Identification of cross-linked peptides from complex samples.

We have developed pLink, software for data analysis of cross-linked proteins coupled with mass-spectrometry analysis. pLink reliably estimates false discovery rate in cross-link identification and is compatible with multiple homo- or hetero-bifunctional cross-linkers. We validated the program with proteins of known structures, and we further tested it on protein complexes, crude immunoprecipitates and whole-cell lysates. We show that it is a robust tool for protein-structure and protein-protein-interaction studies.

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

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.

pParse: a method for accurate determination of monoisotopic peaks in high-resolution mass spectra.

Determining the monoisotopic peak of a precursor is a first step in interpreting mass spectra, which is basic but non-trivial. The reason is that in the isolation window of a precursor, other peaks interfere with the determination of the monoisotopic peak, leading to wrong mass-to-charge ratio or charge state. Here we propose a method, named pParse, to export the most probable monoisotopic peaks for precursors, including co-eluted precursors. We use the relationship between the position of the highest peak and the mass of the first peak to detect candidate clusters. Then, we extract three features to sort the candidate clusters: (i) the sum of the intensity, (ii) the similarity of the experimental and the theoretical isotopic distribution, and (iii) the similarity of elution profiles. We showed that the recall of pParse, MaxQuant, and BioWorks was 98-98.8%, 0.5-17%, and 1.8-36.5% at the same precision, respectively. About 50% of tandem mass spectra are triggered by multiple precursors which are difficult to identify. Then we design a new scoring function to identify the co-eluted precursors. About 26% of all identified peptides were exclusively from co-eluted peptides. Therefore, accurately determining monoisotopic peaks, including co-eluted precursors, can greatly increase peptide identification rate.

Improved peptide identification for proteomic analysis based on comprehensive characterization of electron transfer dissociation spectra.

In recent years, electron transfer dissociation (ETD) has enjoyed widespread applications from sequencing of peptides with or without post-translational modifications to top-down analysis of intact proteins. However, peptide identification rates from ETD spectra compare poorly with those from collision induced dissociation (CID) spectra, especially for doubly charged precursors. This is in part due to an insufficient understanding of the characteristics of ETD and consequently a failure of database search engines to make use of the rich information contained in the ETD spectra. In this study, we statistically characterized ETD fragmentation patterns from a collection of 461 440 spectra and subsequently implemented our findings into pFind, a database search engine developed earlier for CID data. From ETD spectra of doubly charged precursors, pFind 2.1 identified 63-122% more unique peptides than Mascot 2.2 under the same 1% false discovery rate. For higher charged peptides as well as phosphopeptides, pFind 2.1 also consistently obtained more identifications. Of the features built into pFind 2.1, the following two greatly enhanced its performance: (1) refined automatic detection and removal of high-intensity peaks belonging to the precursor, charge-reduced precursor, or related neutral loss species, whose presence often set spectral matching askew; (2) a thorough consideration of hydrogen-rearranged fragment ions such as z + H and c - H for peptide precursors of different charge states. Our study has revealed that different charge states of precursors result in different hydrogen rearrangement patterns. For a fragment ion, its propensity of gaining or losing a hydrogen depends on (1) the ion type (c or z) and (2) the size of the fragment relative to the precursor, and both dependencies are affected by (3) the charge state of the precursor. In addition, we discovered ETD characteristics that are unique for certain types of amino acids (AAs), such as a prominent neutral loss of SCH(2)CONH(2) (90.0014 Da) from z ions with a carbamidomethylated cysteine at the N-terminus and a neutral loss of histidine side chain C(4)N(2)H(5) (81.0453 Da) from precursor ions containing histidine. The comprehensive list of ETD characteristics summarized in this paper should be valuable for automated database search, de novo peptide sequencing, and manual spectral validation.