🚨 New Paper 📜 Alert 🚨 Many congratulations 👏 🎉 to Akhona Myoli for publishing her PhD research chapter! The study describes how new metabolomics profiles and computational metabolomics tools uncov...

Metabolomics is widely used in drug development to reveal mechanisms of action. Here, the authors identify drug-like compounds with potent enzyme engagement by screening 1,020 compounds of unknown fun...

AbstractMetabolite detection with mass spectrometry (MS) in untargeted metabolomics is limited by the wide concentration range of metabolites, where high-abundance ions can restrict the detection of low-abundance features. Full Scan enhanced Dynamic Range (eDR) MS addresses this limitation by partitioning the MS1 mass range into multiple subscans and mass windows, reducing ion competition during accumulation. Here, we systematically evaluated different eDR acquisition strategies for untargeted metabolomics. Across four hepatocellular carcinoma cell lines, Full Scan eDR MS increased detectable compounds up to ∼3.5-fold compared with Full Scan MS and improved signal-to-noise ratios of low-abundance compounds. Among equidistant configurations, 12 windows yielded the highest compound count, while custom window distributions enabled targeted enhancement of selected mass regions. In particular, allocating narrower windows to the low-m/z region selectively increased the detection of low-mass compounds. Increased MS1 coverage also translated into deeper MS2 coverage and increased metabolite identifications. This expanded coverage included cancer-relevant metabolites and improved the detection of metabolic differences between cancer cell lines. Overall, Full Scan eDR MS provides a flexible strategy to increase analytical depth in untargeted metabolomics while maintaining data quality. Customization of window size and distribution enables targeted expansion of the dynamic range within predefined mass regions, allowing MS acquisition to be tailored to sample composition and metabolites of interest.

Dark septate endophytes (DSEs) are widespread fungal symbionts that influence plant performance, yet their encoded and expressed metabolic capacities remain largely uncharacterized. This study provide...

By replacing self-reported food logs with objective biomarkers, metabolic profiling enables personalized precision nutrition and better disease risk prediction.

Read the full paper: Identifying Drug-Induced Metabolic Signatures in Comparative Untargeted NMR Metabolomics Using Difference Spectra

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While developing QUACK (QUAntum Chemistry Kit) – a fast, first-principles quantum-chemical engine originally built to predict tandem mass spectra (MS2 and multi-stage MSn across CID, HCD, EID, UVPD, a...

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AbstractTandem mass spectrometry (MS2) is central to small-molecule structural annotation in metabolomics, yet accurate structure inference remains challenging because of complex fragmentation and structurally similar candidates. Here, we introduce MS2Agent, an end-to-end multi-agent framework that integrates cross-modal representation learning with large language model (LLM) reasoning for MS2 annotation. The agent converts mass spectrometry data and molecular structures into a unified textual representation and utilizes a pre-trained model to calculate similarity. Four functional agents perform spectrum textualization, structure textualization, candidate initialization, and collaborative reranking, enabling evidence-based candidate discrimination. On the CASMI 2022 benchmark, MS2Agent improved Recall@1 from 0.156 for the Codex baseline to 0.738 with the same base LLM. Evaluations on MassSpecGym and an independently constructed isomer benchmark further characterize its generalization and performance in challenging structural cases. To improve computational efficiency, we developed MS2Rep-0.8B-GRPO, a lightweight 0.8B-parameter model optimized by Group Relative Policy Optimization (GRPO), a reinforcement learning (RL) algorithm for MS2 and molecular-structure textualization, achieving a Recall@1 of 0.784 (+8.4 percentage points over the base model) on the CASMI 2022 benchmark. Tests on real samples further demonstrate the practical utility of MS2Agent for metabolite annotation. This multi-agent framework integrating cross-modal representation learning and LLM reasoning offers a promising strategy for automated structural annotation workflows. Future advances in LLMs may further strengthen the annotation capability of the proposed framework.

Abstract. Functional annotation of microbial genes lags behind the pace of genome sequencing, leaving major gaps in our understanding of microbial metaboli