Ryan T. Fellers

Complete Protein Characterization Using Top-Down Mass Spectrometry and Ultraviolet Photodissociation

The top-down approach to proteomics offers compelling advantages due to the potential to provide complete characterization of protein sequence and post-translational modifications. Here we describe the implementation of 193 nm ultraviolet photodissociation (UVPD) in an Orbitrap mass spectrometer for characterization of intact proteins. Near-complete fragmentation of proteins up to 29 kDa is achieved with UVPD including the unambiguous localization of a single residue mutation and several protein modifications on Pin1 (Q13526), a protein implicated in the development of Alzheimers disease and in cancer pathogenesis. The 5 ns, high-energy activation afforded by UVPD exhibits far less precursor ion-charge state dependence than conventional collision- and electron-based dissociation methods.

Large-scale Top-down Proteomics of the Human Proteome: Membrane Proteins, Mitochondria, and Senescence

Top-down proteomics is emerging as a viable method for the routine identification of hundreds to thousands of proteins. In this work we report the largest top-down study to date, with the identification of 1,220 proteins from the transformed human cell line H1299 at a false discovery rate of 1%. Multiple separation strategies were utilized, including the focused isolation of mitochondria, resulting in significantly improved proteome coverage relative to previous work. In all, 347 mitochondrial proteins were identified, including ∼50% of the mitochondrial proteome below 30 kDa and over 75% of the subunits constituting the large complexes of oxidative phosphorylation. Three hundred of the identified proteins were found to be integral membrane proteins containing between 1 and 12 transmembrane helices, requiring no specific enrichment or modified LC-MS parameters. Over 5,000 proteoforms were observed, many harboring post-translational modifications, including over a dozen proteins containing lipid anchors (some previously unknown) and many others with phosphorylation and methylation modifications. Comparison between untreated and senescent H1299 cells revealed several changes to the proteome, including the hyperphosphorylation of HMGA2. This work illustrates the burgeoning ability of top-down proteomics to characterize large numbers of intact proteoforms in a high-throughput fashion.

ProSight Lite: Graphical software to analyze top-down mass spectrometry data

Many top‐down proteomics experiments focus on identifying and localizing PTMs and other potential sources of “mass shift” on a known protein sequence. A simple application to match ion masses and facilitate the iterative hypothesis testing of PTM presence and location would assist with the data analysis in these experiments. ProSight Lite is a free software tool for matching a single candidate sequence against a set of mass spectrometric observations. Fixed or variable modifications, including both PTMs and a select number of glycosylations, can be applied to the amino acid sequence. The application reports multiple scores and a matching fragment list. Fragmentation maps can be exported for publication in either portable network graphic (PNG) or scalable vector graphic (SVG) format. ProSight Lite can be freely downloaded from http://prosightlite.northwestern.edu, installs and updates from the web, and requires Windows 7 or a higher version.

The first pilot project of the consortium for top-down proteomics: a status report.

Pilot Project #1—the identification and characterization of human histone H4 proteoforms by top‐down MS—is the first project launched by the Consortium for Top‐Down Proteomics (CTDP) to refine and validate top‐down MS. Within the initial results from seven participating laboratories, all reported the probability‐based identification of human histone H4 (UniProt accession P62805) with expectation values ranging from 10−13 to 10−105. Regarding characterization, a total of 74 proteoforms were reported, with 21 done so unambiguously; one new PTM, K79ac, was identified. Inter‐laboratory comparison reveals aspects of the results that are consistent, such as the localization of individual PTMs and binary combinations, while other aspects are more variable, such as the accurate characterization of low‐abundance proteoforms harboring >2 PTMs. An open‐access tool and discussion of proteoform scoring are included, along with a description of general challenges that lie ahead including improved proteoform separations prior to mass spectrometric analysis, better instrumentation performance, and software development.

Applying label-free quantitation to top down proteomics.

With the prospect of resolving whole protein molecules into their myriad proteoforms on a proteomic scale, the question of their quantitative analysis in discovery mode comes to the fore. Here, we demonstrate a robust pipeline for the identification and stringent scoring of abundance changes of whole protein forms <30 kDa in a complex system. The input is ∼100–400 μg of total protein for each biological replicate, and the outputs are graphical displays depicting statistical confidence metrics for each proteoform (i.e., a volcano plot and representations of the technical and biological variation). A key part of the pipeline is the hierarchical linear model that is tailored to the original design of the study. Here, we apply this new pipeline to measure the proteoform-level effects of deleting a histone deacetylase (rpd3) in S. cerevisiae. Over 100 proteoform changes were detected above a 5% false positive threshold in WT vs the Δrpd3 mutant, including the validating observation of hyperacetylation of histone H4 and both H2B isoforms. Ultimately, this approach to label-free top down proteomics in discovery mode is a critical technical advance for testing the hypothesis that whole proteoforms can link more tightly to complex phenotypes in cell and disease biology than do peptides created in shotgun proteomics.

Ultraviolet Photodissociation for Characterization of Whole Proteins on a Chromatographic Time Scale.

Intact protein characterization using mass spectrometry thus far has been achieved at the cost of throughput. Presented here is the application of 193 nm ultraviolet photodissociation (UVPD) for top down identification and characterization of proteins in complex mixtures in an online fashion. Liquid chromatographic separation at the intact protein level coupled with fast UVPD and high-resolution detection resulted in confident identification of 46 unique sequences compared to 44 using HCD from prepared Escherichia coli ribosomes. Importantly, nearly all proteins identified in both the UVPD and optimized HCD analyses demonstrated a substantial increase in confidence in identification (as defined by an average decrease in E value of ∼40 orders of magnitude) due to the higher number of matched fragment ions. Also shown is the potential for high-throughput characterization of intact proteins via liquid chromatography (LC)–UVPD-MS of molecular weight-based fractions of a Saccharomyces cerevisiae lysate. In total, protein products from 215 genes were identified and found in 292 distinct proteoforms, 168 of which contained some type of post-translational modification.

Quantitation and Identification of Thousands of Human Proteoforms below 30 kDa

Top-down proteomics is capable of identifying and quantitating unique proteoforms through the analysis of intact proteins. We extended the coverage of the label-free technique, achieving differential analysis of whole proteins <30 kDa from the proteomes of growing and senescent human fibroblasts. By integrating improved control software with more instrument time allocated for quantitation of intact ions, we were able to collect protein data between the two cell states, confidently comparing 1577 proteoform levels. To then identify and characterize proteoforms, our advanced acquisition software, named Autopilot, employed enhanced identification efficiency in identifying 1180 unique Swiss-Prot accession numbers at 1% false-discovery rate. This coverage of the low mass proteome is equivalent to the largest previously reported but was accomplished in 23% of the total acquisition time. By maximizing both the number of quantified proteoforms and their identification rate in an integrated software environment, this work significantly advances proteoform-resolved analyses of complex systems.

The C‑Score: A Bayesian Framework to Sharply Improve Proteoform Scoring in High-Throughput Top Down Proteomics

The automated processing of data generated by top down proteomics would benefit from improved scoring for protein identification and characterization of highly related protein forms (proteoforms). Here we propose the “C-score” (short for Characterization Score), a Bayesian approach to the proteoform identification and characterization problem, implemented within a framework to allow the infusion of expert knowledge into generative models that take advantage of known properties of proteins and top down analytical systems (e.g., fragmentation propensities, “off-by-1 Da” discontinuous errors, and intelligent weighting for site-specific modifications). The performance of the scoring system based on the initial generative models was compared to the current probability-based scoring system used within both ProSightPC and ProSightPTM on a manually curated set of 295 human proteoforms. The current implementation of the C-score framework generated a marked improvement over the existing scoring system as measured by the area under the curve on the resulting ROC chart (AUC of 0.99 versus 0.78).

Integrated Bottom-up and Top-down Proteomics of Patient-derived Breast Tumor Xenografts

Bottom-up proteomics relies on the use of proteases and is the method of choice for identifying thousands of protein groups in complex samples. Top-down proteomics has been shown to be robust for direct analysis of small proteins and offers a solution to the “peptide-to-protein” inference problem inherent with bottom-up approaches. Here, we describe the first large-scale integration of genomic, bottom-up and top-down proteomic data for the comparative analysis of patient-derived mouse xenograft models of basal and luminal B human breast cancer, WHIM2 and WHIM16, respectively. Using these well-characterized xenograft models established by the National Cancer Institutes Clinical Proteomic Tumor Analysis Consortium, we compared and contrasted the performance of bottom-up and top-down proteomics to detect cancer-specific aberrations at the peptide and proteoform levels and to measure differential expression of proteins and proteoforms. Bottom-up proteomic analysis of the tumor xenografts detected almost 10 times as many coding nucleotide polymorphisms and peptides resulting from novel splice junctions than top-down. For proteins in the range of 0–30 kDa, where quantitation was performed using both approaches, bottom-up proteomics quantified 3,519 protein groups from 49,185 peptides, while top-down proteomics quantified 982 proteoforms mapping to 358 proteins. Examples of both concordant and discordant quantitation were found in a ∼60:40 ratio, providing a unique opportunity for top-down to fill in missing information. The two techniques showed complementary performance, with bottom-up yielding eight times more identifications of 0–30 kDa proteins in xenograft proteomes, but failing to detect differences in certain posttranslational modifications (PTMs), such as phosphorylation pattern changes of alpha-endosulfine. This work illustrates the potency of a combined bottom-up and top-down proteomics approach to deepen our knowledge of cancer biology, especially when genomic data are available.

An informatic framework for decoding protein complexes by top-down mass spectrometry

Efforts to map the human protein interactome have resulted in information about thousands of multi-protein assemblies housed in public repositories, but the molecular characterization and stoichiometry of their protein subunits remains largely unknown. Here, we report a computational search strategy that supports hierarchical top-down analysis for precise identification and scoring of multi-proteoform complexes by native mass spectrometry.