DeltAMT: a statistical algorithm for fast detection of protein modifications from LC-MS/MS data

Abstract

Identification of proteins and their modifications via liquid chromatography-tandem mass spectrometry is an important task for the field of proteomics. However, because of the complexity of tandem mass spectra, the majority of the spectra cannot be identified. The presence of unanticipated protein modifications is among the major reasons for the low spectral identification rate. The conventional database search approach to protein identification has inherent difficulties in comprehensive detection of protein modifications. In recent years, increasing efforts have been devoted to developing unrestrictive approaches to modification identification, but they often suffer from their lack of speed. This paper presents a statistical algorithm named DeltAMT (Delta Accurate Mass and Time) for fast detection of abundant protein modifications from tandem mass spectra with high-accuracy precursor masses. The algorithm is based on the fact that the modified and unmodified versions of a peptide are usually present simultaneously in a sample and their spectra are correlated with each other in precursor masses and retention times. By representing each pair of spectra as a delta mass and time vector, bivariate Gaussian mixture models are used to detect modification-related spectral pairs. Unlike previous approaches to unrestrictive modification identification that mainly rely upon the fragment information and the mass dimension in liquid chromatography-tandem mass spectrometry, the proposed algorithm makes the most of precursor information. Thus, it is highly efficient while being accurate and sensitive. On two published data sets, the algorithm effectively detected various modifications and other interesting events, yielding deep insights into the data. Based on these discoveries, the spectral identification rates were significantly increased and many modified peptides were identified.

Fig. 1.

Flowchart of the DeltAMT algorithm (dotted lines indicate optional steps).

Fig. 2.

An example of the distribution of Δ obtained from the ISB standard protein mix data set. In this example, three distribution components were automatically detected by the DeltAMT algorithm. One of them is the random distribution marked by the large dashed square, and the other two are modification-induced distributions marked by the small solid squares. The two modifications are oxidation (right) and the (calcium - sodium) subtractive pseudo-modification (left). These two modifications are well discriminated from each other by the mass and time dimensions.

Fig. 3.

An example of the distribution of Δm. Within each potential modification mass interval, random Δm is assumed to come from a Gaussian distribution. Those mass intervals that contain Δm values of unexpectedly high frequencies (peaks marked by stars) are selected and subjected to two-dimensional (mass and time) distribution fitting for modification detection. In this example, the highest peak corresponds to a real modification (acetaldehyde), and the other three are random signals.

Fig. 4.

Scatter-histogram of observed Δ data points around the nominal mass value of 38 Da for the ISB standard protein mix data set. The dense data cluster in the small square, which was automatically located by the DeltAMT algorithm, was induced by the calcium adduct formation.

Fig. 5.

Modification mass shifts detected by the MS-Alignment algorithm and the corresponding numbers of identified spectra. Those mass shifts with more than three identified spectra are annotated with the nominal mass shift values and modification names.