Biomarker Research,
Journal Year:
2025,
Volume and Issue:
13(1)
Published: March 29, 2025
Antibodies
play
a
crucial
role
in
defending
the
human
body
against
diseases,
including
life-threatening
conditions
like
cancer.
They
mediate
immune
responses
foreign
antigens
and,
some
cases,
self-antigens.
Over
time,
antibody-based
technologies
have
evolved
from
monoclonal
antibodies
(mAbs)
to
chimeric
antigen
receptor
T
cells
(CAR-T
cells),
significantly
impacting
biotechnology,
diagnostics,
and
therapeutics.
Although
these
advancements
enhanced
therapeutic
interventions,
integration
of
artificial
intelligence
(AI)
is
revolutionizing
antibody
design
optimization.
This
review
explores
recent
AI
advancements,
large
language
models
(LLMs),
diffusion
models,
generative
AI-based
applications,
which
transformed
discovery
by
accelerating
de
novo
generation,
enhancing
response
precision,
optimizing
efficacy.
Through
advanced
data
analysis,
enables
prediction
sequences,
3D
structures,
complementarity-determining
regions
(CDRs),
paratopes,
epitopes,
antigen-antibody
interactions.
These
AI-powered
innovations
address
longstanding
challenges
development,
improving
speed,
specificity,
accuracy
design.
By
integrating
computational
with
biomedical
driving
next-generation
cancer
therapies,
transforming
precision
medicine,
patient
outcomes.
Nature Communications,
Journal Year:
2023,
Volume and Issue:
14(1)
Published: April 25, 2023
Abstract
Antibodies
have
the
capacity
to
bind
a
diverse
set
of
antigens,
and
they
become
critical
therapeutics
diagnostic
molecules.
The
binding
antibodies
is
facilitated
by
six
hypervariable
loops
that
are
diversified
through
genetic
recombination
mutation.
Even
with
recent
advances,
accurate
structural
prediction
these
remains
challenge.
Here,
we
present
IgFold,
fast
deep
learning
method
for
antibody
structure
prediction.
IgFold
consists
pre-trained
language
model
trained
on
558
million
natural
sequences
followed
graph
networks
directly
predict
backbone
atom
coordinates.
predicts
structures
similar
or
better
quality
than
alternative
methods
(including
AlphaFold)
in
significantly
less
time
(under
25
s).
Accurate
this
timescale
makes
possible
avenues
investigation
were
previously
infeasible.
As
demonstration
IgFold’s
capabilities,
predicted
1.4
paired
sequences,
providing
insights
500-fold
more
experimentally
determined
structures.
Communications Biology,
Journal Year:
2023,
Volume and Issue:
6(1)
Published: May 29, 2023
Immune
receptor
proteins
play
a
key
role
in
the
immune
system
and
have
shown
great
promise
as
biotherapeutics.
The
structure
of
these
is
critical
for
understanding
their
antigen
binding
properties.
Here,
we
present
ImmuneBuilder,
set
deep
learning
models
trained
to
accurately
predict
antibodies
(ABodyBuilder2),
nanobodies
(NanoBodyBuilder2)
T-Cell
receptors
(TCRBuilder2).
We
show
that
ImmuneBuilder
generates
structures
with
state
art
accuracy
while
being
far
faster
than
AlphaFold2.
For
example,
on
benchmark
34
recently
solved
antibodies,
ABodyBuilder2
predicts
CDR-H3
loops
an
RMSD
2.81Å,
0.09Å
improvement
over
AlphaFold-Multimer,
hundred
times
faster.
Similar
results
are
also
achieved
nanobodies,
(NanoBodyBuilder2
average
2.89Å,
0.55Å
AlphaFold2)
TCRs.
By
predicting
ensemble
structures,
gives
error
estimate
every
residue
its
final
prediction.
made
freely
available,
both
download
(
https://github.com/oxpig/ImmuneBuilder
)
use
via
our
webserver
http://opig.stats.ox.ac.uk/webapps/newsabdab/sabpred
).
make
available
structural
~150
thousand
non-redundant
paired
antibody
sequences
https://doi.org/10.5281/zenodo.7258553
Despite
recent
advances
in
transgenic
animal
models
and
display
technologies,
humanization
of
mouse
sequences
remains
one
the
main
routes
for
therapeutic
antibody
development.
Traditionally,
is
manual,
laborious,
requires
expert
knowledge.
Although
automation
efforts
are
advancing,
existing
methods
either
demonstrated
on
a
small
scale
or
entirely
proprietary.
To
predict
immunogenicity
risk,
human-likeness
can
be
evaluated
using
humanness
scores,
but
these
lack
diversity,
granularity
interpretability.
Meanwhile,
immune
repertoire
sequencing
has
generated
rich
libraries
such
as
Observed
Antibody
Space
(OAS)
that
offer
augmented
diversity
not
yet
exploited
engineering.
Here
we
present
BioPhi,
an
open-source
platform
featuring
novel
(Sapiens)
evaluation
(OASis).
Sapiens
deep
learning
method
trained
OAS
language
modeling.
Based
Patterns,
Journal Year:
2022,
Volume and Issue:
3(7), P. 100513 - 100513
Published: May 18, 2022
An
individual's
B
cell
receptor
(BCR)
repertoire
encodes
information
about
past
immune
responses
and
potential
for
future
disease
protection.
Deciphering
the
stored
in
BCR
sequence
datasets
will
transform
our
understanding
of
enable
discovery
novel
diagnostics
antibody
therapeutics.
A
key
challenge
analysis
is
prediction
properties
from
their
amino
acid
alone.
Here,
we
present
an
antibody-specific
language
model,
Antibody-specific
Bidirectional
Encoder
Representation
Transformers
(AntiBERTa),
which
provides
a
contextualized
representation
sequences.
Following
pre-training,
show
that
AntiBERTa
embeddings
capture
biologically
relevant
information,
generalizable
to
range
applications.
As
case
study,
fine-tune
predict
paratope
positions
sequence,
outperforming
public
tools
across
multiple
metrics.
To
knowledge,
deepest
protein-family-specific
providing
rich
BCRs.
are
primed
downstream
tasks
can
improve
antibodies.
Trends in Pharmacological Sciences,
Journal Year:
2023,
Volume and Issue:
44(3), P. 175 - 189
Published: Jan. 18, 2023
Due
to
their
high
target
specificity
and
binding
affinity,
therapeutic
antibodies
are
currently
the
largest
class
of
biotherapeutics.
The
traditional
largely
empirical
antibody
development
process
is,
while
mature
robust,
cumbersome
has
significant
limitations.
Substantial
recent
advances
in
computational
artificial
intelligence
(AI)
technologies
now
starting
overcome
many
these
limitations
increasingly
integrated
into
pipelines.
Here,
we
provide
an
overview
AI
methods
relevant
for
development,
including
databases,
predictors
properties
structure,
design
with
emphasis
on
machine
learning
(ML)
models,
complementarity-determining
region
(CDR)
loops,
structural
components
critical
binding.
Nature Biotechnology,
Journal Year:
2023,
Volume and Issue:
41(12), P. 1810 - 1819
Published: March 20, 2023
While
AlphaFold2
can
predict
accurate
protein
structures
from
the
primary
sequence,
challenges
remain
for
proteins
that
undergo
conformational
changes
or
which
few
homologous
sequences
are
known.
Here
we
introduce
AlphaLink,
a
modified
version
of
algorithm
incorporates
experimental
distance
restraint
information
into
its
network
architecture.
By
employing
sparse
contacts
as
anchor
points,
AlphaLink
improves
on
performance
in
predicting
challenging
targets.
We
confirm
this
experimentally
by
using
noncanonical
amino
acid
photo-leucine
to
obtain
residue-residue
inside
cells
crosslinking
mass
spectrometry.
The
program
distinct
conformations
basis
restraints
provided,
demonstrating
value
data
driving
structure
prediction.
noise-tolerant
framework
integrating
prediction
presented
here
opens
path
characterization
in-cell
data.
Molecular Biomedicine,
Journal Year:
2022,
Volume and Issue:
3(1)
Published: Nov. 22, 2022
Abstract
Since
the
first
monoclonal
antibody
drug,
muromonab-CD3,
was
approved
for
marketing
in
1986,
165
drugs
have
been
or
are
under
regulatory
review
worldwide.
With
approval
of
new
treating
a
wide
range
diseases,
including
cancer
and
autoimmune
metabolic
disorders,
therapeutic
drug
market
has
experienced
explosive
growth.
Monoclonal
antibodies
sought
after
by
many
biopharmaceutical
companies
scientific
research
institutes
due
to
their
high
specificity,
strong
targeting
abilities,
low
toxicity,
side
effects,
development
success
rate.
The
related
industries
markets
growing
rapidly,
one
most
important
areas
field
biology
medicine.
In
recent
years,
great
progress
made
key
technologies
theoretical
innovations
provided
antibodies,
antibody–drug
conjugates,
antibody-conjugated
nuclides,
bispecific
nanobodies,
other
analogs.
Additionally,
can
be
combined
with
used
fields
create
cross-fields,
such
as
chimeric
antigen
receptor
T
cells
(CAR-T),
CAR-natural
killer
(CAR-NK),
cell
therapy.
This
summarizes
latest
that
worldwide,
well
clinical
on
these
approaches
development,
outlines
discovery
strategies
emerged
during
hybridoma
technology,
phage
display,
preparation
fully
human
from
transgenic
mice,
single
B-cell
artificial
intelligence-assisted
discovery.
Beyond
potency,
a
good
developability
profile
is
key
attribute
of
biological
drug.
Selecting
and
screening
for
such
attributes
early
in
the
drug
development
process
can
save
resources
avoid
costly
late-stage
failures.
Here,
we
review
some
most
important
properties
that
be
assessed
on
biologics.
These
include
influence
source
biologic,
its
biophysical
pharmacokinetic
properties,
how
well
it
expressed
recombinantly.
We
furthermore
present
silico,
vitro,
vivo
methods
techniques
exploited
at
different
stages
discovery
to
identify
molecules
with
liabilities
thereby
facilitate
selection
optimal
leads.
Finally,
reflect
relevant
parameters
injectable
versus
orally
delivered
biologics
provide
an
outlook
toward
what
general
trends
are
expected
rise
npj Vaccines,
Journal Year:
2024,
Volume and Issue:
9(1)
Published: Jan. 20, 2024
Computer-aided
discovery
of
vaccine
targets
has
become
a
cornerstone
rational
design.
In
this
article,
I
discuss
how
Machine
Learning
(ML)
can
inform
and
guide
key
computational
steps
in
design
concerned
with
the
identification
B
T
cell
epitopes
correlates
protection.
provide
examples
ML
models,
as
well
types
data
predictions
for
which
they
are
built.
argue
that
interpretable
potential
to
improve
immunogens
also
tool
scientific
discovery,
by
helping
elucidate
molecular
processes
underlying
vaccine-induced
immune
responses.
outline
limitations
challenges
terms
availability
method
development
need
be
addressed
bridge
gap
between
advances
their
translational
application