scholarly journals Classification of Single Particles from Human Cell Extract Reveals Distinct Structures

2018 ◽  
Author(s):  
Eric J. Verbeke ◽  
Anna L. Mallam ◽  
Kevin Drew ◽  
Edward M. Marcotte ◽  
David W. Taylor
Keyword(s):  
Electron Microscopy ◽  
Mass Spectrometry ◽  
Protein Identification ◽  
Size Structure ◽  
Protein Complexes ◽  
Cell Extract ◽  
Particle Analysis ◽  
Proteomics Data ◽  
Genomics And Proteomics

SummaryMulti-protein complexes are necessary for nearly all cellular processes, and understanding their structure is required for elucidating their function. Current high-resolution strategies in structural biology are effective, but lag behind other fields (e.g. genomics and proteomics) due to their reliance on purified samples rather than characterizing heterogeneous mixtures. Here, we present a method combining single particle analysis by electron microscopy with protein identification by mass spectrometry to structurally characterize macromolecular complexes from extracts of human cells. We obtain three-dimensional structures of native proteasomes directly from ab initio classification of a heterogeneous mixture of protein complexes. In addition, we find an ~1 MDa size structure of unknown composition and reference our proteomics data to suggest possible identities. Our study shows the power of using a shotgun approach to electron microscopy (shotgun EM) when coupled with mass spectrometry as a tool to uncover the structures of macromolecular machines in parallel.

scholarly journals Electron microscopy snapshots of single particles from single cells

2018 ◽  
Vol 294 (5) ◽  
pp. 1602-1608 ◽  
Author(s):  
Xiunan Yi ◽  
Eric J. Verbeke ◽  
Yiran Chang ◽  
Daniel J. Dickinson ◽  
David W. Taylor
Keyword(s):  
Electron Microscopy ◽  
Single Particle ◽  
Protein Complexes ◽  
Signal To Noise Ratio ◽  
Single Cells ◽  
Particle Analysis ◽  
Biological Macromolecules ◽  
Single Particle Analysis ◽  
Whole Cells ◽  
Potential Applications

Cryo-electron microscopy (cryo-EM) has become an indispensable tool for structural studies of biological macromolecules. Two additional predominant methods are available for studying the architectures of multiprotein complexes: 1) single-particle analysis of purified samples and 2) tomography of whole cells or cell sections. The former can produce high-resolution structures but is limited to highly purified samples, whereas the latter can capture proteins in their native state but has a low signal-to-noise ratio and yields lower-resolution structures. Here, we present a simple, adaptable method combining microfluidic single-cell extraction with single-particle analysis by EM to characterize protein complexes from individual Caenorhabditis elegans embryos. Using this approach, we uncover 3D structures of ribosomes directly from single embryo extracts. Moreover, we investigated structural dynamics during development by counting the number of ribosomes per polysome in early and late embryos. This approach has significant potential applications for counting protein complexes and studying protein architectures from single cells in developmental, evolutionary, and disease contexts.

Combining Electron Microscopy (EM) and Cross-Linking Mass Spectrometry (XL-MS) for Structural Characterization of Protein Complexes

2021 ◽  
pp. 217-232
Author(s):  
Lucía Quintana-Gallardo ◽  
Moisés Maestro-López ◽  
Jaime Martín-Benito ◽  
Miguel Marcilla ◽  
Daniel Rutz ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Mass Spectrometry ◽  
Structural Characterization ◽  
Protein Complexes ◽  
Cross Linking

Management and Analysis of Mass Spectrometry Proteomics Data on the Grid

2011 ◽  
pp. 206-227
Author(s):  
Mario Cannataro ◽  
Pietro Hiram Guzzi ◽  
Giuseppe Tradigo ◽  
Pierangelo Veltri
Keyword(s):  
Mass Spectrometry ◽  
Protein Identification ◽  
Biomarker Discovery ◽  
Integrated Management ◽  
Molecular Signature ◽  
Proteomics Data ◽  
Computational Proteomics ◽  
Manual Inspection ◽  
A Cell ◽  
Grid Based

Recent advances in high throughput technologies analysing biological samples enabled the researchers to collect a huge amount of data. In particular, mass spectrometry-based proteomics uses the mass spectrometry to investigate proteins expressed in an organism or a cell. The manual inspection of spectra is unfeasible, so the need to introduce a set of algorithms, tools and platforms to manage and analyze them arises. Computational Proteomics regards the computational methods for analyzing spectra data in qualitative (i.e. peptide/protein identification in tandem mass spectrometry), and quantitative proteomics (i.e. protein expression in samples), as well as in biomarker discovery (i.e. the identification of a molecular signature of a disease directly from spectra). This chapter presents main standards, tools, and technologies for building scalable, reusable, and portable applications in this field. The chapter surveys available solutions for computational proteomics and includes a deep description of MS-Analyzer, a Grid-based software platform for the integrated management and analysis of spectra data. MS-Analyzer provides efficient spectra management through a specialized spectra database, and supports the semantic composition of pre-processing and data mining services to analyze spectra on the Grid.

LOPIT-DC: A simpler approach to high-resolution spatial proteomics

2018 ◽  
Author(s):  
Aikaterini Geladaki ◽  
Nina Kočevar Britovšek ◽  
Lisa M. Breckels ◽  
Tom S. Smith ◽  
Claire M. Mulvey ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
High Resolution Mass Spectrometry ◽  
Protein Complexes ◽  
Subcellular Localisation ◽  
Proteomics Data ◽  
Large Protein ◽  
Subcellular Resolution ◽  
First Time ◽  
Human Dataset

AbstractHyperplexed Localisation of Organelle Proteins by Isotope Tagging (hyperLOPIT) is a well-established method for studying protein subcellular localisation in complex biological samples. As a simpler alternative we developed a second workflow named Localisation of Organelle Proteins by Isotope Tagging after Differential ultraCentrifugation (LOPIT-DC) which is faster and less resource-intensive. We present the most comprehensive high-resolution mass spectrometry-based human dataset to date and deliver a flexible set of subcellular proteomics protocols for sample preparation and data analysis. For the first time, we methodically compare these two different mass spectrometry-based spatial proteomics methods within the same study and also apply QSep, the first tool that objectively and robustly quantifies subcellular resolution in spatial proteomics data. Using both approaches we highlight suborganellar resolution and isoform-specific subcellular niches as well as the locations of large protein complexes and proteins involved in signalling pathways which play important roles in cancer and metabolism. Finally, we showcase an extensive analysis of the multilocalising proteome identified via both methods.

scholarly journals Native Tandem and Ion Mobility Mass Spectrometry Highlight Structural and Modular Similarities in Clustered-Regularly-Interspaced Shot-Palindromic-Repeats (CRISPR)-associated Protein Complexes From Escherichia coli and Pseudomonas aeruginosa

2012 ◽  
Vol 11 (11) ◽  
pp. 1430-1441 ◽  
Author(s):  
Esther van Duijn ◽  
Ioana M. Barbu ◽  
Arjan Barendregt ◽  
Matthijs M. Jore ◽  
Blake Wiedenheft ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Mass Spectrometry ◽  
Ion Mobility ◽  
Protein Complexes ◽  
Acquired Resistance ◽  
Native Mass Spectrometry ◽  
Ion Mobility Mass Spectrometry ◽  
E Coli ◽  
Ribonucleoprotein Complexes ◽  
Specific Association

The CRISPR/Cas (clustered regularly interspaced short palindromic repeats/CRISPR-associated genes) immune system of bacteria and archaea provides acquired resistance against viruses and plasmids, by a strategy analogous to RNA-interference. Key components of the defense system are ribonucleoprotein complexes, the composition of which appears highly variable in different CRISPR/Cas subtypes. Previous studies combined mass spectrometry, electron microscopy, and small angle x-ray scattering to demonstrate that the E. coli Cascade complex (405 kDa) and the P. aeruginosa Csy-complex (350 kDa) are similar in that they share a central spiral-shaped hexameric structure, flanked by associating proteins and one CRISPR RNA. Recently, a cryo-electron microscopy structure of Cascade revealed that the CRISPR RNA molecule resides in a groove of the hexameric backbone. For both complexes we here describe the use of native mass spectrometry in combination with ion mobility mass spectrometry to assign a stable core surrounded by more loosely associated modules. Via computational modeling subcomplex structures were proposed that relate to the experimental IMMS data. Despite the absence of obvious sequence homology between several subunits, detailed analysis of sub-complexes strongly suggests analogy between subunits of the two complexes. Probing the specific association of E. coli Cascade/crRNA to its complementary DNA target reveals a conformational change. All together these findings provide relevant new information about the potential assembly process of the two CRISPR-associated complexes.

scholarly journals PPI-MASS: An Interactive Web Server to Identify Protein-Protein Interactions From Mass Spectrometry-Based Proteomics Data

2021 ◽  
Vol 8 ◽  
Author(s):  
Mariela González-Avendaño ◽  
Simón Zúñiga-Almonacid ◽  
Ian Silva ◽  
Boris Lavanderos ◽  
Felipe Robinson ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Web Server ◽  
Target Protein ◽  
Protein Protein Interactions ◽  
Proteomics Data ◽  
Functional Protein ◽  
Web Platform ◽  
The Web

Mass spectrometry-based proteomics methods are widely used to identify and quantify protein complexes involved in diverse biological processes. Specifically, tandem mass spectrometry methods represent an accurate and sensitive strategy for identifying protein-protein interactions. However, most of these approaches provide only lists of peptide fragments associated with a target protein, without performing further analyses to discriminate physical or functional protein-protein interactions. Here, we present the PPI-MASS web server, which provides an interactive analytics platform to identify protein-protein interactions with pharmacological potential by filtering a large protein set according to different biological features. Starting from a list of proteins detected by MS-based methods, PPI-MASS integrates an automatized pipeline to obtain information of each protein from freely accessible databases. The collected data include protein sequence, functional and structural properties, associated pathologies and drugs, as well as location and expression in human tissues. Based on this information, users can manipulate different filters in the web platform to identify candidate proteins to establish physical contacts with a target protein. Thus, our server offers a simple but powerful tool to detect novel protein-protein interactions, avoiding tedious and time-consuming data postprocessing. To test the web server, we employed the interactome of the TRPM4 and TMPRSS11a proteins as a use case. From these data, protein-protein interactions were identified, which have been validated through biochemical and bioinformatic studies. Accordingly, our web platform provides a comprehensive and complementary tool for identifying protein-protein complexes assisting the future design of associated therapies.

scholarly journals Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis

2020 ◽  
Vol 21 (8) ◽  
pp. 2873 ◽  
Author(s):  
Chen Chen ◽  
Jie Hou ◽  
John J. Tanner ◽  
Jianlin Cheng
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Protein Identification ◽  
Proteomics Data ◽  
Cellular Mechanisms ◽  
Tremendous Progress ◽  
Analysis Strategies ◽  
Bioinformatics Software ◽  
The Relationship ◽  
Proteomics Data Analysis

Recent advances in mass spectrometry (MS)-based proteomics have enabled tremendous progress in the understanding of cellular mechanisms, disease progression, and the relationship between genotype and phenotype. Though many popular bioinformatics methods in proteomics are derived from other omics studies, novel analysis strategies are required to deal with the unique characteristics of proteomics data. In this review, we discuss the current developments in the bioinformatics methods used in proteomics and how they facilitate the mechanistic understanding of biological processes. We first introduce bioinformatics software and tools designed for mass spectrometry-based protein identification and quantification, and then we review the different statistical and machine learning methods that have been developed to perform comprehensive analysis in proteomics studies. We conclude with a discussion of how quantitative protein data can be used to reconstruct protein interactions and signaling networks.

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