Journal of Cheminformatics,
Год журнала:
2025,
Номер
17(1)
Опубликована: Май 5, 2025
Organic
anion
transporting
polypeptides
(OATPs)
are
membrane
transporters
crucial
for
drug
uptake
and
distribution
in
the
human
body.
OATPs
can
mediate
drug-drug
interactions
(DDIs)
which
interaction
of
one
with
an
OATP
impairs
another
drug,
resulting
potentially
fatal
pharmacological
effects.
Predicting
OATP-mediated
DDIs
is
challenging,
due
to
limited
information
on
inhibition
mechanisms
inconsistent
experimental
data
across
different
studies.
This
study
introduces
Heterogeneous
OATP-Ligand
Interaction
Graph
Neural
Network
(HOLIgraph),
a
novel
computational
model
that
integrates
molecular
modeling
graph
neural
network
enhance
prediction
drug-induced
inhibition.
By
combining
ligand
(i.e.,
drug)
features
protein-ligand
from
rigorous
docking
simulations,
HOLIgraph
outperforms
traditional
DDI
models
rely
solely
features.
achieved
median
balanced
accuracy
over
90
percent
when
predicting
inhibitors
OATP1B1,
significantly
outperforming
purely
ligand-based
models.
Beyond
improving
prediction,
used
train
enable
characterization
protein
residues
involved
inhibitory
drug-OATP
interactions.
We
identified
certain
OATP1B1
preferentially
interact
inhibitors,
including
I46
K49.
anticipate
such
will
be
valuable
future
structural
mechanistic
investigations
OATP1B1.
Materials Today Bio,
Год журнала:
2024,
Номер
26, С. 101028 - 101028
Опубликована: Март 24, 2024
The
expansion
applications
of
semiconducting
polymer
dots
(Pdots)
among
optical
nanomaterial
field
have
long
posed
a
challenge
for
researchers,
promoting
their
intelligent
application
in
multifunctional
nano-imaging
systems
and
integrated
nanomedicine
carriers
diagnosis
treatment.
Despite
notable
progress,
several
inadequacies
still
persist
the
Pdots,
including
development
simplified
near-infrared
(NIR)
nanoprobes,
elucidation
inherent
biological
behavior,
integration
information
processing
nanotechnology
into
biomedical
applications.
This
review
aims
to
comprehensively
elucidate
current
status
Pdots
as
classical
nanophotonic
material
by
discussing
its
advantages
limitations
terms
biocompatibility,
adaptability
microenvironments
vivo,
etc.
Multifunctional
surface
chemistry
play
crucial
roles
realizing
Pdots.
Information
visualization
based
on
physicochemical
properties
is
pivotal
achieving
detection,
sensing,
labeling
probes.
Therefore,
we
refined
underlying
mechanisms
constructed
multiple
comprehensive
original
mechanism
summaries
establish
benchmark.
Additionally,
explored
cross-linking
interactions
between
nanomedicine,
potential
yet
complete
metabolic
pathways,
future
research
directions,
innovative
solutions
integrating
treatment
strategies.
presents
possible
expectations
valuable
insights
advancing
specifically
from
chemical,
medical,
photophysical
practitioners'
standpoints.
International Journal of Molecular Sciences,
Год журнала:
2024,
Номер
25(18), С. 10139 - 10139
Опубликована: Сен. 21, 2024
Protein
three-dimensional
(3D)
structure
prediction
is
one
of
the
most
challenging
issues
in
field
computational
biochemistry,
which
has
overwhelmed
scientists
for
almost
half
a
century.
A
significant
breakthrough
structural
biology
been
established
by
developing
artificial
intelligence
(AI)
system
AlphaFold2
(AF2).
The
AF2
provides
state-of-the-art
protein
structures
from
nearly
all
known
sequences
with
high
accuracy.
This
study
examined
reliability
models
compared
to
experimental
drug
discovery,
focusing
on
common
drug-targeted
classes
as
G
protein-coupled
receptors
(GPCRs)
class
A.
total
32
representative
targets
were
selected,
including
X-ray
crystallographic
and
Cryo-EM
their
corresponding
models.
quality
was
assessed
using
different
validation
tools,
pLDDT
score,
RMSD
value,
MolProbity
percentage
Ramachandran
favored,
QMEAN
Z-score,
QMEANDisCo
Global.
molecular
docking
performed
Genetic
Optimization
Ligand
Docking
(GOLD)
software.
models’
virtual
screening
determined
ability
predict
ligand
binding
poses
closest
native
pose
assessing
Root
Mean
Square
Deviation
(RMSD)
metric
scoring
function.
function
evaluated
enrichment
factor
(EF).
Furthermore,
capability
identify
hits
key
protein–ligand
interactions
analyzed.
posing
power
results
showed
that
successfully
predicted
(RMSD
<
2
Å).
However,
they
exhibited
lower
power,
average
EF
values
2.24,
2.42,
1.82
X-ray,
Cryo-EM,
structures,
respectively.
Moreover,
our
revealed
can
competitive
inhibitors.
In
conclusion,
this
found
provided
comparable
particularly
certain
GPCR
targets,
could
potentially
significantly
impact
discovery.
Drug Discovery Today,
Год журнала:
2024,
Номер
29(6), С. 103990 - 103990
Опубликована: Апрель 23, 2024
The
enormous
growth
in
the
amount
of
data
generated
by
life
sciences
is
continuously
shifting
field
from
model-driven
science
towards
data-driven
science.
need
for
efficient
processing
has
led
to
adoption
massively
parallel
accelerators
such
as
graphics
units
(GPUs).
Consequently,
development
bioinformatics
methods
nowadays
often
heavily
depends
on
effective
use
these
powerful
technologies.
Furthermore,
progress
computational
techniques
and
architectures
continues
be
highly
dynamic,
involving
novel
deep
neural
network
models
artificial
intelligence
(AI)
accelerators,
potentially
quantum
future.
These
are
expected
disruptive
a
whole
drug
discovery
particular.
Here,
we
identify
three
waves
acceleration
their
applications
context:
(i)
GPU
computing,
(ii)
AI
(iii)
next-generation
computers.
Abstract
Solute
carriers
(SLC)
are
integral
membrane
proteins
responsible
for
transporting
a
wide
variety
of
metabolites,
signaling
molecules
and
drugs
across
cellular
membranes.
Despite
key
roles
in
metabolism,
pharmacology,
around
one
third
SLC
‘orphans’
whose
substrates
unknown.
Experimental
determination
is
technically
challenging,
given
the
range
possible
physiological
candidates.
Here,
we
develop
predictive
algorithm
to
identify
correlations
between
expression
levels
intracellular
metabolite
concentrations
by
leveraging
existing
cancer
multi-omics
datasets.
Our
predictions
recovered
known
SLC-substrate
pairs
with
high
sensitivity
specificity
compared
simulated
random
pairs.
CRISPR-Cas9
dependency
screen
data
metabolic
pathway
adjacency
further
improved
performance
our
algorithm.
In
parallel,
combined
drug
profiles
predict
new
SLC-drug
interactions.
Together,
provide
novel
bioinformatic
pipeline
substrate
SLCs,
offering
opportunities
de-orphanise
SLCs
important
implications
understanding
their
health
disease.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2023,
Номер
unknown
Опубликована: Дек. 21, 2023
AlphaFold2
(AF2)
and
RosettaFold
have
greatly
expanded
the
number
of
structures
available
for
structure-based
ligand
discovery,
even
though
retrospective
studies
cast
doubt
on
their
direct
usefulness
that
goal.
Here,
we
tested
unrefined
AF2
models
prospectively,
comparing
experimental
hit-rates
affinities
from
large
library
docking
against
vs
same
screens
targeting
receptors.
In
σ2
5-HT2A
receptors,
struggled
to
recapitulate
ligands
had
previously
found
receptors'
structures,
consistent
with
published
results.
Prospective
models,
however,
yielded
similar
hit
rates
both
receptors
versus
experimentally-derived
structures;
hundreds
molecules
were
prioritized
each
model
structure
receptor.
The
success
was
achieved
despite
differences
in
orthosteric
pocket
residue
conformations
targets
structures.
Intriguingly,
receptor
most
potent,
subtype-selective
agonists
discovered
via
model,
not
structure.
To
understand
this
a
molecular
perspective,
cryoEM
determined
one
more
potent
selective
emerge
Our
findings
suggest
may
sample
are
relevant
much
extending
domain
applicability
discovery.
Annual Review of Biomedical Data Science,
Год журнала:
2024,
Номер
7(1), С. 51 - 57
Опубликована: Апрель 11, 2024
Like
the
black
knight
in
classic
Monty
Python
movie,
grand
scientific
challenges
such
as
protein
folding
are
hard
to
finish
off.
Notably,
AlphaFold
is
revolutionizing
structural
biology
by
bringing
highly
accurate
structure
prediction
masses
and
opening
up
innumerable
new
avenues
of
research.
Despite
this
enormous
success,
calling
prediction,
much
less
related
problems,
“solved”
dangerous,
doing
so
could
stymie
further
progress.
Imagine
what
world
would
be
like
if
we
had
declared
flight
solved
after
first
commercial
airlines
opened
stopped
investing
research
development.
Likewise,
there
still
important
limitations
that
benefit
from
addressing.
Moreover,
limited
our
understanding
diversity
different
structures
a
single
can
adopt
(called
conformational
ensemble)
dynamics
which
explores
space.
What
clear
ensembles
critical
function,
aspect
will
advance
ability
design
proteins
drugs.
Wiley Interdisciplinary Reviews Computational Molecular Science,
Год журнала:
2024,
Номер
14(1)
Опубликована: Янв. 1, 2024
Abstract
Potassium
(K
+
)
channels
play
vital
roles
in
various
physiological
functions,
including
regulating
K
flow
cell
membranes,
impacting
nervous
system
signal
transduction,
neuronal
firing,
muscle
contraction,
neurotransmitters,
and
enzyme
secretion.
Their
activation
switch‐off
are
directly
linked
to
diseases
like
arrhythmias,
atrial
fibrillation,
pain
etc.
Although
the
experimental
methods
important
studying
structure
function
of
channels,
they
still
some
limitations
enclose
dynamic
molecular
processes
corresponding
mechanisms
conformational
changes
during
ion
transport,
permeation,
gating
control.
Relatively,
computational
have
obvious
advantages
such
problems
compared
with
methods.
Recently,
more
three‐dimensional
structures
been
disclosed
based
on
silico
prediction
methods,
which
provide
a
good
chance
study
mechanism
related
functional
regulations
channels.
Based
these
structural
details,
dynamics
simulations
together
as
enhanced
sampling
free
energy
calculations,
widely
used
reveal
dynamics,
conductance,
channel
gating,
ligand
binding
mechanisms.
Additionally,
accessibility
also
provides
large
space
for
structure‐based
drug
design.
This
review
mainly
addresses
recent
progress
structure,
mechanism,
design
After
summarizing
fields,
we
give
our
opinion
future
direction
area
research
combined
cutting
edge
article
is
categorized
under:
Molecular
Statistical
Mechanics
>
Dynamics
Monte‐Carlo
Methods
Structure
Mechanism
Computational
Biochemistry
Biophysics
Data
Science
Chemoinformatics
Archiv der Pharmazie,
Год журнала:
2024,
Номер
357(10)
Опубликована: Июль 12, 2024
AlphaFold
is
an
artificial
intelligence
approach
for
predicting
the
three-dimensional
(3D)
structures
of
proteins
with
atomic
accuracy.
One
challenge
that
limits
use
models
drug
discovery
correct
prediction
folding
in
absence
ligands
and
cofactors,
which
compromises
their
direct
use.
We
have
previously
described
optimization
histone
deacetylase
11
(HDAC11)
model
docking
selective
inhibitors
such
as
FT895
SIS17.
Based
on
predicted
binding
mode
optimized
HDAC11
model,
a
new
scaffold
was
designed,
resulting
compounds
were
tested
vitro
against
various
HDAC
isoforms.
Compound
5a
proved
to
be
most
active
compound
IC