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Identifying proteins from living organisms helps us understand the biological functions of cells, discover new molecular mechanisms, and interrogate known mechanisms for improving our understanding. For a comprehensive understanding of cellular functions, identifying the whole protein content, or proteome, of a cell is desirable but challenging. Here, we describe in detail two methods of proteome fractionation at either the protein (SDS-PAGE) or peptide (high-pH reversed-phase fractionation) level, which can be used to maximize the identification of proteins from complex biological samples. We apply these methods to two different sample types commonly processed in our laboratory to demonstrate the versatility of these protocols. These methods produce many more peptide identifications when compared to conventional single-shot analysis and can also be used in combination to generate larger complementary datasets with greater depth of proteome coverage.

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In-Gel and In-Solution Approaches to Protein and Peptide Fractionation

  • 实验方案

  • 发表日期：

    2025年

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  • Dylan H. Multari,
  • Mohadeseh Montazeri Shatouri,
  • Sabrina Grizzi de Oliveira,
  • Zeshan Ali,
  • Fatemeh Habibpourmehraban,
  • Farhad Masoomi-Aladizgeh,
  • Paul A. Haynes

摘要

Identifying proteins from living organisms helps us understand the biological functions of cells, discover new molecular mechanisms, and interrogate known mechanisms for improving our understanding. For a comprehensive understanding of cellular functions, identifying the whole protein content, or proteome, of a cell is desirable but challenging. Here, we describe in detail two methods of proteome fractionation at either the protein (SDS-PAGE) or peptide (high-pH reversed-phase fractionation) level, which can be used to maximize the identification of proteins from complex biological samples. We apply these methods to two different sample types commonly processed in our laboratory to demonstrate the versatility of these protocols. These methods produce many more peptide identifications when compared to conventional single-shot analysis and can also be used in combination to generate larger complementary datasets with greater depth of proteome coverage.