Meticulous
engineering
and
the
yielded
hyperelastic
performance
of
structural
proteins
represent
a
new
frontier
in
developing
next-generation
functional
biomaterials.
These
materials
exhibit
outstanding
programmable
mechanical
properties,
including
elasticity,
resilience,
toughness,
active
biological
characteristics,
such
as
degradability
tissue
repairability,
compared
with
their
chemically
synthetic
counterparts.
However,
there
are
several
critical
issues
regarding
preparation
approaches
protein-based
materials:
limited
natural
sequence
modules,
non-hierarchical
assembly,
imbalance
between
compressive
tensile
leading
to
unmet
demands.
Therefore,
it
is
pivotal
develop
an
alternative
strategy
for
biofabricating
materials.
Herein,
molecular
design,
engineering,
property
regulation
overviewed.
First,
methodologies
deeper
exploration
machine
learning-aided
de
novo
random
mutations
sequences,
multiblock
fusion
techniques,
actively
introduced.
facilitate
generation
elastomeric
protein
modules
demonstrate
enhanced
versatility.
Subsequently,
assembly
tactics
(i.e.,
physical
modulation,
genetic
adaptations,
chemical
modifications)
reviewed,
yielding
hierarchically
ordered
structures.
Finally,
advances
biophysical
techniques
more
nuanced
characterization
ensembles
discussed,
unveiling
tuning
mechanisms
elasticity
across
scales.
Future
developments
biomaterials
also
envisioned.
Nature,
Год журнала:
2024,
Номер
630(8016), С. 493 - 500
Опубликована: Май 8, 2024
Abstract
The
introduction
of
AlphaFold
2
1
has
spurred
a
revolution
in
modelling
the
structure
proteins
and
their
interactions,
enabling
huge
range
applications
protein
design
2–6
.
Here
we
describe
our
3
model
with
substantially
updated
diffusion-based
architecture
that
is
capable
predicting
joint
complexes
including
proteins,
nucleic
acids,
small
molecules,
ions
modified
residues.
new
demonstrates
improved
accuracy
over
many
previous
specialized
tools:
far
greater
for
protein–ligand
interactions
compared
state-of-the-art
docking
tools,
much
higher
protein–nucleic
acid
nucleic-acid-specific
predictors
antibody–antigen
prediction
AlphaFold-Multimer
v.2.3
7,8
Together,
these
results
show
high-accuracy
across
biomolecular
space
possible
within
single
unified
deep-learning
framework.
Deep-learning
methods
have
revolutionized
protein
structure
prediction
and
design
but
are
presently
limited
to
protein-only
systems.
We
describe
RoseTTAFold
All-Atom
(RFAA),
which
combines
a
residue-based
representation
of
amino
acids
DNA
bases
with
an
atomic
all
other
groups
model
assemblies
that
contain
proteins,
nucleic
acids,
small
molecules,
metals,
covalent
modifications,
given
their
sequences
chemical
structures.
By
fine-tuning
on
denoising
tasks,
we
developed
RFdiffusion
(RFdiffusionAA),
builds
structures
around
molecules.
Starting
from
random
distributions
acid
residues
surrounding
target
designed
experimentally
validated,
through
crystallography
binding
measurements,
proteins
bind
the
cardiac
disease
therapeutic
digoxigenin,
enzymatic
cofactor
heme,
light-harvesting
molecule
bilin.
Cell,
Год журнала:
2024,
Номер
187(3), С. 526 - 544
Опубликована: Фев. 1, 2024
Methods
from
artificial
intelligence
(AI)
trained
on
large
datasets
of
sequences
and
structures
can
now
"write"
proteins
with
new
shapes
molecular
functions
de
novo,
without
starting
found
in
nature.
In
this
Perspective,
I
will
discuss
the
state
field
novo
protein
design
at
juncture
physics-based
modeling
approaches
AI.
New
folds
higher-order
assemblies
be
designed
considerable
experimental
success
rates,
difficult
problems
requiring
tunable
control
over
conformations
precise
shape
complementarity
for
recognition
are
coming
into
reach.
Emerging
incorporate
engineering
principles-tunability,
controllability,
modularity-into
process
beginning.
Exciting
frontiers
lie
deconstructing
cellular
and,
conversely,
constructing
synthetic
signaling
ground
up.
As
methods
improve,
many
more
challenges
unsolved.
ACS Central Science,
Год журнала:
2024,
Номер
10(2), С. 226 - 241
Опубликована: Фев. 5, 2024
Enzymes
can
be
engineered
at
the
level
of
their
amino
acid
sequences
to
optimize
key
properties
such
as
expression,
stability,
substrate
range,
and
catalytic
efficiency-or
even
unlock
new
activities
not
found
in
nature.
Because
search
space
possible
proteins
is
vast,
enzyme
engineering
usually
involves
discovering
an
starting
point
that
has
some
desired
activity
followed
by
directed
evolution
improve
its
"fitness"
for
a
application.
Recently,
machine
learning
(ML)
emerged
powerful
tool
complement
this
empirical
process.
ML
models
contribute
(1)
discovery
functional
annotation
known
protein
or
generating
novel
with
functions
(2)
navigating
fitness
landscapes
optimization
mappings
between
associated
values.
In
Outlook,
we
explain
how
complements
discuss
future
potential
improved
outcomes.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Март 18, 2024
Despite
the
central
role
that
antibodies
play
in
modern
medicine,
there
is
currently
no
way
to
rationally
design
novel
bind
a
specific
epitope
on
target.
Instead,
antibody
discovery
involves
time-consuming
immunization
of
an
animal
or
library
screening
approaches.
Here
we
demonstrate
fine-tuned
RFdiffusion
network
capable
designing
de
novo
variable
heavy
chains
(VHH's)
user-specified
epitopes.
We
experimentally
confirm
binders
four
disease-relevant
epitopes,
and
cryo-EM
structure
designed
VHH
bound
influenza
hemagglutinin
nearly
identical
model
both
configuration
CDR
loops
overall
binding
pose.
AlphaFold2
(AF2)
models
have
had
wide
impact
but
mixed
success
in
retrospective
ligand
recognition.
We
prospectively
docked
large
libraries
against
unrefined
AF2
of
the
σ
Nature Communications,
Год журнала:
2024,
Номер
15(1)
Опубликована: Май 28, 2024
Abstract
Protein-ligand
docking
is
an
established
tool
in
drug
discovery
and
development
to
narrow
down
potential
therapeutics
for
experimental
testing.
However,
a
high-quality
protein
structure
required
often
the
treated
as
fully
or
partially
rigid.
Here
we
develop
AI
system
that
can
predict
flexible
all-atom
of
protein-ligand
complexes
directly
from
sequence
information.
We
find
classical
methods
are
still
superior,
but
depend
upon
having
crystal
structures
target
protein.
In
addition
predicting
structures,
predicted
confidence
metrics
(plDDT)
be
used
select
accurate
predictions
well
distinguish
between
strong
weak
binders.
The
advances
presented
here
suggest
goal
AI-based
one
step
closer,
there
way
go
grasp
complexity
interactions
fully.
Umol
available
at:
https://github.com/patrickbryant1/Umol
.
