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A Comprehensive Study of Gradient Conditions for Deep Proteome Discovery in a Complex Protein Matrix

Bottom–up mass-spectrometry-based proteomics is a well-developed technology based on complex peptide mixtures from proteolytic cleavage of proteins and is widely applied in protein identification, characterization, and quantitation. A tims-ToF mass spectrometer is an excellent platform for bottom–up...

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Autores principales: Wei, Xing, Liu, Pei N., Mooney, Brian P., Nguyen, Thao Thi, Greenlief, C. Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9569591/
https://www.ncbi.nlm.nih.gov/pubmed/36233016
http://dx.doi.org/10.3390/ijms231911714
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author Wei, Xing
Liu, Pei N.
Mooney, Brian P.
Nguyen, Thao Thi
Greenlief, C. Michael
author_facet Wei, Xing
Liu, Pei N.
Mooney, Brian P.
Nguyen, Thao Thi
Greenlief, C. Michael
author_sort Wei, Xing
collection PubMed
description Bottom–up mass-spectrometry-based proteomics is a well-developed technology based on complex peptide mixtures from proteolytic cleavage of proteins and is widely applied in protein identification, characterization, and quantitation. A tims-ToF mass spectrometer is an excellent platform for bottom–up proteomics studies due to its rapid acquisition with high sensitivity. It remains challenging for bottom–up proteomics approaches to achieve 100% proteome coverage. Liquid chromatography (LC) is commonly used prior to mass spectrometry (MS) analysis to fractionate peptide mixtures, and the LC gradient can affect the peptide fractionation and proteome coverage. We investigated the effects of gradient type and time duration to find optimal gradient conditions. Five gradient types (linear, logarithm-like, exponent-like, stepwise, and step-linear), three different gradient lengths (22 min, 44 min, and 66 min), two sample loading amounts (100 ng and 200 ng), and two loading conditions (the use of trap column and no trap column) were studied. The effect of these chromatography variables on protein groups, peptides, and spectral counts using HeLa cell digests was explored. The results indicate that (1) a step-linear gradient performs best among the five gradient types studied; (2) the optimal gradient duration depends on protein sample loading amount; (3) the use of a trap column helps to enhance protein identification, especially low-abundance proteins; (4) MSFragger and PEAKS Studio have high similarity in protein group identification; (5) MSFragger identified more protein groups among the different gradient conditions compared to PEAKS Studio; and (6) combining results from both database search engines can expand identified protein groups by 9–11%.
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spelling pubmed-95695912022-10-17 A Comprehensive Study of Gradient Conditions for Deep Proteome Discovery in a Complex Protein Matrix Wei, Xing Liu, Pei N. Mooney, Brian P. Nguyen, Thao Thi Greenlief, C. Michael Int J Mol Sci Article Bottom–up mass-spectrometry-based proteomics is a well-developed technology based on complex peptide mixtures from proteolytic cleavage of proteins and is widely applied in protein identification, characterization, and quantitation. A tims-ToF mass spectrometer is an excellent platform for bottom–up proteomics studies due to its rapid acquisition with high sensitivity. It remains challenging for bottom–up proteomics approaches to achieve 100% proteome coverage. Liquid chromatography (LC) is commonly used prior to mass spectrometry (MS) analysis to fractionate peptide mixtures, and the LC gradient can affect the peptide fractionation and proteome coverage. We investigated the effects of gradient type and time duration to find optimal gradient conditions. Five gradient types (linear, logarithm-like, exponent-like, stepwise, and step-linear), three different gradient lengths (22 min, 44 min, and 66 min), two sample loading amounts (100 ng and 200 ng), and two loading conditions (the use of trap column and no trap column) were studied. The effect of these chromatography variables on protein groups, peptides, and spectral counts using HeLa cell digests was explored. The results indicate that (1) a step-linear gradient performs best among the five gradient types studied; (2) the optimal gradient duration depends on protein sample loading amount; (3) the use of a trap column helps to enhance protein identification, especially low-abundance proteins; (4) MSFragger and PEAKS Studio have high similarity in protein group identification; (5) MSFragger identified more protein groups among the different gradient conditions compared to PEAKS Studio; and (6) combining results from both database search engines can expand identified protein groups by 9–11%. MDPI 2022-10-03 /pmc/articles/PMC9569591/ /pubmed/36233016 http://dx.doi.org/10.3390/ijms231911714 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wei, Xing
Liu, Pei N.
Mooney, Brian P.
Nguyen, Thao Thi
Greenlief, C. Michael
A Comprehensive Study of Gradient Conditions for Deep Proteome Discovery in a Complex Protein Matrix
title A Comprehensive Study of Gradient Conditions for Deep Proteome Discovery in a Complex Protein Matrix
title_full A Comprehensive Study of Gradient Conditions for Deep Proteome Discovery in a Complex Protein Matrix
title_fullStr A Comprehensive Study of Gradient Conditions for Deep Proteome Discovery in a Complex Protein Matrix
title_full_unstemmed A Comprehensive Study of Gradient Conditions for Deep Proteome Discovery in a Complex Protein Matrix
title_short A Comprehensive Study of Gradient Conditions for Deep Proteome Discovery in a Complex Protein Matrix
title_sort comprehensive study of gradient conditions for deep proteome discovery in a complex protein matrix
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9569591/
https://www.ncbi.nlm.nih.gov/pubmed/36233016
http://dx.doi.org/10.3390/ijms231911714
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