Autonomous Dissociation-type Selection for Glycoproteomics Using a Real-Time Library Search
Tandem mass spectrometry (MS/MS) is the gold standard for intact glycopeptide identification, enabling peptide sequence elucidation and site-specific localization of glycan compositions. Beam-type collisional activation is generally sufficient for N-glycopeptides, while electron-driven dissociation...
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Abstract | Tandem mass spectrometry (MS/MS) is the gold standard for intact glycopeptide identification, enabling peptide sequence elucidation and site-specific localization of glycan compositions. Beam-type collisional activation is generally sufficient for N-glycopeptides, while electron-driven dissociation is crucial for site localization in O-glycopeptides. Modern glycoproteomic methods often employ multiple dissociation techniques within a single LC-MS/MS analysis, but this approach frequently sacrifices sensitivity when analyzing multiple glycopeptide classes simultaneously. Here we explore the utility of intelligent data acquisition for glycoproteomics through real-time library searching (RTLS) to match oxonium ion patterns for on-the-fly selection of the appropriate dissociation method. By matching dissociation method with glycopeptide class, this autonomous dissociation-type selection (ADS) generates equivalent numbers of N-glycopeptide identifications relative to traditional beam-type collisional activation methods while also yielding comparable numbers of site-localized O-glycopeptide identifications relative to conventional electron transfer dissociation-based methods. The ADS approach represents a step forward in glycoproteomics throughput by enabling site-specific characterization of both N-and O-glycopeptides within the same LC-MS/MS acquisition.Tandem mass spectrometry (MS/MS) is the gold standard for intact glycopeptide identification, enabling peptide sequence elucidation and site-specific localization of glycan compositions. Beam-type collisional activation is generally sufficient for N-glycopeptides, while electron-driven dissociation is crucial for site localization in O-glycopeptides. Modern glycoproteomic methods often employ multiple dissociation techniques within a single LC-MS/MS analysis, but this approach frequently sacrifices sensitivity when analyzing multiple glycopeptide classes simultaneously. Here we explore the utility of intelligent data acquisition for glycoproteomics through real-time library searching (RTLS) to match oxonium ion patterns for on-the-fly selection of the appropriate dissociation method. By matching dissociation method with glycopeptide class, this autonomous dissociation-type selection (ADS) generates equivalent numbers of N-glycopeptide identifications relative to traditional beam-type collisional activation methods while also yielding comparable numbers of site-localized O-glycopeptide identifications relative to conventional electron transfer dissociation-based methods. The ADS approach represents a step forward in glycoproteomics throughput by enabling site-specific characterization of both N-and O-glycopeptides within the same LC-MS/MS acquisition. |
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AbstractList | Tandem mass spectrometry (MS/MS) is the gold standard for intact glycopeptide identification, enabling peptide sequence elucidation and site-specific localization of glycan compositions. Beam-type collisional activation is generally sufficient for N-glycopeptides, while electron-driven dissociation is crucial for site localization in O-glycopeptides. Modern glycoproteomic methods often employ multiple dissociation techniques within a single LC-MS/MS analysis, but this approach frequently sacrifices sensitivity when analyzing multiple glycopeptide classes simultaneously. Here we explore the utility of intelligent data acquisition for glycoproteomics through real-time library searching (RTLS) to match oxonium ion patterns for on-the-fly selection of the appropriate dissociation method. By matching dissociation method with glycopeptide class, this autonomous dissociation-type selection (ADS) generates equivalent numbers of N-glycopeptide identifications relative to traditional beam-type collisional activation methods while also yielding comparable numbers of site-localized O-glycopeptide identifications relative to conventional electron transfer dissociation-based methods. The ADS approach represents a step forward in glycoproteomics throughput by enabling site-specific characterization of both N-and O-glycopeptides within the same LC-MS/MS acquisition.Tandem mass spectrometry (MS/MS) is the gold standard for intact glycopeptide identification, enabling peptide sequence elucidation and site-specific localization of glycan compositions. Beam-type collisional activation is generally sufficient for N-glycopeptides, while electron-driven dissociation is crucial for site localization in O-glycopeptides. Modern glycoproteomic methods often employ multiple dissociation techniques within a single LC-MS/MS analysis, but this approach frequently sacrifices sensitivity when analyzing multiple glycopeptide classes simultaneously. Here we explore the utility of intelligent data acquisition for glycoproteomics through real-time library searching (RTLS) to match oxonium ion patterns for on-the-fly selection of the appropriate dissociation method. By matching dissociation method with glycopeptide class, this autonomous dissociation-type selection (ADS) generates equivalent numbers of N-glycopeptide identifications relative to traditional beam-type collisional activation methods while also yielding comparable numbers of site-localized O-glycopeptide identifications relative to conventional electron transfer dissociation-based methods. The ADS approach represents a step forward in glycoproteomics throughput by enabling site-specific characterization of both N-and O-glycopeptides within the same LC-MS/MS acquisition. |
Author | Kothlow, Kathryn Huguet, Romain McAlister, Graeme C. Russell, Jacob H. Bergen, David Huang, Jingjing Sutherland, Emmajay Riley, Nicholas M. Veth, Tim S. Canterbury, Jesse D. Mullen, Christopher Zabrouskov, Vlad Barshop, William D. |
Author_xml | – sequence: 1 givenname: Emmajay orcidid: 0000-0001-5150-4346 surname: Sutherland fullname: Sutherland, Emmajay organization: Department of Chemistry, University of Washington, Seattle, Washington 98195, United States – sequence: 2 givenname: Tim S. orcidid: 0000-0002-2561-5437 surname: Veth fullname: Veth, Tim S. organization: Department of Chemistry, University of Washington, Seattle, Washington 98195, United States – sequence: 3 givenname: William D. orcidid: 0000-0001-9517-2339 surname: Barshop fullname: Barshop, William D. organization: Thermo Fisher Scientific, San Jose, California 95134, United States – sequence: 4 givenname: Jacob H. orcidid: 0009-0006-8054-7082 surname: Russell fullname: Russell, Jacob H. organization: Department of Chemistry, University of Washington, Seattle, Washington 98195, United States – sequence: 5 givenname: Kathryn orcidid: 0009-0007-3252-0616 surname: Kothlow fullname: Kothlow, Kathryn organization: Department of Chemistry, University of Washington, Seattle, Washington 98195, United States – sequence: 6 givenname: Jesse D. surname: Canterbury fullname: Canterbury, Jesse D. organization: Thermo Fisher Scientific, San Jose, California 95134, United States – sequence: 7 givenname: Christopher surname: Mullen fullname: Mullen, Christopher organization: Thermo Fisher Scientific, San Jose, California 95134, United States – sequence: 8 givenname: David surname: Bergen fullname: Bergen, David organization: Thermo Fisher Scientific, San Jose, California 95134, United States – sequence: 9 givenname: Jingjing surname: Huang fullname: Huang, Jingjing organization: Thermo Fisher Scientific, San Jose, California 95134, United States – sequence: 10 givenname: Vlad orcidid: 0000-0003-3567-9407 surname: Zabrouskov fullname: Zabrouskov, Vlad organization: Thermo Fisher Scientific, San Jose, California 95134, United States – sequence: 11 givenname: Romain surname: Huguet fullname: Huguet, Romain organization: Thermo Fisher Scientific, San Jose, California 95134, United States – sequence: 12 givenname: Graeme C. surname: McAlister fullname: McAlister, Graeme C. organization: Thermo Fisher Scientific, San Jose, California 95134, United States – sequence: 13 givenname: Nicholas M. orcidid: 0000-0002-1536-2966 surname: Riley fullname: Riley, Nicholas M. organization: Department of Chemistry, University of Washington, Seattle, Washington 98195, United States |
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