New Nature Communications Publication by Mann & Theis Groups Harnesses the Benefits of Large-scale Peptide Collisional Cross ...
February 25 2021 - 8:41AM
Business Wire
- Measured more than a million collision cross sections (CCS)
from whole-proteome digests of five organisms with trapped ion
mobility spectrometry (TIMS) and parallel accumulation-serial
fragmentation (PASEF) on several timsTOF Pro systems
- Large-scale CCS data from 360 LC-TIMS-MS/MS runs, processed
with MaxQuant
- With CCS alignment, across 347,885 peptide CCS values
measured in duplicate, the median coefficient of variation (CV) was
0.4%; highlights excellent reproducibility of TIMS CCS over longer
periods of time and across instruments
- Precision (CV < 1%) of CCS data is sufficient to train a
deep recurrent neural network that accurately predicts CCS values
solely based on proteogenomic peptide sequences (R >
0.99)
- Harnessing deep learning, CCS values can now be predicted
for any peptide and organism, forming a basis for advanced 4D
proteomics TIMS/PASEF workflows that make full use of the
additional peptide CCS information
Bruker Corporation (Nasdaq: BRKR) today announces a seminal
publication from the groups of Professors Matthias Mann and Fabian
Theis in the journal Nature Communications with the title ‘Deep
learning the collisional cross sections of the peptide universe
from a million experimental values’ by Florian Meier et al.
(doi.org/10.1038/s41467-021-21352-8)1.
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Fig. 1: Large-scale peptide collisional
cross section (CCS) measurement with TIMS and PASEF. From "Deep
learning the collisional cross sections of the peptide universe
from a million experimental values". (a) Workflow from extraction
of whole-cell proteomes through digestion, fractionation, and
chromatographic separation of each fraction. The TIMS-quadrupole
TOF mass spectrometer was operated in PASEF mode. (b) Overview of
the CCS dataset in this study by organism. (c) Frequency of peptide
C-terminal amino acids. (d) Frequency of peptide N-terminal amino
acids. (e) Distribution of 559,979 unique data points, including
modified sequence and charge state, in the CCS vs. m/z space
color-coded by charge state. Density distributions for m/z and CCS
are projected on the top and right axes, respectively. Source data
are provided as a Source Data file. (Graphic: Business Wire)
The Nature Communications paper describes CCS values measured on
the timsTOF pro as an essentially intrinsic property of the peptide
ions, which can be used to improve confidence in peptide and
protein group identification in 4D shotgun proteomics. Since mass
spectrometry-based proteomics relies on accurate matching of
acquired spectra against a database of protein sequences, accurate
CCS values offer the benefit of narrowing down the list of
candidates. This is essential for high sensitivity proteomics where
low levels of peptide signals need to be accurately measured in a
complex mixture, e.g. in plasma proteomics, peptidomics,
immunopeptidomics or metaproteomics.
The publication summarizes a collaborative research effort led
by Professor Matthias Mann, who holds dual appointments at the Max
Planck Institute of Biochemistry in Martinsried, Germany and the
Novo Nordisk Foundation Center for Protein Research at the
University of Copenhagen in Denmark, together with the group of
Professor Fabian Theis, who also holds dual appointments at the
Helmholtz Center Munich in the German Research Center for
Environmental Health, and in the Department of Mathematics at TU
Munich, in Germany.
Lead author Dr. Florian Meier, now an Assistant Professor in
Functional Proteomics at the Jena University Hospital in Germany,
said: “The scale and precision of peptide CCS values in our data
from the timsTOF pro was sufficient to train our deep learning
model to accurately predict CCS values based only on the peptide
sequence. This connection between the amino acids contained within
a peptide sequence and its measured CCS has tremendous potential to
increase the confidence of protein identification. Since the
peptide CCS values are entirely determined by their linear amino
acid sequences, they should be predictable with high accuracy and
our deep learning model accurately predicted CCS values even for
previously unobserved peptides. We acquired data from
whole-proteome digests of five organisms, which resulted in the
measurement of over two million CCS values, including about 500,000
unique peptides, making it by far the most comprehensive CCS data
set to date.”
Professor Matthias Mann added: “The source code is publicly
available so that further developments can be accelerated for
training and prediction models of the human peptide universe.
Conceptually, our CCS model could make dia-PASEF faster and less
expensive by reducing the effort to generate libraries.
Additionally, predicted CCS values should allow for the use of
community libraries, such as the Pan Human library, a repository of
over 10,000 human proteins, for targeted proteomics.”
Professor Fabian Theis stated: “Deep learning, in particular the
used recurrent neural networks need a lot of samples to be
predictive, so I was very happy when Matthias approached me and we
jointly were able to predict and interpolate biochemical properties
of peptides based only on their sequence. I personally liked the
fact that we could thus impute CCS values also for many never
before measured peptides."
Dr. Gary Kruppa, the Bruker Vice President for Proteomics,
commented: “This paper showcases the tremendous potential of
accurate CCS values for TIMS-PASEF methods in unbiased, deep 4D
proteomics. The proven robustness, higher throughput and ultra-high
sensitivity of the timsTOF platform is highly suitable for
translational research. Large-scale peptide CCS values provide a
fundamental advantage in the confidence of protein identification
and quantitation in biomarker research in large cohort studies.
Furthermore, the benefits of CCS values for improving confidence of
identification are also applicable to other multiomics timsTOF
workflows, such as metabolomics, lipidomics and glycomics. These
are exciting times for our rapidly growing timsTOF user
community.”
About Bruker Corporation (Nasdaq: BRKR)
Bruker is enabling scientists to make breakthrough discoveries
and develop new applications that improve the quality of human
life. Bruker’s high performance scientific instruments and high
value analytical and diagnostic solutions enable scientists to
explore life and materials at molecular, cellular and microscopic
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innovation, improved productivity and customer success in life
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industrial applications. Bruker offers differentiated, high-value
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imaging, clinical phenomics research, proteomics and multiomics,
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molecular diagnostics. For more information, please visit:
www.bruker.com.
1 Meier, F., K�hler, N.D., Brunner, AD. et al. Deep learning the
collisional cross sections of the peptide universe from a million
experimental values. Nat Commun 12, 1185 (2021).
https://doi.org/10.1038/s41467-021-21352-8
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