MIND: A Double-Linear Model To Accurately Determine Monoisotopic Precursor Mass in High-Resolution Top-Down Proteomics

Lermyte, Frederik; Dittwald, Piotr; Claesen, Jürgen; Baggerman, Geert; Sobott, Frank; O’Connor, Peter B.; Laukens, Kris; Hooyberghs, Jef; Gambin, Anna; Valkenborg, Dirk

doi:10.1021/acs.analchem.9b02682

Cited by 5 publications

(11 citation statements)

References 25 publications

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“…Automation of data analysis with no loss in accuracy is evolving with the recent developments of both open source and commercial software workflows for biopharma applications. 33 Recent advancements in algorithms for data analysis of protein mass spectra aim at more accurate peak assignment (for accurate monoisotopic or average mass calculation) 99 as well as improved deconvolution of complex product ion mass spectra and attempts to perform de novo TD MS sequencing. 100 In principle, TD/MD MS could be integrated into the next generation MAM workflows, which presently employ BU-derived methods.…”

Section: Further Considerations and Outlookmentioning

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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Section: Further Considerations and Outlookmentioning

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 first row shows the error between the experimentally determined (intensity-weighted) and the theoretical average mass. Lermyte et al . and Claesen et al have observed that the average mass is less “precise” and less robust to various MS-related errors as a mass metric than, for instance, the most-abundant mass (second row).…”

Section: Resultsmentioning

confidence: 99%

MIND4OLIGOS: Determining the Monoisotopic Mass of Oligonucleotides Observed in High-Resolution Mass Spectrometry

Prostko,

Radziński,

Ciach

et al. 2024

Anal. Chem.

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Oligonucleotide therapeutics have emerged as an important class of drugs offering targeted therapeutic strategies that complement traditional modalities, such as monoclonal antibodies and small molecules. Their unique ability to precisely modulate gene expression makes them vital for addressing previously undruggable targets. A critical aspect of developing these therapies is characterizing their molecular composition accurately. This includes determining the monoisotopic mass of oligonucleotides, which is essential for identifying impurities, degradants, and modifications that can affect the drug efficacy and safety. Mass spectrometry (MS) plays a pivotal role in this process, yet the accurate interpretation of complex mass spectra remains challenging, especially for large molecules, where the monoisotopic peak is often undetectable. To address this issue, we have adapted the MIND algorithm, originally developed for top-down proteomics, for use with oligonucleotide data. This adaptation allows for the prediction of monoisotopic mass from the more readily detectable, most-abundant peak mass, enhancing the ability to annotate complex spectra of oligonucleotides. Our comprehensive validation of this modified algorithm on both in silico and real-world oligonucleotide data sets has demonstrated its effectiveness and reliability. To facilitate wider adoption of this advanced analytical technique, we have encapsulated the enhanced MIND algorithm in a user-friendly Shiny application. This online platform simplifies the process of annotating complex oligonucleotide spectra, making advanced mass spectrometry analysis accessible to researchers and drug developers. The application is available at https:// valkenborg-lab.shinyapps.io/mind4oligos/.

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“…Firstly, the same model can be devised but with a different covariate, for example, the average or most abundant mass of the molecules. Secondly, a model similar in spirit as MIND by Lermyte et al [ 35 ] can be proposed for DNA that predicts the monoisotopic mass based on the partially observed isotope distribution. Thirdly, we can use the model in combination with the fitting procedure of Senko et al [ 36 ] that scales the elemental composition of an average amino acid, denoted as averagine.…”

Section: Discussionmentioning

confidence: 99%

A Compositional Model to Predict the Aggregated Isotope Distribution for Average DNA and RNA Oligonucleotides

et al. 2021

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Structural modifications of DNA and RNA molecules play a pivotal role in epigenetic and posttranscriptional regulation. To characterise these modifications, more and more MS and MS/MS- based tools for the analysis of nucleic acids are being developed. To identify an oligonucleotide in a mass spectrum, it is useful to compare the obtained isotope pattern of the molecule of interest to the one that is theoretically expected based on its elemental composition. However, this is not straightforward when the identity of the molecule under investigation is unknown. Here, we present a modelling approach for the prediction of the aggregated isotope distribution of an average DNA or RNA molecule when a particular (monoisotopic) mass is available. For this purpose, a theoretical database of all possible DNA/RNA oligonucleotides up to a mass of 25 kDa is created, and the aggregated isotope distribution for the entire database of oligonucleotides is generated using the BRAIN algorithm. Since this isotope information is compositional in nature, the modelling method is based on the additive log-ratio analysis of Aitchison. As a result, a univariate weighted polynomial regression model of order 10 is fitted to predict the first 20 isotope peaks for DNA and RNA molecules. The performance of the prediction model is assessed by using a mean squared error approach and a modified Pearson’s χ² goodness-of-fit measure on experimental data. Our analysis has indicated that the variability in spectral accuracy contributed more to the errors than the approximation of the theoretical isotope distribution by our proposed average DNA/RNA model. The prediction model is implemented as an online tool. An R function can be downloaded to incorporate the method in custom analysis workflows to process mass spectral data.

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MIND: A Double-Linear Model To Accurately Determine Monoisotopic Precursor Mass in High-Resolution Top-Down Proteomics

Cited by 5 publications

References 25 publications

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

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

MIND4OLIGOS: Determining the Monoisotopic Mass of Oligonucleotides Observed in High-Resolution Mass Spectrometry

A Compositional Model to Predict the Aggregated Isotope Distribution for Average DNA and RNA Oligonucleotides

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