We present an open-source, interactive program named Proteoform Suite that uses proteoform mass and intensity measurements from complex biological samples to identify and quantify proteoforms. It constructs families of proteoforms derived from the same gene, assesses proteoform function using gene ontology (GO) analysis, and enables visualization of quantified proteoform families and their changes. It is applied here to reveal systemic proteoform variations in the yeast response to salt stress.

Background The detection of physiologically relevant protein isoforms encoded by the human genome is critical to biomedicine. Mass spectrometry (MS)-based proteomics is the preeminent method for protein detection, but isoform-resolved proteomic analysis relies on accurate reference databases that match the sample; neither a subset nor a superset database is ideal. Long-read RNA sequencing (e.g., PacBio or Oxford Nanopore) provides full-length transcripts which can be used to predict full-length protein isoforms. Results We describe here a long-read proteogenomics approach for integrating sample-matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discover novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data to enable detection of protein isoforms previously intractable to MS-based detection. We have released an open-source Nextflow pipeline that integrates long-read sequencing in a proteomic workflow for isoform-resolved analysis. Conclusions Our work suggests that the incorporation of long-read sequencing and proteomic data can facilitate improved characterization of human protein isoform diversity. Our first-generation pipeline provides a strong foundation for future development of long-read proteogenomics and its adoption for both basic and translational research.

A proteoform family is a group of related molecular forms of a protein (proteoforms) derived from the same gene. We have previously described a strategy to identify proteoforms and elucidate proteoform families in complex mixtures of intact proteins. The strategy is based upon measurements of two properties for each proteoform: (i) the accurate proteoform intact-mass, measured by liquid chromatography/mass spectrometry (LC–MS), and (ii) the number of lysine residues in each proteoform, determined using an isotopic labeling approach. These measured properties are then compared with those extracted from a catalog of theoretical proteoforms containing protein sequences and localized post-translational modifications (PTMs) for the organism under study. A match between the measured properties and those in the catalog constitutes an identification of the proteoform. In the present study, this strategy is extended by utilizing a global PTM discovery database and is applied to the widely studied model organism Escherichia coli, providing the most comprehensive elucidation of E. coli proteoforms and proteoform families to date.

Complex human biomolecular processes are made possible by the diversity of human proteoforms. Constructing proteoform families, groups of proteoforms derived from the same gene, is one way to represent this diversity. Comprehensive, high-confidence identification of human proteoforms remains a central challenge in mass spectrometry-based proteomics. We have previously reported a strategy for proteoform identification using intact-mass measurements, and we have since improved that strategy by mass calibration based on search results, the use of a global post-translational modification discovery database, and the integration of top-down proteomics results with intact-mass analysis. In the present study, we combine these strategies for enhanced proteoform identification in total cell lysate from the Jurkat human T lymphocyte cell line. We collected, processed, and integrated three types of proteomics data (NeuCode-labeled intact-mass, label-free top-down, and multi-protease bottom-up) to maximize the number of confident proteoform identifications. The integrated analysis revealed 5950 unique experimentally observed proteoforms, which were assembled into 848 proteoform families. Twenty percent of the observed proteoforms were confidently identified at a 3.9% false discovery rate, representing 1207 unique proteoforms derived from 484 genes.

Comprehensive understanding of a gene’s expression and regulation at the molecular level requires identification of all proteins interacting with the gene. HyCCAPP (Hybridization Capture of Chromatin Associated Proteins for Proteomics) is an approach that uses single-stranded DNA oligonucleotides to capture specific genomic sequences in cross-linked chromatin fragments and identify associated proteins by mass spectrometry. Previous studies have shown HyCCAPP to provide useful information on protein–DNA interactions, revealing the proteins associated with the GAL110 region in yeast. We present here a multiplexed version of HyCCAPP. Utilizing a toehold-mediated capture/release strategy, HyCCAPP is targeted to multiple genomic loci in parallel, and the protein binders at each locus are eluted in a programmable and selective fashion. Multiplexed HyCCAPP was applied to four genes (25S rDNA, ARX1, CTT1, and RPL30) in S. cerevisiae under normal and stressed conditions. Capture and release efficiencies and specificities were comparable to those obtained without multiplexing. Using mass spectrometry-based bottom-up proteomics, hundreds of proteins were discovered at each locus in each condition. Statistical analysis revealed 34–88 enriched proteins in each gene capture. Many of these proteins had expected functions, including DNA-related and ribosome biogenesis-associated activities. Multiplexed HyCCAPP provides a useful strategy for the identification of proteins interacting with specific chromatin regions.