Glycopeptides

Modules for binomial fitting were developed and implemented with Visual Basic scripts as follows. Isotopic peaks in the spectra were converted to integer values by subtracting the monoisotopic peak m/z and multiplying by the charge state (z). The peptide natural abundance isotopic distribution was either read directly from the undeuterated spectra or calculated from the amino acid (and carbohydrate) composition [5 (link)]. The number of slow-exchanging amides was based on the peptide sequence and used as the number of events (n) in calculation of the binomial distribution function (eq. 1). n was initially estimated as the number of amino acids minus the number of prolines, minus 1 for the N-terminal residue. In some cases peptides contained fast exchanging residues, which back-exchange rapidly [23 (link)] and therefore n was set slightly lower. For glycopeptides the glycan groups need to be taken into account as some carbohydrate groups (primarily N-Acetyl hexosamines) will also retain deuterium [24 (link)]. Examination of the fit to a fully deuterated standard was used to assess whether the value of n generated the correct envelope width, and with the majority of peptides the initial estimate was accurate. For a few highly protected peptides, better fits were achieved with a slightly lower n for the early time points, presumably because some amides have yet to exchange by then.
The centroid shift in each spectrum relative to the undeuterated profile served as an initial estimate of the binomial distribution probability (“p”). Each theoretical peak (I_m^calc) was reconstructed by applying the natural abundance profile to each peak in the binomial distribution with up to 3 points of zero padding on both sides of the mass envelope as described by Chik et al [19 (link)]. The peak intensities were scaled by a weighting term (A), using the intensity of the highest data point as an initial guess. Least squares regression was performed using the Gauss-Newton algorithm implemented within the Excel Solver module (Microsoft, Redmond WA) to minimize the discrepancy (χ²) between the isotopic peaks and the calculated binomial profile by varying p and A (eq. 2). For data sets showing overlap with an interfering ion, the asymmetry term (λ) was user defined (typically between 2 and 10, based on visual assessment) and applied to points where the fit exceeded the data. The resulting degree of deuteration was calculated as either the percentage relative to the undeuterated (p_UN) and fully deuterated (p_TD) values (p_t-p_UN)/(p_TD-p_UN) or average deuterium uptake (p_t·n), and was plotted in the summary page for each time point (t) and condition. $${I_m}^{\mathit{\text{calc}}}=A·\frac{n!}{m!(n-m)!}{p}^x{(1-p)}^{n-m}$$
$$\begin{gathered} {\chi }^2=\sum _{m=0}^n(\lambda )·{({I_m}^{\mathit{\text{calc}}}-{I_m}^{\text{exp}})}^2 \\ \lambda =1\text{if}{I_m}^{\mathit{\text{calc}}}<{I_m}^{\mathit{\text{exp}}} \\ \lambda >1\text{if}{I_m}^{\mathit{\text{calc}}}<{I_m}^{\mathit{\text{exp}}} \end{gathered}$$

Deisotoping has been incorporated as an option in MSFragger starting with version 2.3. All searches described here were performed in a beta version of MSFragger (2.4-RC6-glyco) that included glycoproteomics search capabilities to enable searches of glyco data, but all beta capabilities have since been incorporated into the released version of MSFragger (starting with v3.0)^{28 (link)}. Full parameters for all MSFragger searches and Philosopher validation and filtering can be found in the supporting information. Parameter files are named to match the labels provided in figures and tables. Briefly, searches were performed as follows. The reviewed Human proteome from UniProt (downloaded 8/22/2019, 20464 sequences) was used with reversed decoys appended in Philosopher^{29 (link)} for all searches except the mouse N-glycopeptide data of Riley et al. and mouse neuropeptide data of Anapindi et al.. For searching data from Anapindi et al., the reviewed mouse (Mus musculus) proteome from Uniprot (downloaded 9/24/2019, 17019 sequences) was used with decoys appended in Philosopher. For Riley et al., the glycoprotein-focused database with 3,574 sequences used by Riley et al. was used with reversed decoys appended in Philosopher. For all searches except the peptidomics (nonspecific) search of neuropeptide data, trypsin digestion with 1 or 2 missed cleavages was allowed with resulting peptide lengths of 7 to 50 amino acids. Variable modifications of Met oxidation (up to 2 per peptide), protein N-terminal acetylation and peptide N-terminal pyroglutamate formation (Gln, Cys) were allowed with no more than 3 total modifications per peptide. For closed searches, the precursor mass tolerance was 20 ppm and the fragment mass tolerance was 10 ppm (Orbitrap) or 20 ppm (TOF) with mass calibration enabled (without parameter optimization) and isotope off-by-X errors of 0/1/2 allowed. Spectra were square root transformed and had precursor peaks removed prior to analysis. For open searches, precursor mass tolerance was −150 to 500 Da, with the same fragment tolerances as closed searches, calibration enabled, no isotope errors, and localization of delta masses enabled³⁰ . Each search was performed with and without deisotoping, with all other parameters identical.
For nonspecific searches of endogenous neuropeptides, no enzyme was specified and variable modifications of oxidation (Met), deamidation (peptide C-terminus), acetylation (protein N-terminus, Lys), pyroglutamate formation (N-terminal Gln), and water loss (N-terminal Glu) were allowed, with a maximum of 1 each and 3 total modifications per peptide. Peptide lengths of 7 to 70 amino acids were allowed with masses of 500 to 12,000 Da. Three matched ions were required for modeling and five matched ions were required for reporting to pepXML output file. Precursor and fragment mass tolerances were 20 and 25 ppm, no calibration was performed, and isotope errors of 0/1/2 were allowed. Since the number of candidate peptides exceeded the maximum possible within MSFragger (~ 2 billion), it was run using the split database method available within FragPipe.
For N-glycopeptide searches in the data of Riley et al., raw files were split into separate mzML files by activation type (HCD or AI-ETD). Up to 3 missed cleavages were allowed and a list of 182 glycan masses (identical to those used in Riley et al.) were set as N-glycan mass offsets. For HCD spectra, b, y, Y, b~, and y~ ions were considered (~ refers to the addition of one HexNAc, 203.0794 Da), without localization of delta masses. AI-ETD spectra considered b, y, c, z, and Y ions, used delta mass localization, and removed precursor and M + e⁻ peaks. Default Y and oxonium ion lists were used.
FDR filtering was performed with the Philosopher^{29 (link)} pipeline, including PeptideProphet^{31 (link)} modeling of peptide probabilities, ProteinProphet^{32 (link)} protein inference, and Philosopher’s internal FDR filter. In PeptideProphet, closed searches used the accmass, ppm, nonparam, expectscore and decoyprobs options, while open searches used nonparam, expectscore, and decoyprobs options along with the extended mass model^{1 (link)} (masswidth 1000) and clevel −2. All searches used ProteinProphet with default options except maxppmdiff increased to 20,000,000. All searches were filtered to 1% PSM and protein level FDR using the razor peptides method with a sequential filtering step to remove any PSMs from proteins that did not pass FDR.

Selection of the component programs in the data analysis pipeline was based on the diversity of sample processing and instruments used in the studies. Since both label-free and iTRAQ^™ 4plex strategies were used during early “system suitability” (xenograft analysis) evaluation studies, the pipeline was designed to handle both data types as well as data from phosphopeptide and glycopeptide enrichment studies. All laboratories used Thermo Fisher Orbitrap^™-based high-resolution mass spectrometers for TCGA samples. In general, the design of the pipeline was based on group consensus by a steering committee of collaborators as well as the availability of tools for the NIST hardware and operating system infrastructure. Details of the pipeline are given below. Importantly, the database file and software versions were not changed throughout the processing of the “system suitability” and TCGA tumor analysis data files. (See Table 1 for a detailed list of software and parameters used.)