Informed-Proteomics: open-source software package for top-down proteomics
Jungkap Park,Paul D. Piehowski,Christopher S. Wilkins,Mowei Zhou,Joshua Mendoza,Grant M. Fujimoto,Bryson C. Gibbons,Jared B. Shaw,Yufeng Shen,Anil K. Shukla,Ronald J. Moore,Tao Liu,Vladislav A. Petyuk,Nikola Tolić,Ljiljana Paša-Tolić,Richard D. Smith,Samuel H. Payne,Sangtae Kim +17 more
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TLDR
Informed-Proteomics is presented, an open-source software suite for top-down proteomics analysis that consists of an LC-MS feature-finding algorithm, a database search algorithm, and an interactive results viewer that is compared with several other popular tools using human-in-mouse xenograft luminal and basal breast tumor samples.Abstract:
Top-down proteomics, the analysis of intact proteins in their endogenous form, preserves valuable information about post-translation modifications, isoforms and proteolytic processing. The quality of top-down liquid chromatography-tandem MS (LC-MS/MS) data sets is rapidly increasing on account of advances in instrumentation and sample-processing protocols. However, top-down mass spectra are substantially more complex than conventional bottom-up data. New algorithms and software tools for confident proteoform identification and quantification are needed. Here we present Informed-Proteomics, an open-source software suite for top-down proteomics analysis that consists of an LC-MS feature-finding algorithm, a database search algorithm, and an interactive results viewer. We compare our tool with several other popular tools using human-in-mouse xenograft luminal and basal breast tumor samples that are known to have significant differences in protein abundance based on bottom-up analysis.read more
Citations
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Journal ArticleDOI
High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis.
Shivani Tiwary,Roie Levy,Petra Gutenbrunner,Favio Salinas Soto,Krishnan K. Palaniappan,Laura Deming,Marc Berndl,Arthur Brant,Peter Cimermancic,Jürgen Cox,Jürgen Cox +10 more
TL;DR: It is demonstrated that machine learning can predict peptide fragmentation patterns in mass spectrometers with accuracy within the uncertainty of measurement, and analysis of the models reveals that peptides fragmentation depends on long-range interactions within a peptide sequence.
Journal ArticleDOI
Top-Down Proteomics: Ready for Prime Time?
TL;DR: The top-down approach retains in-tact protein mass information, providing a “bird’s-eye” view of the proteome and allowing for identification of novel proteoforms, in-depth sequence characterization, and quantification of disease-associated PTMs.
Journal ArticleDOI
Identification and Quantification of Proteoforms by Mass Spectrometry.
Leah V. Schaffer,Robert J. Millikin,Rachel M. Miller,Lissa C. Anderson,Ryan T. Fellers,Ying Ge,Neil L. Kelleher,Richard D. LeDuc,Xiaowen Liu,Xiaowen Liu,Samuel H. Payne,Liangliang Sun,Paul M. Thomas,Trisha Tucholski,Zhe Wang,Si Wu,Zhijie Wu,Dahang Yu,Michael R. Shortreed,Lloyd M. Smith +19 more
TL;DR: Recent technological developments have enabled comprehensive proteoform analyses in complex samples, and an increasing number of laboratories are adopting top‐down proteomic workflows.
TopPIC: a software tool for top-down mass spectrometry-based proteoform identification and characterization
TL;DR: Liu et al. as discussed by the authors presented TopPIC, a tool that identifies and characterizes complex proteoforms with unknown primary structure alterations, such as amino acid mutations and post-translational modifications, by searching top-down tandem mass spectra against a protein database.
Journal ArticleDOI
High-Resolution Native Mass Spectrometry.
TL;DR: A review of recent developments in high-resolution native mass spectrometer applications can be found in this article, which describes applications in the structural analysis of protein assemblies, proteoform profiling of biopharmaceuticals and plasma proteins.
References
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