6 research outputs found
Creating a Mass Spectral Reference Library for Oligosaccharides in Human Milk
We report the development
and availability of a mass spectral reference
library for oligosaccharides in human milk. This represents a new
variety of spectral library that includes consensus spectra of compounds
annotated through various data analysis methods, a concept that can
be extended to other varieties of biological fluids. Oligosaccharides
from the NIST Standard Reference Material (SRM) 1953, composed of
human milk pooled from 100 breastfeeding mothers, were identified
and characterized using hydrophilic interaction liquid chromatography
electrospray ionization tandem mass spectrometry (HILIC-ESI-MS/MS)
and the NIST 17 Tandem MS Library. Consensus reference spectra were
generated, incorporated into a searchable library, and matched using
the newly developed hybrid search algorithm to elucidate unknown oligosaccharides.
The NIST hybrid search program facilitates the structural assignment
of complex oligosaccharides especially when reference standards are
not commercially available. High accuracy mass measurement for precursor
and product ions, as well as the relatively high MS/MS signal intensities
of various oligosaccharide precursors with Fourier transform ion trap
(FT-IT) and higher energy dissociation (HCD) fragmentation techniques,
enabled the assignment of multiple free and underivatized fucosyllacto-
and sialyllacto-oligosaccharide spectra. Neutral and sialylated isomeric
oligosaccharides have distinct retention times, allowing the identification
of 74 oligosaccharides in the reference material. This collection
of newly characterized spectra based on a searchable, reference MS
library of annotated oligosaccharides can be applied to analyze similar
compounds in other types of milk or any biological fluid containing
milk oligosaccharides
Creation of Libraries of Recurring Mass Spectra from Large Data Sets Assisted by a Dual-Column Workflow
An analytical methodology has been
developed for extracting recurrent
unidentified spectra (RUS) from large GC/MS data sets. Spectra were
first extracted from original data files by the Automated Mass Spectral
Deconvolution and Identification System (AMDIS; Stein, S. E. J. Am. Soc. Mass Spectrom. 1999, 10, 770–781) using settings designed to minimize spurious
spectra, followed by searching the NIST library with all unidentified
spectra. The spectra that could not be identified were then filtered
to remove poorly deconvoluted data and clustered. The results were
assumed to be unidentified components. This was tested by requiring
each unidentified spectrum to be found in two chromatographic columns
with slightly different stationary phases. This methodology has been
applied to a large set of pediatric urine samples. A library of spectra
and retention indices for derivatized urine components, both identified
and recurrent unidentified, has been created and is available for
download
The Hybrid Search: A Mass Spectral Library Search Method for Discovery of Modifications in Proteomics
We present a mass spectral library-based
method to identify tandem
mass spectra of peptides that contain unanticipated modifications
and amino acid variants. We describe this as a “hybrid”
method because it combines matching both ion <i>m</i>/<i>z</i> and mass losses. The mass loss is the difference between
the mass of an ion peak and the mass of its precursor. This difference,
termed DeltaMass, is used to shift the product ions in the library
spectrum that contain the modification, thereby allowing library product
ions that contain the unexpected modification to match the query spectrum.
Clustered unidentified spectra from the Clinical Proteomic Tumor Analysis
Consortium (CPTAC) and Chinese hamster ovary cells were used to evaluate
this method. The results demonstrate the ability of the hybrid method
to identify unanticipated modifications, insertions, and deletions,
which may include those due to an incomplete protein sequence database
or to search settings that exclude the correct identification, in
high-resolution tandem mass spectra without regard to their precursor
mass. This has been made possible by indexing of the <i>m</i>/<i>z</i> value of each fragment ion and its difference
in mass from its precursor ion
Reverse and Random Decoy Methods for False Discovery Rate Estimation in High Mass Accuracy Peptide Spectral Library Searches
Spectral library searching (SLS)
is an attractive alternative to
sequence database searching (SDS) for peptide identification due to
its speed, sensitivity, and ability to include any selected mass spectra.
While decoy methods for SLS have been developed for low mass accuracy
peptide spectral libraries, it is not clear that they are optimal
or directly applicable to high mass accuracy spectra. Therefore, we
report the development and validation of methods for high mass accuracy
decoy libraries. Two types of decoy libraries were found to be suitable
for this purpose. The first, referred to as Reverse, constructs spectra
by reversing a library’s peptide sequences except for the C-terminal
residue. The second, termed Random, randomly replaces all non-C-terminal
residues and either retains the original C-terminal residue or replaces
it based on the amino-acid frequency of the library’s C-terminus.
In both cases the <i>m</i>/<i>z</i> values of
fragment ions are shifted accordingly. Determination of FDR is performed
in a manner equivalent to SDS, concatenating a library with its decoy
prior to a search. The utility of Reverse and Random libraries for
target-decoy SLS in estimating false-positives and FDRs was demonstrated
using spectra derived from a recently published synthetic human proteome
project (Zolg, D. P.; et al. <i>Nat. Methods</i> <b>2017</b>, 14<i>,</i> 259–262). For data sets from two large-scale
label-free and iTRAQ experiments, these decoy building methods yielded
highly similar score thresholds and spectral identifications at 1%
FDR. The results were also found to be equivalent to those of using
the decoy-free PeptideProphet algorithm. Using these new methods for
FDR estimation, MSPepSearch, which is freely available search software,
led to 18% more identifications at 1% FDR and 23% more at 0.1% FDR
when compared with other widely used SDS engines coupled to postprocessing
approaches such as Percolator. An application of these methods for
FDR estimation for the recently reported “hybrid” library
search (Burke, M. C.; et al. <i>J. Proteome Res.</i> <b>2017</b>, <i>16</i>, 1924–1935) method is also
made. The application of decoy methods for high mass accuracy SLS
permits the merging of these results with those of SDS, thereby increasing
the assignment of more peptides, leading to deeper proteome coverage
Reverse and Random Decoy Methods for False Discovery Rate Estimation in High Mass Accuracy Peptide Spectral Library Searches
Spectral library searching (SLS)
is an attractive alternative to
sequence database searching (SDS) for peptide identification due to
its speed, sensitivity, and ability to include any selected mass spectra.
While decoy methods for SLS have been developed for low mass accuracy
peptide spectral libraries, it is not clear that they are optimal
or directly applicable to high mass accuracy spectra. Therefore, we
report the development and validation of methods for high mass accuracy
decoy libraries. Two types of decoy libraries were found to be suitable
for this purpose. The first, referred to as Reverse, constructs spectra
by reversing a library’s peptide sequences except for the C-terminal
residue. The second, termed Random, randomly replaces all non-C-terminal
residues and either retains the original C-terminal residue or replaces
it based on the amino-acid frequency of the library’s C-terminus.
In both cases the <i>m</i>/<i>z</i> values of
fragment ions are shifted accordingly. Determination of FDR is performed
in a manner equivalent to SDS, concatenating a library with its decoy
prior to a search. The utility of Reverse and Random libraries for
target-decoy SLS in estimating false-positives and FDRs was demonstrated
using spectra derived from a recently published synthetic human proteome
project (Zolg, D. P.; et al. <i>Nat. Methods</i> <b>2017</b>, 14<i>,</i> 259–262). For data sets from two large-scale
label-free and iTRAQ experiments, these decoy building methods yielded
highly similar score thresholds and spectral identifications at 1%
FDR. The results were also found to be equivalent to those of using
the decoy-free PeptideProphet algorithm. Using these new methods for
FDR estimation, MSPepSearch, which is freely available search software,
led to 18% more identifications at 1% FDR and 23% more at 0.1% FDR
when compared with other widely used SDS engines coupled to postprocessing
approaches such as Percolator. An application of these methods for
FDR estimation for the recently reported “hybrid” library
search (Burke, M. C.; et al. <i>J. Proteome Res.</i> <b>2017</b>, <i>16</i>, 1924–1935) method is also
made. The application of decoy methods for high mass accuracy SLS
permits the merging of these results with those of SDS, thereby increasing
the assignment of more peptides, leading to deeper proteome coverage
Reverse and Random Decoy Methods for False Discovery Rate Estimation in High Mass Accuracy Peptide Spectral Library Searches
Spectral library searching (SLS)
is an attractive alternative to
sequence database searching (SDS) for peptide identification due to
its speed, sensitivity, and ability to include any selected mass spectra.
While decoy methods for SLS have been developed for low mass accuracy
peptide spectral libraries, it is not clear that they are optimal
or directly applicable to high mass accuracy spectra. Therefore, we
report the development and validation of methods for high mass accuracy
decoy libraries. Two types of decoy libraries were found to be suitable
for this purpose. The first, referred to as Reverse, constructs spectra
by reversing a library’s peptide sequences except for the C-terminal
residue. The second, termed Random, randomly replaces all non-C-terminal
residues and either retains the original C-terminal residue or replaces
it based on the amino-acid frequency of the library’s C-terminus.
In both cases the <i>m</i>/<i>z</i> values of
fragment ions are shifted accordingly. Determination of FDR is performed
in a manner equivalent to SDS, concatenating a library with its decoy
prior to a search. The utility of Reverse and Random libraries for
target-decoy SLS in estimating false-positives and FDRs was demonstrated
using spectra derived from a recently published synthetic human proteome
project (Zolg, D. P.; et al. <i>Nat. Methods</i> <b>2017</b>, 14<i>,</i> 259–262). For data sets from two large-scale
label-free and iTRAQ experiments, these decoy building methods yielded
highly similar score thresholds and spectral identifications at 1%
FDR. The results were also found to be equivalent to those of using
the decoy-free PeptideProphet algorithm. Using these new methods for
FDR estimation, MSPepSearch, which is freely available search software,
led to 18% more identifications at 1% FDR and 23% more at 0.1% FDR
when compared with other widely used SDS engines coupled to postprocessing
approaches such as Percolator. An application of these methods for
FDR estimation for the recently reported “hybrid” library
search (Burke, M. C.; et al. <i>J. Proteome Res.</i> <b>2017</b>, <i>16</i>, 1924–1935) method is also
made. The application of decoy methods for high mass accuracy SLS
permits the merging of these results with those of SDS, thereby increasing
the assignment of more peptides, leading to deeper proteome coverage