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Proteomes
Special Issues
Pattern Recognition and Discovery Methods for Genomic and Proteomic Data
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Pattern Recognition and Discovery Methods for Genomic and Proteomic Data

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

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 8142

Special Issue Editors

Prof. Dr. Andrew K. C. Wong

E-Mail Website
Guest Editor
Systems Design Engineering, 5th, 6th Floor, 200 University Avenue West, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Interests: machine intelligence; pattern recognition; knowledge representation; bioinformatics; protein binding; protein sequencing analysis; statistical modeling
Dr. Peiyuan Zhou

E-Mail Website
Guest Editor
Systems Design Engineering, 5th, 6th Floor, 200 University Avenue West, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Interests: machine learning; pattern recognition; data mining; statistical modeling; time series analysis; clinical data analysis
Dr. Annie En-Shiun Lee

E-Mail Website
Guest Editor
York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
Interests: machine learning; pattern recognition; bioinformatics; sentiment analysis; sequence analysis
Dr. Luis Rueda

E-Mail Website
Guest Editor
School of Computer Science, University of Windsor, 401, Sunset Ave, Windsor, ON N9B 3P4, Canada
Interests: machine learning; transcriptomics; interactomics

Special Issue Information

Dear Colleagues,

Pattern recognition is concerned with solving a given problem using a set of example instances with a number of features. The use of pattern recognition algorithms in bioinformatics is pervasive. For example, the identification of functional regions from protein family sequences is still a primal challenge in bioinformatics and proteomics. Such knowledge, if discovered effectively, could reveal the functionality and crucial mutation hotspots, not only enabling us to have a better understanding of biological mechanisms, but also helping toward the design of new drugs and the curing of genetic diseases.

Besides, in the field of proteomic studies, pattern recognition and knowledge discovery are significant for identifying interactions between protein sequences, detecting mutations, and so on. 

Hence, for this Special Issue, we invite authors to contribute original research articles, method papers, as well as review articles that will address recent developments in the area of proteomic analytics.

Potential topics include, but are not limited to, the following:

• Novel methods for the analysis of proteins and their interactions
• Discovery and analysis of protein structural/functional domains
• Bioinformatics for pattern discovery/analysis of proteomic data
• Integration of proteomics with other omics approaches 

Prof. Dr. Andrew K. C. Wong
Dr. Peiyuan Zhou
Dr. Annie En-Shiun Lee
Dr. Luis Rueda 
Guest Editors

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
  • AI
  • Pattern analysis
  • Protein binding
  • Protein sequencing analysis

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Published Papers (2 papers)

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Research

17 pages, 3652 KiB  
Article
Pattern Analysis of Organellar Maps for Interpretation of Proteomic Data
by Jordan B. Burton, Nicholas J. Carruthers, Zhanjun Hou, Larry H. Matherly and Paul M. Stemmer
Proteomes 2022, 10(2), 18; https://doi.org/10.3390/proteomes10020018 - 23 May 2022
Cited by 9 | Viewed by 3874
Abstract
Localization of organelle proteins by isotope tagging (LOPIT) maps are a coordinate-directed representation of proteome data that can aid in biological interpretation. Analysis of organellar association for proteins as displayed using LOPIT is evaluated and interpreted for two types of proteomic data sets. [...] Read more.
Localization of organelle proteins by isotope tagging (LOPIT) maps are a coordinate-directed representation of proteome data that can aid in biological interpretation. Analysis of organellar association for proteins as displayed using LOPIT is evaluated and interpreted for two types of proteomic data sets. First, test and control group protein abundances and fold change data obtained in a proximity labeling experiment are plotted on a LOPIT map to evaluate the likelihood of true protein interactions. Selection of true positives based on co-localization of proteins in the organellar space is shown to be consistent with carboxylase enrichment which serves as a positive control for biotinylation in streptavidin affinity selected proteome data sets. The mapping in organellar space facilitates discrimination between the test and control groups and aids in identification of proteins of interest. The same representation of proteins in organellar space is used in the analysis of extracellular vesicle proteomes for which protein abundance and fold change data are evaluated. Vesicular protein organellar localization patterns provide information about the subcellular origin of the proteins in the samples which are isolates from the extracellular milieu. The organellar localization patterns are indicative of the provenance of the vesicular proteome origin and allow discrimination between proteomes prepared using different enrichment methods. The patterns in LOPIT displays are easy to understand and compare which aids in the biological interpretation of proteome data. Full article
(This article belongs to the Special Issue Pattern Recognition and Discovery Methods for Genomic and Proteomic Data)
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9 pages, 1269 KiB  
Article
Novel Binding Partners for CCT and PhLP1 Suggest a Common Folding Mechanism for WD40 Proteins with a 7-Bladed Beta-Propeller Structure
by Wai Shun Mak, Tsz Ming Tsang, Tsz Yin Chan and Georgi L. Lukov
Proteomes 2021, 9(4), 40; https://doi.org/10.3390/proteomes9040040 - 2 Oct 2021
Cited by 1 | Viewed by 3277
Abstract
This study investigates whether selected WD40 proteins with a 7-bladed β-propeller structure, similar to that of the β subunit of the G protein heterotrimer, interact with the cytosolic chaperonin CCT and its known binding partner, PhLP1. Previous studies have shown that CCT is [...] Read more.
This study investigates whether selected WD40 proteins with a 7-bladed β-propeller structure, similar to that of the β subunit of the G protein heterotrimer, interact with the cytosolic chaperonin CCT and its known binding partner, PhLP1. Previous studies have shown that CCT is required for the folding of the Gβ subunit and other WD40 proteins. The role of PhLP1 in the folding of Gβ has also been established, but it is unknown if PhLP1 assists in the folding of other Gβ-like proteins. The binding of three Gβ-like proteins, TBL2, MLST8 and CDC20, to CCT and PhLP1, was demonstrated in this study. Co-immunoprecipitation assays identified one novel binding partner for CCT and three new interactors for PhLP1. All three of the studied proteins interact with CCT and PhLP1, suggesting that these proteins may have a folding machinery in common with that of Gβ and that the well-established Gβ folding mechanism may have significantly broader biological implications than previously thought. These findings contribute to continuous efforts to determine common traits and unique differences in the folding mechanism of the WD40 β-propeller protein family, and the role PhLP1 has in this process. Full article
(This article belongs to the Special Issue Pattern Recognition and Discovery Methods for Genomic and Proteomic Data)
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