Protein quantification is a crucial data processing step that combines quantitative values at the peptide or fragment level into protein levels in mass spectrometry-based proteomics. However, many of ...

Complex reaction networks can reach out-of-equilibrium steady states with multiple product-determining steps. Now it has been shown that a four-component photochemical network enables selective GlcNAc...

When we launched medRxiv in mid-2019, we never could have anticipated that just a few months later, New York would be at the epicenter of a rapidly escalating global pandemic, and we would be handling...

Only in Halobacterium salinarum: Sugar modifications unique to an archaeal N-linked glycan

Mass spectrometry proteomics creates complex data representing the peptide/protein contents of biological samples. Various types of machine learning have been central to computational methods used to identify peptides from tandem mass spectra and numerous other aspects of the data analysis process. As deep learning has emerged as a powerful machine learning method for modeling and interpreting data, computational proteomics researchers have leveraged large publicly available data sets to train machine learning models to predict peptide fragmentation spectra and liquid chromatography retention time. Resources like proteomicsML offer extensive demonstrative tutorials for these learning tasks and are closing the gap between the proteomics and machine learning communities. However, in these and other educational materials on deep learning, the critical step of preparing data for learning is frequently omitted. Prior to learning, peptide strings must be converted into a numeric format─an embedding. There are many different peptide embeddings, and some vastly outperform others. Yet the process for creating an embedding, and also the rationale for choosing a specific embedding, is rarely discussed in our proteomics literature. In this technical note, we introduce four Google Colab notebooks to teach peptide embeddings. The series walks users through five different peptide-embedding strategies─ from simplistic single-number encodings to state-of-the-art pretrained embeddings─ through both code examples and narrative descriptions. The final notebook compares the five embeddings in a head-to-head benchmark. By making these notebooks free, we hope to lower the barrier for researchers who want to bring modern deep learning into their proteomics workflows.

Call for Academic and Industrial Postdoctoral Fellowships in Data-Driven Life Science 2026 Generic Description of the DDLS Postdoc Program The SciLifeLab and Wallenberg National Program for Data-Drive...

Six oligosaccharides with the degree of polymerization (DP) 2 to 7 were isolated from the active fraction of Trillium tschonoskii using a high-temperature

Chirality has become a fundamental design principle to craft peptide materials. In contrast, the systematic exploitation of chirality to build glycan materials remains largely unexplored, despite the ...

Charge detection mass spectrometry (CDMS) enables direct measurement of mass and charge for individual ions and overcomes fundamental limitations of conventional electrospray mass spectrometry for large, heterogeneous biomolecules. Messenger RNAs (mRNAs) represent a particularly challenging analytical target, as transcripts often span thousands of nucleotides, exhibit substantial heterogeneity, and form higher-order oligomers that are difficult to assess using existing workflows. Here, we optimize and evaluate native CDMS for intact analysis of long mRNAs and conduct systematic comparison of positive and negative analysis polarities. Using a panel of commercially available mRNAs ranging from approximately 1,000 to 4,500 nucleotides, we demonstrate that native CDMS enables direct, and accurate mass measurement of intact mRNAs alongside various process-related impurities (truncations, dimers etc.). Collisional activation improves mass accuracy by reducing adduct heterogeneity while preserving the ability to resolve impurity populations. Across all constructs, CDMS provides quantitative access to charge distributions that scale with transcript length and respond predictably to activation, denaturation, and polarity, offering insight into ion charging behavior and charge accommodation for large RNAs. Negative polarity measurements generally access higher charge states than positive polarity, particularly for the largest transcripts, improving sensitivity and relative size discrimination, while intact mass accuracy remains comparable between polarities when appropriate ion transmission strategies are employed. Together, these results establish native CDMS as a versatile and information-rich platform for intact mRNA characterization, providing simultaneous access to mass, charge, and impurity information that complements existing analytical methods and supports emerging needs in RNA biotherapeutic development.

Open to: academia, industry, and government Criteria: (1) PhD scientist, (2) Within 10 years of graduation, (3) Actively involved in the field of intact protein analysis by mass spectrometry (broadly ...

Gaining control of existing biomanufacturing chassis organisms, such as Escherichia coli K12 , and novel isolates, such as the salt tolerant Halomonas bluephagenesis sp TD01 studied here may be facili...