scholarly journals Protein mass spectra data analysis for clinical biomarker discovery: a global review

2010 ◽  
Vol 12 (2) ◽  
pp. 176-186 ◽  
Author(s):  
P. Roy ◽  
C. Truntzer ◽  
D. Maucort-Boulch ◽  
T. Jouve ◽  
N. Molinari
Keyword(s):  
Data Analysis ◽  
Mass Spectra ◽  
Biomarker Discovery ◽  
Protein Mass ◽  
Clinical Biomarker

A Universal Score for Deconvolution of Intact Protein and Native Electrospray Mass Spectra

2019 ◽  
Author(s):  
Michael Marty
Keyword(s):  
Data Analysis ◽  
Mass Spectra ◽  
Weighted Average ◽  
Simulated Data ◽  
Charge State Distribution ◽  
Native Mass Spectrometry ◽  
Intact Protein ◽  
Protein Mass ◽  
Charge States

The growing use of intact protein mass analysis, top-down proteomics, and native mass spectrometry have created a need for improved data analysis pipelines for deconvolution of electrospray (ESI) mass spectra containing multiple charge states and potentially without isotopic resolution. Although there are multiple deconvolution algorithms, there is no consensus for how to judge the quality of the deconvolution, and many scoring schemes are not published. Here, an intuitive universal score (UniScore) for ESI deconvolution is presented. The UniScore is the weighted average of deconvolution scores (DScores) for each peak. Each DScore is composed of separate components to score 1) the uniqueness and fit of the deconvolution to the data, 2) the consistency of the peak shape across different charge states, 3) the smoothness of the charge state distribution, and 4) symmetry and separation of the peak. Example scores are provided for a range of experimental and simulated data. By providing a means of judging the quality of the overall deconvolution as well as individual mass peaks, the UniScore scheme provides a foundation for standardizing ESI data analysis of larger molecules and enabling the use of ESI deconvolution in automated data analysis pipelines.

scholarly journals Deconvolving Native and Intact Protein Mass Spectra with UniDec

2020 ◽  
Author(s):  
Marius Kostelic ◽  
Michael Marty
Keyword(s):  
Data Analysis ◽  
Mass Spectra ◽  
Charge State Distribution ◽  
Native Mass Spectrometry ◽  
Intact Protein ◽  
Top Down ◽  
Protein Mass ◽  
Isotopic Resolution ◽  
Charge States ◽  
User Friendly

Intact protein, top-down, and native mass spectrometry (MS) generally require the deconvolution of electrospray ionization (ESI) mass spectra to assign the mass of components from their charge state distribution. For small, well-resolved proteins, the charge can usually be assigned based on the isotope distribution. However, it can be challenging to determine charge states with larger proteins that lack isotopic resolution, in complex mass spectra with overlapping charge states, and in native spectra that show adduction. To overcome these challenges, UniDec uses Bayesian deconvolution to assign charge states and to create a zero-charge mass distribution. UniDec is fast, user-friendly, and includes a range of advanced tools to assist in intact protein, top-down, and native MS data analysis. This chapter provides a step-by-step protocol, an in-depth explanation of the UniDec algorithm, and highlights the parameters that affect the deconvolution. It also covers advanced data analysis tools, such as macromolecular mass defect analysis and tools for assigning potential PTMs and bound ligands. Overall, the chapter provides users with a deeper understanding of UniDec, which will enhance the quality of deconvolutions and allow for more intricate MS experiments.<br>

scholarly journals Deconvolving Native and Intact Protein Mass Spectra with UniDec

2020 ◽  
Author(s):  
Marius Kostelic ◽  
Michael Marty
Keyword(s):  
Data Analysis ◽  
Mass Spectra ◽  
Charge State Distribution ◽  
Native Mass Spectrometry ◽  
Intact Protein ◽  
Top Down ◽  
Protein Mass ◽  
Isotopic Resolution ◽  
Charge States ◽  
User Friendly

Intact protein, top-down, and native mass spectrometry (MS) generally require the deconvolution of electrospray ionization (ESI) mass spectra to assign the mass of components from their charge state distribution. For small, well-resolved proteins, the charge can usually be assigned based on the isotope distribution. However, it can be challenging to determine charge states with larger proteins that lack isotopic resolution, in complex mass spectra with overlapping charge states, and in native spectra that show adduction. To overcome these challenges, UniDec uses Bayesian deconvolution to assign charge states and to create a zero-charge mass distribution. UniDec is fast, user-friendly, and includes a range of advanced tools to assist in intact protein, top-down, and native MS data analysis. This chapter provides a step-by-step protocol, an in-depth explanation of the UniDec algorithm, and highlights the parameters that affect the deconvolution. It also covers advanced data analysis tools, such as macromolecular mass defect analysis and tools for assigning potential PTMs and bound ligands. Overall, the chapter provides users with a deeper understanding of UniDec, which will enhance the quality of deconvolutions and allow for more intricate MS experiments.<br>

A Universal Score for Deconvolution of Intact Protein and Native Electrospray Mass Spectra

2019 ◽  
Author(s):  
Michael Marty
Keyword(s):  
Data Analysis ◽  
Mass Spectra ◽  
Weighted Average ◽  
Simulated Data ◽  
Charge State Distribution ◽  
Native Mass Spectrometry ◽  
Intact Protein ◽  
Protein Mass ◽  
Charge States

The growing use of intact protein mass analysis, top-down proteomics, and native mass spectrometry have created a need for improved data analysis pipelines for deconvolution of electrospray (ESI) mass spectra containing multiple charge states and potentially without isotopic resolution. Although there are multiple deconvolution algorithms, there is no consensus for how to judge the quality of the deconvolution, and many scoring schemes are not published. Here, an intuitive universal score (UniScore) for ESI deconvolution is presented. The UniScore is the weighted average of deconvolution scores (DScores) for each peak. Each DScore is composed of separate components to score 1) the uniqueness and fit of the deconvolution to the data, 2) the consistency of the peak shape across different charge states, 3) the smoothness of the charge state distribution, and 4) symmetry and separation of the peak. Example scores are provided for a range of experimental and simulated data. By providing a means of judging the quality of the overall deconvolution as well as individual mass peaks, the UniScore scheme provides a foundation for standardizing ESI data analysis of larger molecules and enabling the use of ESI deconvolution in automated data analysis pipelines.

scholarly journals L-Carnitine and Acylcarnitines: Mitochondrial Biomarkers for Precision Medicine

Metabolites ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 51
Author(s):  
Marc R. McCann ◽  
Mery Vet George De la Rosa ◽  
Gus R. Rosania ◽  
Kathleen A. Stringer
Keyword(s):  
Precision Medicine ◽  
Inborn Errors Of Metabolism ◽  
Clinical Utility ◽  
Biomarker Discovery ◽  
Genetic Disorders ◽  
Acid Oxidation ◽  
Inborn Errors ◽  
Therapeutic Decisions ◽  
Literature Evidence ◽  
Clinical Biomarker

Biomarker discovery and implementation are at the forefront of the precision medicine movement. Modern advances in the field of metabolomics afford the opportunity to readily identify new metabolite biomarkers across a wide array of disciplines. Many of the metabolites are derived from or directly reflective of mitochondrial metabolism. L-carnitine and acylcarnitines are established mitochondrial biomarkers used to screen neonates for a series of genetic disorders affecting fatty acid oxidation, known as the inborn errors of metabolism. However, L-carnitine and acylcarnitines are not routinely measured beyond this screening, despite the growing evidence that shows their clinical utility outside of these disorders. Measurements of the carnitine pool have been used to identify the disease and prognosticate mortality among disorders such as diabetes, sepsis, cancer, and heart failure, as well as identify subjects experiencing adverse drug reactions from various medications like valproic acid, clofazimine, zidovudine, cisplatin, propofol, and cyclosporine. The aim of this review is to collect and interpret the literature evidence supporting the clinical biomarker application of L-carnitine and acylcarnitines. Further study of these metabolites could ultimately provide mechanistic insights that guide therapeutic decisions and elucidate new pharmacologic targets.

scholarly journals NEW PARAMETERIZATION OF THE RESONANT PRODUCTION AMPLITUDES NEAR AN INELASTIC THRESHOLD

2009 ◽  
Vol 24 (02n03) ◽  
pp. 549-552 ◽  
Author(s):  
LEONARD LEŚNIAK
Keyword(s):  
Data Analysis ◽  
Mass Spectra ◽  
Resonant Scattering ◽  
Resonance Pole ◽  
Inelastic Threshold ◽  
Production Processes ◽  
Resonance Properties ◽  
Resonant Production ◽  
Watson Theorem ◽  
Scalar Resonances

New formulae for the resonant scattering and the production amplitudes near an inelastic threshold are derived. It is shown that the Flatté formula, frequently used in experimental analyses, is not sufficiently accurate. Its application to data analysis can lead to a substantial distortion of the effective mass spectra and of the resonance pole positions. A unitary parameterization, satisfying a generalized Watson theorem for the production amplitudes, is proposed. It can be easily applied to study production processes, multichannel meson-meson interactions and the resonance properties, including among others the scalar resonances a0(980) and f0(980).

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