Research Square (Research Square),
Journal Year:
2022,
Volume and Issue:
unknown
Published: May 18, 2022
Abstract
AlphaFold
2
(AF2)
has
placed
Molecular
Biology
in
a
new
era
where
we
can
visualize,
analyze
and
interpret
the
structures
functions
of
all
proteins
solely
from
their
primary
sequences.
We
performed
AF2
structure
predictions
for
various
protein
systems,
including
globular
proteins,
multi-domain
protein,
an
intrinsically
disordered
(IDP),
randomized
two
larger
(>
1000
AA),
heterodimer
homodimer
complex.
Our
results
show
that
along
with
three
dimensional
(3D)
structures,
also
decodes
sequences
into
residue
flexibilities
via
both
predicted
local
distance
difference
test
(pLDDT)
scores
models,
aligned
error
(PAE)
maps.
PAE
maps
are
correlated
variation
(DV)
matrices
molecular
dynamics
(MD)
simulations,
which
reveals
predict
dynamical
nature
residues.
Here,
introduce
AF2-scores,
simply
derived
pLDDT
range
[0,
1].
found
good
multisequence
alignment
(MSA)
depths,
large
complexes,
AF2-scores
highly
root
mean
square
fluctuations
(RMSF)
calculated
MD
simulations.
For
little
or
no
MSA
hits
(the
IDP
protein),
do
not
correlate
RMSF
MD,
especially
(IDPs).
indicate
by
convey
information
flexibility,
i.e.,
dynamics.
Scientific Reports,
Journal Year:
2022,
Volume and Issue:
12(1)
Published: June 23, 2022
AlphaFold
2
(AF2)
has
placed
Molecular
Biology
in
a
new
era
where
we
can
visualize,
analyze
and
interpret
the
structures
functions
of
all
proteins
solely
from
their
primary
sequences.
We
performed
AF2
structure
predictions
for
various
protein
systems,
including
globular
proteins,
multi-domain
protein,
an
intrinsically
disordered
(IDP),
randomized
two
larger
(>
1000
AA),
heterodimer
homodimer
complex.
Our
results
show
that
along
with
three
dimensional
(3D)
structures,
also
decodes
sequences
into
residue
flexibilities
via
both
predicted
local
distance
difference
test
(pLDDT)
scores
models,
aligned
error
(PAE)
maps.
PAE
maps
are
correlated
variation
(DV)
matrices
molecular
dynamics
(MD)
simulations,
which
reveals
predict
dynamical
nature
residues.
Here,
introduce
AF2-scores,
simply
derived
pLDDT
range
[0,
1].
found
most
large
complexes,
AF2-scores
highly
root
mean
square
fluctuations
(RMSF)
calculated
MD
simulations.
However,
IDP
do
not
correlate
RMSF
MD,
especially
IDP.
indicate
by
convey
information
flexibility,
i.e.,
dynamics.
Protein Science,
Journal Year:
2022,
Volume and Issue:
31(6)
Published: May 26, 2022
AlphaFold2
has
revolutionized
protein
structure
prediction
by
leveraging
sequence
information
to
rapidly
model
folds
with
atomic-level
accuracy.
Nevertheless,
previous
work
shown
that
these
predictions
tend
be
inaccurate
for
structurally
heterogeneous
proteins.
To
systematically
assess
factors
contribute
this
inaccuracy,
we
tested
AlphaFold2's
performance
on
98-fold-switching
proteins,
which
assume
at
least
two
distinct-yet-stable
secondary
and
tertiary
structures.
Topological
similarities
were
quantified
between
five
predicted
experimentally
determined
structures
of
each
fold-switching
protein.
Overall,
94%
captured
one
conformation
but
not
the
other.
Despite
biased
results,
estimated
confidences
moderate-to-high
74%
residues,
a
result
contrasts
overall
low
intrinsically
disordered
are
also
heterogeneous.
investigate
contributing
disparity,
variation
within
multiple
alignments
used
generate
Unlike
regions,
whose
show
conservation,
regions
had
conservation
rates
statistically
similar
canonical
single-fold
Furthermore,
lower
than
either
or
regardless
conservation.
high
fold
switchers
indicate
it
uses
sophisticated
pattern
recognition
search
most
probable
conformer
rather
biophysics
protein's
structural
ensemble.
Thus,
is
surprising
its
often
fail
proteins
properties
fully
apparent
from
solved
Our
results
emphasize
need
look
as
an
ensemble
suggest
systematic
examination
sequences
may
reveal
propensities
stable
International Journal of Molecular Sciences,
Journal Year:
2022,
Volume and Issue:
23(9), P. 4591 - 4591
Published: April 21, 2022
The
development
of
AlphaFold2
marked
a
paradigm-shift
in
the
structural
biology
community.
Herein,
we
assess
ability
to
predict
disordered
regions
against
traditional
sequence-based
disorder
predictors.
We
find
that
performs
well
at
discriminating
regions,
but
also
note
predictor
one
constructs
from
an
structure
determines
accuracy.
In
particular,
naïve,
non-trivial
assumption
residues
assigned
helices,
strands,
and
H-bond
stabilized
turns
are
likely
ordered
all
other
results
dramatic
overestimation
disorder;
conversely,
predicted
local
distance
difference
test
(pLDDT)
provides
excellent
measure
residue-wise
disorder.
Furthermore,
by
employing
molecular
dynamics
(MD)
simulations,
interesting
relationship
between
pLDDT
secondary
structure,
may
explain
our
observations
suggests
broader
application
for
characterizing
intrinsically
proteins
(IDPs/IDRs).
The Journal of Physical Chemistry B,
Journal Year:
2022,
Volume and Issue:
126(34), P. 6372 - 6383
Published: Aug. 17, 2022
AlphaFold
has
burst
into
our
lives.
A
powerful
algorithm
that
underscores
the
strength
of
biological
sequence
data
and
artificial
intelligence
(AI).
appended
projects
research
directions.
The
database
it
been
creating
promises
an
untold
number
applications
with
vast
potential
impacts
are
still
difficult
to
surmise.
AI
approaches
can
revolutionize
personalized
treatments
usher
in
better-informed
clinical
trials.
They
promise
make
giant
leaps
toward
reshaping
revamping
drug
discovery
strategies,
selecting
prioritizing
combinations
targets.
Here,
we
briefly
overview
structural
biology,
including
molecular
dynamics
simulations
prediction
microbiota-human
protein-protein
interactions.
We
highlight
advancements
accomplished
by
deep-learning-powered
protein
structure
their
impact
on
life
sciences.
At
same
time,
does
not
resolve
decades-long
folding
challenge,
nor
identify
pathways.
models
provides
do
capture
conformational
mechanisms
like
frustration
allostery,
which
rooted
ensembles,
controlled
dynamic
distributions.
Allostery
signaling
properties
populations.
also
generate
ensembles
intrinsically
disordered
proteins
regions,
instead
describing
them
low
probabilities.
Since
generates
single
ranked
structures,
rather
than
cannot
elucidate
allosteric
activating
driver
hotspot
mutations
resistance.
However,
capturing
key
features,
deep
learning
techniques
use
predicted
conformation
as
basis
for
generating
a
diverse
ensemble.
International Journal of Molecular Sciences,
Journal Year:
2022,
Volume and Issue:
23(22), P. 14050 - 14050
Published: Nov. 14, 2022
Many
proteins
and
protein
segments
cannot
attain
a
single
stable
three-dimensional
structure
under
physiological
conditions;
instead,
they
adopt
multiple
interconverting
conformational
states.
Such
intrinsically
disordered
or
are
highly
abundant
across
proteomes,
involved
in
various
effector
functions.
This
review
focuses
on
different
aspects
of
regions,
which
form
the
basis
so-called
“Disorder–function
paradigm”
proteins.
Additionally,
experimental
approaches
computational
tools
used
for
characterizing
regions
discussed.
Finally,
role
diseases
their
utility
as
potential
drug
targets
explored.
Proteins Structure Function and Bioinformatics,
Journal Year:
2023,
Volume and Issue:
91(6), P. 847 - 855
Published: Jan. 21, 2023
Abstract
AlphaFold2
has
revolutionized
protein
structure
prediction
from
amino‐acid
sequence.
In
addition
to
structures,
high‐resolution
dynamics
information
about
various
regions
is
important
for
understanding
function.
Although
neither
been
designed
nor
trained
predict
dynamics,
it
shown
here
how
the
returned
by
can
be
used
dynamic
at
individual
residue
level.
The
approach,
which
termed
cdsAF2,
uses
3D
backbone
NMR
NH
S
2
order
parameters
using
a
local
contact
model
that
takes
into
account
contacts
made
each
peptide
plane
along
with
its
environment.
By
combining
AlphaFold2's
pLDDT
confidence
score
accuracy
predicted
value
model,
an
estimator
obtained
semi‐quantitatively
captures
many
of
features
observed
in
experimental
parameter
profiles.
method
demonstrated
set
nine
proteins
different
sizes
and
variable
amounts
disorder.
