In silico based Collision Energy optimization for targeted MRM assay of Hemoglobin variants using Skyline

Abstract: Hemoglobinopathies are among the most prevalent monogenic disorders worldwide, resulting from structural mutations or imbalances in globin chain synthesis. Accurate detection of hemoglobin variants such as HbS, |Hb Q-India, Hb A2-India and HbE is critical for diagnosis and management. Traditional methods like electrophoresis and HPLC can lack specificity or resolution. Mass spectrometry-based Multiple Reaction Monitoring (MRM) offers a promising alternative, but its development is often time-consuming and experimentally intensive.This study presents an in silico workflow for the development of a targeted MRM assay using Skyline software to detect clinically relevant hemoglobin variants. The goal is to streamline assay design, optimize collision energy (CE), and identify variant-specific peptides for improved diagnostic sensitivity and specificity.Amino acid sequences of normal and variant hemoglobin subunits were retrieved from UniProt and Hb variant databases. In silico tryptic digestion was performed using Expasy, followed by rigorous peptide filtering based on uniqueness, ionization efficiency, and absence of interfering modifications. Skyline was employed to predict transitions, optimize CE for different instruments (SCIEX, Agilent, Thermo, Shimadzu), and export MRM methods tailored to variant-specific peptides.The in silico workflow successfully identified unique signature peptides for key hemoglobin variants. Optimized transition lists with instrument-specific CE values were generated for HbS and HbE, enhancing assay sensitivity and reproducibility. The method reduced assay development time while ensuring high confidence in peptide selection and transition prediction.This study demonstrates a robust, cost-effective, and reproducible in silico approach for MRM method development targeting hemoglobin variants. The use of Skyline enables precise CE optimization and transition design, laying the foundation for clinical implementation of mass spectrometry-based diagnostics. While experimental validation is needed, the workflow shows strong potential for integration into diagnostic pipelines and expansion to other clinically relevant protein variants.