Biomedical Chemistry: Research and Methods 2019, 2(4), e00100

The Prediction of the Ion Fraction of the Peptide with Selected Charge in Mass Spectrometry with Positive Electrospray Ionization

V.S. Skvortsov*, N.N. Alekseychuk, Yu.V. Miroshnichenko, A.V. Rybina

Institute of Biomedical Chemistry, 10 Pogodinskaya str., Moscow, 119121 Russia; *e-mail: vladlen@ibmh.msk.su

Keywords:peptide; mass-spectrometry; electrospray ionization; property prediction

DOI:10.18097/BMCRM00100

The whole version of this paper is available in Russian.

The possibility of prediction of selected ion fraction in the total peptide fraction obtained during mass spectrometry with positive ionization by electrospray was investigated on the basis of the amino acid sequence. The data obtained in the MS / MS experiment [Ramus et al., 2015]] using the standardized UPS1 kit (48 highly purified human proteins) and deposited in ProteomeXchange (identifier PXD001819) were used as the initial data set. For each of the identified peptides belonging to one of the proteins of the UPS kit, a list of detected ions of different charge was formed. The sum of the peak intensities detected for the primary ion was used as a measure of quantity. Since the ratio of the peptide fractions of ions with different charges does not depend on the concentration in the experimental sample, the total sample was assembled by combining the data obtained for different dilutions of UPS1. A set of equations of prediction of the fraction of 1+, 2+, and 3+ ions has been constructed. This computational analysis has shown applicability of the proposed for prediction of the ion fraction of the peptide with selected charge in mass spectrometry with positive electrospray ionization.

Figure 1. The distribution of peptides in serial dilutions obtained in [3] and data combined into a virtual pulled set. Ramos et al. - data from [3]. Sample identification: peptides identified for a particular dilution were used. All identifications: all the peptides identified in [3] were used, regardless of dilution. Selected: additional filtration (accuracy of primary ions comparison was 5 ppm, peptides with N-terminal acetylation was removed).
Figure 2. Examples of comparison of the of peptide ion fraction of a particular charge (ωn+) obtained for different dilutions. Variant A includes peptides with the ωn+ value in these dilutions equal to 1 (only one variant of the ion is detected) or 0 (an ion with this charge has not been registered). Others – at least 2 fractions exist.
Figure 3. The comparison of the ratios between the sums of intensities of differently charged ions (Сn/m) obtained for different dilutions. Сn/m values did not change from dilution to dilution.
Figure 4. The comparison of observed and predicted Cn/m values in learning procedure. The linear regression equation used the amino acid composition of peptides as independent variables.
Figure 5. The comparison of observed and predicted Cn/m values in learning procedure. The linear regression equation used the amino acid composition of peptides as independent variables.

CLOSE
Table 1. The parameters of linear regression equations that predict the values of ωn+ and log(Сn/m) obtained during learning and in the leave-one-out procedure

FUNDING

This work was performed within the framework of the Program for Basic Research of State Academies of Sciences for 2013-2020.

SUPPLEMENTARY

Supplementary materials are available at http://dx.doi.org/10.18097/BMCRM00100

REFERENCES

  1. Yates, J. R., Ruse, C. I., & Nakorchevsky, A. (2009). Proteomics by mass spectrometry: approaches, advances, and applications. Annual review of biomedical engineering, 11, 49-79. DOI
  2. Iavarone, A. T., Jurchen, J. C., & Williams, E. R. (2000). Effects of solvent on the maximum charge state and charge state distribution of protein ions produced by electrospray ionization. Journal of the American Society for Mass Spectrometry, 11(11), 976-985. DOI
  3. Ramus, C., Hovasse, A., Marcellin, M., Hesse, A. M., Mouton-Barbosa, E., Bouyssié, D., ... & Garin, J. (2016). Spiked proteomic standard dataset for testing label-free quantitative software and statistical methods. Data in brief, 6, 286-294. DOI
  4. https://www.ebi.ac.uk/pride, Submission Reference: PXD001819.
  5. Cox, J., & Mann, M. (2008). MaxQuant enables high peptide identification rates, individualized ppb-range mass accuracies and proteome-wide protein quantification. Nature biotechnology, 26(12), 1367. DOI
  6. Cox, J., Neuhauser, N., Michalski, A., Scheltema, R. A., Olsen, J. V., & Mann, M. (2011). Andromeda: a peptide search engine integrated into the MaxQuant environment. Journal of proteome research, 10(4), 1794-1805. DOI
  7. Teleman, J., Chawade, A., Sandin, M., Levander, F., & Malmström, J. (2016). Dinosaur: a refined open-source peptide MS feature detector. Journal of proteome research, 15(7), 2143-2151. DOI