bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Апрель 19, 2024
Abstract
Transcription
factor
(TF)
are
proteins
that
regulates
the
transcription
of
genetic
information
from
DNA
to
messenger
RNA
by
binding
a
specific
sequence.
Nucleic
acid-protein
interactions
crucial
in
regulating
biological
systems.
This
work
presents
quick
and
convenient
method
for
constructing
tight-binding
models
offers
physical
insights
into
electronic
structure
properties
complexes
motifs.
The
tight
Hamiltonian
parameters
generated
using
random
forest
regression
algorithm,
which
reproduces
given
ab-initio
level
calculations
with
reasonable
accuracy.
We
present
library
residue-level
derived
extensive
over
various
possible
combinations
nucleobases
amino
acid
side
chains
high-quality
DNA-protein
complex
structures.
As
an
example,
our
approach
can
reasonably
generate
subtle
details
orthologous
factors
human
AP-1
Epstein-Barr
virus
Zta
within
few
seconds
on
laptop.
potentially
enhances
understanding
variations
gene-protein
interaction
complexes,
even
those
involving
dozens
genes.
hope
this
study
powerful
tool
analyzing
regulation
mechanisms
at
structural
level.
Topic
Content
bind
modulate
gene
expression,
stability
reactivity
their
elucidated
eigenvalues
model.
Visualization
these
reveals
Highest
Occupied
Molecular
Orbital
(HOMO)
Lowest
Unoccupied
(LUMO),
gap
between
determines
molecular
complex.
advances
revealing
dynamics
charge
transfer
states
factor-DNA
complexes.
International Journal of Low-Carbon Technologies,
Год журнала:
2025,
Номер
20, С. 341 - 352
Опубликована: Янв. 1, 2025
Abstract
To
improve
the
performance
of
new
energy-powered
robots,
a
method
for
optimizing
robots
has
been
proposed,
based
on
low-carbon
power
demand
forecasting
model.
The
approach
advocated
leveraging
to
optimize
system
design
and
control
strategies.
Then,
model
robotic
demands
was
established,
alongside
refinement
evaluation
mechanism.
Results
indicated
significant
correlation
between
operational
parameters
linked
performance.
precision
our
notably
high,
enabling
provision
specific
optimization
strategies
tailored
diverse
contexts.
Applied Sciences,
Год журнала:
2025,
Номер
15(5), С. 2266 - 2266
Опубликована: Фев. 20, 2025
The
Six
Sigma
methodology
for
quality
improvement
enabled
a
high
degree
of
process
compliance
and
enhanced
capability.
This
research
develops
new
model
optimizing
the
offset
printing
based
on
approach,
with
aim
reducing
variability
achieving
stable,
predictable
production
outcomes.
Special
focus
was
placed
defining
Critical
Product
Characteristics
(CPCs)
to
Quality
(CTQs)
points
analysing
their
impact
output
quality,
defined
by
sigma
level.
Based
research,
limits
parameters
were
ensure
consistency
product
quality.
integration
Artificial
Intelligence
(AI)
within
framework
allowed
additional
automation
adaptation
changing
conditions.
use
Random
Forest
efficient
analysis
critical
points,
prediction
potential
deviations,
real-time
adjustment.
AI
is
utilized
improve
precision
efficiency
in
management,
which
further
enhances
stability
optimization
line
dynamic
demands
modern
production.
proposed
represents
an
innovative
approach
that
facilitates
maintaining
stable
results
provides
sustainable
foundation
future
optimizations
industry.
