Cumulating MS Signal enables polyclonal antibody analysis

Abstract

Unraveling the complexities of protein systems via Mass Spectrometry (MS), particularly polyclonal antibodies, demands innovative analytical strategies. Here, we introduce the cumulative MS score (cMS), a novel mathematical framework that transcends traditional spectrum-matching, integrating MS evidence across multiple sample injections to achieve robust de novo peptide sequencing annotation. This approach, shifting from isolated spectrum analysis to a holistic MS signal-based methodology, was rigorously evaluated and validated across diverse sample types and experimental conditions. We applied this framework to characterize a complex polyclonal antibody mixture of Streptococcus pyogenes M1 protein binders derived from intravenous immunoglobulin (IVIG), revealing predominant variable heavy (VH) and light (VL) chain subgroups consistent with established genetic studies. Furthermore, we successfully identified conserved complementarity-determining region (CDR) features and predicted stable antibody-antigen interactions through molecular dynamics simulations, demonstrating the methods potential for dissecting intricate antibody responses. This work establishes a powerful alternative to conventional tandem mass spectrometry MS/MS data analysis, enabling deeper insights into protein systems and paving the way for targeted therapeutic development.