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Related Experiment Video

Updated: Oct 13, 2025

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

Computed structures of core eukaryotic protein complexes.

Ian R Humphreys^1,2, Jimin Pei^3,4, Minkyung Baek^1,2

¹Department of Biochemistry, University of Washington, Seattle, WA, USA.

Science (New York, N.Y.)

|November 11, 2021

Summary

This summary is machine-generated.

Researchers used deep learning and coevolution analysis to predict protein structures in yeast. This identified 1505 interacting protein pairs, including 106 novel complexes, advancing our understanding of cellular functions.

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Last Updated: Oct 13, 2025

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

High-Resolution Complexome Profiling by Cryoslicing BN-MS Analysis

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Published on: October 15, 2019

Analyzing Large Protein Complexes by Structural Mass Spectrometry

Analyzing Large Protein Complexes by Structural Mass Spectrometry

Published on: June 19, 2010

Area of Science:

Molecular Biology
Structural Biology
Computational Biology

Background:

Protein-protein interactions are fundamental to cellular processes, but many eukaryotic protein complex structures remain uncharacterized.
Identifying and structurally modeling these complexes is crucial for understanding biological function.

Purpose of the Study:

To systematically identify and model core eukaryotic protein complexes in the *Saccharomyces cerevisiae* proteome.
To leverage advances in amino acid coevolution analysis and deep learning for structure prediction.

Main Methods:

Utilized RoseTTAFold and AlphaFold for structure modeling.
Screened 8.3 million protein pairs from yeast proteome using multiple sequence alignments.
Applied amino acid coevolution analysis to predict interacting proteins.

Main Results:

Identified 1505 yeast protein pairs likely to interact.
Generated structure models for 106 previously unidentified protein assemblies.
Provided structural models for 806 complexes lacking prior structural characterization.
Characterized complexes with up to five subunits involved in essential cellular processes.

Conclusions:

This study systematically mapped and structurally modeled numerous yeast protein complexes, including novel ones.
The findings provide significant insights into eukaryotic cellular functions and protein complex organization.
The integrated approach of coevolution analysis and deep learning is effective for discovering and modeling protein interactions.