Transient and weak protein–protein interactions are essential to many biochemical reactions, yet are technically challenging to study. Chemical cross-linking of proteins coupled with mass spectrometry analysis (CXMS) provides a powerful tool in the analysis of such interactions. Central to this technology are chemical cross-linkers. Here, using two transient heterodimeric complexes EIN/HPr and EIIAGlc/EIIBGlc as our model systems, we evaluated the effects of two amine-specific homo-bifunctional cross-linkers with different reactivities. We showed previously that DOPA2 (di-ortho-phthalaldehyde with a di-ethylene glycol spacer arm) cross-links proteins 60–120 times faster than DSS (disuccinimidyl suberate). We found that though most of the intermolecular cross-links of either cross-linker are consistent with the encounter complexes (ECs), an ensemble of short-lived binding intermediates, more DOPA2 intermolecular cross-links could be assigned to the stereospecific complex (SC), the final lowest-energy conformational state for the two interacting proteins. Our finding suggests that faster cross-linking captures the SC more effectively and cross-linkers of different reactivities potentially probe protein–protein interaction dynamics across multiple timescales.
Identifying peptides directly from data-independent acquisition (DIA) data remains challenging due to the highly multiplexed MS/MS spectra. Spectral library-based peptide detection is sensitive, but it is limited to the depth of the library and mutes the discovery potential of DIA data. We present here, DIA-MS2pep, a library-free framework for comprehensive peptide identification from DIA data. DIA-MS2pep uses a data-driven algorithm for MS/MS spectrum demultiplexing using the fragments data without the need of a precursor. With a large precursor mass tolerance database search, DIA-MS2pep can identify the peptides and their modified forms. We demonstrate the performance of DIA-MS2pep by comparing it to conventional library-free tools in accuracy and sensitivity of peptide identifications using publicly available DIA datasets of varying samples, including HeLa cell lysates, phosphopeptides, plasma, etc. Compared with data-dependent acquisition-based spectral libraries, spectral libraries built directly from DIA data with DIA-MS2pep improve the accuracy and reproducibility of the quantitative proteome.
In recent years, an open search of tandem mass spectra has greatly promoted the detection of post-translational modifications (PTMs) in shotgun proteomics. However, post-processing of the results from open searches remains an unsatisfactorily resolved problem, which hinders the open search mode from wide practical use. PTMiner is a software tool based on dedicated statistical algorithms for reliable filtering, localization and annotation of the modifications (mass shifts) detected by open search. Furthermore, PTMiner also supports quality control and re-localization of modifications identified by the traditional closed search. In this protocol, we describe how to use PTMiner for the two search modes. Currently, the search engines supported by PTMiner include pFind, MSFragger, MaxQuant, Comet, MS-GF + and SEQUEST.
Protein glycosylation is of great importance in many biological processes. Glycosylation has been increasingly analyzed at the intact glycopeptide level using mass spectrometry to study site-specific glycosylation changes under different physiological and pathological conditions. StrucGP is a glycan database-independent search engine for the structural interpretation of N-glycoproteins at the site-specific level. To ensure the accuracy of results, two collision energies are implemented in instrument settings for each precursor to separate fragments of peptides and glycans. In addition, the false discovery rates (FDR) of peptides and glycans as well as probabilities of detailed structures are estimated. In this protocol, the use of StrucGP is demonstrated, including environment configuration, data preprocessing as well as result inspection and visualization using our in-house software “GlycoVisualTool”. The described workflow should be able to be performed by anyone with basic proteomic knowledge.
