Database Search Strategies for Proteomic Data Sets Generated by Electron Capture Dissociation Mass Spectrometry



research articles

Sweet et al.


charged precursors, these neutral loss peaks occupy a region
of the
m/z range which is either free of c, z and y fragment
ions or can only contain certain large c, z and y ions. Previous
observations in our laboratory reveal that ECD mass spectra
from doubly charged precursors typically give lower scores and
identification rates than more highly charged precursors. In
part this is likely due to the fragmentation process itself:
electron capture efficiency increases as the square of the ion
charge hence doubly charged precursors are less likely to
capture an electron. In addition, only one of the resulting
fragments can retain the single positive charge. We test whether
the identification rate for ECD mass spectra from doubly
charged precursors can be increased through removal of some
of the aforementioned uninformative peak types. Recently,
Good et al.9 developed a similar presearch processing algorithm
for electron transfer dissociation (ETD) data. The algorithm was
tested on a low resolution ETD data set. The Good algorithm
differs from that described here: (1) rather than remove the
entire neutral loss region from the mass spectrum, our algo-
rithm retains all potential true peaks; (2) our algorithm does
not remove neutral loss regions from higher (
>2+) charge-state
precursor ions; (3) our algorithm allows removal of noise peaks.
We compare our algorithm with the Good algorithm for high
resolution ECD data.

Methods

Cell Culture and Sample Preparation. Mouse fibroblast NIH
3T3 cells were cultured and lysed as previously described.5
Proteins were reduced and alkylated (DTT and iodoacetamide),
digested using trypsin and the resulting peptides separated by
SCX chromatography, again as described previously.5

Liquid Chromatography Tandem Mass Spectrometry (LC-
MS/MS).
Online liquid chromatography was performed by use
of a Micro AS autosampler and Surveyor MS pump (Thermo
Fisher Scientific, Bremen, Germany). Peptides were loaded onto
a 75
μm (internal diameter) Integrafrit (New Objective) C8
resolving column (length 10 cm) and separated over a 40 min
gradient from 0% to 40% acetonitrile (Baker, Holland). Peptides
eluted directly (
~350 nL/min) via a Triversa nanospray source
(Advion Biosciences, NY) intoa7TLTQFTmass spectrometer
(Thermo Fisher Scientific), where they were subjected to data-
dependent CID and ECD.

Data-Dependent CID and ECD (DD-CID-ECD). The mass
spectrometer alternated between a full FT-MS scan (
m/z
400-1600) and subsequent CID and ECD MS/MS scans of the
most abundant ion above a threshold of 40 000. Survey scans
were acquired in the ICR cell with a resolution of 100 000 at
m/z 400. Precursor ions were subjected to CID in the linear
ion trap. The width of the precursor isolation window was 6
m/z. Only multiply charged precursor ions were selected for
MS/MS. CID was performed with helium gas at a normalized
collision energy of 35%. Automated gain control was used to
accumulate sufficient precursor ions (target value 5
× 104,
maximum fill time 0.2 s). For the ECD event, precursor ions
were isolated in the ion trap and transferred to the ICR cell.
Isolation width was 6
m/z. Automated gain control was used
to accumulate sufficient precursor ions (target value 1
× 106
per microscan, maximum fill time 1 s). The electrons for ECD
were produced by an indirectly heated barium tungsten
cylindrical dispenser cathode (5.1 mm diameter, 154 mm from
the cell, 1 mm off-axis). The current across the electrode was
~1.1 A. Ions were irradiated for 60 ms at 5% energy (corre-
sponding to a cathode potential of -2.775 V). Each ECD scan

5476 Journal of Proteome Research Vol. 8, No. 12, 2009
comprised 4 coadded microscans, acquired with a resolution
of25 000 at
m/z 400. Dynamic exclusion was used with a repeat
count of 1 and an exclusion duration of 60 s. Data acquisition
was controlled by Xcalibur 2.0 and Tune 2.2 software (Thermo
Fisher Scientific, Inc.).

Data Analysis. DTA files were created from the raw data
using Bioworks 3.3.1 (Thermo Fisher Scientific, Inc.). The DTA
files were either searched directly, or preprocessed using a perl
script to remove non-c,z,y peaks as described in the text. The
perl script is available as a Supplementary File online. The
regions removed from all ECD mass spectra consisted of (1) a
prominent noise peak (
m/z 101.7-102.1) and (2) the isolation
window around the precursor ion (precursor ( 3
m/z). For ECD
mass spectra of [M + 2H]2+ ions, the region containing neutral
loss peaks from the charge-reduced precursor was modified
as follows: in the region [M + 2H]+
m/z([M + 2H]+ - 57),
no peaks were retained. (No c,z,y peaks can fall in this region
as the smallest amino acid residue, glycine, has a mass of 57
Da.) Within the region ([M + 2H]+ - 57)
m/z([M + 2H]+
- 140), the peaks that could correspond to c ions, z prime ions,
z dot ions, and y ions were retained. The list of masses retained,
annotated with their corresponding fragment, is given in
Supporting Information Table 1. The window around each
retained
m/z was set at (12 ppm.

For comparison, DTA files were also generated using the DTA
Generator developed for postacquisition ETD processing by
Good, et al.9 These DTA files were either left unprocessed (1),
processed with the Good ETD algorithm (2) or processed as
above with our algorithm (3). Options: Fragment ion intensity,
0.7% (relative). Assumed precursor charge state range: 2-8.
ETD Cleaning algorithm: none (1 and 3). Output: individual
DTAs. Or for (2) ETD Cleaning algorithm: smart. Clean precur-
sor, charge-reduced precursor, clean NL from charge-reduced
precursor.

Searches were carried out against a concatenated database
consisting of the mouse IPI database (Version 3.40) supple-
mented with common contaminants (including keratins, trypsin,
BSA) and the reversed-sequence version of the same database.
The final database contained 107 688 protein entries (53 844
of which were reversed-sequence versions). CID and ECD data
were searched separately.

Mascot (version 2.2.; Matrix Science, U.K.) was searched
using the following parameters for ECD data: enzyme, trypsin;
maximum missed cleavages, 2; fixed modification, carbami-
domethyl (C); variable modifications, acetyl (protein N-termi-
nus), Oxidation (M); Peptide tolerance, 1.1 Da (or as stated in
the text); MS/MS tolerance, 0.02 Da; Instrument, FTMS-ECD
(2+ fragments if precursor 3+ or higher; ion types, c, y, z + 1,
z + 2 [corresponds to z-dot and z-prime]). Settings for CID data
were as above, except MS/MS tolerance, 0.5 Da; Instrument,
ESI-TRAP (2+ fragments if precursor 2+ or higher; ion types,
b, b with NH
3 loss ifb significant and fragment contains RKNQ,
b with water loss if b significant and fragment contains STED,
y, y with NH
3 and water losses (as for b)).

Mascot search results were exported (“Format as: export
search results”) using the following settings: export format, CSV;
significance threshold
<1; Max. number of hits, 16 000. All other
settings were left as default. The number of proteins exported
was checked, to ensure it did not reach the number specified
in “Max. number of hits”, above. This indicates that all
identifications, however low-scoring, were exported. A perl
script was used to copy the protein identifier into every peptide
identification row for each protein identified.



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