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.

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.

scholarly journals Improvement of Capture Compound Mass Spectrometry Technology (CCMS) for the Profiling of Human Kinases by Combination with 2D LC-MS/MS

2011 ◽  
Vol 2011 ◽  
pp. 1-5 ◽  
Author(s):  
Jenny J. Fischer ◽  
Olivia Graebner ◽  
Mathias Dreger ◽  
Mirko Glinski ◽  
Sabine Baumgart ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
Small Molecule ◽  
Protein Interactions ◽  
Protein Identification ◽  
Spectrometric Analysis ◽  
Proteomic Profiling ◽  
Protein Conjugates ◽  
Input Material ◽  
Mass Spectrometry Technology

An increasingly popular and promising field in functional proteomics is the isolation of proteome subsets based on small molecule-protein interactions. One platform approach in this field are Capture Compounds that contain a small molecule of interest to bind target proteins, a photo-activatable reactivity function to covalently trap bound proteins, and a sorting function to isolate captured protein conjugates from complex biological samples for direct protein identification by liquid chromatography/mass spectrometry (nLC-MS/MS). In this study we used staurosporine as a selectivity group for analysis in HepG2 cells derived from human liver. In the present study, we combined the functional isolation of kinases with different separation workflows of automated split-free nanoflow liquid chromatography prior to mass spectrometric analysis. Two different CCMS setups, CCMS technology combined with 1D LC-MS and 2D LC-MS, were compared regarding the total number of kinase identifications. By extending the chromatographic separation of the tryptic digested captured proteins from 1D LC linear gradients to 2D LC we were able to identify 97 kinases. This result is similar to the 1D LC setup we previously reported but this time 4 times less input material was needed. This makes CCMS of kinases an even more powerful tool for the proteomic profiling of this important protein family.

Applications of Mass Spectrometry in Proteomics

2013 ◽  
Vol 66 (7) ◽  
pp. 721 ◽  
Author(s):  
Izabela Sokolowska ◽  
Armand G. Ngounou Wetie ◽  
Alisa G. Woods ◽  
Costel C. Darie
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Protein Identification ◽  
Protein Protein Interactions ◽  
Post Translational Modification ◽  
Cellular Processes ◽  
Physiological Processes ◽  
Protein Functions ◽  
Instruments And Techniques ◽  
Accurate Mass Spectrometry

Characterisation of proteins and whole proteomes can provide a foundation to our understanding of physiological and pathological states and biological diseases or disorders. Constant development of more reliable and accurate mass spectrometry (MS) instruments and techniques has allowed for better identification and quantification of the thousands of proteins involved in basic physiological processes. Therefore, MS-based proteomics has been widely applied to the analysis of biological samples and has greatly contributed to our understanding of protein functions, interactions, and dynamics, advancing our knowledge of cellular processes as well as the physiology and pathology of the human body. This review will discuss current proteomic approaches for protein identification and characterisation, including post-translational modification (PTM) analysis and quantitative proteomics as well as investigation of protein–protein interactions (PPIs).

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 Progress in Establishing Common Standards for Exchanging Proteomics Data: The Second Meeting of the HUPO Proteomics Standards Initiative

2003 ◽  
Vol 4 (2) ◽  
pp. 203-206 ◽  
Author(s):  
Sandra Orchard ◽  
Paul Kersey ◽  
Weimin Zhu ◽  
Luisa Montecchi-Palazzi ◽  
Henning Hermjakob ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Data Exchange ◽  
Data Representation ◽  
Early Summer ◽  
Protein Protein Interactions ◽  
Proteomics Data ◽  
Proteomics Experiment ◽  
Level 2 ◽  
Made In

The Proteomics Standards Initiative (PSI) aims to define community standards for data representation in proteomics and to facilitate data comparison, exchange and verification. Rapid progress has been made in the development of common standards for data exchange in the fields of both mass spectrometry and protein–protein interactions since the first PSI meeting [1]. Both hardware and software manufacturers have agreed to work to ensure that a proteomics-specific extension is created for the emerging ASTM mass spectrometry standard and the data model for a proteomics experiment has advanced significantly. The Protein–Protein Interactions (PPI) group expects to publish the Level 1 PSI data exchange format for protein–protein interactions by early summer this year, and discussion as to the additional content of Level 2 has been initiated.

scholarly journals Protein Identification False Discovery Rates for Very Large Proteomics Data Sets Generated by Tandem Mass Spectrometry

2009 ◽  
Vol 8 (11) ◽  
pp. 2405-2417 ◽  
Author(s):  
Lukas Reiter ◽  
Manfred Claassen ◽  
Sabine P. Schrimpf ◽  
Marko Jovanovic ◽  
Alexander Schmidt ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Tandem Mass Spectrometry ◽  
Protein Identification ◽  
Data Sets ◽  
Tandem Mass ◽  
Proteomics Data ◽  
False Discovery Rates ◽  
False Discovery ◽  
Discovery Rates

scholarly journals Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata

2014 ◽  
Vol 30 (9) ◽  
pp. 1322-1324 ◽  
Author(s):  
L. Gatto ◽  
L. M. Breckels ◽  
S. Wieczorek ◽  
T. Burger ◽  
K. S. Lilley
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Proteomics Data ◽  
Proteomics Data Analysis

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.

Sign in / Sign up

Export Citation Format

Share Document