Protein Science,
Journal Year:
2024,
Volume and Issue:
33(9)
Published: Aug. 24, 2024
Abstract
In
protein
design,
the
ultimate
test
of
success
is
that
designs
function
as
desired.
Here,
we
discuss
utility
cell
free
synthesis
(CFPS)
a
rapid,
convenient
and
versatile
method
to
screen
for
activity.
We
champion
use
CFPS
in
screening
potential
designs.
Compared
vivo
screening,
wider
range
different
activities
can
be
evaluated
using
CFPS,
scale
on
which
it
easily
used—screening
tens
hundreds
designed
proteins—is
ideally
suited
current
needs.
Protein
design
physics‐based
strategies
tended
have
relatively
low
rate,
compared
with
machine‐learning
based
methods.
Screening
steps
(such
yeast
display)
were
often
used
identify
proteins
displayed
desired
activity
from
many
highly
ranked
computationally.
also
describe
how
well‐suited
reasons
fail,
may
include
problems
transcription,
translation,
solubility,
addition
not
achieving
structure
function.
ACS Central Science,
Journal Year:
2024,
Volume and Issue:
10(2), P. 226 - 241
Published: Feb. 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.
Science,
Journal Year:
2024,
Volume and Issue:
385(6704), P. 46 - 53
Published: July 4, 2024
Large
language
models
trained
on
sequence
information
alone
can
learn
high-level
principles
of
protein
design.
However,
beyond
sequence,
the
three-dimensional
structures
proteins
determine
their
specific
function,
activity,
and
evolvability.
Here,
we
show
that
a
general
model
augmented
with
structure
backbone
coordinates
guide
evolution
for
diverse
without
need
to
individual
functional
tasks.
We
also
demonstrate
ESM-IF1,
which
was
only
single-chain
structures,
be
extended
engineer
complexes.
Using
this
approach,
screened
about
30
variants
two
therapeutic
clinical
antibodies
used
treat
severe
acute
respiratory
syndrome
coronavirus
2
(SARS-CoV-2)
infection.
achieved
up
25-fold
improvement
in
neutralization
37-fold
affinity
against
antibody-escaped
viral
concern
BQ.1.1
XBB.1.5,
respectively.
These
findings
highlight
advantage
integrating
structural
identify
efficient
trajectories
requiring
any
task-specific
training
data.
Chemical Reviews,
Journal Year:
2024,
Volume and Issue:
124(14), P. 8740 - 8786
Published: July 3, 2024
In
recent
years,
powerful
genetic
code
reprogramming
methods
have
emerged
that
allow
new
functional
components
to
be
embedded
into
proteins
as
noncanonical
amino
acid
(ncAA)
side
chains.
this
review,
we
will
illustrate
how
the
availability
of
an
expanded
set
building
blocks
has
opened
a
wealth
opportunities
in
enzymology
and
biocatalysis
research.
Genetic
provided
insights
enzyme
mechanisms
by
allowing
introduction
spectroscopic
probes
targeted
replacement
individual
atoms
or
groups.
NcAAs
also
been
used
develop
engineered
biocatalysts
with
improved
activity,
selectivity,
stability,
well
enzymes
artificial
regulatory
elements
are
responsive
external
stimuli.
Perhaps
most
ambitiously,
combination
laboratory
evolution
given
rise
classes
use
ncAAs
key
catalytic
elements.
With
framework
for
developing
ncAA-containing
now
firmly
established,
optimistic
become
progressively
more
tool
armory
designers
engineers
coming
years.
Nature Communications,
Journal Year:
2025,
Volume and Issue:
16(1)
Published: Jan. 2, 2025
Computational
methods
for
predicting
protein
function
are
of
great
significance
in
understanding
biological
mechanisms
and
treating
complex
diseases.
However,
existing
computational
approaches
prediction
lack
interpretability,
making
it
difficult
to
understand
the
relations
between
structures
functions.
In
this
study,
we
propose
a
deep
learning-based
solution,
named
DPFunc,
accurate
with
domain-guided
structure
information.
DPFunc
can
detect
significant
regions
accurately
predict
corresponding
functions
under
guidance
domain
It
outperforms
current
state-of-the-art
achieves
improvement
over
structure-based
methods.
Detailed
analyses
demonstrate
that
information
contributes
prediction,
enabling
our
method
key
residues
or
structures,
which
closely
related
their
summary,
serves
as
an
effective
tool
large-scale
pushes
border
systems.
deep-learning-based
tool,
uses
structures.
prediction.
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: March 24, 2024
Abstract
Optimizing
enzymes
to
function
in
novel
chemical
environments
is
a
central
goal
of
synthetic
biology,
but
optimization
often
hindered
by
rugged,
expansive
protein
search
space
and
costly
experiments.
In
this
work,
we
present
TeleProt,
an
ML
framework
that
blends
evolutionary
experimental
data
design
diverse
variant
libraries,
employ
it
improve
the
catalytic
activity
nuclease
enzyme
degrades
biofilms
accumulate
on
chronic
wounds.
After
multiple
rounds
high-throughput
experiments
using
both
TeleProt
standard
directed
evolution
(DE)
approaches
parallel,
find
our
approach
found
significantly
better
top-performing
than
DE,
had
hit
rate
at
finding
diverse,
high-activity
variants,
was
even
able
high-performance
initial
library
no
prior
data.
We
have
released
dataset
55K
one
most
extensive
genotype-phenotype
landscapes
date,
drive
further
progress
ML-guided
design.
Journal of Medicinal Chemistry,
Journal Year:
2024,
Volume and Issue:
67(12), P. 10336 - 10349
Published: June 5, 2024
While
large-scale
artificial
intelligence
(AI)
models
for
protein
structure
prediction
and
design
are
advancing
rapidly,
the
translation
of
deep
learning
practical
macromolecular
drug
development
remains
limited.
This
investigation
aims
to
bridge
this
gap
by
combining
cutting-edge
methodologies
create
a
novel
peptide-based
PROTAC
paradigm.
Using
ProteinMPNN
RFdiffusion,
we
identified
binding
peptides
androgen
receptor
(AR)
Von
Hippel-Lindau
(VHL),
followed
computational
modeling
with
Alphafold2-multimer
ZDOCK
predict
spatial
interrelationships.
Experimental
validation
confirmed
designed
peptide's
ability
AR
VHL.
Transdermal
microneedle
patching
technology
was
seamlessly
integrated
peptide
delivery
in
androgenic
alopecia
treatment.
In
summary,
our
approach
provides
generic
method
generating
PROTACs
offers
application
designing
potential
therapeutic
drugs
androgenetic
alopecia.
showcases
interdisciplinary
approaches
personalized
medicine.
Nature Communications,
Journal Year:
2024,
Volume and Issue:
15(1)
Published: June 20, 2024
Abstract
A
major
challenge
in
protein
design
is
to
augment
existing
functional
proteins
with
multiple
property
enhancements.
Altering
several
properties
likely
necessitates
numerous
primary
sequence
changes,
and
novel
methods
are
needed
accurately
predict
combinations
of
mutations
that
maintain
or
enhance
function.
Models
co-variation
(e.g.,
EVcouplings),
which
leverage
extensive
information
about
various
activities
from
homologous
sequences,
have
proven
effective
for
many
applications
including
structure
determination
mutation
effect
prediction.
We
apply
EVcouplings
computationally
variants
the
model
TEM-1
β
-lactamase.
Nearly
all
14
experimentally
characterized
designs
were
functional,
one
84
nearest
natural
homolog.
The
also
had
large
increases
thermostability,
increased
activity
on
substrates,
nearly
identical
wild
type
enzyme.
This
study
highlights
efficacy
evolutionary
models
guiding
alterations
generate
diversity
applications.
Frontiers in Bioengineering and Biotechnology,
Journal Year:
2025,
Volume and Issue:
13
Published: Feb. 5, 2025
The
integration
of
artificial
intelligence
(AI)
in
protein
design
presents
unparalleled
opportunities
for
innovation
bioengineering
and
biotechnology.
However,
it
also
raises
significant
biosecurity
concerns.
This
review
examines
the
changing
landscape
bioweapon
risks,
dual-use
potential
AI-driven
tools,
necessary
safeguards
to
prevent
misuse
while
fostering
innovation.
It
highlights
emerging
policy
frameworks,
technical
safeguards,
community
responses
aimed
at
mitigating
risks
enabling
responsible
development
application
AI
design.