimental
quality.
The
astounding
success
even
led
to
claims
that
folding
problem
is
"solved".
However,
more
than
just
sequence.
Presently,
it
unknown
if
AlphaFold-triggered
revolution
could
help
solve
other
problems
related
folding.
Here
we
assay
ability
predict
impact
single
mutations
on
stability
(ΔΔG)
and
function.
To
study
question
extracted
pLDDT
metrics
predictions
before
after
mutation
in
a
correlated
predicted
change
with
experimentally
known
ΔΔG
values.
Additionally,
same
using
large
scale
dataset
GFP
assayed
levels
fluorescence.
We
found
very
weak
or
no
correlation
between
output
Our
results
imply
may
not
be
immediately
applied
applications
iScience,
Год журнала:
2024,
Номер
27(5), С. 109713 - 109713
Опубликована: Апрель 23, 2024
This
study
systematically
reviewed
the
application
of
large
language
models
(LLMs)
in
medicine,
analyzing
550
selected
studies
from
a
vast
literature
search.
LLMs
like
ChatGPT
transformed
healthcare
by
enhancing
diagnostics,
medical
writing,
education,
and
project
management.
They
assisted
drafting
documents,
creating
training
simulations,
streamlining
research
processes.
Despite
their
growing
utility
diagnosis
improving
doctor-patient
communication,
challenges
persisted,
including
limitations
contextual
understanding
risk
over-reliance.
The
surge
LLM-related
indicated
focus
on
patient
but
highlighted
need
for
careful
integration,
considering
validation,
ethical
concerns,
balance
with
traditional
practice.
Future
directions
suggested
multimodal
LLMs,
deeper
algorithmic
understanding,
ensuring
responsible,
effective
use
healthcare.
Abstract
Single
amino
acid
substitutions
can
profoundly
affect
protein
folding,
dynamics,
and
function.
The
ability
to
discern
between
benign
pathogenic
is
pivotal
for
therapeutic
interventions
research
directions.
Given
the
limitations
in
experimental
examination
of
these
variants,
AlphaMissense
has
emerged
as
a
promising
predictor
pathogenicity
missense
variants.
Since
heterogenous
performance
on
different
types
proteins
be
expected,
we
assessed
efficacy
across
several
groups
(e.g.
soluble,
transmembrane,
mitochondrial
proteins)
regions
intramembrane,
membrane
interacting,
high
confidence
AlphaFold
segments)
using
ClinVar
data
validation.
Our
comprehensive
evaluation
showed
that
delivers
outstanding
performance,
with
MCC
scores
predominantly
0.6
0.74.
We
observed
low
disordered
datasets
related
CFTR
ABC
protein.
However,
superior
was
shown
when
benchmarked
against
quality
CFTR2
database.
results
emphasizes
AlphaMissense’s
potential
pinpointing
functional
hot
spots,
its
likely
surpassing
benchmarks
calculated
from
ProteinGym
datasets.
Pharmacology & Therapeutics,
Год журнала:
2025,
Номер
unknown, С. 108797 - 108797
Опубликована: Янв. 1, 2025
The
traditional
model
of
protein
structure
determined
by
the
amino
acid
sequence
is
today
seriously
challenged
fact
that
approximately
half
human
proteome
made
up
proteins
do
not
have
a
stable
3D
structure,
either
partially
or
in
totality.
These
proteins,
called
intrinsically
disordered
(IDPs),
are
involved
numerous
physiological
functions
and
associated
with
severe
pathologies,
e.g.
Alzheimer,
Parkinson,
Creutzfeldt-Jakob,
amyotrophic
lateral
sclerosis
(ALS),
type
2
diabetes.
Targeting
these
challenging
for
two
reasons:
i)
we
need
to
preserve
their
functions,
ii)
drug
design
molecular
docking
possible
due
lack
reliable
starting
conditions.
Faced
this
challenge,
solutions
proposed
artificial
intelligence
(AI)
such
as
AlphaFold
clearly
unsuitable.
Instead,
suggest
an
innovative
approach
consisting
mimicking,
short
synthetic
peptides,
conformational
flexibility
IDPs.
which
call
adaptive
derived
from
domains
IDPs
become
structured
after
interacting
ligand.
Adaptive
peptides
designed
aim
selectively
antagonizing
harmful
effects
IDPs,
without
targeting
them
directly
but
through
selected
ligands,
affecting
properties.
This"target
target,
arrow"
strategy
promised
open
new
route
discovery
currently
undruggable
proteins.
Frontiers in Molecular Biosciences,
Год журнала:
2022,
Номер
9
Опубликована: Май 17, 2022
The
artificial
intelligence
program
AlphaFold
2
is
revolutionizing
the
field
of
protein
structure
determination
as
it
accurately
predicts
3D
two
thirds
human
proteome.
Its
predictions
can
be
used
directly
structural
models
or
indirectly
aids
for
experimental
using
X-ray
crystallography,
CryoEM
NMR
spectroscopy.
Nevertheless,
neither
afford
insight
into
how
proteins
fold,
nor
determine
stability
dynamics.
Rare
folds
minor
alternative
conformations
are
also
not
predicted
by
and
does
forecast
impact
post
translational
modifications,
mutations
ligand
binding.
remaining
third
proteome
which
poorly
largely
corresponds
to
intrinsically
disordered
regions
proteins.
Key
regulation
signaling
networks,
these
often
form
biomolecular
condensates
amyloids.
Fortunately,
limitations
complemented
This
approach
provides
information
on
folding
dynamics
well
amyloids
their
modulation
conditions,
small
molecules,
mutations,
flanking
sequence,
interactions
with
other
proteins,
RNA
virus.
Together,
spectroscopy
collaborate
advance
our
comprehension
BMC Bioinformatics,
Год журнала:
2022,
Номер
23(1)
Опубликована: Авг. 8, 2022
Despite
the
immense
importance
of
transmembrane
proteins
(TMP)
for
molecular
biology
and
medicine,
experimental
3D
structures
TMPs
remain
about
4-5
times
underrepresented
compared
to
non-TMPs.
Today's
top
methods
such
as
AlphaFold2
accurately
predict
many
TMPs,
but
annotating
regions
remains
a
limiting
step
proteome-wide
predictions.