Enhancing Proteomic Analytics for Biopharmaceutical Engineering

• James Anoshkumar Christian

  Author

Mass spectrometry-based proteomics plays a pivotal role in biopharmaceutical engineering, enabling the high-resolution analysis of protein composition, abundance, and post-translational modifications, critical parameters in biologics development and quality control. This study presents a comprehensive statistical framework to improve both protein inference and quantification from LC-MS/MS data, addressing core challenges in interpreting complex peptide datasets. By integrating expectation-maximization algorithms, signal-to-noise-based variance modeling, and probabilistic treatment of degenerate and divergent peptides, we enhance the accuracy and confidence in protein-level quantification. The approach is compatible with both label-free and isotopically labeled quantification strategies, and is particularly suited for detecting subtle expression changes and modifications relevant to therapeutic protein characterization. This work contributes directly to biopharmaceutical engineering by optimizing proteomic pipelines used in drug target validation and formulation consistency. The framework is positioned as a research-grade analytical tool; formal regulatory qualification would require additional validation studies under GLP/GMP conditions.

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