Small
molecule
drug
design
hinges
on
obtaining
co-crystallized
ligand-protein
structures.
Despite
AlphaFold2’s
strides
in
protein
native
structure
prediction,
its
focus
apo
structures
overlooks
ligands
and
associated
holo
Moreover,
designing
selective
drugs
often
benefits
from
the
targeting
of
diverse
metastable
conformations.
Therefore,
direct
application
AlphaFold2
models
virtual
screening
dis-covery
remains
tentative.
Here,
we
demonstrate
an
based
framework
combined
with
all-atom
enhanced
sampling
molecular
dynamics
induced
fit
docking,
named
AF2RAVE-Glide,
to
conduct
computational
model
small
binding
kinase
conformations,
initiated
sequences.
We
AF2RAVE-Glide
workflow
three
different
kinases
their
type
I
II
inhibitors,
special
emphasis
known
inhibitors
which
target
classical
DFG-out
state.
These
states
are
not
easy
sample
AlphaFold2.
Here
how
AF2RAVE
these
conformations
can
be
sampled
for
high
enough
ac-
curacy
enable
subsequent
docking
more
than
50%
success
rates
across
calculations.
believe
protocol
should
deployable
other
proteins
generally.
HDAC11
is
a
class
IV
histone
deacylase
with
no
crystal
structure
reported
so
far.
The
catalytic
domain
of
shares
low
sequence
identity
other
HDAC
isoforms
which
makes
the
conventional
homology
modeling
less
reliable.
AlphaFold
neural
network
machine
learning
approach
that
can
predict
3D
proteins
high
accuracy
even
in
absence
similar
structures.
However
fact
models
are
predicted
small
molecules
and
ions/cofactors
complicate
their
utilization
for
drug
design.
Previously
we
optimized
an
model
by
adding
zinc
ion
minimization
presence
inhibitors.
In
current
study
implement
comparative
structure-based
virtual
screening
utilizing
previously
to
identify
novel
selective
stepwise
was
successful
identifying
hit
subsequently
tested
using
vitro
enzymatic
assay.
compound
showed
IC50
value
3.5
µM
could
selectively
inhibit
over
subtypes
at
10
concentration.
addition
carried
out
molecular
dynamics
simulations
further
confirm
binding
hypothesis
obtained
docking
study.
These
results
reinforce
presented
optimization
applicability
search
inhibitors
discovery.
Small
molecule
drug
design
hinges
on
obtaining
co-crystallized
ligand-protein
structures.
Despite
AlphaFold2's
strides
in
protein
native
structure
prediction,
its
focus
apo
structures
overlooks
ligands
and
associated
holo
Moreover,
designing
selective
drugs
often
benefits
from
the
targeting
of
diverse
metastable
conformations.
Therefore,
direct
application
AlphaFold2
models
virtual
screening
discovery
remains
tentative.
Here,
we
demonstrate
an
based
framework
combined
with
all-atom
enhanced
sampling
molecular
dynamics
induced
fit
docking,
named
AF2RAVE-Glide,
to
conduct
computational
model
small
binding
kinase
conformations,
initiated
sequences.
We
AF2RAVE-Glide
workflow
three
different
kinases
their
type
I
II
inhibitors,
special
emphasis
known
inhibitors
which
target
classical
DFG-out
state.
These
states
are
not
easy
sample
AlphaFold2.
Here
how
AF2RAVE
these
conformations
can
be
sampled
for
high
enough
accuracy
enable
subsequent
docking
more
than
50%
success
rates
across
calculations.
believe
protocol
should
deployable
other
proteins
generally.
Beni-Suef University Journal of Basic and Applied Sciences,
Год журнала:
2024,
Номер
13(1)
Опубликована: Май 17, 2024
Abstract
Breakthrough
achievements
in
protein
structure
prediction
have
occurred
recently,
mostly
due
to
the
advent
of
sophisticated
machine
learning
methods
and
significant
advancements
algorithmic
approaches.
The
most
recent
version
AlphaFold
model,
known
as
“AlphaFold-latest,”
which
expands
functionalities
groundbreaking
AlphaFold2,
is
subject
this
article.
goal
novel
model
predict
three-dimensional
structures
various
biomolecules,
such
ions,
proteins,
nucleic
acids,
small
molecules,
non-standard
residues.
We
demonstrate
notable
gains
precision,
surpassing
specialized
tools
across
multiple
domains,
including
protein–ligand
interactions,
protein–nucleic
acid
antibody–antigen
predictions.
In
conclusion,
framework
has
ability
yield
atomically-accurate
structural
predictions
for
a
variety
biomolecular
hence
facilitating
drug
discovery.
iScience,
Год журнала:
2024,
Номер
27(6), С. 110032 - 110032
Опубликована: Май 20, 2024
Evaluation
of
the
binding
affinities
drugs
to
proteins
is
a
crucial
process
for
identifying
drug
pharmacological
actions,
but
it
requires
three
dimensional
structures
proteins.
Herein,
we
propose
novel
computational
methods
predict
therapeutic
indications
and
side
effects
candidate
compounds
from
human
protein
on
proteome-wide
scale.
Large-scale
docking
simulations
were
performed
7,582
with
19,135
revealed
by
AlphaFold
(including
experimentally
unresolved
proteins),
machine
learning
models
affinity
score
(PBAS)
profiles
constructed.
We
demonstrated
usefulness
method
predicting
559
diseases
285
toxicities.
The
enabled
which
related
had
not
been
determined
successfully
extract
eliciting
effects.
proposed
will
be
useful
in
various
applications
discovery.
Small
molecule
drug
design
hinges
on
obtaining
co-crystallized
ligand-protein
structures.
Despite
AlphaFold2’s
strides
in
protein
native
structure
prediction,
its
focus
apo
structures
overlooks
ligands
and
associated
holo
Moreover,
designing
selective
drugs
often
benefits
from
the
targeting
of
diverse
metastable
conformations.
Therefore,
direct
application
AlphaFold2
models
virtual
screening
dis-covery
remains
tentative.
Here,
we
demonstrate
an
based
framework
combined
with
all-atom
enhanced
sampling
molecular
dynamics
induced
fit
docking,
named
AF2RAVE-Glide,
to
conduct
computational
model
small
binding
kinase
conformations,
initiated
sequences.
We
AF2RAVE-Glide
workflow
three
different
kinases
their
type
I
II
inhibitors,
special
emphasis
known
inhibitors
which
target
classical
DFG-out
state.
These
states
are
not
easy
sample
AlphaFold2.
Here
how
AF2RAVE
these
conformations
can
be
sampled
for
high
enough
ac-
curacy
enable
subsequent
docking
more
than
50%
success
rates
across
calculations.
believe
protocol
should
deployable
other
proteins
generally.