bims-mascan 2026-01-18 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2026–01–18
sixteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Development of an untargeted metabolomics analytical protocol for fecal samples by liquid chromatography-mass spectrometry.
Higher-Throughput Proteome Profiling Enabled by Parallelized Pre-Accumulation and Optimized Ion Processing in the Orbitrap Astral Zoom Mass Spectrometer.
A Systematic Approach to Selecting the Right Tool for Mass Spectrometry Data Analysis in Protein and Peptide Bioanalysis.
Multiplexed Data-Independent Acquisition (mDIA) to Profile Extracellular Vesicle Proteomes.
Synthesis-driven reverse metabolomics reveals 3-hydroxy N -acyl amides as gut microbial molecules.
Direct Injection Mass Spectrometry and iFishMass for the High-Throughput Analysis of Antibody Modifications.
TidyMass2: advancing LC-MS untargeted metabolomics through metabolite origin inference and metabolic feature-based functional module analysis.
Demonstrating Voxel-by-Voxel (V × V) Single-Point Calibration in Whole-Body Zebrafish by IR-MALDESI Quantitative MSI.
Automated strain-to-peptide conversion: a high-throughput proteome analysis platform empowering rational design of microbial cell factories.
Temperature-Responsive Agarose-Based Digital Microfluidics: An In-Chip Sample Preparation To Mass Spectrometry Analysis for Trace Cells and Single-Cell Proteomics.
Quantitative Mass Spectrometry Imaging of Intact Proteins in Tissues.
RNA Methylation in Cancer Metabolism: from Mechanisms to Therapeutic Opportunities.
A Chemical Proteomics Method to Quantify Cysteine S-Acylation.
Better Inputs, Better Learning: A Peptide Embedding Tutorial for Proteomic Mass Spectrometry.
Language model-guided anticipation and discovery of mammalian metabolites.
A New Detailed Mass Offset Search in MSFragger for Improved Interpretation of Complex PTMs.

J Food Drug Anal. 2025 Dec 15. 33(4): 460-470

Development of an untargeted metabolomics analytical protocol for fecal samples by liquid chromatography-mass spectrometry.

Tsai-Wei Ting, Chih-Ning Cheng, Chieh-Chang Chen, Ching-Hua Kuo.

Gut microbiota has recently gained attention for its role in regulating multiple host pathways and contributing to disease developments. Fecal metabolomics using liquid chromatography-mass spectrometry (LC-MS) offers a promising approach to study gut microbial metabolites; however, it remains technically challenging due to the complex, heterogeneous nature of fecal samples and the lack of standardized protocols. This study aimed to establish a robust and reproducible untargeted fecal metabolomics workflow. We systematically evaluated sample preparation parameters-including sample amount, extraction solvent, numbers of extraction, and sample-to-solvent ratio-and assessed method reproducibility. Additionally, we compared three LC-MS data acquisition workflows using 10 samples from inflammatory bowel disease (IBD) patients and healthy controls (HC) to improve the identification of biologically relevant metabolites. In sample preparation, our results showed that 50 mg of lyophilized feces was sufficient to capture inter-individual metabolic variation. Additionally, methanol outperformed acetonitrile and showed comparable results to three binary solvent mixtures. A single extraction with methanol was sufficient, and a 1:20 (w/v) sample-to-solvent ratio maximized feature detection. Among the acquisition methods, data-dependent acquisition (DDA) with simultaneous MS1 and MS2 scans provided the highest metabolite coverage with acceptable annotation reliability. In summary, we recommend a single extraction of 50 mg lyophilized feces with 1 mL methanol and the use of DDA for sample acquisition to ensure comprehensive and reproducible untargeted analysis. This optimized protocol improves metabolite detection in human feces and offers a practical strategy to support future studies exploring gut microbial contributions to human health and disease.

DOI: https://doi.org/10.38212/2224-6614.3571
Mol Cell Proteomics. 2026 Jan 09. pii: S1535-9476(25)00603-6. [Epub ahead of print] 101504

Higher-Throughput Proteome Profiling Enabled by Parallelized Pre-Accumulation and Optimized Ion Processing in the Orbitrap Astral Zoom Mass Spectrometer.

Ulises H Guzman, Martin Rykar, Ivo A Hendriks, Hamish Stewart, Eduard Denisov, Bernd Hagedorn, Johannes Petzoldt, Arne Kreutzmann, Yannick Mueller, Tabiwang N Arrey, Immo Colonius, Ole Østergaard, Claire Koenig, Julia Kraegenbring, Kyle L Fort, Erik Couzijn, Jan-Peter Hauschild, Daniel Hermanson, Vlad Zabrouskov, Christian Hock, Eugen Damoc, Jesper V Olsen.

High-throughput proteomics is critical for understanding biological processes, enabling large-scale studies such as biomarker discovery and systems biology. However, current mass spectrometry technologies face limitations in speed, sensitivity, and scalability for analyzing large sample cohorts. The Thermo Scientific™ Orbitrap™ Astral™ Zoom mass spectrometer (MS) was developed to address these limitations by improving acquisition speed, ion utilization, and spectral processing, which are all essential for advancing proteome depth in high-throughput proteomics. The Orbitrap Astral Zoom MS achieves ultra-fast MS/MS scan rates of up to 270 Hz with enhanced ion utilization through pre-accumulation, enabling the identification of ∼100,000 unique peptides and >8,400 proteins in a single 300 samples-per-day (SPD) analysis of human cell lysate. The optimized system reduces analysis time by 40%, achieves near-complete proteome coverage (>12,000 proteins) in 2.7 hours, and enables ultra-high-throughput workflows, identifying >7,000 proteins in a 500 SPD method with exceptional reproducibility (Pairwise Pearson correlations >0.99). These advancements establish the Orbitrap Astral Zoom MS among the fastest and most sensitive instruments under the tested conditions, significantly enhancing speed, sensitivity, and scalability, paving the way for routine large-scale proteomics with applications in clinical research and systems biology.

DOI: https://doi.org/10.1016/j.mcpro.2025.101504
J Mass Spectrom. 2026 Feb;61(2): e70025

A Systematic Approach to Selecting the Right Tool for Mass Spectrometry Data Analysis in Protein and Peptide Bioanalysis.

Prajita Pandey.

Mass spectrometry (MS) has emerged as an imperative technology in protein and peptide bioanalysis, offering unparalleled sensitivity, specificity, and dynamic range. However, the complexity of MS datasets, particularly those arising from advanced acquisition methods and diverse biological samples, necessitates the use of specialized data analysis tools. Therefore, selecting the appropriate software is crucial for accurate data interpretation, reproducibility, and regulatory compliance. This review presents a systematic framework to guide researchers in selecting the most suitable MS data analysis tools, spanning key categories such as quantitative analysis, post-translational modification (PTM) identification, and data-independent acquisition (DIA) workflows, tailored to specific experimental goals in protein and peptide bioanalysis.

DOI: https://doi.org/10.1002/jms.70025
Mol Cell Proteomics. 2026 Jan 08. pii: S1535-9476(26)00002-2. [Epub ahead of print] 101507

Multiplexed Data-Independent Acquisition (mDIA) to Profile Extracellular Vesicle Proteomes.

Yi-Kai Liu, Nathaniel Miller, Marco Hadisurya, Zheng Zhang, W Andy Tao.

  Extracellular vesicles (EVs) have gained increasing attention with their intriguing biological functions and their molecular cargoes serving as potential biomarkers for various diseases, including cancers. A relatively lower abundance of EV proteins compared to cellular counterparts necessitates sensitive and accurate quantitative proteomic strategies. Multiplexed proteomics combined with data-independent acquisition (mDIA) has shown promise for improving sensitivity and quantification over traditional DDA and label-free methods. Despite this, mDIA pipelines that utilize various types of spectral libraries and search software suites have not been thoroughly evaluated with EV proteome samples. In this study, we aim to establish a robust mDIA pipeline based on dimethyl labeling for quantitative proteomics of EVs. EVs were isolated using the extracellular vesicle total recovery and purification (EVtrap) technique and processed directly through an on-bead one-pot sample preparation workflow to obtain digested peptides. We evaluated different mDIA pipelines, including library-free and library-based DIA on the timsTOF HT platform. Results showed that library-based DIA, with project-specific spectral libraries generated from StageTip-based fractionation, outperformed other pipelines in protein identification and quantification. We demonstrated for the first time EV proteome landscape changes caused by the IDH1 mutation and inhibitor treatment in intrahepatic cholangiocarcinoma, highlighting the utility of mDIA in EV-based biomarker discovery.

Keywords:  AG-120; Extracellular vesicles; IDH1 mutation; Intrahepatic cholangiocarcinoma; Proteomics; StageTip-based fractionation; mDIA

DOI:  https://doi.org/10.1016/j.mcpro.2026.101507
bioRxiv. 2026 Jan 08. pii: 2026.01.07.698252. [Epub ahead of print]

Synthesis-driven reverse metabolomics reveals 3-hydroxy N -acyl amides as gut microbial molecules.

Victoria Deleray, Vincent Charron-Lamoureux, Kyle Vittali, Helena Mannochio-Russo, Crystal X Wang, Corinn Walker, Karsten Zengler, Dionicio Siegel, Pieter C Dorrestein.

3-hydroxy N -acyl amides are bioactive lipids with reported anti-obesity and glucose-regulating effects, yet they are rarely detected in untargeted metabolomics studies because they are largely absent from existing spectral reference libraries. To address this gap, we synthesized an MS2 spectral resource comprising 436 structurally diverse 3-hydroxy N -acyl amides, spanning 3- to 18-carbon chains with a wide range of amine headgroups such as ornithine, valine, and dopamine. Using a synthesis-driven reverse metabolomics approach, we found 161,626 spectral matches across 54,744 publicly available files in untargeted metabolomics datasets revealing widespread occurrences in biological samples, including human-derived specimens. Of these molecules detected through MS2 spectral matching, 334 represent newly reported biological entities. We further confirmed their presence in human saliva, stool, and skin using retention time and ion mobility measurements. Frequent detection in microbial datasets and validation in communities of human-derived gut bacteria support microbial production. Several metabolites also showed altered abundance in individuals with diabetes mellitus, showing that this lipid class is modulated in human metabolic disease. Together, these findings establish 3-hydroxy N -acyl amides as a distinct and biologically relevant lipid class, and the accompanying MS2 spectral resource will enable their broader recognition and study in untargeted metabolomics data.

DOI: https://doi.org/10.64898/2026.01.07.698252
ACS Pharmacol Transl Sci. 2026 Jan 09. 9(1): 165-176

Direct Injection Mass Spectrometry and iFishMass for the High-Throughput Analysis of Antibody Modifications.

Jennifer Aguilan, Carlos Madrid-Aliste, Fereshteh Zandkarimi, Alexey Makarov, Alycia Shoultz, Umme Ayesa, Hang Hu, Zachary E X Dance, Anumita Saha-Shah, Simone Sidoli.

  High-throughput analysis has become a critical component in chemical biology and analytical chemistry due to the large libraries of compounds that are screened every day for drug development. Mass spectrometry (MS)-based proteomics is the methodology of choice for large-scale identification and quantification of protein modifications, both chemically deposited and biological post-translational modifications (PTMs). With the advent of antibody drug conjugates (ADCs) and other novel protein-based conjugates, the demand for such an analysis has skyrocketed. Here, we present a new protocol that achieves quantitative data for modified peptides in approximately 30 s of MS acquisition time. This platform includes a direct injection MS approach coupled with new software named iFishMass to extract targeted signals from hundreds of runs. iFishMass automatically generates plots and statistics. This platform will enable a faster analysis of synthetic modifications installed on monoclonal antibodies to create ADCs, and it is potentially scalable to biological PTMs. Sample preparation can be parallelized for 384 samples by using multichannel pipettes and 96-well plates, paving the way to an inexpensive but effective platform for high-throughput screening of conjugation sites on proteins.

Keywords:  NanoMate; antibody−drug conjugates; automation; direct injection; high-throughput screening; iFishMass; mass spectrometry

DOI:  https://doi.org/10.1021/acsptsci.5c00658
Nat Commun. 2026 Jan 17.

TidyMass2: advancing LC-MS untargeted metabolomics through metabolite origin inference and metabolic feature-based functional module analysis.

Xiao Wang, Yijiang Liu, Chao Jiang, Zinuo Huang, Hong Yan, Sunny H Wong, Caroline H Johnson, Jingxiang Zhang, Yifei Ge, Feifan Zhang, Junli Zhang, Renfu Lai, Peng Gao, Xuebin Zhang, Xiaotao Shen.

Untargeted metabolomics provides a direct window into biochemical activities but faces critical challenges in determining metabolite origins and interpreting unannotated metabolic features. Here, we present TidyMass2, an enhanced computational framework for Liquid Chromatography-Mass Spectrometry (LC-MS) untargeted metabolomics that addresses these limitations. TidyMass2 introduces three major innovations compared to its predecessor, TidyMass: (1) a comprehensive metabolite origin inference capability that traces metabolites to human, microbial, dietary, pharmaceutical, and environmental sources through integration of 11 metabolite databases containing 532,488 metabolites with source information; (2) a metabolic feature-based functional module analysis approach that bypasses the annotation bottleneck by leveraging metabolic network topology to extract biological insights from unannotated metabolic features; and (3) a graphical interface that makes advanced metabolomics analyses accessible to researchers without programming expertise. Applied to longitudinal urine metabolomics data from human pregnancy, TidyMass2 identified diverse metabolites originating from human, microbiome, and environment, and uncovered 27 dysregulated metabolic modules. It increased the proportion of biologically interpretable metabolic features from 5.8% to 58.8%, revealing coordinated changes in steroid hormone biosynthesis, carbohydrate metabolism, and amino acid processing. By expanding biological interpretation beyond MS2 spectra-based annotated metabolites, TidyMass2 enables more comprehensive metabolic phenotyping while upholding open-source principles of reproducibility, traceability, and transparency.

DOI: https://doi.org/10.1038/s41467-026-68464-7
J Mass Spectrom. 2026 Feb;61(2): e70022

Demonstrating Voxel-by-Voxel (V × V) Single-Point Calibration in Whole-Body Zebrafish by IR-MALDESI Quantitative MSI.

Alena N Joignant, Stephen J Vega, David C Muddiman.

  This work describes a quantitative mass spectrometry imaging (qMSI) method comparison for the absolute quantification of arachidonic acid (AA) in whole-body zebrafish using infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI). Whole-body zebrafish are structurally heterogeneous samples that are complex to analyze by many qMSI methods largely due to practical sample preparation limitations. However, the multi-organ quantification is valuable in zebrafish, especially for lipid-related investigations. In the standard workflow, the ion abundance of AA was normalized to a structural analogue that was sprayed on the slide before mounting the tissue. A series of calibration spots of stable isotope label (SIL) deuterated AA were spotted onto tissue to construct a calibration curve and subsequently calculate the concentration of endogenous AA in the tissue sections. The calculated values of AA using this method provided values significantly lower than the literature. In the subsequent workflow, the structural analogue was considered in a voxel-by-voxel (V × V) calculation of the concentration of AA in the tissues resulting in an AA concentration similar to the literature within whole-body zebrafish. The V × V method proved simpler in sample preparation, and more accurately quantified AA. This implies the potential utility of single-point V × V calibration for the qMSI of highly heterogeneous tissues.

Keywords:  IR‐MALDESI; mass spectrometry imaging; quantification; voxel‐by‐voxel calibration

DOI:  https://doi.org/10.1002/jms.70022
Bioresour Technol. 2026 Jan 13. pii: S0960-8524(26)00091-X. [Epub ahead of print] 134010

Automated strain-to-peptide conversion: a high-throughput proteome analysis platform empowering rational design of microbial cell factories.

Yujie Wu, Xueying Wang, Kaiguang Yang, Zhigang Sui, Huiming Yuan, Lihua Zhang, Yukui Zhang.

  Mass spectrometry-based proteomics offers a powerful tool for characterizing enzyme expression in engineered strains, yet rapid generation of large strain libraries creates proteomic analysis bottlenecks. The critical limitation lies in manual sample preparation-protein extraction, denaturation, reduction, desalting, and digestion-which is time-consuming and risks compromising reproducibility. To overcome this bottleneck, we developed a novel "strain-to-peptide conversion" (SPC) strategy for high-throughput proteome profiling in microbial cell factories. This automated workflow integrates bacterial lysis, magnetic solid-phase alkylation (mSPA)-based protein enrichment, contaminant removal, and rapid digestion through a commercial liquid handling system, processing 96 samples within 1 h. Compared to the well-established single-pot solid-phase-enhanced sample preparation (SP3) method, SPC achieves a 94% reduction in processing time while maintaining equivalent protein identification depth. Furthermore, the quantification of membrane proteins was increased by 28%. Meanwhile, the method demonstrated exceptional reproducibility, with intra- and inter-batch Pearson correlation coefficients exceeding 0.95. Leveraging this platform, we processed 96 E. coli samples simultaneously, with reliable quantitative data revealing significant regulation of proteins primarily associated with translation, transmembrane transport, and metabolic processes following overexpression of key tricarboxylic acid (TCA) cycle enzymes. These results establish the SPC strategy as an efficient high-throughput solution for large-scale strain proteome analysis, advancing rational cell factory design in synthetic biology and metabolic engineering.

Keywords:  Automation; Biomaterials; High-throughput; Microbial cell factory; Proteome analysis; Synthetic biology

DOI:  https://doi.org/10.1016/j.biortech.2026.134010
Anal Chem. 2026 Jan 11.

Temperature-Responsive Agarose-Based Digital Microfluidics: An In-Chip Sample Preparation To Mass Spectrometry Analysis for Trace Cells and Single-Cell Proteomics.

Ling Yan, Jiacheng Fang, Huimin Zhang, Yali Lin, Dan Li, Yingyan Zhou, Lin Zhu, Zhi Zhu, Pengyuan Yang, Zongwei Cai.

Mass spectrometry (MS)-based single- and trace-cell proteomics provides critical insights into cellular phenotypes, but widespread use is limited by the cost and complexity of advanced MS systems. We present a cost-effective, accessible workflow compatible with standard MS platforms and scalable for multiomics. The temperature-responsive agarose-based digital microfluidics (TRA-DMF) platform enables one-step sample processing, including lysis, reduction/alkylation, and digestion, in a parallel four-channel format. Unlike conventional droplet-based microfluidics or fluorescence-activated cell sorting (FACS) approaches, our DMF system ensures real-time visualization and confirmation of single-cell capture and 98.3% sample recovery, minimizing losses through nonpipetting transfer. The TRA-DMF system also overcomes the MS-incompatibility of oil-phase microfluidics, allowing high-efficiency droplet transfer to MS vials. The entire TRA-DMF for single cell proteomics (TRA-DMF-SCP) workflow is completed in ∼3 h (including single-cell capture), with seamless sample introduction into standard LC-MS/MS systems. Using a regular benchtop MS instrument Orbitrap Fusion, we identified over 4000 protein groups (PGs) from 50 293T cells with excellent reproducibility and robustness. This system offers a practical and scalable solution for trace- and single-cell proteomics and holds strong potential for integration into routine multiomics workflows.

DOI: https://doi.org/10.1021/acs.analchem.5c06267
Anal Chem. 2026 Jan 15.

Quantitative Mass Spectrometry Imaging of Intact Proteins in Tissues.

Mushfeqa Iqfath, Andrew K Swansiger, Manxi Yang, Lily Miller, Alyssa M Moore, Emerson Hernly, Yunpeng Bai, Zhong-Yin Zhang, James S Prell, Julia Laskin.

Quantitative imaging of endogenous proteins in biological tissues is essential for understanding their roles in cellular signaling and metabolism. Mass spectrometry imaging (MSI) using nanospray desorption electrospray ionization (nano-DESI) is a label-free approach for mapping intact proteins in biological tissues with minimal sample preparation. However, signal suppression during ionization presents a challenge for quantification. In this study, we introduce a quantitative nano-DESI MSI approach by incorporating protein internal standards (IS) into the extraction solvent. Ion images were generated using iFAMS deconvolution software, which efficiently isolates protein-specific signals from a complex background. A systematic evaluation of normalization strategies indicates that accurate quantification in each pixel is achieved using charge-weighted signal normalization to the IS signal. We demonstrate quantitative imaging of endogenous proteins using only one IS and validate the approach using immunofluorescence imaging and bulk analysis. This robust approach enables accurate protein mapping and quantification in MSI experiments, providing deeper insights into protein function in complex biological systems.

DOI: https://doi.org/10.1021/acs.analchem.5c05187
Int J Biol Sci. 2026 ;22(2): 920-950

RNA Methylation in Cancer Metabolism: from Mechanisms to Therapeutic Opportunities.

Zeyu Wu, Yuncan Xing, Shiwen Mei, Tixian Xiao, Fangze Wei, Qian Liu.

  One of the most important changes in the transformation of normal cells into tumor cells is metabolism. In order to satisfy the more active proliferation, migration and metastasis of cancer cells, abnormal changes occur in various pathways and molecules involved in metabolism, which eventually lead to metabolic reprogramming of tumor cells. This process involves the uptake of nutrients and changes in major metabolic forms. As an important part of post-transcriptional epigenetics, RNA methylation modifications can regulate RNA processing and metabolism, while dynamically and reversibly influencing the expression of specific molecules, thereby ultimately affecting diverse biological processes and cellular phenotypes. In this review, various types of RNA methylation modifications involved in cancer are summarized. Subsequently, we systematically elucidate the mechanism of RNA modification for metabolic reprogramming in cancer, including glucose, lipid, amino acid and mitochondrial metabolism. Most importantly, we discuss in depth the clinical significance of RNA modification in metabolic targeted therapy and immunotherapy from mechanism to therapeutic application.

Keywords:  RNA methylation; cancer; clinical application; metabolism

DOI:  https://doi.org/10.7150/ijbs.124177
ACS Chem Biol. 2026 Jan 13.

A Chemical Proteomics Method to Quantify Cysteine S-Acylation.

Chloé Freyermuth, Jean-William Dupuy, Thibaud T Renault, Stéphane Claverol, Anne-Aurélie Raymond, Emmanuelle Thinon.

S-acylation, often referred to as S-palmitoylation, is a reversible and dynamic posttranslational modification that corresponds to the addition of a long-chain fatty acid to cysteine (Cys) residues. Established mass spectrometry-based chemoproteomics methods have improved our understanding of the S-acylation proteome, notably by identifying hundreds of S-acylated proteins, sometimes with the modified Cys. However, the precise quantification of S-acylation levels for each Cys within a single sample remains challenging at the proteome level. Quantification of S-acylation levels is critical to further our understanding of protein S-acylation in cellular function and its role in health and diseases. We report here the development of an S-acylation quantification workflow based on the sequential labeling of free Cys and S-acylated Cys with isotopic labeling reagents. The workflow was extensively optimized, notably by comparing the number of sites identified with two alkyne-tagged Cys-reactive isotopic probes and four azido-tagged biotin-based capture reagents. By integrating this enhanced workflow with high-field asymmetric waveform ion mobility spectrometry (FAIMS) on LC-MS/MS instruments for the separation of labeled peptides, over 17,000 unique Cys could be quantified in biological samples. Application of the S-acylation quantification workflow to cellular proteomes allowed for the quantification of S-acylation levels in a HeLa proteome. We also identified dynamic S-acylation changes in response to autophagy induction.

DOI: https://doi.org/10.1021/acschembio.5c00824
J Proteome Res. 2026 Jan 13.

Better Inputs, Better Learning: A Peptide Embedding Tutorial for Proteomic Mass Spectrometry.

Luke Squires, Jose Humberto Giraldez Chavez, Alfred Nilsson, Lukas Käll, Samuel H Payne.

  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.

Keywords:  embedding; encoding; machine learning; peptide; proteomics AI; proteomics education; tutorials

DOI:  https://doi.org/10.1021/acs.jproteome.5c00563
Nature. 2026 Jan 14.

Language model-guided anticipation and discovery of mammalian metabolites.

Hantao Qiang, Fei Wang, Wenyun Lu, Xi Xing, Hahn Kim, Sandrine A M Mérette, Lucas B Ayres, Eponine Oler, Jenna E AbuSalim, Asael Roichman, Michael Neinast, Ricardo A Cordova, Won Dong Lee, Ehud Herbst, Vishu Gupta, Samuel L Neff, Mickel Hiebert-Giesbrecht, Adamo Young, Vasuk Gautam, Siyang Tian, Bo Wang, Hannes Röst, Jatinder Baidwan, Russell Greiner, Li Chen, Chad W Johnston, Leonard J Foster, Aaron M Shapiro, David S Wishart, Joshua D Rabinowitz, Michael A Skinnider.

Despite decades of study, large parts of the mammalian metabolome remain unexplored1. Mass spectrometry-based metabolomics routinely detects thousands of small molecule-associated peaks in human tissues and biofluids, but typically only a small fraction of these can be identified, and structure elucidation of novel metabolites remains challenging2-4. Biochemical language models have transformed the interpretation of DNA, RNA and protein sequences, but have not yet had a comparable impact on understanding small molecule metabolism. Here we present an approach that leverages chemical language models5-7 to anticipate the existence of previously uncharacterized metabolites. We introduce DeepMet, a chemical language model that learns from the structures of known metabolites to anticipate the existence of previously unrecognized metabolites. Integration of DeepMet with mass spectrometry-based metabolomics data facilitates metabolite discovery. We harness DeepMet to reveal several dozen structurally diverse mammalian metabolites. Our work demonstrates the potential for language models to advance the mapping of the mammalian metabolome.

DOI: https://doi.org/10.1038/s41586-025-09969-x
J Proteome Res. 2026 Jan 13.

A New Detailed Mass Offset Search in MSFragger for Improved Interpretation of Complex PTMs.

Carolina Rojas Ramírez, Fengchao Yu, Daniel A Polasky, Alexey I Nesvizhskii.

  Conventional database search methods for proteomics struggle when tasked with identifying dozens or hundreds of modifications simultaneously. Open or error-tolerant searches can address this limitation but at the cost of increased difficulty in downstream interpretation of the results and quantification. We and others have previously described "mass offset" or multinotch searches that sit in between closed and open searches, allowing simultaneous search for hundreds of modifications with more straightforward downstream interpretation than open search. The original mass offset searches were closer to the open search, lacking the ability to restrict modifications to specific amino acids. Here, we describe a new "detailed" mass offset (DMO) search implemented in the MSFragger search engine, which allows each mass offset to have its own site restrictions and fragmentation rules. The benefits of the DMO search over existing mass offset searches are shown with three example searches of complex modification sets: nearly one hundred post-translational modifications, fast photochemical oxidation of proteins (FPOP)-derived modifications, and amino acid substitutions. The DMO search further improves the interpretability of results by reducing ambiguity in site localization, particularly when modifications have overlapping masses, and provides benefits that scale with the complexity of the search.

Keywords:  PTMs; database search; mass offset; open search; proteomics; software

DOI:  https://doi.org/10.1021/acs.jproteome.5c00775