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Western Blotting

Western Blotting

Western blotting is an analytical technique for protein identification. It has various applications in immunology and medicine, including detecting diseases like bovine spongiform encephalopathy, mad cow disease, and human and feline immunodeficiency virus from biological samples.
The technique begins with separating proteins from the sample using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), followed by protein transfer, immunoblotting, and finally, protein detection.

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Related Experiment Video

Updated: May 12, 2026

Analyzing Dynamic Protein Complexes Assembled On and Released From Biolayer Interferometry Biosensor Using Mass Spectrometry and Electron Microscopy

Analyzing Dynamic Protein Complexes Assembled On and Released From Biolayer Interferometry Biosensor Using Mass Spectrometry and Electron Microscopy

Published on: August 6, 2018

Improved method for protein complex detection using bottleneck proteins.

Jaegyoon Ahn¹, Dae Hyun Lee, Youngmi Yoon

¹Department of Computer Science, Yonsei University, 3rd Engineering Bldg, 533-1, Shinchon-dong, Seodaemun-gu, Seoul, Korea.

BMC Medical Informatics and Decision Making

|April 10, 2013

Summary

This summary is machine-generated.

This study introduces a new algorithm for detecting protein complexes by identifying dense protein subnetworks. The method effectively finds overlapping complexes with improved accuracy and recall, advancing biological understanding.

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Last Updated: May 12, 2026

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High-Resolution Complexome Profiling by Cryoslicing BN-MS Analysis

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Published on: October 15, 2019

Area of Science:

Computational Biology
Systems Biology
Bioinformatics

Background:

Accurate identification of protein complexes is crucial for understanding biological functions.
Protein complexes play fundamental roles in cellular processes.

Purpose of the Study:

To develop a novel algorithm for more accurate detection of protein complexes.
To identify dense protein subnetworks characterized by bottleneck proteins.

Main Methods:

Proposed an algorithm to detect dense protein subnetworks.
Algorithm identifies bottleneck proteins within subnetworks.
Algorithm allows for the detection of overlapping protein complexes.

Main Results:

Applied the algorithm to Protein-Protein Interaction (PPI) networks of Saccharomyces cerevisiae and Homo sapiens.
Validated results using public protein complex databases.
Achieved improved prediction accuracy, particularly for small-sized complexes, over previous methods.

Conclusions:

The algorithm successfully identifies overlapping protein complexes with a significantly improved F1 score.
High recall is attributed to effective network search.
High precision is achieved through the strategic use of bottleneck information.