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Related Experiment Video

Updated: Jul 11, 2026

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

Published on: April 1, 2017

WeSA: a web server for improving analysis of affinity proteomics data.

Magdalena M Shtetinska^1,2, Juan-Carlos González-Sánchez^1,2, Tina Beyer³

¹BioQuant, Heidelberg University, 69120 Heidelberg, Germany.

Nucleic Acids Research

|May 25, 2024

Summary

We developed Weighted SocioAffinity (WeSA) to identify false positive protein-protein interactions using large proteomics datasets. WeSA reliably flags unreliable interactions, improving confidence in experimental results.

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Last Updated: Jul 11, 2026

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

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Published on: April 1, 2017

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

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An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Protein-protein interactions (PPIs) are crucial for cellular functions.
Experimental PPI studies frequently produce false positive results, complicating biological interpretation.
Existing metrics for assessing interaction confidence have limitations.

Purpose of the Study:

To introduce Weighted SocioAffinity (WeSA), a novel metric for scoring human protein interactions.
To develop a robust method for distinguishing true PPIs from false positives in large-scale proteomics data.
To provide a user-friendly web server for analyzing PPI confidence.

Main Methods:

WeSA integrates data from major interaction proteomics databases (IntAct, BioGRID, BioPlex).
Statistical analysis and ROC analysis using curated positive (CORUM-PDB) and negative (Negatome) datasets were performed.
A web server was developed for users to submit data or query pre-computed interaction scores.

Main Results:

WeSA demonstrates improved performance over existing interaction confidence measures.
High accuracy was achieved across multiple datasets, with an Area Under the Curve (AUC) up to 0.93.
The method effectively identifies likely false positives with high precision (0.98) and low false positive rate (0.11).

Conclusions:

WeSA offers a statistically robust approach to assess the reliability of protein-protein interactions.
The WeSA web server provides valuable tools for researchers to evaluate and visualize PPI data.
This method enhances the interpretation of proteomics data and reduces reliance on potentially erroneous interactions.