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2026 Mass Analytica User Meeting and Training
An exciting opportunity to shape the future of our software and gain valuable training in the process
2026 Mass Analytica 1-Day Workshops
Join us for an engaging and hands-on workshop designed to deliver practical skills and new insights on our IT solutions – all in a single day!
Scalable Peptide MRM Transition Prediction for High-Throughput Proteomics via Hashing-Based Sequence Encoding
Scalable Peptide MRM Transition Prediction for High-Throughput Proteomics via Hashing-Based Sequence Encoding
Peptide analysis via Multiple Reaction Monitoring (MRM) is indispensable for quantification and/or biomarker validation and drug development, yet its reliance on experimental transition optimization limits scalability. Current computational models for small molecules fail to address peptide-specific complexities, such as sequence-dependent fragmentation and charge-state variability. We introduce a novel framework that combines hashing-based peptide fragment encoding with gradient-boosted decision trees to predict MRM transitions efficiently. This method eliminates bottlenecks in experimental workflows, enabling rapid, resource-efficient transition identification without compromising accuracy—a critical advancement for high-throughput proteomics pipelines.
Molecular Structure and Mass Spectral Data Quality Driven Processing of High-Resolution Mass Spectrometry Data for Pharmacokinetics Studies
Molecular Structure and Mass Spectral Data Quality Driven Processing of High-Resolution Mass Spectrometry Data for Pharmacokinetics Studies
Our inability to comprehensively process high resolution mass spectrometry data for quantitative analysis has long been an impediment to the broader adoption of this powerful technique. We have developed an approach that agnostically and automatically identifies all ions related to the compound in both the MS and MSMS data. The algorithm uses the structure of the molecule to automatically select the optimal compound related MS and MSMS signals, and parameters (extraction window, S/N) to provide the best overall method to meet the assay acceptance criteria defined by the user. Results using this structure and data driven approach are presented for pharmacokinetic data that were collected using the same set of samples analyzed on both QQQ and HRMS instruments.