scholarly journals The effect of pressure cycling on proteolytic cleavage efficiency, reaction time and protein sequence coverage

2010 ◽  
Vol 24 (S1) ◽  
Author(s):  
Roger Biringer ◽  
Andreas F. R. Hühmer
Keyword(s):  
Reaction Time ◽  
Protein Sequence ◽  
Proteolytic Cleavage ◽  
Sequence Coverage ◽  
Effect Of Pressure ◽  
Pressure Cycling ◽  
Cleavage Efficiency ◽  
Protein Sequence Coverage

scholarly journals Increased Single-Spectrum Top-Down Protein Sequence Coverage in Trapping Mass Spectrometers with Chimeric Ion Loading

2020 ◽  
Vol 92 (18) ◽  
pp. 12193-12200
Author(s):  
Chad R. Weisbrod ◽  
Lissa C. Anderson ◽  
Joseph B. Greer ◽  
Caroline J. DeHart ◽  
Christopher L. Hendrickson
Keyword(s):  
Protein Sequence ◽  
Sequence Coverage ◽  
Top Down ◽  
Single Spectrum ◽  
Mass Spectrometers ◽  
Protein Sequence Coverage

Improved protein sequence coverage by on resin deglycosylation and cysteine modification for biomarker discovery

PROTEOMICS ◽  
2009 ◽  
Vol 9 (3) ◽  
pp. 783-787 ◽  
Author(s):  
Haruhiko Kamada ◽  
Tim Fugmann ◽  
Dario Neri ◽  
Christoph Roesli
Keyword(s):  
Protein Sequence ◽  
Biomarker Discovery ◽  
Cysteine Modification ◽  
Sequence Coverage ◽  
Protein Sequence Coverage

100% protein sequence coverage: a modern form of surrealism in proteomics

Amino Acids ◽  
2010 ◽  
Vol 41 (2) ◽  
pp. 291-310 ◽  
Author(s):  
Bjoern Meyer ◽  
Dimitrios G. Papasotiriou ◽  
Michael Karas
Keyword(s):  
Protein Sequence ◽  
Sequence Coverage ◽  
Modern Form ◽  
Protein Sequence Coverage

scholarly journals Sequence Coverage Visualizer: A web application for protein sequence coverage 3D visualization

2022 ◽  
Author(s):  
Xinhao Shao ◽  
Christopher Grams ◽  
Yu Gao
Keyword(s):  
Protein Structure ◽  
Protein Sequence ◽  
Web Application ◽  
3D Visualization ◽  
Protein Structures ◽  
Limited Proteolysis ◽  
Protein Characterization ◽  
Series Data ◽  
Sequence Coverage ◽  
Protein Sequence Coverage

Protein structure is connected with its function and interaction and plays an extremely important role in protein characterization. As one of the most important analytical methods for protein characterization, Proteomics is widely used to determine protein composition, quantitation, interaction, and even structures. However, due to the gap between identified proteins by proteomics and available 3D structures, it was very challenging, if not impossible, to visualize proteomics results in 3D and further explore the structural aspects of proteomics experiments. Recently, two groups of researchers from DeepMind and Baker lab have independently published protein structure prediction tools that can help us obtain predicted protein structures for the whole human proteome. Although there is still debate on the validity of some of the predicted structures, it is no doubt that these represent the most accurate predictions to date. More importantly, this enabled us to visualize the majority of human proteins for the first time. To help other researchers best utilize these protein structure predictions, we present the Sequence Coverage Visualizer (SCV), http://scv.lab.gy, a web application for protein sequence coverage 3D visualization. Here we showed a few possible usages of the SCV, including the labeling of post-translational modifications and isotope labeling experiments. These results highlight the usefulness of such 3D visualization for proteomics experiments and how SCV can turn a regular result list into structural insights. Furthermore, when used together with limited proteolysis, we demonstrated that SCV can help validate and compare different protein structures, including predicted ones and existing PDB entries. By performing limited proteolysis on native proteins at various time points, SCV can visualize the progress of the digestion. This time-series data further allowed us to compare the predicted structure and existing PDB entries. Although not deterministic, these comparisons could be used to refine current predictions further and represent an important step towards a complete and correct protein structure database. Overall, SCV is a convenient and powerful tool for visualizing proteomics results.

scholarly journals Data-independent acquisition protease-multiplexing enables increased proteome sequence coverage across multiple fragmentation modes

2021 ◽  
Author(s):  
Alicia L Richards ◽  
Kuei-Ho Chen ◽  
Damien B Wilburn ◽  
Erica Stevenson ◽  
Benjamin Polacco ◽  
...  
Keyword(s):  
Protein Sequence ◽  
Routine Data ◽  
Resonance Excitation ◽  
Sequence Coverage ◽  
Data Independent Acquisition ◽  
Beam Type ◽  
Quantitative Performance ◽  
Mode Beam ◽  
Time Required ◽  
Protein Sequence Coverage

The use of multiple proteases has been shown to increase protein sequence coverage in proteomics experiments, however due to the additional analysis time required, it has not been widely adapted in routine data-dependent acquisition (DDA) proteomic workflows. Alternatively, data-independent acquisition (DIA) has the potential to analyze multiplexed samples from different protease digests, but has been primarily optimized for fragmenting tryptic peptides. Here we evaluate a DIA multiplexing approach that combines three proteolytic digests (Trypsin, AspN, and GluC) into a single sample. We first optimize data acquisition conditions for each protease individually with both the canonical DIA fragmentation mode (beam type CID), as well as resonance excitation CID, to determine optimal consensus conditions across proteases. Next, we demonstrate that application of these conditions to a protease-multiplexed sample of human peptides results in similar protein identifications and quantitative performance as compared to trypsin alone, but enables up to a 63% increase in peptide detections, and a 27% increase non-redundant amino acid detections. Importantly, this resulted in 100% sequence coverage for numerous proteins, suggesting the utility of this approach in applications where sequence coverage is critical, such as proteoform analysis.

Dual Matrix-Based Immobilized Trypsin for Complementary Proteolytic Digestion and Fast Proteomics Analysis with Higher Protein Sequence Coverage

2014 ◽  
Vol 86 (3) ◽  
pp. 1452-1458 ◽  
Author(s):  
Chao Fan ◽  
Zhaomei Shi ◽  
Yiting Pan ◽  
Zifeng Song ◽  
Wanjun Zhang ◽  
...  
Keyword(s):  
Protein Sequence ◽  
Proteolytic Digestion ◽  
Sequence Coverage ◽  
Proteomics Analysis ◽  
Immobilized Trypsin ◽  
Protein Sequence Coverage
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