Mass spectrometry-based<i>de novo</i>sequencing of the anti-FLAG-M2 antibody using multiple proteases and a dual fragmentation scheme

Peng, Weiwei; Snijder, Joost

doi:10.1101/2021.01.07.425675

Cited by 3 publications

(3 citation statements)

References 45 publications

(54 reference statements)

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“…In terms of its sequence, we could find three different sequences in public repositories, germline sequence, the modified germline sequence based on electron-density maps in X-ray crystallography, and the one sequenced by mass spectrometry (Figure S4). , The group who performed the X-ray crystallography replaced certain residues of germline with alanine if the residues did not match to the electron-density maps. For the MS-based sequencing, Peng et al digested 27 μg of the antibody with 9 proteases and utilized two mass spectrometric fragmentation strategies, and the peptides were de novo sequenced .…”

Section: Resultsmentioning

confidence: 99%

“… , The group who performed the X-ray crystallography replaced certain residues of germline with alanine if the residues did not match to the electron-density maps. For the MS-based sequencing, Peng et al digested 27 μg of the antibody with 9 proteases and utilized two mass spectrometric fragmentation strategies, and the peptides were de novo sequenced . In the Fab domain of the antibody, there were 8 different sites for the light chain and 40 for the heavy chain between the one with X-ray and the MS-based sequence.…”

Section: Resultsmentioning

confidence: 99%

“…For the MS-based sequencing, Peng et al digested 27 μg of the antibody with 9 proteases and utilized two mass spectrometric fragmentation strategies, and the peptides were de novo sequenced. 59 In the Fab domain of the antibody, there were 8 different sites for the light chain and 40 for the heavy chain between the one with X-ray and the MSbased sequence. All three sequences were considered in our analysis and included in the database.…”

Section: Maldi-time Of Flight-based Peptide Fingerprintingmentioning

confidence: 99%

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tipNrich: A Tip-Based N-Terminal Proteome Enrichment Method

Lee

Kim

et al. 2021

Anal. Chem.

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The mass spectrometry-based analysis of protein post-translational modifications requires large amounts of sample, complicating the analysis of samples with limited amounts of proteins such as clinical biopsies. Here, we present a tip-based Nterminal analysis method, tipNrich. The entire procedure is processed in a single pipette tip to minimize sample loss, which is so highly optimized to analyze small amounts of proteins, even femtomole-scale of a single protein. With tipNrich, we investigated various single proteins purified from different organisms using a low-resolution mass spectrometer and identified several N-terminal peptides with different Nt-modifications such as ragged N-termini. Furthermore, we applied matrix-assisted laser desorption ionization time-of-flight mass spectrometry to our method for shortening the analysis time. Moreover, we showed that our method could be utilized in disease diagnosis as exemplified by the characterization of wild-type transthyretin amyloidosis patients compared to the healthy individuals based on N-terminome profiling. In summary, tipNrich will satisfy the need of identifying N-terminal peptides even with highly scarce amounts of proteins and of having faster processing time to check the quality of protein products or to characterize N-terminal proteoform-related diseases.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Maldi-time Of Flight-based Peptide Fingerprintingmentioning

confidence: 99%

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tipNrich: A Tip-Based N-Terminal Proteome Enrichment Method

Lee

Kim

et al. 2021

Anal. Chem.

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Current state, existing challenges, and promising progress for de novo sequencing and assembly of monoclonal antibodies

Beslic

Tscheuschner

Renard

et al. 2022

Preprint

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Monoclonal antibodies (mAbs) are biotechnologically produced proteins with various applications in research, therapeutics, and diagnostics. Their ability to recognize and bind to specific molecule structures makes them essential research tools and therapeutic agents. Sequence information of antibodies is helpful for understanding antibody-antigen interactions and ensuring their affinity and specificity. De novo sequencing based on mass spectrometry is a useful method to obtain the amino acid sequence of peptides and proteins without a priori knowledge. Deep learning-based approaches have been developed and applied more frequently to increase the accuracy of de novo sequencing. In this study, we evaluated five recently developed de novo sequencing algorithms (Novor, pNovo 3, DeepNovo, SMSNet, and PointNovo) in their ability to identify and assemble antibody sequences. The deep learning-based tools PointNovo and SMSNet showed an increased peptide recall across different enzymes and datasets compared to spectrum-graph-based approaches. We evaluated different error types of de novo peptide sequencing tools and their performance for different numbers of missing cleavage sites, noisy spectra, and peptides of various lengths. We achieved a sequence coverage of 93.15% to 99.07% on the light chains of three different antibody datasets using the de Bruijn assembler ALPS and the predictions from PointNovo. However, low sequence coverage and accuracy on the heavy chains demonstrate that complete de novo protein sequencing remains a challenging issue in proteomics that requires improved de novo error correction, alternative digestion strategies, and hybrid approaches such as homology search to achieve high accuracy on long protein sequences.

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No patient is the same; lessons learned from antibody repertoire profiling in hospitalized severe COVID-19 patients

Bondt

Hoek

Dingess

et al. 2022

Preprint

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Here, by using mass spectrometry-based methods IgG1 and IgA1 clonal repertoires were monitored quantitatively and longitudinally in more than 50 individual serum samples obtained from 17 COVID-19 patients admitted to intensive care units because of acute respiratory distress syndrome. These serological clonal profiles were used to examine how each patient reacted to a severe SARS-CoV-2 infection. All 17 donors revealed unique polyclonal repertoires and changes after infection. Substantial changes over time in the IgG1 and/or IgA1 clonal repertoires were observed in individual patients, with several new clones appearing following the infection, in a few cases leading to a few very high abundant IgG1 and/or IgA1 clones dominating the repertoire. Several of these clones were de novo sequenced through combinations of top-down, middle-down and bottom-up proteomics approaches. This revealed several sequence features in line with sequences deposited in the SARS-CoV-specific database of antibodies. In other patients, the serological Ig profiles revealed the treatment with tocilizumab, as after treatment, this IgG1-mAb dominated the serological IgG1 repertoire. Tocilizumab clearance could be monitored and a half-life of approximately 6 days was established in these patients. Overall, our longitudinal monitoring of IgG1 and IgA1 repertoires of individual donors reveals that antibody responses are highly personalized traits of each patient, affected by the disease and the chosen clinical treatment. The impact of these observations argues for a more personalized and longitudinal approach in patients diagnostics, both in serum proteomics as well as in monitoring immune responses.

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Mass spectrometry-basedde novosequencing of the anti-FLAG-M2 antibody using multiple proteases and a dual fragmentation scheme

Cited by 3 publications

References 45 publications

tipNrich: A Tip-Based N-Terminal Proteome Enrichment Method

tipNrich: A Tip-Based N-Terminal Proteome Enrichment Method

Current state, existing challenges, and promising progress for de novo sequencing and assembly of monoclonal antibodies

No patient is the same; lessons learned from antibody repertoire profiling in hospitalized severe COVID-19 patients

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