This tutorial review describes the principles and practices of electron capture and transfer dissociation (ECD/ETD or ExD) mass spectrometry (MS) employed for peptide and protein structure analysis. ExD MS relies on interactions between gas phase peptide or protein ions carrying multiple positive charges with either free low-energy (~1 eV) electrons (ECD), or with reagent radical anions possessing an electron available for transfer (ETD). As a result of recent implementation on sensitive, high resolution, high mass accuracy, and liquid chromatography timescale-compatible mass spectrometers, ExD, more specifically, ETD MS has received particular interest in life science research. In addition to describing the fundamental aspects of ExD radical ion chemistry, this tutorial provides practical guidelines for peptide de novo sequencing with ExD MS, as well as reviews some of the current capabilities and limitations of these techniques. The merits of ExD MS are discussed primarily within the context of life science research.

Despite the recent advances in structural analysis of monoclonal antibodies with bottom-up, middle-down, and top-down mass spectrometry (MS), further improvements in analysis accuracy, depth, and speed are needed. The remaining challenges include quantitatively accurate assignment of post-translational modifications, reduction of artifacts introduced during sample preparation, increased sequence coverage per liquid chromatography (LC) MS experiment, and ability to extend the detailed characterization to simple antibody cocktails and more complex antibody mixtures. Here, we evaluate the recently introduced extended bottom-up proteomics (eBUP) approach based on proteolysis with secreted aspartic protease 9, Sap9, for analysis of monoclonal antibodies. Key findings of the Sap9-based proteomics analysis of a single antibody include: (i) extensive antibody sequence coverage with up to 100% for the light chain and up to 99-100% for the heavy chain in a single LC-MS run; (ii) connectivity of complementarity-determining regions (CDRs) via Sap9-produced large proteolytic peptides (3.4 kDa on average) containing up to two CDRs per peptide; (iii) reduced artifact introduction (e. g., deamidation) during proteolysis with Sap9 compared to conventional bottom-up proteomics workflows. The analysis of a mixture of six antibodies via Sap9-based eBUP produced comparable results. Due to the reasons specified above, Sap9-produced proteolytic peptides improve the identification confidence of antibodies from the mixtures compared to conventional bottom-up proteomics dealing with shorter proteolytic peptides.

High-resolution mass spectrometry and quantum mechanics/molecular mechanics studies were employed for characterizing the formation of two gold finger (GF) domains from the reaction of zinc fingers (ZF) with gold complexes. The influence of both the gold oxidation state and the ZF coordination sphere in GF formation provided useful insights into the possible design of new gold complexes targeting specific ZF motifs.

Data-dependent tandem mass spectrometry (MS/MS) is one of the main techniques for protein identification in shotgun proteomics. In a typical LC-MS/MS workflow, peptide product ion mass spectra (MS/MS spectra) are compared with those derived theoretically from a protein sequence database. Scoring of these matches results in peptide identifications. A set of peptide identifications is characterized by false discovery rate (FDR), which determines the fraction of false identifications in the set. The total number of peptides targeted for fragmentation is in the range of 10,000 to 20,000 for a several-hour LC-MS/MS run. Typically, <50% of these MS/MS spectra result in peptide-spectrum matches (PSMs). A small fraction of PSMs pass the preset FDR level (commonly 1%) giving a list of identified proteins, yet a large number of correct PSMs corresponding to the peptides originally present in the sample are left behind in the "grey area" below the identity threshold. Following the numerous efforts to recover these correct PSMs, here we investigate the utility of a scoring scheme based on the multiple PSM descriptors available from the experimental data. These descriptors include retention time, deviation between experimental and theoretical mass, number of missed cleavages upon in-solution protein digestion, precursor ion fraction (PIF), PSM count per sequence, potential modifications, median fragment mass error, (13)C isotope mass difference, charge states, and number of PSMs per protein. The proposed scheme utilizes a set of metrics obtained for the corresponding distributions of each of the descriptors. We found that the proposed PSM scoring algorithm differentiates equally or more efficiently between correct and incorrect identifications compared with existing postsearch validation approaches.