PTMap--a sequence alignment software for unrestricted, accurate, and full-spectrum identification of post-translational modification sites

Abstract

We present sequence alignment software, called PTMap, for the accurate identification of full-spectrum protein post-translational modifications (PTMs) and polymorphisms. The software incorporates several features to improve searching speed and accuracy, including peak selection, adjustment of inaccurate mass shifts, and precise localization of PTM sites. PTMap also automates rules, based mainly on unmatched peaks, for manual verification of identified peptides. To evaluate the quality of sequence alignment, we developed a scoring system that takes into account both matched and unmatched peaks in the mass spectrum. Incorporation of these features dramatically increased both accuracy and sensitivity of the peptide- and PTM-identifications. To our knowledge, PTMap is the first algorithm that emphasizes unmatched peaks to eliminate false positives. The superior performance and reliability of PTMap were demonstrated by confident identification of PTMs on 156 peptides from four proteins and validated by MS/MS of the synthetic peptides. Our results demonstrate that PTMap is a powerful algorithm capable of identification of all possible protein PTMs with high confidence.

Fig. 1.

Flow chart of the PTMap algorithm.

Fig. 2.

Evaluation of the PTMap score distribution. (A) PTMap score distributions (S_Unmatched = 4.0:10.0 in high and low mass ranges respectively) of unmodified peptides identified from normal and scrambled protein sequences of the four selected proteins—histone H4, SGK1, HMG2 and BSA—using PTMap (155 peptides generated from normal database and 15 peptides generated from scrambled database); (B) PTMap score distributions (S_Unmatched = 4.0:10.0) of both unmodified and modified peptides identified from normal and scrambled of the same four protein sequences using PTMap (1,478 peptides generated from normal database and 917 peptides generated from scrambled database); (C) Correlation of Mascot scores with PTMap scores (S_Unmatched = 4.0:10.0) for identification of unmodified peptides. The highest Mascot score and PTMap score of each peptide were used. Those peptides identified with Mascot only (PTMap score = 0) were found to be false positives by manual verification methods (21).

Fig. 3.

Evaluation of the two PTMap strategies: peak selection and automatic mass shift adjustment. (A) The number of unmodified peptides (PTMap score cutoff = 0.5) identified with or without incorporation of the peak-selection function in PTMap; (B) The distribution of the mass changes (ΔM_after − ΔM_before) made by PTMap after automatic mass-shift adjustment for all identified spectra bearing PTMs (PTMap score cutoff = 1.0); (C) The number of modified peptides identified with or without automatic mass-shift adjustment strategy in PTMap (PTMap score cutoff = 1.0);(D) scatter plot showing the distribution of the mass errors of the spectra that identified unmodified peptides in the four proteins (PTMap score cutoff = 0.5)

Fig. 4.

Evaluation of the PTMap strategy for exclusive site localization. (A) A strategy for precise mapping of a PTM site that will distinguish two peptide isoforms that are modified on adjacent sites; (B) The number of modified peptides identified by PTMap before and after the implementation of exclusive site localization.