MSTopDiff: A Tool for the Visualization of Mass Shifts in Deconvoluted Top-Down Proteomics Data for the Database-Independent Detection of Protein Modifications

Author(s):  
Philipp T. Kaulich ◽  
Konrad Winkels ◽  
Tobias B. Kaulich ◽  
Christian Treitz ◽  
Liam Cassidy ◽  
...  
2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Abdul Rehman Basharat ◽  
Kanzal Iman ◽  
Muhammad Farhan Khalid ◽  
Zohra Anwar ◽  
Rashid Hussain ◽  
...  

2019 ◽  
Author(s):  
Ahmed Arslan

AbstractMotivationPosttranslational modifications (PTMs) modulate proteins activity depending on the dynamics of cellular conditions, in the highly regulated processes that control the reversible nature of these modifications and a cellular state. Due to the unique importance of PTMs, a number of resources are available to analyze the protein modification data for different organisms. These databases are quite informative on a limited number of popular organisms, mostly human and yeast. However there has not been a single database to date that makes it possible to analyze the modified protein residue data for up to 83 model organisms. Moreover, there are limited resources that rely on both protein mutations and modifications in evaluating a phenotype.ResultsI am presenting a comprehensive python tool Pyntheon that enables users to analyze protein modifications and mutations data. This resource can be used in different ways to know: (i) if the proteins of interest have modifications and (ii) if the modified residues overlap with mutated sites. Additional functions include, analyzing if a PTM-site is present in a functional protein region, like domain and structural regions. In summary, Pyntheon makes it possible for a larger community of researchers to evaluate their curated proteomics data and interpret the impact of mutations on phenotypes.ConclusionPyntheon has multifold functions that can help analyzing the protein mutations impact on the modified residues for a large number of popular model organisms.Code-Availabilityhttps://github.com/AhmedArslan/[email protected]


protocols.io ◽  
2020 ◽  
Author(s):  
Jeannie Camarillo ◽  
Bryon Drown ◽  
Rafael Melani ◽  
Neil Kelleher

Author(s):  
Diogo B Lima ◽  
Mathieu Dupré ◽  
Magalie Duchateau ◽  
Quentin Giai Gianetto ◽  
Martial Rey ◽  
...  

Abstract Motivation We present a high-performance software integrating shotgun with top-down proteomic data. The tool can deal with multiple experiments and search engines. Enable rapid and easy visualization, manual validation and comparison of the identified proteoform sequences including the post-translational modification characterization. Results We demonstrate the effectiveness of our approach on a large-scale Escherichia coli dataset; ProteoCombiner unambiguously shortlisted proteoforms among those identified by the multiple search engines. Availability and implementation ProteoCombiner, a demonstration video and user tutorial are freely available at https://proteocombiner.pasteur.fr, for academic use; all data are thus available from the ProteomeXchange consortium (identifier PXD017618). Supplementary information Supplementary data are available at Bioinformatics online.


2020 ◽  
Author(s):  
Lev I. Levitsky ◽  
Julia A. Bubis ◽  
Mikhail V. Gorshkov ◽  
Irina A. Tarasova

ABSTRACTWe report on AA_stat, a bioinformatic approach for panoramic profiling of artificial and post-translational modifications and their localization sites in large-scale proteomics data. Presented version of AA_stat provides validation of ultra-tolerant (open) search results followed by interpretation of the observed mass shifts and recommendation of the optimized sets of fixed and variable modifications for subsequent regular searches. Localization of modification sites is based on relative amino acid frequencies and analysis of tandem mass spectra. AA_stat determines groups of peptide identifications with mass shifts from the validated results of the open search and then scores each possible mass shift location by matching the MS/MS spectrum across the theoretical peptide isoforms. Here we demonstrate the utility of AA_stat for blind scanning of abundant and rare amino acid modifications of both artificial and biological origins and analyze advantages and limitations of open search strategies. AA_stat is implemented as an open-source command line tool available at https://github.com/SimpleNumber/aa_stat.


Author(s):  
David J. Degnan ◽  
Lisa M. Bramer ◽  
Amanda M. White ◽  
Mowei Zhou ◽  
Aivett Bilbao ◽  
...  

2020 ◽  
Vol 31 (5) ◽  
pp. 1104-1113 ◽  
Author(s):  
Sean J. McIlwain ◽  
Zhijie Wu ◽  
Molly Wetzel ◽  
Daniel Belongia ◽  
Yutong Jin ◽  
...  

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