BioMedInformatics,
Journal Year:
2024,
Volume and Issue:
4(3), P. 2022 - 2042
Published: Sept. 10, 2024
Cell-free
protein
synthesis
(CFPS)
has
emerged
as
a
powerful
tool
for
production,
with
applications
ranging
from
basic
research
to
biotechnology
and
pharmaceutical
development.
However,
enhancing
the
efficiency
of
CFPS
systems
remains
crucial
challenge
realizing
their
full
potential.
Computational
strategies
offer
promising
avenues
optimizing
by
providing
insights
into
complex
biological
processes
enabling
rational
design
approaches.
This
review
provides
comprehensive
overview
computational
approaches
aimed
at
efficiency.
The
introduction
outlines
significance
role
methods
in
addressing
limitations.
It
discusses
mathematical
modeling
simulation-based
predicting
kinetics
reactions.
also
delves
DNA
templates,
including
codon
optimization
mRNA
secondary
structure
prediction
tools,
improve
Furthermore,
it
explores
techniques
engineering
cell-free
transcription
translation
machinery,
such
expression
predictive
ribosome
dynamics.
metabolic
pathways
energy
utilization
is
discussed,
highlighting
flux
analysis
resource
allocation
strategies.
Machine
learning
artificial
intelligence
are
being
increasingly
employed
optimization,
neural
network
models,
deep
algorithms,
reinforcement
adaptive
control.
presents
case
studies
showcasing
successful
using
synthetic
biology,
biotechnology,
pharmaceuticals.
challenges
limitations
current
addressed,
along
future
perspectives
emerging
trends,
integration
multi-omics
data
advances
high-throughput
screening.
conclusion
summarizes
key
findings,
implications
directions
applications,
emphasizes
opportunities
interdisciplinary
collaboration.
offers
valuable
prospects
regarding
enhance
serves
resource,
consolidating
knowledge
field
guiding
further
advancements.
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 5, 2024
Abstract
Accurate
prediction
of
mutation
effects
on
antibody-antigen
interactions
is
critical
for
antibody
engineering
and
drug
design.
In
this
study,
we
present
abCAN,
a
practical
novel
attention
network
designed
to
predict
changes
in
binding
affinity
caused
by
mutations.
abCAN
requires
only
the
pre-mutant
complex
structure
information
perform
its
predictions.
introduces
an
innovative
approach,
Progressive
Encoding,
which
progressively
integrates
structural,
residue-level,
sequential
construct
representation
systematic
manner,
effectively
capturing
both
topological
features
contextual
sequence.
During
which,
extra
weight
interface
residues
would
also
be
applied
through
mechanisms.
These
learned
representations
are
then
transferred
predictor
that
estimates
induced
On
benchmark
dataset,
achieved
root-mean-square
error
(RMSE)
1.195
(kcal/mol
-1
)
Pearson
correlation
coefficient
(PCC)
0.841,
setting
new
state-of-the-art
(SOTA)
accuracy
field
prediction.
