Stress Granules and RNA Processing Bodies are Novel Autoantibody Targets in Systemic Sclerosis

Autoantibody profiles represent important patient stratification markers in systemic sclerosis (SSc). Here, we performed serum-immunoprecipitations with patient antibodies followed by mass spectrometry (LC-MS/MS) to obtain an unbiased view of all possible autoantibody targets and their associated molecular complexes recognized by SSc

Sample multiplexing-based targeted pathway proteomics with real-time analytics reveals the impact of genetic variation on protein expression.

Targeted proteomics enables hypothesis-driven research by measuring the cellular expression of protein cohorts related by function, disease, or class after perturbation. Here, we present a pathway-centric approach and an assay builder resource for targeting entire pathways of up to 200 proteins selected from \u3e10,000 expressed proteins to directly measure their abundances, exploiting sample multiplexing to increase throughput by 16-fold. The strategy, termed GoDig, requires only a single-shot LC-MS analysis, ~1 µg combined peptide material, a list of up to 200 proteins, and real-time analytics to trigger simultaneous quantification of up to 16 samples for hundreds of analytes. We apply GoDig to quantify the impact of genetic variation on protein expression in mice fed a high-fat diet. We create several GoDig assays to quantify the expression of multiple protein families (kinases, lipid metabolism- and lipid droplet-associated proteins) across 480 fully-genotyped Diversity Outbred mice, revealing protein quantitative trait loci and establishing potential linkages between specific proteins and lipid homeostasis

CysDB: A Human Cysteine Database based on Experimental Quantitative Chemoproteomics

Cysteine chemoproteomics studies provide proteome-wide portraits of the ligandability or potential ‘druggability’ of thousands of cysteine residues. Consequently, these studies are enabling resources for closing the druggability gap, namely achieving pharmacological manipulation of ~99% of the human proteome that remains untargeted by FDA approved small molecules. Recent interactive dataset repositories, such as OxiMouse and SLCABPP, have enabled users to interface more readily with cysteine chemoproteomics studies1,2. However, these databases remain limited to single studies and therefore do not provide a mechanism to perform cross-study analyses. Here we report CysDB as a curated community-wide repository of cysteine chemoproteomics data that incorporates high coverage data derived from nine studies generated by the Backus, Cravatt, Gygi, Wang, and Yang research groups. CysDB is a SQL relational database that is publicly available at https://backuslab.shinyapps.io/cysdb/ and features chemoproteomic measures of identification, hyperreactivity, and ligandability for 62,888 cysteines (24% of all cysteines the human proteome). The CysDB web application also includes annotations of functionality (UniProtKB/Swiss-Prot, Pfam, Panther), known druggability (FDA approved targets, DrugBank, ChEMBL), disease-relevance and genetic variation (ClinVar, Cancer Gene Census, Online Mendelian Inheritance in Man), and structural features (Protein Data Bank). Showcasing the utility of CysDB, here we report the discovery and enrichment of ligandable cysteines in undruggable classes of proteins, the observation that a subset of cysteines showed marked preference for specific classes of electrophiles (chloroacetamide vs acrylamide), and that ligandable cysteines are present in numerous undrugged disease-relevant proteins. Most importantly, we have designed CysDB for the incorporation of new datasets and features to support the continued growth of the druggable cysteineome

Global and tissue-specific aging effects on murine proteomes

Summary: Maintenance of protein homeostasis degrades with age, contributing to aging-related decline and disease. Previous studies have primarily surveyed transcriptional aging changes. To define the effects of age directly at the protein level, we perform discovery-based proteomics in 10 tissues from 20 C57BL/6J mice, representing both sexes at adult and late midlife ages (8 and 18 months). Consistent with previous studies, age-related changes in protein abundance often have no corresponding transcriptional change. Aging results in increases in immune proteins across all tissues, consistent with a global pattern of immune infiltration with age. Our protein-centric data reveal tissue-specific aging changes with functional consequences, including altered endoplasmic reticulum and protein trafficking in the spleen. We further observe changes in the stoichiometry of protein complexes with important roles in protein homeostasis, including the CCT/TriC complex and large ribosomal subunit. These data provide a foundation for understanding how proteins contribute to systemic aging across tissues

Integrating Cross-Linking Experiments with Ab Initio Protein-Protein Docking

Ab initio protein-protein docking algorithms often rely on experimental data to identify the most likely complex structure. We integrated protein-protein docking with the experimental data of chemical cross-linking followed by mass spectrometry. We tested our approach using 19 cases that resulted from an exhaustive search of the Protein Data Bank for protein complexes with cross-links identified in our experiments. We implemented cross-links as constraints based on Euclidean distance or void-volume distance. For most test cases, the rank of the top-scoring near-native prediction was improved by at least twofold compared with docking without the cross-link information, and the success rate for the top 5 predictions nearly tripled. Our results demonstrate the delicate balance between retaining correct predictions and eliminating false positives. Several test cases had multiple components with distinct interfaces, and we present an approach for assigning cross-links to the interfaces. Employing the symmetry information for these cases further improved the performance of complex structure prediction

Conditional Fragment Ion Probabilities Improve Database Searching for Nonmonoisotopic Precursors

Stochastic, intensity-based precursor isolation can result in isotopically enriched fragment ions. This problem is exacerbated for large peptides and stable isotope labeling experiments using deuterium or 15N. For stable isotope labeling experiments, incomplete and ubiquitous labeling strategies result in the isolation of peptide ions composed of many distinct structural isomers. Unfortunately, existing proteomics search algorithms do not account for this variability in isotopic incorporation, and thus often yield poor peptide and protein identification rates. We sought to resolve this shortcoming by deriving the expected isotopic distributions of each fragment ion and incorporating them into the theoretical mass spectra used for peptide-spectrum-matching. We adapted the Comet search platform to integrate a modified spectral prediction algorithm we term Conditional fragment Ion Distribution Search (CIDS). Comet-CIDS uses a traditional database searching strategy, but for each candidate peptide we compute the isotopic distribution of each fragment to better match the observed m/z distributions. Evaluating previously generated D2O and 15N labeled data sets, we found that Comet-CIDS identified more confident peptide spectral matches and higher protein sequence coverage compared to traditional theoretical spectra generation, with the magnitude of improvement largely determined by the amount of labeling in the sample