In Vitro and In Vivo Chemical Labeling of Ribosomal Proteins: A Quantitative Comparison

Thioimidates have emerged as reagents for probing the protein structure, folding, and interactions under physiological conditions. The same properties that give thioimidates biological relevance make these molecules ideal candidates for use in vivo. Through labeling of ribosomal proteins, we have quantified the in vivo and in vitro reactivity of two thioimidates: <i>S</i>-methylthioacetimidate (SMTA) and a novel, charge-carrying analogue, <i>S</i>-sulfethylthioacetimidate (SSETA). In vitro experiments demonstrate that both amidinating reagents can probe the protein structure. Under comparable in vivo conditions, SMTA is found to be membrane-permeable while SSETA is not. The use of mass spectrometry with permeant and impermeant thioimidates promises insights into the membrane topology and protein structure in the native environment

XLSearch: a Probabilistic Database Search Algorithm for Identifying Cross-Linked Peptides

Chemical cross-linking combined with mass spectrometric analysis has become an important technique for probing protein three-dimensional structure and protein–protein interactions. A key step in this process is the accurate identification and validation of cross-linked peptides from tandem mass spectra. The identification of cross-linked peptides, however, presents challenges related to the expanded nature of the search space (all pairs of peptides in a sequence database) and the fact that some peptide-spectrum matches (PSMs) contain one correct and one incorrect peptide but often receive scores that are comparable to those in which both peptides are correctly identified. To address these problems and improve detection of cross-linked peptides, we propose a new database search algorithm, XLSearch, for identifying cross-linked peptides. Our approach is based on a data-driven scoring scheme that independently estimates the probability of correctly identifying each individual peptide in the cross-link given knowledge of the correct or incorrect identification of the other peptide. These conditional probabilities are subsequently used to estimate the joint posterior probability that both peptides are correctly identified. Using the data from two previous cross-link studies, we show the effectiveness of this scoring scheme, particularly in distinguishing between true identifications and those containing one incorrect peptide. We also provide evidence that XLSearch achieves more identifications than two alternative methods at the same false discovery rate (availability: https://github.com/COL-IU/XLSearch)

Impact of Amidination on Peptide Fragmentation and Identification in Shotgun Proteomics

Peptide amidination labeling using <i>S</i>-methyl thioacetimidate (SMTA) is investigated in an attempt to increase the number and types of peptides that can be detected in a bottom-up proteomics experiment. This derivatization method affects the basicity of lysine residues and is shown here to significantly impact the idiosyncracies of peptide fragmentation and peptide detectability. The unique and highly reproducible fragmentation properties of SMTA-labeled peptides, such as the strong propensity for forming b₁ fragment ions, can be further exploited to modify the scoring of peptide-spectrum pairs and improve peptide identification. To this end, we have developed a supervised postprocessing algorithm to exploit these characteristics of peptides labeled by SMTA. Our experiments show that although the overall number of identifications are similar, the SMTA modification enabled the detection of 16–26% peptides not previously observed in comparable CID/HCD tandem mass spectrometry experiments without SMTA labeling