Protein Science,
Journal Year:
2024,
Volume and Issue:
33(10)
Published: Sept. 14, 2024
Abstract
Alzheimer's
disease
(AD)
is
one
of
the
most
common
forms
dementia
and
neurodegenerative
diseases,
characterized
by
formation
neuritic
plaques
neurofibrillary
tangles.
Many
different
proteins
participate
in
this
complicated
pathogenic
mechanism,
missense
mutations
can
alter
folding
functions
these
proteins,
significantly
increasing
risk
AD.
However,
many
methods
to
identify
AD‐causing
variants
did
not
consider
effect
from
perspective
a
protein
three‐dimensional
environment.
Here,
we
present
machine
learning‐based
analysis
classify
their
benign
counterparts
21
AD‐related
leveraging
both
sequence‐
structure‐based
features.
Using
computational
tools
estimate
on
stability,
first
observed
bias
with
significant
destabilizing
effects
family
proteins.
Combining
insight,
built
generic
predictive
model,
improved
performance
tuning
sample
weights
training
process.
Our
final
model
achieved
area
under
receiver
operating
characteristic
curve
up
0.95
blind
test
0.70
an
independent
clinical
validation,
outperforming
all
state‐of‐the‐art
methods.
Feature
interpretation
indicated
that
hydrophobic
environment
polar
interaction
contacts
were
crucial
decision
phenotypes
mutations.
Finally,
presented
user‐friendly
web
server,
AlzDiscovery,
for
researchers
browse
predicted
possible
study
will
be
valuable
resource
AD
screening
development
personalized
treatment.
Molecular Neurodegeneration,
Journal Year:
2024,
Volume and Issue:
19(1)
Published: Feb. 20, 2024
The
conversion
of
native
peptides
and
proteins
into
amyloid
aggregates
is
a
hallmark
over
50
human
disorders,
including
Alzheimer's
Parkinson's
diseases.
Increasing
evidence
implicates
misfolded
protein
oligomers
produced
during
the
formation
process
as
primary
cytotoxic
agents
in
many
these
devastating
conditions.
In
this
review,
we
analyze
processes
by
which
are
formed,
their
structures,
physicochemical
properties,
population
dynamics,
mechanisms
cytotoxicity.
We
then
focus
on
drug
discovery
strategies
that
target
ability
to
disrupt
cell
physiology
trigger
degenerative
processes.
Nature Communications,
Journal Year:
2022,
Volume and Issue:
13(1)
Published: Oct. 27, 2022
Abstract
Intrinsically
disordered
proteins,
which
do
not
adopt
well-defined
structures
under
physiological
conditions,
are
implicated
in
many
human
diseases.
Small
molecules
that
target
the
transactivation
domain
of
androgen
receptor
have
entered
trials
for
treatment
castration-resistant
prostate
cancer
(CRPC),
but
no
structural
or
mechanistic
rationale
exists
to
explain
their
inhibition
mechanisms
relative
potencies.
Here,
we
utilize
all-atom
molecular
dynamics
computer
simulations
elucidate
atomically
detailed
binding
compounds
EPI-002
and
EPI-7170
receptor.
Our
reveal
both
bind
at
interface
two
transiently
helical
regions
induce
formation
partially
folded
collapsed
states.
We
find
binds
more
tightly
than
identify
a
network
intermolecular
interactions
drives
higher
affinity
binding.
results
suggest
strategies
developing
potent
inhibitors
general
protein
drug
design.
Journal of the American Chemical Society,
Journal Year:
2024,
Volume and Issue:
146(4), P. 2319 - 2324
Published: Jan. 22, 2024
Intrinsically
disordered
proteins
(IDPs)
are
highly
dynamic
biomolecules
that
rapidly
interconvert
among
many
structural
conformations.
These
involved
in
cancers,
neurodegeneration,
cardiovascular
illnesses,
and
viral
infections.
Despite
their
enormous
therapeutic
potential,
IDPs
have
generally
been
considered
undruggable
because
of
lack
classical
long-lived
binding
pockets
for
small
molecules.
Currently,
only
a
few
instances
known
where
molecules
observed
to
interact
with
IDPs,
this
situation
is
further
exacerbated
by
the
limited
sensitivity
experimental
techniques
detect
such
events.
Here,
using
nuclear
magnetic
resonance
(NMR)
spectroscopy
Proceedings of the National Academy of Sciences,
Journal Year:
2024,
Volume and Issue:
121(6)
Published: Jan. 31, 2024
A
central
challenge
in
the
study
of
intrinsically
disordered
proteins
is
characterization
mechanisms
by
which
they
bind
their
physiological
interaction
partners.
Here,
we
utilize
a
deep
learning–based
Markov
state
modeling
approach
to
characterize
folding-upon-binding
pathways
observed
long
timescale
molecular
dynamics
simulation
region
measles
virus
nucleoprotein
N
TAIL
reversibly
binding
X
domain
phosphoprotein
complex.
We
find
that
predominantly
occurs
via
two
distinct
encounter
complexes
are
differentiated
orientation,
helical
content,
and
conformational
heterogeneity
.
observe
proceeds
through
multi-step
induced
fit
mechanism
with
several
intermediates
do
not
evidence
for
existence
canonical
selection
pathways.
four
kinetically
separated
native-like
bound
states
interconvert
on
timescales
eighty
five
hundred
nanoseconds.
These
share
core
set
native
intermolecular
contacts
stable
helices
sequential
formation
non-native
additional
turns.
Our
analyses
provide
an
atomic
resolution
structural
description
intermediate
pathway
elucidate
nature
kinetic
barriers
between
metastable
dynamic
heterogenous,
or
“fuzzy”,
protein
Current Opinion in Structural Biology,
Journal Year:
2024,
Volume and Issue:
87, P. 102834 - 102834
Published: May 16, 2024
Predicting
protein
interactions
in
the
cellular
environment
still
remains
a
challenge
AlphaFold
era.
Protein
interactions,
similarly
to
their
structures,
sample
continuum
from
ordered
disordered
states,
with
specific
partners
many
bound
configurations.
A
multiplicity
of
binding
modes
(MBM)
enables
transition
between
these
states
under
different
conditions.
This
review
focuses
on
how
affects
highlighting
molecular
mechanisms,
biophysical
origin,
and
sequence-based
principles
context-dependent,
fuzzy
interactions.
It
summarises
experimental
computational
approaches
address
interaction
heterogeneity
its
contribution
wide
range
biological
functions.
These
insights
will
help
understanding
complex
processes,
involving
conversions
assembly
such
as
liquid-like
droplet
state
amyloid
state.
Abstract
Intrinsically
disordered
proteins
(IDPs)
are
that
perform
important
biological
functions
without
well‐defined
structures
under
physiological
conditions.
IDPs
can
form
fuzzy
complexes
with
other
molecules,
participate
in
the
formation
of
membraneless
organelles,
and
function
as
hubs
protein–protein
interaction
networks.
The
malfunction
causes
major
human
diseases.
However,
drug
design
targeting
remains
challenging
due
to
their
highly
dynamic
interactions.
Turning
into
druggable
targets
provides
a
great
opportunity
extend
target‐space
for
novel
discovery.
Integrative
structural
biology
approaches
combine
information
derived
from
computational
simulations,
artificial
intelligence/data‐driven
analysis
experimental
studies
have
been
used
uncover
interactions
IDPs.
An
increasing
number
ligands
directly
bind
found
either
by
target‐based
screening
or
phenotypic
screening.
Along
understanding
IDP
binding
its
partners,
structure‐based
strategies,
especially
conformational
ensemble‐based
ligand
computer‐aided
optimization
algorithms,
greatly
accelerated
development
ligands.
It
is
inspiring
several
IDP‐targeting
small‐molecule
peptide
drugs
advanced
clinical
trials.
new
methods
need
be
further
developed
efficiently
discovering
optimizing
specific
potent
vast
interactions,
expected
become
valuable
treasure
targets.
This
article
categorized
under:
Structure
Mechanism
>
Computational
Biochemistry
Biophysics
JACS Au,
Journal Year:
2024,
Volume and Issue:
4(6), P. 2228 - 2245
Published: May 28, 2024
Computational
study
of
the
effect
drug
candidates
on
intrinsically
disordered
biomolecules
is
challenging
due
to
their
vast
and
complex
conformational
space.
Here,
we
developed
a
comparative
Markov
state
analysis
(CoVAMPnet)
framework
quantify
changes
in
distribution
dynamics
biomolecule
presence
absence
small
organic
candidate
molecules.
First,
molecular
trajectories
are
generated
using
enhanced
sampling,
molecule
candidates,
ensembles
soft
models
(MSMs)
learned
for
each
system
unsupervised
machine
learning.
Second,
these
MSMs
aligned
across
different
systems
based
solution
an
optimal
transport
problem.
Third,
directional
importance
inter-residue
distances
assignment
states
assessed
by
discriminative
aggregated
neural
network
gradients.
This
final
step
provides
interpretability
biophysical
context
MSMs.
We
applied
this
novel
computational
assess
effects
ongoing
phase
3
therapeutics
tramiprosate
(TMP)
its
metabolite
3-sulfopropanoic
acid
(SPA)
Aβ42
peptide
involved
Alzheimer's
disease.
Based
adaptive
sampling
CoVAMPnet
analysis,
observed
that
both
TMP
SPA
preserved
more
structured
conformations
interacting
nonspecifically
with
charged
residues.
impacted
than
TMP,
protecting
α-helices
suppressing
formation
aggregation-prone
β-strands.
Experimental
analyses
showed
only
mild
TMP/SPA
activity
enhancement
endogenous
metabolization
into
SPA.
Our
data
suggest
may
also
target
other
Aβ
peptides.
The
method
broadly
applicable
behavior
biomolecules.
The
mis-folding
and
aggregation
of
intrinsically
disordered
proteins
(IDPs)
such
as
α
-synuclein
(
S)
underlie
the
pathogenesis
various
neurodegenerative
disorders.
However,
targeting
S
with
small
molecules
faces
challenges
due
to
its
lack
defined
ligand-binding
pockets
in
structure.
Here,
we
implement
a
deep
artificial
neural
network
based
machine
learning
approach,
which
is
able
statistically
distinguish
fuzzy
ensemble
conformational
substates
neat
water
from
those
aqueous
fasudil
(small
molecule
interest)
solution.
In
particular,
presence
milieu
either
modulates
pre-existing
states
or
gives
rise
new
S,
akin
an
ensemble-expansion
mechanism.
ensembles
display
strong
conformation-dependence
residue-wise
interaction
molecule.
A
thermodynamic
analysis
indicates
that
small-molecule
structural
repertoire
via
tuning
protein
backbone
entropy,
however
keeping
entropic
ordering
surrounding
solvent
unperturbed.
Together,
this
study
sheds
light
on
intricate
interplay
between
IDPs,
offering
insights
into
modulation
expansion
key
biophysical
mechanisms
driving
potential
therapeutics.