The Prediction of the Isoelectric Point Value of Peptides and Proteins with a Wide Range of Chemical Modifications

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V.S. Skvortsov
A.I. Voronina
Y.O. Ivanova
A.V. Rybina


The scale of virtual pKa values for calculating the isoelectric point of peptides and proteins with chemical and post-translational modifications (PTM) is presented. The learning set of pKa values is based on data from 25 experiments of isoelectric focusing of peptides with subsequent mass spectrometric identification (ProteomeXchange accession codes: PXD000065, PXD005410, PXD006291, PXD010006 and PXD017201). In order to enrich the resulting sets with peptides containing modifications the identification of peptides was repeated using raw mass spectrometry data of all datasets. In the final learning set have included peptides satisfying the following conditions: the peptide was found in the fraction with scoring function maximum and maximum peptide abundance; the peptide was found in more than one experiment, and differences of the pI value between experiments was less than 0.15 pH unit. Two variants of the scales were created. In the first variant, pKa values depended only on the residue position relative to the ends of the sequence (N- or C-terminal residue or inside the chain). In the second variant, the effect of neighboring residues was also taken into account. The prediction accuracy of the second variant was higher. The comparison with other methods of pI prediction was carried out. Although the scale was calculated from set containing only peptides, it would be applicable for pI prediction of proteins with and without PTM. The software for prediction of pI values using the resulting pKa scales is available at

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Skvortsov, V., Voronina, A., Ivanova, Y., & Rybina, A. (2021). The Prediction of the Isoelectric Point Value of Peptides and Proteins with a Wide Range of Chemical Modifications. Biomedical Chemistry: Research and Methods, 4(4), e00161.


  1. Giglione, C., Boularot, A., Meinnel, T. (2004) Protein N-terminal methionine excision. Cellular and Molecular Life Sciences CMLS, 61, 1455–1474. DOI
  2. Heller, M., Ye, M., Michel, P.E., Morier, P., Stalder, D., Jünger, M.A., Aebersold, R., Reymond, F., Rossier, J. (2005) Journal of proteome research, 4(6), 2273-2282. DOI
  3. Pernemalm, M., & Lehtiö, J. (2013) A novel prefractionation method combining protein and peptide isoelectric focusing in immobilized pH gradient strips. Journal of proteome research, 12(2), 1014–1019. DOI
  4. Zhu, M., Rodriguez,R., Wehr, T. (1991) Optimizing separation parameters in capillary isoelectric focusing. Journal of chromatography, 559, 479–488.
  5. Kirkwood, J., Hargreaves, D., O'Keefe, S., & Wilson, J. (2015) Using isoelectric point to determine the pH for initial protein crystallization trials. Bioinformatics (Oxford, England), 31(9), 1444–1451. DOI
  6. Branca, R. M., Orre, L. M., Johansson, H. J., Granholm, V., Huss, M., Pérez-Bercoff, Å., Forshed, J., Käll, L., & Lehtiö, J. (2014) HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics. Nature methods, 11(1), 59–62. DOI
  7. Naryzhny, S. N., Legina, O. K. (2019) Structural-functional diversity of p53 proteoforms. Biomeditsinskaya khimiya, 65(4), 263-276. DOI
  8. Po, H. N., Senozan, N. M. (2001) The Henderson-Hasselbalch Equation: Its History and Limitations. Journal of Chemical Education, 78, 1499-1503. DOI
  9. Bjellqvist, B., Hughes, G. J., Pasquali, C., Paquet, N., Ravier, F., Sanchez, J. C., Frutiger, S., & Hochstrasser, D. (1993) The focusing positions of polypeptides in immobilized pH gradients can be predicted from their amino acid sequences. Electrophoresis, 14(10), 1023–1031. DOI
  10. Gasteiger, E., Hoogland, C., Gattiker, A., Duvaud, S., Wilkins, M. R., Appel, R. D., Bairoch, A. (2005) The Proteomics Protocols Handbook, pp. 571-607. DOI
  11. Chemaxon, Budapest, Hungary,
  12. Patrickios, C. S. (1995) Journal of Colloid and Interface Science, 175, 256-256. DOI
  13. Skvortsov, V. S., Alekseychuk, N. N., Khudyakov, D. V., Romero Reyes, I. V. (2015) pIPredict: a computer tool for predicting isoelectric points of peptides and proteins. Biomeditsinskaya khimiya, 61(1), 83-91. DOI
  14. Branca, R., Orre, L., Johansson, H., Granholm, V., Huss, M., Pérez-Bercoff, A., Forshed, J., Käll, L., Lehtiö, J. (2014) HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics. Nat Methods, 11, 59–62. DOI
  15. Kozlowski, L. P. (2021) IPC 2.0: prediction of isoelectric point and pKa dissociation constants. Nucleic Acids Research, 49(W1, 2), W285–W292. DOI
  16. Halligan, B. D., Ruotti, V., Jin, W., Laffoon, S., Twigger, S. N., & Dratz, E. A. (2004) ProMoST (Protein Modification Screening Tool): a web-based tool for mapping protein modifications on two-dimensional gels. Nucleic acids research, 32(suppl_2), W638-W644. DOI
  17. Cargile, B. J., Sevinsky, J. R., Essader, A. S., Eu, J. P., & Stephenson, J. L., Jr (2008) Calculation of the isoelectric point of tryptic peptides in the pH 3.5-4.5 range based on adjacent amino acid effects. Electrophoresis, 29(13), 2768–2778. DOI
  18. Perez-Riverol, Y., Audain, E., Millan, A., Ramos, Y., Sanchez, A., Vizcaíno, J. A., Wang, R., Müller, M., Machado, Y. J., Betancourt, L. H., González, L. J., Padrón, G., & Besada, V. (2012) Isoelectric point optimization using peptide descriptors and support vector machines. Journal of proteomics, 75(7), 2269–2274. DOI
  19. Panizza, E., Branca, R. M. M., Oliviusson, P. et al. (2017) Isoelectric point-based fractionation by HiRIEF coupled to LC-MS allows for in-depth quantitative analysis of the phosphoproteome. Scientific Reports, 7, 4513. DOI
  20. Zhu, Y., Orre, L. M., Johansson, H. J. et al. (2018) Discovery of coding regions in the human genome by integrated proteogenomics analysis workflow. Nat Commun, 9, 903. DOI
  21. Panizza, E., Zhang, L., Fontana, J. M., Hamada, K., Svensson, D., Akkuratov, E. E., Scott, L., Mikoshiba, K., Brismar, H., Lehtiö, J., & Aperia, A. (2019) Ouabain-regulated phosphoproteome reveals molecular mechanisms for Na+, K+-ATPase control of cell adhesion, proliferation, and survival. FASEB journal : official publication of the Federation of American Societies for Experimental Biology, 33(9), 10193–10206. DOI
  22. Babačić, H., Lehtiö, J., Pico de Coaña, Y., Pernemalm, M., & Eriksson, H. (2020) In-depth plasma proteomics reveals increase in circulating PD-1 during anti-PD-1 immunotherapy in patients with metastatic cutaneous melanoma. Journal for immunotherapy of cancer, 8(1), e000204. DOI
  23. Ma, B., Zhang, K., Hendrie, C., Liang, C., Li, M., Doherty-Kirby, A., & Lajoie, G. (2003) PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry. Rapid communications in mass spectrometry : RCM, 17(20), 2337–2342. DOI
  24. Plikat, U., Voshol, H., Dangendorf, Y., Wiedmann, B., Devay, P., Müller, D., Wirth, U., Szustakowski, J., Chirn, G. W., Inverardi, B., Puyang, X., Brown, K., Kamp, H., Hoving, S., Ruchti, A., Brendlen, N., Peterson, R., Buco, J., Oostrum, J. v., & Peitsch, M. C. (2007) From proteomics to systems biology of bacterial pathogens: approaches, tools, and applications. Proteomics, 7(6), 992–1003. DOI
  25. Hoogland, C., Mostaguir, K., Appel, R. D., & Lisacek, F. (2008) The World-2DPAGE Constellation to promote and publish gel-base d proteomics data through the ExPASy server. Journal of proteomics, 71(2), 245–248. DOI