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Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Identification of Protein Interacting Partners Using Tandem Affinity Purification

Identification of Protein Interacting Partners Using Tandem Affinity Purification

Published on: February 25, 2012

Inferring interaction partners from protein sequences.

Anne-Florence Bitbol¹, Robert S Dwyer², Lucy J Colwell³

¹Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544; Department of Physics, Princeton University, Princeton, NJ 08544; Sorbonne Universités, Université Pierre et Marie Curie - Paris 6, CNRS, Laboratoire Jean Perrin, UMR 8237, F-75005 Paris, France; wingreen@princeton.edu anne-florence.bitbol@upmc.fr ljc37@cam.ac.uk.

Proceedings of the National Academy of Sciences of the United States of America

|September 25, 2016

Summary

This summary is machine-generated.

We developed a novel computational method to predict specific protein interactions using only protein sequence data. This approach accurately identifies interacting protein partners, advancing our understanding of cellular processes and complex formation.

Keywords:

coevolution direct coupling analysis maximum entropy paralogs protein−protein interactions

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Identification of Protein Interaction Partners in Mammalian Cells Using SILAC-immunoprecipitation Quantitative Proteomics

Identification of Protein Interaction Partners in Mammalian Cells Using SILAC-immunoprecipitation Quantitative Proteomics

Published on: July 6, 2014

Area of Science:

Computational Biology
Bioinformatics
Molecular Biology

Background:

Specific protein-protein interactions are fundamental for cellular functions, including complex formation and signal transduction.
Coevolution between interacting proteins leads to correlated sequences, offering a basis for prediction.
Existing methods may require prior knowledge or extensive experimental data.

Purpose of the Study:

To develop and validate a novel algorithm for predicting specific protein-protein interaction partners solely from sequence data.
To assess the algorithm's performance on well-characterized biological systems.
To establish metrics for distinguishing interacting from non-interacting protein families based on sequence information.

Main Methods:

Utilized a pairwise maximum entropy model to infer residue couplings, adapted from protein structure prediction.
Developed an iterative algorithm to predict specific interaction partners between protein families.
Applied the algorithm to bacterial two-component systems (histidine kinases and response regulators) and ATP-binding cassette (ABC) transporter complexes.

Main Results:

Achieved a high true positive fraction of 0.93 for predicting interactions in bacterial two-component systems without prior knowledge.
Demonstrated accurate predictions for proteins within ATP-binding cassette (ABC) transporter complexes.
Developed two novel metrics capable of distinguishing interacting protein families from non-interacting ones using sequence data.

Conclusions:

The developed iterative algorithm effectively predicts specific protein-protein interactions using only sequence data.
The approach holds significant potential for large-scale interactome mapping and understanding protein complex assembly.
Sequence-based metrics provide a valuable tool for identifying potential functional associations between protein families.