Improved mass defect model for theoretical tryptic peptides

Abstract

Improvements in the mass accuracy and resolution of mass spectrometers have greatly aided mass spectrometry-based proteomics in profiling complex biological mixtures. With the use of innovative bioinformatics approaches, high mass accuracy and resolution information can be used for filtering chemical noise in mass spectral data. Using our recent algorithmic developments, we have generated the mass distributions of all theoretical tryptic peptides composed of 20 natural amino acids and with masses limited to 3.5 kDa. Peptide masses are distributed discretely, with well-defined peak clusters separated by empty or sparsely populated trough regions. Accurate models for peak centers and widths can be used to filter peptide signals from chemical noise. We modeled mass defects, the difference between monoisotopic and nominal masses, and peak centers and widths in the peptide mass distributions. We found that peak widths encompassing 95% of all peptide sequences are substantially smaller than previously thought. The result has implications for filtering out larger stretches of the mass axis. Mass defects of peptides exhibit an oscillatory behavior which is damped at high mass values. The periodicity of the oscillations is about 14 Da which is the most common difference between the masses of the 20 natural amino acids. To determine the effects of amino acid modifications on our findings, we examined the mass distributions of peptides composed of the 20 natural amino acids, oxidized Met, and phosphorylated Ser, Thr, and Tyr. We found that extension of the amino acid set by modifications increases the 95% peak width. Mass defects decrease, reflecting the fact that the average mass defect of natural amino acids is larger than that of oxidized Met. We propose that a new model for mass defects and peak widths of peptides may improve peptide identifications by filtering chemical noise in mass spectral data.

FIGURE 1

Peak dynamics of theoretical tryptic peptides for selected mass intervals. As peptides become larger, the peak centers “move” with respect to the integer masses. Peak center of theoretical tryptic peptides “crosses” the integer mass at 2128 Da (see the main text). Forbidden zones are seen in the distributions of peptides up to 1.5 kDa mass.

FIGURE 2

MD distributions for all theoretical tryptic peptides with masses up to 3.5 kDa. The color bar is in base 2 log scale for the number of peptide sequences for each pair of monoisotopic mass and MD, with violet and blue indicating low-populated areas and red indicating the most populated areas.

FIGURE 3

Mass defect as a function of nominal mass for peak centers of theoretical tryptic peptides. The MD grows (with oscillations) for peptides in the mass range up to 2128 Da. At 2128 Da, MD passes through an inflection point, i.e., its value drops to 0. After this abrupt change MD increases again. The empirical distribution of MD necessitates two separate fits: one for mass values up to 2128 Da and one for mass values between 2128 Da and 3.5 kDa.

FIGURE 4

Distribution of residuals of the MDs of the peak centers obtained by using Eq. 2. This distribution exhibits an oscillatory behavior and for large mass values the amplitude of the oscillations is damped.

FIGURE 5

Normalized plots of the total mass defects for Gr I (black solid line), Gr II (black broken line) and Gr III (red broken line). MD modeling and the necessity of two separate fittings stems from the relative abundances of Gr I and Gr II peptides. For Gr I peptides TMD is 1 Da and Eq. 2 (a) is an adequate description. In the mass range where Gr II peptides constitute the majority, Eq. 2 (b) is a better description. Note that even for higher masses there still will be peptides for which Eq. 2 (a) describes the correct MD.

FIGURE 6

95% PWs as a function of peptide nominal masses for peak centers (black lines). Also shown are predictions from the model distributions of Eq. 3 (blue line) and Eq. 1 (red line). The model using Eq. 1 is more conservative is more conservative in estimating PWs. PWs can be substantially reduced by using a combination of nonlinear (small mass values) and linear (larger mass values) models.

FIGURE 7

Spectral power of MD residual values from Figure 4. A prominent frequency, f_k k = 220, is observed. When converted back to mass domain, this frequency corresponds to a mass value of 14 Da (see the main text).