A five-level classification system for proteoform identifications

Smith, Lloyd M.; Thomas, Paul M.; Shortreed, Michael R.; Schaffer, Leah V.; Fellers, Ryan T.; LeDuc, Richard D.; Tucholski, Trisha; Ge, Ying; Agar, Jeffrey N.; Anderson, Lissa C.; Chamot‐Rooke, Julia; Gault, Joseph; Loo, Joseph A.; Paša‐Tolić, Ljiljana; Robinson, Carol V.; Schlüter, Hartmut; Tsybin, Yury O.; Vilaseca, Marta; Vizcaíno, Juan Antonio; Danis, Paul O.; Kelleher, Neil L.

doi:10.1038/s41592-019-0573-x

Cited by 67 publications

(79 citation statements)

References 10 publications

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“…1d ). Inspired by early adoption of class levels for phosphopeptide localizations 23 and more recently for proteoforms 24 , we developed this classification system to more accurately describe the quality and confidence of glycopeptide and glycosite identifications. Level 1 glycopeptide identifications indicate that all glycans identified in the total glycan mass modification are localized to specific serine and threonine residues with a localization probability > 0.75.…”

Section: Resultsmentioning

confidence: 99%

O-Pair Search with MetaMorpheus for O-glycopeptide characterization

et al. 2020

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We report O-Pair Search, a new approach to identify O-glycopeptides and localize O-glycosites. Using paired collision- and electron-based dissociation spectra, O-Pair Search identifies O-glycopeptides using an ion-indexed open modification search and localizes O-glycosites using graph theory and probability-based localization. O-Pair Search reduces search times more than 2,000-fold compared to current O-glycopeptide processing software, while defining O-glycosite localization confidence levels and generating more O-glycopeptide identifications. Beyond the mucin-type O-glycopeptides discussed here, O-Pair Search also accepts user-defined glycan databases, making it compatible with many types of O-glycosylation. O-Pair Search is freely available within the open-source MetaMorpheus platform at https://github.com/smith-chem-wisc/MetaMorpheus .

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Section: Resultsmentioning

confidence: 99%

O-Pair Search with MetaMorpheus for O-glycopeptide characterization

et al. 2020

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“…These technological advances have been supported by the corresponding TD/MD-specific developments in data analysis tools, such as deconvolution of highly convoluted product ion distributions and product ion annotation approaches. [43][44][45][46][47] As a result, TD MS/proteomics has emerged as a powerful tool in basic, translational, and clinical research, not only for protein identification, but also for large-scale proteoform elucidation.…”

Section: (Introduction)mentioning

confidence: 99%

Interlaboratory Study for Characterizing Monoclonal Antibodies by Top-Down and Middle-Down Mass Spectrometry

Srzentić

Fornelli

Tsybin

et al. 2020

J. Am. Soc. Mass Spectrom.

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The Consortium for Top-Down Proteomics (www.topdownproteomics.org) launched the present study to assess the current state of top-down mass spectrometry (TD MS) and middle-down mass spectrometry (MD MS) for characterizing monoclonal antibody (mAb) primary structures, including their modifications. To meet the needs of the rapidly growing therapeutic antibody market, it is important to develop analytical strategies to characterize the heterogeneity of a therapeutic product's primary structure accurately and reproducibly. The major objective of the present study is to determine whether current TD/MD MS technologies and protocols can add value to the more commonly employed bottom-up (BU) approaches with regard to confirming protein integrity, sequencing variable domains, avoiding artifacts, and revealing modifications and their locations. We also aim to gather information

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“…In the following sections we will use the five-level classification system for proteoform identifications proposed by Smith et al [50]. We will refer to levels 1, 2A, 2B and 3.…”

Section: Resultsmentioning

confidence: 99%

Protamine Characterization by Top-Down Proteomics: Boosting Proteoform Identification with DBSCAN

et al. 2021

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Protamines replace histones as the main nuclear protein in the sperm cells of many species and play a crucial role in compacting the paternal genome. Human spermatozoa contain protamine 1 (P1) and the family of protamine 2 (P2) proteins. Alterations in protamine PTMs or the P1/P2 ratio may be associated with male infertility. Top-down proteomics enables large-scale analysis of intact proteoforms derived from alternative splicing, missense or nonsense genetic variants or PTMs. In contrast to current gold standard techniques, top-down proteomics permits a more in-depth analysis of protamine PTMs and proteoforms, thereby opening up new perspectives to unravel their impact on male fertility. We report on the analysis of two normozoospermic semen samples by top-down proteomics. We discuss the difficulties encountered with the data analysis and propose solutions as this step is one of the current bottlenecks in top-down proteomics with the bioinformatics tools currently available. Our strategy for the data analysis combines two software packages, ProSight PD (PS) and TopPIC suite (TP), with a clustering algorithm to decipher protamine proteoforms. We identified up to 32 protamine proteoforms at different levels of characterization. This in-depth analysis of the protamine proteoform landscape of normozoospermic individuals represents the first step towards the future study of sperm pathological conditions opening up the potential personalized diagnosis of male infertility.

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A five-level classification system for proteoform identifications

Cited by 67 publications

References 10 publications

O-Pair Search with MetaMorpheus for O-glycopeptide characterization

O-Pair Search with MetaMorpheus for O-glycopeptide characterization

Interlaboratory Study for Characterizing Monoclonal Antibodies by Top-Down and Middle-Down Mass Spectrometry

Protamine Characterization by Top-Down Proteomics: Boosting Proteoform Identification with DBSCAN

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