Journal of Proteome Research,
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 25, 2024
In
metaproteomics
studies,
constructing
a
reference
protein
sequence
database
that
is
both
comprehensive
and
not
overly
large
critical
for
the
peptide
identification
step.
Therefore,
availability
of
well-curated
databases
tools
custom
construction
essential
to
enhance
performance
analyses.
this
review,
we
first
provide
an
overview
by
presenting
concise
historical
background,
outlining
typical
experimental
bioinformatics
workflow,
emphasizing
crucial
step
metaproteomics.
We
then
delve
into
current
available
building
such
databases,
highlighting
their
individual
approaches,
utility,
advantages
limitations.
Next,
examine
existing
detailing
scope
relevance
in
research.
Then,
practical
recommendations
metaproteomics,
along
with
challenges
area.
conclude
discussion
anticipated
advancements,
emerging
trends,
future
directions
Briefings in Bioinformatics,
Год журнала:
2025,
Номер
26(2)
Опубликована: Март 1, 2025
Abstract
For
metaproteomics
data
derived
from
the
collective
protein
composition
of
dynamic
multi-organism
systems,
proportion
missing
values
and
dimensions
exceeds
that
observed
in
single-organism
experiments.
Consequently,
evaluations
differential
analysis
strategies
other
mass
spectrometry
(MS)
(such
as
proteomics
metabolomics)
may
not
be
directly
applicable
to
data.
In
this
study,
we
systematically
evaluated
five
imputation
methods
[sample
minimum,
quantile
regression,
k-nearest
neighbors
(KNN),
Bayesian
principal
component
(bPCA),
random
forest
(RF)]
six
imputation-free
(moderated
t-test,
two-part
Wilcoxon
test,
semiparametric
abundance
analysis,
with
Bayes
shrinkage
estimation
variance
method,
Mixture)
for
simulated
metaproteomic
datasets
based
on
both
data-dependent
acquisition
MS
experiments
emerging
data-independent
The
simulation
comprised
588
scenarios
by
considering
impacts
sample
size,
fold
change
between
case
control,
value
ratio
at
nonrandom.
Compared
methods,
KNN,
bPCA,
RF
performed
poorly
a
high
missingness
large
size
resulted
false-positive
risk.
We
made
empirical
recommendations
balance
sensitivity
control
false
positives.
moderated
t-test
was
optimal
low
ratio.
test
recommended
small
or
comprehensive
our
study
can
provide
guidance
metaproteomics.
Fermentation,
Год журнала:
2025,
Номер
11(5), С. 259 - 259
Опубликована: Май 5, 2025
This
review
focuses
on
the
potential
utilization
of
artificial
intelligence
(AI)
tools
to
deepen
our
understanding
probiotics,
their
mode
action,
and
technological
characteristics
such
as
survival.
To
that
end,
this
provides
an
overview
current
knowledge
probiotics
well
next-generation
probiotics.
AI-aided
omics
technologies,
including
genomics,
transcriptomics,
proteomics,
offer
new
insights
into
genetic
functional
properties
Furthermore,
AI
can
be
used
elucidate
key
probiotic
activities
microbiota
modulation,
metabolite
production,
immune
system
interactions
enable
improved
health
impacts.
Additionally,
technologies
facilitate
precision
in
identifying
impacts,
role
gut
health,
anticancer
activity,
antiaging
effects.
Beyond
applications,
expand
use
optimizing
storage
survival
broadening
biotechnological
approaches.
In
context,
addresses
how
AI-driven
approaches
facilitated
by
strengthening
evaluation
characteristics,
explaining
mechanisms
enhancing
applications.
Moreover,
enhance
impact
assessments
optimize
industrial
applications
is
highlighted,
concluding
with
future
perspectives
transformative
research.
Abstract
Metaproteomics
is
an
emerging
approach
for
studying
microbiomes,
offering
the
ability
to
characterize
proteins
that
underpin
microbial
functionality
within
diverse
ecosystems.
As
primary
catalytic
and
structural
components
of
provide
unique
insights
into
active
processes
ecological
roles
communities.
By
integrating
metaproteomics
with
other
omics
disciplines,
researchers
can
gain
a
comprehensive
understanding
ecology,
interactions,
functional
dynamics.
This
review,
developed
by
Initiative
(
www.metaproteomics.org
),
serves
as
practical
guide
both
microbiome
proteomics
researchers,
presenting
key
principles,
state‐of‐the‐art
methodologies,
analytical
workflows
essential
metaproteomics.
Topics
covered
include
experimental
design,
sample
preparation,
mass
spectrometry
techniques,
data
analysis
strategies,
statistical
approaches.
ISME Communications,
Год журнала:
2024,
Номер
4(1)
Опубликована: Янв. 1, 2024
Abstract
Tremendous
advances
in
mass
spectrometric
and
bioinformatic
approaches
have
expanded
proteomics
into
the
field
of
microbial
ecology.
The
commonly
used
spectral
annotation
method
for
metaproteomics
data
relies
on
database
searching,
which
requires
sample-specific
databases
obtained
from
whole
metagenome
sequencing
experiments.
However,
creating
these
is
complex,
time-consuming,
prone
to
errors,
potentially
biasing
experimental
outcomes
conclusions.
This
asks
alternative
that
can
provide
rapid
orthogonal
insights
data.
Here,
we
present
NovoLign,
a
de
novo
pipeline
performs
sequence
alignment
sequences
complete
enables
taxonomic
profiling
complex
communities
evaluates
coverage
searches.
Furthermore,
NovoLign
supports
creation
reference
searching
ensure
comprehensive
coverage.
We
assessed
false
positive
annotations
using
wide
range
silico
data,
including
pure
strains,
laboratory
enrichment
cultures,
synthetic
communities,
environmental
communities.
In
summary,
employs
large-scale
enable
profiling,
evaluation
outcomes,
databases.
publicly
available
via:
https://github.com/hbckleikamp/NovoLign.
Communications Chemistry,
Год журнала:
2024,
Номер
7(1)
Опубликована: Авг. 16, 2024
The
gut
microbiota
offers
an
extensive
resource
of
enzymes,
but
many
remain
uncharacterized.
To
distinguish
the
activities
similar
annotated
proteins
and
mine
potentially
applicable
ones
in
microbiome,
we
applied
effective
Activity-Based
Metaproteomics
(ABMP)
strategy
using
a
specific
activity-based
probe
(ABP)
to
screen
entire
microbiome
for
directly
discovering
active
enzymes
their
potential
applications,
not
exploring
host-microbiome
interactions.
By
cyclophellitol
aziridine
α-galactosidases
(AGAL),
successfully
identified
characterized
several
possessing
AGAL
activities.
Cryo-electron
microscopy
analysis
newly
enzyme
(AGLA5)
revealed
covalent
binding
conformations
between
AGAL5
site
ABP,
which
could
provide
insights
into
enzyme's
catalytic
mechanism.
four
AGALs
have
diverse
activities,
including
raffinose
family
oligosaccharides
(RFOs)
hydrolysis
enzymatic
blood
group
transformation.
Collectively,
present
ABMP
platform
that
facilitates
discovery,
biochemical
activity
annotations
industrial
or
biopharmaceutical
applications.
however,
Here,
authors
apply
metaproteomics
identify
characterize
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 15, 2024
Abstract
While
metaproteomics
provides
invaluable
insight
into
microbial
communities
and
functions,
significant
bioinformatics
challenges
persist
due
to
data
complexity
the
limitations
of
database
searching.
We
introduce
Orthrus
,
a
hybrid
approach
combining
transformer-based
de
novo
sequencing
(
Casanovo
)
searching
with
rescoring
Sage
+
Mokapot
).
Benchmarking
against
PEAKS
®
11
MaxQuant
MetaNovo
demonstrates
high
peptide
outputs,
taxonomic
diversity,
proteome
coverage.
is
Python-based
accessible
all
via
Google
Colaboratory.
The
human
gut
microbiota
(GM)
is
a
community
of
microorganisms
that
resides
in
the
gastrointestinal
(GI)
tract.
Recognized
as
critical
element
health,
functions
GM
extend
beyond
GI
well-being
to
influence
overall
systemic
health
and
susceptibility
disease.
Among
other
omic
sciences,
metaproteomics
highlights
additional
facets
make
it
highly
valuable
discipline
study
GM.
Indeed,
allows
protein
inventory
complex
microbial
communities.
Proteins
with
associated
taxonomic
membership
function
are
identified
quantified
from
their
constituent
peptides
by
liquid
chromatography
coupled
mass
spectrometry
analyses
querying
specific
databases
(DBs).
aim
this
review
was
compile
comprehensive
information
on
metaproteomic
studies
GM,
focus
bacterial
component,
assist
newcomers
understanding
methods
types
research
conducted
field.
outlines
key
steps
metaproteomic-based
study,
such
extraction,
DB
selection,
bioinformatic
workflow.
importance
standardization
emphasized.
In
addition,
list
previously
published
provided
hints
for
researchers
interested
investigating
role
disease
states.
