bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2023,
Номер
unknown
Опубликована: Дек. 23, 2023
Summary
The
high
binding
affinity
of
antibodies
towards
their
cognate
targets
is
key
to
eliciting
effective
immune
responses,
as
well
the
use
research
and
therapeutic
tools.
Here,
we
propose
ANTIPASTI,
a
Convolutional
Neural
Network
model
that
achieves
state-of-the-art
performance
in
prediction
antibody
using
input
representation
antibody-antigen
structures
terms
Normal
Mode
correlation
maps
derived
from
Elastic
Models.
This
captures
not
only
structural
features
but
energetic
patterns
local
global
residue
fluctuations.
learnt
representations
are
interpretable:
they
reveal
similarities
among
targeting
same
antigen
type,
can
be
used
quantify
importance
regions
contributing
affinity.
Our
results
show
imprint
landscape,
dominance
cooperative
effects
long-range
correlations
between
determine
ACS Catalysis,
Год журнала:
2023,
Номер
13(19), С. 12774 - 12802
Опубликована: Сен. 15, 2023
The
review
by
Christianson,
published
in
2017
on
the
twentieth
anniversary
of
emergence
field,
summarizes
foundational
discoveries
and
key
advances
terpene
synthase/cyclase
(TS)
biocatalysis
(Christianson,
D.
W.
Chemical Society Reviews,
Год журнала:
2024,
Номер
53(16), С. 8202 - 8239
Опубликована: Янв. 1, 2024
Global
environmental
issues
and
sustainable
development
call
for
new
technologies
fine
chemical
synthesis
waste
valorization.
Biocatalysis
has
attracted
great
attention
as
the
alternative
to
traditional
organic
synthesis.
However,
it
is
challenging
navigate
vast
sequence
space
identify
those
proteins
with
admirable
biocatalytic
functions.
The
recent
of
deep-learning
based
structure
prediction
methods
such
AlphaFold2
reinforced
by
different
computational
simulations
or
multiscale
calculations
largely
expanded
3D
databases
enabled
structure-based
design.
While
approaches
shed
light
on
site-specific
enzyme
engineering,
they
are
not
suitable
large-scale
screening
potential
biocatalysts.
Effective
utilization
big
data
using
machine
learning
techniques
opens
up
a
era
accelerated
predictions.
Here,
we
review
applications
machine-learning
guided
We
also
provide
our
view
challenges
perspectives
effectively
employing
design
integrating
molecular
learning,
importance
database
construction
algorithm
in
attaining
predictive
ML
models
explore
fitness
landscape
Journal of Chemical Information and Modeling,
Год журнала:
2024,
Номер
64(10), С. 4310 - 4321
Опубликована: Май 13, 2024
Currently,
antimicrobial
resistance
constitutes
a
serious
threat
to
human
health.
Drugs
based
on
peptides
(AMPs)
constitute
one
of
the
alternatives
address
it.
Shallow
and
deep
learning
(DL)-based
models
have
mainly
been
built
from
amino
acid
sequences
predict
AMPs.
Recent
advances
in
tertiary
(3D)
structure
prediction
opened
new
opportunities
this
field.
In
sense,
graphs
derived
predicted
peptide
structures
recently
proposed.
However,
these
are
not
correspondence
with
state-of-the-art
approaches
codify
evolutionary
information,
and,
addition,
they
memory-
time-consuming
because
depend
multiple
sequence
alignment.
Herein,
we
presented
framework
create
alignment-free
graph
representations
generated
ESMFold-predicted
structures,
whose
nodes
characterized
acid-level
information
Evolutionary
Scale
Modeling
(ESM-2)
models.
A
attention
network
(GAT)
was
implemented
assess
usefulness
AMP
classification.
To
end,
set
comprised
67,058
used.
It
demonstrated
that
proposed
methodology
allowed
build
GAT
generalization
abilities
consistently
better
than
20
non-DL-based
DL-based
The
best
were
developed
using
36-
33-layer
ESM-2
Similarity
studies
showed
best-built
codified
different
chemical
spaces,
thus
fused
significantly
improve
general,
results
suggest
esm-AxP-GDL
is
promissory
tool
develop
good,
structure-dependent,
can
be
successfully
applied
screening
large
data
sets.
This
should
only
useful
classify
AMPs
but
also
for
modeling
other
protein
activities.
Current Opinion in Structural Biology,
Год журнала:
2025,
Номер
92, С. 103023 - 103023
Опубликована: Фев. 22, 2025
Despite
massive
sequencing
efforts,
understanding
the
difference
between
human
pathogenic
and
benign
variants
remains
a
challenge.
Computational
variant
effect
predictors
(VEPs)
have
emerged
as
essential
tools
for
assessing
impact
of
genetic
variants,
although
their
performance
varies.
Initially,
sequence-based
methods
dominated
field,
but
recent
advances,
particularly
in
protein
structure
prediction
technologies
like
AlphaFold,
led
to
an
increased
utilization
structural
information
by
VEPs
aimed
at
scoring
missense
variants.
This
review
highlights
progress
integrating
into
VEPs,
showcasing
novel
models
such
AlphaMissense,
PrimateAI-3D,
CPT-1
that
demonstrate
improved
evaluation.
Structural
data
offers
more
interpretability,
especially
non-loss-of-function
provides
insights
complex
interactions
vivo.
As
field
utilizing
biomolecular
structures
will
be
pivotal
future
VEP
development,
with
breakthroughs
protein-ligand
protein-nucleic
acid
offering
new
avenues.
Communications Biology,
Год журнала:
2025,
Номер
8(1)
Опубликована: Фев. 4, 2025
Peptide-based
drugs
often
fail
in
clinical
trials
due
to
their
toxicity
or
hemolytic
activity
against
red
blood
cells
(RBCs).
Existing
methods
predict
peptides
but
not
the
concentration
(HC50)
required
lyse
50%
of
RBCs.
This
study
develops
classification
and
regression
models
identify
quantify
activity.
These
train
on
1926
with
experimentally
determined
HC50
mammalian
Analysis
indicates
that
hydrophobic
positively
charged
residues
were
associated
higher
Among
models,
including
machine
learning
(ML),
quantum
ML,
protein
language
a
hybrid
model
combining
random
forest
(RF)
motif-based
approach
achieves
highest
area
under
receiver
operating
characteristic
curve
(AUROC)
0.921.
Regression
achieve
Pearson
correlation
coefficient
(R)
0.739
determination
(R²)
0.543.
outperform
existing
are
implemented
HemoPI2,
web-based
platform
standalone
software
for
designing
desired
values
(
http://webs.iiitd.edu.in/raghava/hemopi2/
).
Deleted Journal,
Год журнала:
2024,
Номер
1, С. 100008 - 100008
Опубликована: Июнь 2, 2024
The
electronic
density
of
states
is
a
property
the
material
that
extensively
used
in
quantum
systems
condensed
matter
physics.
It
refers
to
energy
level
electrons
solid
crystal.
One
most
current
ways
compute
it
by
Density
Functional
Tight
Binding
(DFTB),
given
geometry
material.
Nevertheless,
this
computation
could
be
very
computationally
demanding,
although
applied
some
materials
with
reduced
number
atoms.
This
paper
presents
method
deduce
states,
which
based
on
neural
network,
thus,
almost
linear
respect
atoms
Specifically,
we
have
our
metal
oxide
structure
interacting
nucleic
base
guanine.
We
focused
stoichiometric
and
O-defective
anatase
TiO2
(101)
surfaces.
data
set
needed
train
network
has
been
obtained
DFTB+
numerical
solver
an
initial
molecular
model,
computed
track
time-dependent
their
associated
states.
validated
predicted
deduced
DFTB
tends
similar,
opening
door
other
computations
such
introducing
process
generating
analysis.
