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Mass Spectrometric Proteomics 3.0

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 1920

Special Issue Editor


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Guest Editor
Department of Biology and Biotechnology “L. Spallanzani”, Universita degli Studi di Pavia, Pavia, Italy
Interests: purification and characterization of enzymes and structural proteins; investigation of the proteome of different tissues/fluids by using the conventional methods of proteomics/metabolomics
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Special Issue Information

Dear Colleagues,

This Special Issue is the second volume of our previous Special Issue on “Mass Spectrometric Proteomics 2.0”. Proteomics is a still-growing field of molecular biology whose the goal is the systematic identification and quantification of the entire set of proteins (the proteome) expressed at a given time in a biological system (organism, tissue, cell, or biological fluid). Assuming that the variations observed in the proteomes of a system at different times, in response to a specific stimulus, would highlight differences between them, most proteomic (in parallel with metabolomics and genomics) efforts to date have been mainly directed toward biomarker research for a variety of disorders. As proteomics and genomics are complementary techniques, it is questionable what the former adds to the latter. Indeed, the variety of proteins that may be produced both as a result of alternative splicing at the RNA level and after translation (via processes such as phosphorylation, glycosylation, and proteolytic cleavage) makes proteomics more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. Understanding how proteins function and interact with one another is another goal of proteomics that makes this approach even more intriguing. Because of their ability to handle the complexity of the events mentioned above, mass spectrometry (MS)-based methods have become the primary technology to identify proteins that may be separated by one- and two-dimensional gel electrophoresis (1- and 2-DE) and/or via liquid chromatographic techniques (1- and 2D-LC). Currently, proteomics relies mainly on MS, and the numerous applications thus far described have contributed heavily to providing new insights into the roles played by some proteins in human disorders.

The aim of this Special Issue is to attract contributions on all aspects of MS-based proteomics, with special emphasis on recent/novel technologies that, by pushing the boundaries of MS capabilities, are able to address biological problems that have not yet been resolved.

Prof. Dr. Paolo Iadarola
Guest Editor

Manuscript Submission Information

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Keywords

  • proteome
  • mass spectrometry
  • biological system
  • genome
  • protein forms
  • biological phenotype
  • expression, localization, interaction and domain structure of proteomics

Published Papers (2 papers)

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Research

16 pages, 1491 KiB  
Article
Prefrontal Cortex Cytosolic Proteome and Machine Learning-Based Predictors of Resilience toward Chronic Social Isolation in Rats
by Dragana Filipović, Božidar Novak, Jinqiu Xiao, Predrag Tadić and Christoph W. Turck
Int. J. Mol. Sci. 2024, 25(5), 3026; https://doi.org/10.3390/ijms25053026 - 06 Mar 2024
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Abstract
Chronic social isolation (CSIS) generates two stress-related phenotypes: resilience and susceptibility. However, the molecular mechanisms underlying CSIS resilience remain unclear. We identified altered proteome components and biochemical pathways and processes in the prefrontal cortex cytosolic fraction in CSIS-resilient rats compared to CSIS-susceptible and [...] Read more.
Chronic social isolation (CSIS) generates two stress-related phenotypes: resilience and susceptibility. However, the molecular mechanisms underlying CSIS resilience remain unclear. We identified altered proteome components and biochemical pathways and processes in the prefrontal cortex cytosolic fraction in CSIS-resilient rats compared to CSIS-susceptible and control rats using liquid chromatography coupled with tandem mass spectrometry followed by label-free quantification and STRING bioinformatics. A sucrose preference test was performed to distinguish rat phenotypes. Potential predictive proteins discriminating between the CSIS-resilient and CSIS-susceptible groups were identified using machine learning (ML) algorithms: support vector machine-based sequential feature selection and random forest-based feature importance scores. Predominantly, decreased levels of some glycolytic enzymes, G protein-coupled receptor proteins, the Ras subfamily of GTPases proteins, and antioxidant proteins were found in the CSIS-resilient vs. CSIS-susceptible groups. Altered levels of Gapdh, microtubular, cytoskeletal, and calcium-binding proteins were identified between the two phenotypes. Increased levels of proteins involved in GABA synthesis, the proteasome system, nitrogen metabolism, and chaperone-mediated protein folding were identified. Predictive proteins make CSIS-resilient vs. CSIS-susceptible groups linearly separable, whereby a 100% validation accuracy was achieved by ML models. The overall ratio of significantly up- and downregulated cytosolic proteins suggests adaptive cellular alterations as part of the stress-coping process specific for the CSIS-resilient phenotype. Full article
(This article belongs to the Special Issue Mass Spectrometric Proteomics 3.0)
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14 pages, 5559 KiB  
Article
Flexible Quality Control for Protein Turnover Rates Using d2ome
by Henock M. Deberneh and Rovshan G. Sadygov
Int. J. Mol. Sci. 2023, 24(21), 15553; https://doi.org/10.3390/ijms242115553 - 25 Oct 2023
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Abstract
Bioinformatics tools are used to estimate in vivo protein turnover rates from the LC-MS data of heavy water labeled samples in high throughput. The quantification includes peak detection and integration in the LC-MS domain of complex input data of the mammalian proteome, which [...] Read more.
Bioinformatics tools are used to estimate in vivo protein turnover rates from the LC-MS data of heavy water labeled samples in high throughput. The quantification includes peak detection and integration in the LC-MS domain of complex input data of the mammalian proteome, which requires the integration of results from different experiments. The existing software tools for the estimation of turnover rate use predefined, built-in, stringent filtering criteria to select well-fitted peptides and determine turnover rates for proteins. The flexible control of filtering and quality measures will help to reduce the effects of fluctuations and interferences to the signals from target peptides while retaining an adequate number of peptides. This work describes an approach for flexible error control and filtering measures implemented in the computational tool d2ome for automating protein turnover rates. The error control measures (based on spectral properties and signal features) reduced the standard deviation and tightened the confidence intervals of the estimated turnover rates. Full article
(This article belongs to the Special Issue Mass Spectrometric Proteomics 3.0)
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