Bioinformatics Tools for Mass Spectrometry-Based High-Throughput Quantitative Proteomics Platforms

An Integrated Strategy to Identify Tyrosine Sulfation from the Therapeutic Proteins

Posttranslational modifications (PTMs) are potential critical quality attributes in biotherapeutic development, as they can affect drug efficacy and safety. Tyrosine sulfation plays a critical role in protein-protein interactions and has been found on many surface receptors as well as antibody complementarity-determining regions (CDR). However, the presence and function of tyrosine sulfation in therapeutic proteins have not been broadly investigated due to difficulties in detecting the modification. Here, we establish an integrated strategy to identify tyrosine sulfation in biotherapeutic proteins. In silico prediction was used to estimate possible modification sites, followed by the elucidation with intact LCMS and native SCX-MS. The combination of these three steps takes less than 1 h, which provides quick and confident preliminary detection of potential CQAs. Taking NB1 as an example, three +80 Da mass shifts were observed from intact mass analysis and three acidic peaks were monitored by SCX, allowing confirmation of modification as either phosphorylation or sulfation. Peptide mapping, Fe3+-IMAC enrichment, and dephosphorylation were further conducted to provide improved signal intensity and differentiation of modification such as sulfation or phosphorylation. With this integrated strategy, we were able to identify for the first time both tyrosine sulfation and serine phosphorylation in one therapeutic protein.

MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale

The MSstats R-Bioconductor family of packages is widely used for statistical analyses of quantitative bottom-up mass spectrometry-based proteomic experiments to detect differentially abundant proteins. It is applicable to a variety of experimental designs and data acquisition strategies and is compatible with many data processing tools used to identify and quantify spectral features. In the face of ever-increasing complexities of experiments and data processing strategies, the core package of the family, with the same name MSstats, has undergone a series of substantial updates. Its new version MSstats v4.0 improves the usability, versatility, and accuracy of statistical methodology, and the usage of computational resources. New converters integrate the output of upstream processing tools directly with MSstats, requiring less manual work by the user. The package's statistical models have been updated to a more robust workflow. Finally, MSstats' code has been substantially refactored to improve memory use and computation speed. Here we detail these updates, highlighting methodological differences between the new and old versions. An empirical comparison of MSstats v4.0 to its previous implementations, as well as to the packages MSqRob and DEqMS, on controlled mixtures and biological experiments demonstrated a stronger performance and better usability of MSstats v4.0 as compared to existing methods.

A Critical Review of Bottom-Up Proteomics: The Good, the Bad, and the Future of this Field

Proteomics is the field of study that includes the analysis of proteins, from either a basic science prospective or a clinical one. Proteins can be investigated for their abundance, variety of proteoforms due to post-translational modifications (PTMs), and their stable or transient protein–protein interactions. This can be especially beneficial in the clinical setting when studying proteins involved in different diseases and conditions. Here, we aim to describe a bottom-up proteomics workflow from sample preparation to data analysis, including all of its benefits and pitfalls. We also describe potential improvements in this type of proteomics workflow for the future.