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Protrec2: Tissue-specific Network-based Missing Protein Recovery Method.

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Protrec2: Tissue-specific Network-based Missing Protein Recovery Method.

Related Experiment Video

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

Published on: April 18, 2025

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

Weijia Kong^1,2,3, Wilson Wen Bin Goh^1,2,4,5,6, Limsoon Wong³

¹Lee Kong Chian School of Medicine, Nanyang Technological University, Experimental Medicine Building, 59 Nanyang Drive, Singapore 636798, Singapore.

Briefings in Bioinformatics

|December 26, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Protrec2 is a new computational framework that effectively recovers missing proteins in proteomic data. It significantly improves protein discovery and has broad applications in biological and clinical research.

Keywords:

Bayesian inference missing proteins protein complex proteomics tissue specificity

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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

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.