skip to main content

Explore Research Products in the NSF-PAR

Title: Mass spectrometry-based technologies for probing the 3D world of plant proteins
Abstract Over the past two decades, mass spectrometric (MS)-based proteomics technologies have facilitated the study of signaling pathways throughout biology. Nowhere is this needed more than in plants, where an evolutionary history of genome duplications has resulted in large gene families involved in posttranslational modifications and regulatory pathways. For example, at least 5% of the Arabidopsis thaliana genome (ca. 1,200 genes) encodes protein kinases and protein phosphatases that regulate nearly all aspects of plant growth and development. MS-based technologies that quantify covalent changes in the side-chain of amino acids are critically important, but they only address one piece of the puzzle. A more crucially important mechanistic question is how noncovalent interactions—which are more difficult to study—dynamically regulate the proteome’s 3D structure. The advent of improvements in protein 3D technologies such as cryo-electron microscopy, nuclear magnetic resonance, and X-ray crystallography has allowed considerable progress to be made at this level, but these methods are typically limited to analyzing proteins, which can be expressed and purified in milligram quantities. Newly emerging MS-based technologies have recently been developed for studying the 3D structure of proteins. Importantly, these methods do not require protein samples to be purified and require smaller amounts of sample, opening the wider proteome for structural analysis in complex mixtures, crude lysates, and even in intact cells. These MS-based methods include covalent labeling, crosslinking, thermal proteome profiling, and limited proteolysis, all of which can be leveraged by established MS workflows, as well as newly emerging methods capable of analyzing intact macromolecules and the complexes they form. In this review, we discuss these recent innovations in MS-based “structural” proteomics to provide readers with an understanding of the opportunities they offer and the remaining challenges for understanding the molecular underpinnings of plant structure and function.  more » « less
Award ID(s):
2010789
NSF-PAR ID:
10423512
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Plant Physiology
Volume:
189
Issue:
1
ISSN:
0032-0889
Page Range / eLocation ID:
12 to 22
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; ; ; ( , Rapid Communications in Mass Spectrometry)
    Rationale

    Tandem‐ion mobility spectrometry/mass spectrometry methods have recently gained traction for the structural characterization of proteins and protein complexes. However, ion activation techniques currently coupled with tandem‐ion mobility spectrometry/mass spectrometry methods are limited in their ability to characterize structures of proteins and protein complexes.

     Methods

    Here, we describe the coupling of the separation capabilities of tandem‐trapped ion mobility spectrometry/mass spectrometry (tTIMS/MS) with the dissociation capabilities of ultraviolet photodissociation (UVPD) for protein structure analysis.

     Results

    We establish the feasibility of dissociating intact proteins by UV irradiation at 213 nm between the two TIMS devices in tTIMS/MS and at pressure conditions compatible with ion mobility spectrometry (2–3 mbar). We validate that the fragments produced by UVPD under these conditions result from a radical‐based mechanism in accordance with prior literature on UVPD. The data suggest stabilization of fragment ions produced from UVPD by collisional cooling due to the elevated pressures used here (“UVnoD2”), which otherwise do not survive to detection. The data account for a sequence coverage for the protein ubiquitin comparable to recent reports, demonstrating the analytical utility of our instrument in mobility‐separating fragment ions produced from UVPD.

     Conclusions

    The data demonstrate that UVPD carried out at elevated pressures of 2–3 mbar yields extensive fragment ions rich in information about the protein and that their exhaustive analysis requires IMS separation post‐UVPD. Therefore, because UVPD and tTIMS/MS each have been shown to be valuable techniques on their own merit in proteomics, our contribution here underscores the potential of combining tTIMS/MS with UVPD for structural proteomics.

     
    more » « less
  2. ; ; ; ; ( , Proceedings of the National Academy of Sciences)

    The stability of proteins influences their tendency to aggregate, undergo degradation, or become modified in cells. Despite their significance to understanding protein folding and function, quantitative analyses of thermodynamic stabilities have been mostly limited to soluble proteins in purified systems. We have used a highly multiplexed proteomics approach, based on analyses of methionine oxidation rates, to quantify stabilities of ∼10,000 unique regions within ∼3,000 proteins in human cell extracts. The data identify lysosomal and extracellular proteins as the most stable ontological subsets of the proteome. We show that the stability of proteins impacts their tendency to become oxidized and is globally altered by the osmolyte trimethylamineN-oxide (TMAO). We also show that most proteins designated as intrinsically disordered retain their unfolded structure in the complex environment of the cell. Together, the data provide a census of the stability of the human proteome and validate a methodology for global quantitation of folding thermodynamics.

     
    more » « less
  3. ; ( , Methods in molecular biology)
    Jacob, S. (Ed.)
    Protein–protein interactions underlie cellular structure and function. In recent years, a number of methods have been developed for the identification of protein complexes and component proteins involved in the control of various biological pathways. Tandem affinity purification (TAP) coupled with mass spectrometry (MS) is a powerful method enabling the isolation of high-purity native protein complexes under mild conditions by performing two sequential purification steps using two different epitope tags. In this protocol, we describe a TAP-MS methodology for identifying protein-protein interactions present at very low levels in the fungal cell. Using the 6xHis-3xFLAG double tag, we start the affinity purification process for our protein of interest using high-capacity Ni²⁺ columns. This allows for greatly increased sample input compared to antibody-based first-step purification in conventional TAP protocols and provides a large amount of highly concentrated and preliminarily purified protein complexes to be used in a second purification step involving FLAG immunoprecipitation. The second step greatly facilitates the capture of low-level interacting partners under in vivo conditions. Our TAP-MS method has been proven to secure the characterization of low-abundance protein complexes under physiological conditions with high efficiency, specificity, and economy in the filamentous fungus Magnaporthe oryzae and might benefit gene function and proteomics studies in plants and other research fields. 
    more » « less
  4. ; ; ; ; ; ; ; ; ( , BMC Bioinformatics)
    Abstract Background Protein kinases are a large family of druggable proteins that are genomically and proteomically altered in many human cancers. Kinase-targeted drugs are emerging as promising avenues for personalized medicine because of the differential response shown by altered kinases to drug treatment in patients and cell-based assays. However, an incomplete understanding of the relationships connecting genome, proteome and drug sensitivity profiles present a major bottleneck in targeting kinases for personalized medicine. Results In this study, we propose a multi-component Quantitative Structure–Mutation–Activity Relationship Tests (QSMART) model and neural networks framework for providing explainable models of protein kinase inhibition and drug response ( $$\hbox {IC}_{50}$$ IC 50 ) profiles in cell lines. Using non-small cell lung cancer as a case study, we show that interaction terms that capture associations between drugs, pathways, and mutant kinases quantitatively contribute to the response of two EGFR inhibitors (afatinib and lapatinib). In particular, protein–protein interactions associated with the JNK apoptotic pathway, associations between lung development and axon extension, and interaction terms connecting drug substructures and the volume/charge of mutant residues at specific structural locations contribute significantly to the observed $$\hbox {IC}_{50}$$ IC 50 values in cell-based assays. Conclusions By integrating multi-omics data in the QSMART model, we not only predict drug responses in cancer cell lines with high accuracy but also identify features and explainable interaction terms contributing to the accuracy. Although we have tested our multi-component explainable framework on protein kinase inhibitors, it can be extended across the proteome to investigate the complex relationships connecting genotypes and drug sensitivity profiles. 
    more » « less
  5. ; ; ; ; ; ; ; ( , Briefings in Bioinformatics)
    null (Ed.)
    Deregulation of gene expression is associated with the pathogenesis of numerous human diseases including cancer. Current data analyses on gene expression are mostly focused on differential gene/transcript expression in big data-driven studies. However, a poor connection to the proteome changes is a widespread problem in current data analyses. This is partly due to the complexity of gene regulatory pathways at the post-transcriptional level. In this study, we overcome these limitations and introduce a graph-based learning model, PTNet, which simulates the microRNAs (miRNAs) that regulate gene expression post-transcriptionally in silico. Our model does not require large-scale proteomics studies to measure the protein expression and can successfully predict the protein levels by considering the miRNA–mRNA interaction network, the mRNA expression, and the miRNA expression. Large-scale experiments on simulations and real cancer high-throughput datasets using PTNet validated that (i) the miRNA-mediated interaction network affects the abundance of corresponding proteins and (ii) the predicted protein expression has a higher correlation with the proteomics data (ground-truth) than the mRNA expression data. The classification performance also shows that the predicted protein expression has an improved prediction power on cancer outcomes compared to the prediction done by the mRNA expression data only or considering both mRNA and miRNA. Availability: PTNet toolbox is available at http://github.com/CompbioLabUCF/PTNet 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email