Mass spectrometry (MS)-based proteomics is a well-established strategy for analyzing complex biological mixtures. Many MS instruments and data acquisition strategies are available, and the data they a...

Post-translational modifications (PTMs) serve as a dynamic chemical language regulating protein function, yet current proteomic methods remain blind to a vast portion of the modified proteome. Standar...

Single-cell proteomics by mass spectrometry (scp-MS) detects thousands of proteins per cell, yet low-abundant proteins routinely detected by antibody-based methods are challenging to quantify in the s...

Metaproteomics characterizes and compares molecular phenotypes of organisms in communities by comprehensively analyzing their protein expression profiles using statistical methods. However, not all st...

Microbial community proteomics is rapidly gaining traction as it allows exploration of functional processes in microbial ecosystems. Consequently, there is a growing need for user-friendly tools that ...

Chain-of-Thought (CoT) prompting has significantly advanced task-solving capabilities in natural language processing with large language models. Unlike standard prompting, CoT encourages the model to ...

Post-translational modifications (PTMs) play a central role in cellular regulation and are implicated in numerous diseases. Database searching remains the standard for identifying modified peptides fr...

Tandem mass spectrometry (MS/MS) is a core technology for small molecule structural elucidation in nontargeted metabolomics. However, the limited coverage of experimental spectral libraries presents a major challenge to MS-based small molecule identification. Although spectrum prediction methods offer a promising alternative, existing approaches often suffer from limitations in combinatorial enumeration strategies or insufficient molecular representation capabilities, leading to poor generalizability. To address this, we propose HDSE-MS, an MS/MS spectrum prediction model that integrates a message passing neural network (MPNN) with a Transformer architecture enhanced by hierarchical distance structural encoding (HDSE). By applying graph coarsening, the model transforms molecular graphs into multilevel cluster structures and encodes the hierarchical distances between clusters as structural biases in the Transformer. This enables the joint modeling of molecular substructures, their hierarchical relationships, and long-range dependencies, thereby improving the model’s ability to represent complex molecular structures. We conducted a comprehensive evaluation of HDSE-MS on three benchmark data sets: NIST23, MoNA, and MassBank. Experimental results show that HDSE-MS outperforms existing methods, achieving mean spectral entropy similarities of 0.759, 0.567, and 0.483 under the [M + H]+ ionization mode, respectively. Furthermore, on the external CASMI2022 test set, HDSE-MS achieved a Rank of 220.8 and a Top-1 accuracy of 0.098, demonstrating its strong predictive accuracy, robust generalization, and scalability. The source code is publicly available at https://github.com/lzjforyou/HDSE-MS, and an interactive web service is accessible at https://huggingface.co/spaces/liuzhijin/hdse-ms-attn-viz.

In a new study published in the journal Nucleic Acid Research, a research team at Karolinska Institutet has performed a large-scale genetic screen to uncover the hidden roles of tiny proteins, so-call...

The MIRAGE (Minimum Information Required for A Glycomics Experiment) guidelines for mass spectrometry (MS) data were initially developed to standardize the reporting of instrumentation, data acquisiti...

Analyses of German medical manuals from 1531 reveal what ingredients those who handled the books used

Abstract. The ProteomeXchange consortium of proteomics resources (http://www.proteomexchange.org) was established to standardize open data practices in the

Franklin was no victim in how the DNA double helix was solved. An overlooked letter and an unpublished news article, both written in 1953, reveal that she was an equal player.

Analysis of RNA polymerase hallmark gene phylogenies supported by protein structure relationships of flaviviruses and ‘flavi-like’ viruses underpins the taxonomic expansion and reorganization of Flavi...

Deep learning has notably advanced the field of liquid chromatography–mass spectrometry-based proteomics. Accurate prediction of peptide retention times significantly enhances our ability to match LC-...

Accurate spectral libraries are essential for analyzing data-independent acquisition (DIA) proteomics data. Here, the authors present Carafe, which trains on DIA data to build experiment-specific spec...

This Review reflects on the major challenges in bacterial mRNA vaccine design, provides strategies for tailoring mRNA construct design to promote humoral or cellular immunity, and provides an overview...