Protrec2: tissue-specific network-based missing protein recovery method.

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• Missing proteins present a significant challenge in proteomics, hindering the identification of biologically and clinically relevant proteins.
• Existing methods struggle to accurately recover these unannotated proteins from complex proteomic datasets.

Purpose of the Study:

• To introduce Protrec2, a novel probabilistic framework designed to recover missing proteins by integrating tissue-specific protein complex annotations with Bayesian inference.
• To evaluate Protrec2's performance against state-of-the-art methods in both upper-bound and lower-bound scenarios using HeLa and A549 proteomes.

Main Methods:

• Protrec2 utilizes Bayesian inference and incorporates tissue-specific protein complex information to predict the presence of unreported proteins.
• Benchmarking involved comparative analysis with PROTein RECovery, Functional Class Scoring, Hypergeometric Enrichment, and Gene Set Enrichment Analysis.
• The framework was applied to lung tumor-normal proteomic pairs and validated against CPTAC data.

Main Results:

• Protrec2 demonstrated superior performance in upper-bound evaluations, achieving high recovery rates (up to 98.4%) and outperforming existing methods.
• In lower-bound evaluations, Protrec2 maintained high precision (over 90% in A549), unlike other methods that showed performance degradation.
• Application to lung cancer data revealed biologically relevant protein changes, with over 85% of predicted proteins supported by CPTAC validation.

Conclusions:

• Protrec2 is a robust and biologically grounded tool for the recovery of missing proteins in proteomics.
• The framework shows significant potential for advancing discovery proteomics and translational research by enabling more comprehensive protein identification.
• Protrec2's ability to identify key proteins in lung cancer highlights its clinical relevance and broad applicability.