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.