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Proteomes
Special Issues
Computational Proteomics
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Computational Proteomics

A special issue of Proteomes (ISSN 2227-7382).

Deadline for manuscript submissions: closed (31 December 2016) | Viewed by 12704

Special Issue Editor

Prof. Dr. Shenheng Guan

E-Mail Website
Guest Editor
Department of Pharmaceutical Chemistry and Institute for Neurodegenerative Diseases, University of California, San Francisco, CA, USA
Interests: machine learning and data mining in proteomics; global protein turnover; posttranslational modifications; native mass spectrometry; high throughput screening; chemical instrumentation; microfludics

Special Issue Information

Dear Colleagues,

The astonishing performance improvements in chemical instrumentation, especially in mass spectrometry, in the past ten years has allowed us to “see” more, “dig” deeper, and to “zoom in” for more details in proteomes. A direct consequence of the “hardware” development is the confounding challenge to handle the high data generation speed by those high-throughput instruments. The primary goal of the computational proteomics field has being to develop efficient algorithms and practical software tools to extract protein identification, quantification, posttranslational modification states, protein/protein interaction, high order structure, localization, and dynamics information from large-scale experiments. Coincidentally, the emerging “Big Data” science, driven by media sharing, social networks, and e-commerce has arrived, providing us with rich information handling tools, which we can borrow, adapt, and improve. We start to witness the impact of the “machine learning revolution” in the field of proteomics, such as automated instrument tuning, high-speed data analysis for peptide identification and quantification, and optimized information extraction from multi-sources of data. We wish for this Special Issue of Proteomes, dedicated to the area of computational proteomics, to provide readers with the current status of this exciting and interdisciplinary field.

Porf. Dr. Shenheng Guan
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Proteomes is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Bioinformatics
  • Proteomics
  • Protein identification
  • Qunatitative proteomics
  • Algorithms
  • Machine learning

Published Papers (2 papers)

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7608 KiB  
Article
mzStudio: A Dynamic Digital Canvas for User-Driven Interrogation of Mass Spectrometry Data
by Scott B. Ficarro, William M. Alexander and Jarrod A. Marto
Proteomes 2017, 5(3), 20; https://doi.org/10.3390/proteomes5030020 - 01 Aug 2017
Cited by 18 | Viewed by 4956
Abstract
Although not yet truly ‘comprehensive’, modern mass spectrometry-based experiments can generate quantitative data for a meaningful fraction of the human proteome. Importantly for large-scale protein expression analysis, robust data pipelines are in place for identification of un-modified peptide sequences and aggregation of these [...] Read more.
Although not yet truly ‘comprehensive’, modern mass spectrometry-based experiments can generate quantitative data for a meaningful fraction of the human proteome. Importantly for large-scale protein expression analysis, robust data pipelines are in place for identification of un-modified peptide sequences and aggregation of these data to protein-level quantification. However, interoperable software tools that enable scientists to computationally explore and document novel hypotheses for peptide sequence, modification status, or fragmentation behavior are not well-developed. Here, we introduce mzStudio, an open-source Python module built on our multiplierz project. This desktop application provides a highly-interactive graphical user interface (GUI) through which scientists can examine and annotate spectral features, re-search existing PSMs to test different modifications or new spectral matching algorithms, share results with colleagues, integrate other domain-specific software tools, and finally create publication-quality graphics. mzStudio leverages our common application programming interface (mzAPI) for access to native data files from multiple instrument platforms, including ion trap, quadrupole time-of-flight, Orbitrap, matrix-assisted laser desorption ionization, and triple quadrupole mass spectrometers and is compatible with several popular search engines including Mascot, Proteome Discoverer, X!Tandem, and Comet. The mzStudio toolkit enables researchers to create a digital provenance of data analytics and other evidence that support specific peptide sequence assignments. Full article
(This article belongs to the Special Issue Computational Proteomics)
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Article
A Proof of Concept to Bridge the Gap between Mass Spectrometry Imaging, Protein Identification and Relative Quantitation: MSI~LC-MS/MS-LF
by Laëtitia Théron, Delphine Centeno, Cécile Coudy-Gandilhon, Estelle Pujos-Guillot, Thierry Astruc, Didier Rémond, Jean-Claude Barthelemy, Frédéric Roche, Léonard Feasson, Michel Hébraud, Daniel Béchet and Christophe Chambon
Proteomes 2016, 4(4), 32; https://doi.org/10.3390/proteomes4040032 - 26 Oct 2016
Cited by 13 | Viewed by 7270
Abstract
Mass spectrometry imaging (MSI) is a powerful tool to visualize the spatial distribution of molecules on a tissue section. The main limitation of MALDI-MSI of proteins is the lack of direct identification. Therefore, this study focuses on a MSI~LC-MS/MS-LF workflow to link the [...] Read more.
Mass spectrometry imaging (MSI) is a powerful tool to visualize the spatial distribution of molecules on a tissue section. The main limitation of MALDI-MSI of proteins is the lack of direct identification. Therefore, this study focuses on a MSI~LC-MS/MS-LF workflow to link the results from MALDI-MSI with potential peak identification and label-free quantitation, using only one tissue section. At first, we studied the impact of matrix deposition and laser ablation on protein extraction from the tissue section. Then, we did a back-correlation of the m/z of the proteins detected by MALDI-MSI to those identified by label-free quantitation. This allowed us to compare the label-free quantitation of proteins obtained in LC-MS/MS with the peak intensities observed in MALDI-MSI. We managed to link identification to nine peaks observed by MALDI-MSI. The results showed that the MSI~LC-MS/MS-LF workflow (i) allowed us to study a representative muscle proteome compared to a classical bottom-up workflow; and (ii) was sparsely impacted by matrix deposition and laser ablation. This workflow, performed as a proof-of-concept, suggests that a single tissue section can be used to perform MALDI-MSI and protein extraction, identification, and relative quantitation. Full article
(This article belongs to the Special Issue Computational Proteomics)
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