Computational and Structural Biotechnology Journal,
Journal Year:
2024,
Volume and Issue:
23, P. 3989 - 3998
Published: Nov. 7, 2024
In
today's
scientific
landscape,
research
software
has
evolved
from
being
a
supportive
tool
to
becoming
fundamental
driver
of
discovery,
particularly
in
life
sciences.
Beyond
its
roots
engineering,
now
plays
crucial
role
facilitating
efficient
data
analysis
and
enabling
the
exploration
complex
natural
phenomena.
The
advancements
simulations
modeling
through
have
significantly
accelerated
pace
while
reducing
associated
costs.
This
growing
reliance
underscores
importance
ensuring
reproducibility
-
cornerstone
rigor
trustworthiness.
Although
verifying
presents
challenges,
well-developed
openly
accessible
enhances
transparency
aids
early
detection
errors.
can
be
challenging,
improves
facilitates
error
detection.
mini-review
examines
characteristics
summarizes
key
events
that
shaped
development,
alongside
changes
requirements
guidelines.
Moreover,
we
propose
two
additional
principles
Many
naturally
occurring
protein
assemblies
have
dynamic
structures
that
allow
them
to
perform
specialized
functions.
Although
computational
methods
for
designing
novel
self-assembling
proteins
advanced
substantially
over
the
past
decade,
they
primarily
focus
on
static
structures.
Here
we
characterize
three
distinct
computationally
designed
exhibit
unanticipated
structural
diversity
arising
from
flexibility
in
their
subunits.
Cryo-EM
single-particle
reconstructions
and
native
mass
spectrometry
reveal
two
architectures
assemblies,
while
six
cryo-EM
third
likely
represent
a
subset
of
its
solution-phase
Structural
modeling
molecular
dynamics
simulations
indicate
constrained
within
subunits
each
assembly
promotes
defined
range
rather
than
nonspecific
aggregation.
Redesigning
flexible
region
one
building
block
rescues
intended
monomorphic
assembly.
These
findings
highlight
as
powerful
design
principle,
enabling
exploration
new
functional
spaces
design.
This
study
reports
unexpected
due
subunit
flexibility.
Fixing
restores
architecture,
suggesting
strategy
assemblies.
Nature Communications,
Journal Year:
2024,
Volume and Issue:
15(1)
Published: Nov. 29, 2024
The
de
novo
design
of
self-assembling
peptides
has
garnered
significant
attention
in
scientific
research.
While
alpha-helical
assemblies
have
been
extensively
studied,
exploration
polyproline
type
II
helices,
such
as
those
found
collagen,
remains
relatively
limited.
In
this
study,
we
focus
on
understanding
the
sequence-structure
relationship
hierarchical
collagen-like
peptides,
using
defense
collagen
Surfactant
Protein
A
a
model.
By
dissecting
sequence
derived
from
and
synthesizing
short
successfully
construct
discrete
bundle
hollow
triple
helices.
Amino
acid
substitution
studies
pinpoint
hydrophobic
charged
residues
that
are
critical
for
oligomer
formation.
These
insights
guide
resulting
formation
diverse
quaternary
structures,
including
heterogenous
bundled
oligomers,
two-dimensional
nanosheets,
pH-responsive
nanoribbons.
Our
study
represents
advancement
harnessing
higher-order
beyond
helix.
Despite
advances
machine
learning
approaches,
collagen-based
materials
difficult.
based
natural
structure
family
proteins,
designed
helical
peptide
to
form
ribbons
variety
bundled,
porous
architectures.
Current Opinion in Biotechnology,
Journal Year:
2025,
Volume and Issue:
92, P. 103256 - 103256
Published: Jan. 18, 2025
Recent
advances
in
protein
engineering
have
revolutionized
the
design
of
bionanomolecular
assemblies
for
functional
therapeutic
and
biotechnological
applications.
This
review
highlights
progress
creating
complex
architectures,
encompassing
both
finite
extended
assemblies.
AI
tools,
including
AlphaFold,
RFDiffusion,
ProteinMPNN,
significantly
enhanced
scalability
success
de
novo
designs.
Finite
assemblies,
like
nanocages
coiled-coil-based
structures,
enable
precise
molecular
encapsulation
or
domain
presentation.
Extended
filaments
2D/3D
lattices,
offer
unparalleled
structural
versatility
applications
such
as
vaccine
development,
responsive
biomaterials,
engineered
cellular
scaffolds.
The
convergence
artificial
intelligence-driven
experimental
validation
promises
strong
acceleration
development
tailored
offering
new
opportunities
synthetic
biology,
materials
science,
biotechnology,
biomedicine.
Journal of the American Chemical Society,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 27, 2025
The
advent
of
autonomous
nanomotors
presents
exciting
opportunities
for
nanodrug
delivery.
However,
significant
potential
remains
enhancing
the
asymmetry
and
advancing
development
second
near-infrared
(NIR-II)
light-propelled
capable
operating
within
deep
tissues.
Herein,
we
developed
a
dual-ligand
assisted
anisotropic
assembly
strategy
that
enables
precise
regulation
interfacial
energy
between
selenium
(Se)
nanoparticle
periodic
mesoporous
organosilica
(PMO).
This
facilitates
controllable
growth
PMO
on
Se
nanoparticle,
leading
to
formation
Se&PMO
Janus
nanohybrids.
exposure
ratio
subunit
nanohybrids
can
be
finely
tuned
from
0%
75%.
Leveraging
transformability
subunit,
variety
functional
MxSe&PMO
nanocomposites
(MxSe
denotes
metal
selenide)
were
further
derived.
As
proof
concept,
CuSe&PMO
nanohybrids,
with
NIR-II
photothermal
properties,
employed
as
light-driven
nanomotors.
By
precisely
controlling
CuSe
nanostructure,
these
achieved
optimal
self-propulsion,
thus
cellular
uptake
promoting
tumor
penetration.
Furthermore,
high
loading
capacity
hydrophobic
framework
enabled
incorporation
disulfiram,
thereby
significantly
boosting
efficacy
synergistic
active-motion
therapy.
Biophysics and Physicobiology,
Journal Year:
2025,
Volume and Issue:
22(1), P. n/a - n/a
Published: Jan. 1, 2025
Visualization
of
hydration
structures
over
the
entire
protein
surface
is
necessary
to
understand
why
aqueous
environment
essential
for
folding
and
functions.
However,
it
still
difficult
experiments.
Recently,
we
developed
a
convolutional
neural
network
(CNN)
predict
probability
distribution
water
molecules
surfaces
in
cavities.
The
deep
was
optimized
using
solely
patterns
atoms
surrounding
each
molecule
high-resolution
X-ray
crystal
successfully
provided
distributions
molecules.
Despite
effectiveness
distribution,
positional
differences
predicted
positions
obtained
from
local
maxima
as
sites
remained
inadequate
reproducing
structure
models.
In
this
work,
modified
by
subdividing
atomic
classes
based
on
electronic
properties
composing
amino
acids.
addition,
exclusion
volumes
atom
were
taken
distribution.
These
information
chemical
leads
an
improvement
prediction
accuracy.
We
selected
best
CNN
47
CNNs
constructed
systematically
varying
number
channels
layers
networks.
Here,
report
improvements
accuracy
reorganized
together
with
details
architecture,
training
data,
peak
search
algorithm.
Macromolecular Bioscience,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 22, 2025
Abstract
Computer‐aided
protein
design
(CAPD)
has
become
a
transformative
field,
harnessing
advances
in
computational
power
and
deep
learning
to
deepen
the
understanding
of
structure,
function,
design.
This
review
provides
comprehensive
overview
CAPD
techniques,
with
focus
on
their
application
protein‐based
therapeutics
such
as
monoclonal
antibodies,
drugs,
antigens,
polymers.
starts
key
methods,
particularly
those
integrating
learning‐based
predictions
generative
models.
These
approaches
have
significantly
enhanced
drug
properties,
including
binding
affinity,
specificity,
reduction
immunogenicity.
also
covers
CAPD's
role
optimizing
vaccine
antigen
design,
improving
stability,
customizing
polymers
for
delivery
applications.
Despite
considerable
progress,
faces
challenges
model
overfitting,
limited
data
rare
families,
need
efficient
experimental
validation.
Nevertheless,
ongoing
advancements
coupled
interdisciplinary
collaborations,
are
poised
overcome
these
obstacles,
advancing
engineering
therapeutic
development.
In
conclusion,
this
highlights
future
potential
transform
development,
personalized
medicine,
biotechnology.
