Physics Reports,
Journal Year:
2024,
Volume and Issue:
1075, P. 1 - 137
Published: May 16, 2024
The
last
years
have
witnessed
remarkable
advances
in
our
understanding
of
the
emergence
and
consequences
topological
constraints
biological
soft
matter.
Examples
are
abundant
relation
to
(bio)polymeric
systems
range
from
characterization
knots
single
polymers
proteins
that
whole
chromosomes
polymer
melts.
At
same
time,
considerable
been
made
description
interplay
between
physical
properties
complex
fluids,
with
development
techniques
now
allow
researchers
control
formation
interaction
defects
diverse
classes
liquid
crystals.
Thanks
technological
progress
integration
experiments
increasingly
sophisticated
numerical
simulations,
matter
is
a
vibrant
area
research
attracting
scientists
broad
disciplines.
However,
owing
high
degree
specialization
modern
science,
many
results
remained
confined
their
own
particular
fields,
different
jargon
making
it
difficult
for
share
ideas
work
together
towards
comprehensive
view
phenomena
at
play.
Compelled
by
these
motivations,
here
we
present
overview
effects
ranging
DNA
genome
organization
entangled
proteins,
polymeric
materials,
crystals,
theoretical
physics,
intention
reducing
barriers
fields
biophysics.
Particular
care
has
taken
providing
coherent
formal
introduction
continuum
materials
highlighting
underlying
common
aspects
concerning
emergence,
characterization,
objects
systems.
second
half
review
dedicated
presentation
latest
selected
problems,
specifically,
on
viscoelastic
materials;
organization;
discussion
possible
other
entanglements
proteins;
solitons
fluids.
This
memory
Marek
Cieplak.
Science,
Journal Year:
2023,
Volume and Issue:
379(6637), P. 1123 - 1130
Published: March 16, 2023
Recent
advances
in
machine
learning
have
leveraged
evolutionary
information
multiple
sequence
alignments
to
predict
protein
structure.
We
demonstrate
direct
inference
of
full
atomic-level
structure
from
primary
using
a
large
language
model.
As
models
sequences
are
scaled
up
15
billion
parameters,
an
atomic-resolution
picture
emerges
the
learned
representations.
This
results
order-of-magnitude
acceleration
high-resolution
prediction,
which
enables
large-scale
structural
characterization
metagenomic
proteins.
apply
this
capability
construct
ESM
Metagenomic
Atlas
by
predicting
structures
for
>617
million
sequences,
including
>225
that
predicted
with
high
confidence,
gives
view
into
vast
breadth
and
diversity
natural
Nature Structural & Molecular Biology,
Journal Year:
2022,
Volume and Issue:
29(11), P. 1056 - 1067
Published: Nov. 1, 2022
Most
proteins
fold
into
3D
structures
that
determine
how
they
function
and
orchestrate
the
biological
processes
of
cell.
Recent
developments
in
computational
methods
for
protein
structure
predictions
have
reached
accuracy
experimentally
determined
models.
Although
this
has
been
independently
verified,
implementation
these
across
structural-biology
applications
remains
to
be
tested.
Here,
we
evaluate
use
AlphaFold2
(AF2)
study
characteristic
structural
elements;
impact
missense
variants;
ligand
binding
site
predictions;
modeling
interactions;
experimental
data.
For
11
proteomes,
an
average
25%
additional
residues
can
confidently
modeled
when
compared
with
homology
modeling,
identifying
features
rarely
seen
Protein
Data
Bank.
AF2-based
disorder
complexes
surpass
dedicated
tools,
AF2
models
used
diverse
equally
well
structures,
confidence
metrics
are
critically
considered.
In
summary,
find
advances
likely
a
transformative
biology
broader
life-science
research.
Signal Transduction and Targeted Therapy,
Journal Year:
2023,
Volume and Issue:
8(1)
Published: March 14, 2023
Abstract
AlphaFold2
(AF2)
is
an
artificial
intelligence
(AI)
system
developed
by
DeepMind
that
can
predict
three-dimensional
(3D)
structures
of
proteins
from
amino
acid
sequences
with
atomic-level
accuracy.
Protein
structure
prediction
one
the
most
challenging
problems
in
computational
biology
and
chemistry,
has
puzzled
scientists
for
50
years.
The
advent
AF2
presents
unprecedented
progress
protein
attracted
much
attention.
Subsequent
release
more
than
200
million
predicted
further
aroused
great
enthusiasm
science
community,
especially
fields
medicine.
thought
to
have
a
significant
impact
on
structural
research
areas
need
information,
such
as
drug
discovery,
design,
function,
et
al.
Though
time
not
long
since
was
developed,
there
are
already
quite
few
application
studies
medicine,
many
them
having
preliminarily
proved
potential
AF2.
To
better
understand
promote
its
applications,
we
will
this
article
summarize
principle
architecture
well
recipe
success,
particularly
focus
reviewing
applications
Limitations
current
also
be
discussed.
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.
Nature,
Journal Year:
2023,
Volume and Issue:
622(7983), P. 637 - 645
Published: Sept. 13, 2023
Proteins
are
key
to
all
cellular
processes
and
their
structure
is
important
in
understanding
function
evolution.
Sequence-based
predictions
of
protein
structures
have
increased
accuracy1,
over
214
million
predicted
available
the
AlphaFold
database2.
However,
studying
at
this
scale
requires
highly
efficient
methods.
Here,
we
developed
a
structural-alignment-based
clustering
algorithm-Foldseek
cluster-that
can
cluster
hundreds
millions
structures.
Using
method,
clustered
database,
identifying
2.30
non-singleton
structural
clusters,
which
31%
lack
annotations
representing
probable
previously
undescribed
Clusters
without
annotation
tend
few
representatives
covering
only
4%
proteins
database.
Evolutionary
analysis
suggests
that
most
clusters
ancient
origin
but
seem
be
species
specific,
lower-quality
or
examples
de
novo
gene
birth.
We
also
show
how
comparisons
used
predict
domain
families
relationships,
remote
similarity.
On
basis
these
analyses,
identify
several
human
immune-related
with
putative
homology
prokaryotic
species,
illustrating
value
resource
for
evolution
across
tree
life.
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2022,
Volume and Issue:
unknown
Published: Nov. 22, 2022
Abstract
AlphaFold2
revolutionized
structural
biology
with
the
ability
to
predict
protein
structures
exceptionally
high
accuracy.
Its
implementation,
however,
lacks
code
and
data
required
train
new
models.
These
are
necessary
(i)
tackle
tasks,
like
protein-ligand
complex
structure
prediction,
(ii)
investigate
process
by
which
model
learns,
remains
poorly
understood,
(iii)
assess
model’s
generalization
capacity
unseen
regions
of
fold
space.
Here
we
report
OpenFold,
a
fast,
memory-efficient,
trainable
implementation
AlphaFold2.
We
OpenFold
from
scratch,
fully
matching
accuracy
Having
established
parity,
OpenFold’s
generalize
across
space
retraining
it
using
carefully
designed
datasets.
find
that
is
remarkably
robust
at
generalizing
despite
extreme
reductions
in
training
set
size
diversity,
including
near-complete
elisions
classes
secondary
elements.
By
analyzing
intermediate
produced
during
training,
also
gain
surprising
insights
into
manner
learns
proteins,
discovering
spatial
dimensions
learned
sequentially.
Taken
together,
our
studies
demonstrate
power
utility
believe
will
prove
be
crucial
resource
for
modeling
community.
Nature Methods,
Journal Year:
2024,
Volume and Issue:
21(8), P. 1514 - 1524
Published: May 14, 2024
AlphaFold2
revolutionized
structural
biology
with
the
ability
to
predict
protein
structures
exceptionally
high
accuracy.
Its
implementation,
however,
lacks
code
and
data
required
train
new
models.
These
are
necessary
(1)
tackle
tasks,
like
protein–ligand
complex
structure
prediction,
(2)
investigate
process
by
which
model
learns
(3)
assess
model's
capacity
generalize
unseen
regions
of
fold
space.
Here
we
report
OpenFold,
a
fast,
memory
efficient
trainable
implementation
AlphaFold2.
We
OpenFold
from
scratch,
matching
accuracy
Having
established
parity,
find
that
is
remarkably
robust
at
generalizing
even
when
size
diversity
its
training
set
deliberately
limited,
including
near-complete
elisions
classes
secondary
elements.
By
analyzing
intermediate
produced
during
training,
also
gain
insights
into
hierarchical
manner
in
fold.
In
sum,
our
studies
demonstrate
power
utility
believe
will
prove
be
crucial
resource
for
modeling
community.
open-source
It
fast
efficient,
available
under
permissive
license.
Recent
developments
in
deep
learning,
coupled
with
an
increasing
number
of
sequenced
proteins,
have
led
to
a
breakthrough
life
science
applications,
particular
protein
property
prediction.
There
is
hope
that
learning
can
close
the
gap
between
proteins
and
known
properties
based
on
lab
experiments.
Language
models
from
field
natural
language
processing
gained
popularity
for
predictions
new
computational
revolution
biology,
where
old
prediction
results
are
being
improved
regularly.
Such
learn
useful
multipurpose
representations
large
open
repositories
sequences
be
used,
instance,
predict
properties.
The
growing
quickly
because
class
model-the
Transformer
model.
We
review
recent
use
large-scale
applications
predicting
characteristics
how
such
used
predict,
example,
post-translational
modifications.
shortcomings
other
explain
proven
very
promising
way
unravel
information
hidden
amino
acids.
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.