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Related Experiment Video

Updated: Feb 3, 2026

Identifying Protein-protein Interaction Sites Using Peptide Arrays

Identifying Protein-protein Interaction Sites Using Peptide Arrays

Published on: November 18, 2014

Peptide design by optimization on a data-parameterized protein interaction landscape.

Justin M Jenson¹, Vincent Xue², Lindsey Stretz¹

¹Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139.

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

|October 17, 2018

Summary

This summary is machine-generated.

Researchers developed a computational model to design novel peptides that precisely bind to specific Bcl-2 family proteins. This protein engineering advance enables targeted therapies and diagnostic tools.

Keywords:

Bcl-2 inhibitor energy landscape machine learning peptide design protein–protein interactions

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Published on: December 20, 2017

Area of Science:

Biochemistry
Computational Biology
Protein Engineering

Background:

Optimizing multiple protein properties simultaneously is crucial for applications like targeted drug design.
Designing proteins with specific binding profiles requires navigating complex sequence-function relationships.

Purpose of the Study:

To develop a predictive model of protein-peptide binding interactions for the Bcl-2 family.
To computationally design novel peptides with high affinity and specificity for target proteins.

Main Methods:

Measured thousands of protein-peptide binding affinities using the amped SORTCERY assay.
Parameterized a computational model of the alpha-helical peptide-binding landscape for Bcl-xL, Mcl-1, and Bfl-1.
Applied optimization protocols to discover peptides with desired interaction profiles.

Main Results:

Generated 36 peptides with high affinity and specificity for individual Bcl-2 family proteins (Bcl-xL, Mcl-1, Bfl-1).
Designed additional peptides with selective binding to two out of the three target proteins.
Confirmed target engagement via high-resolution crystal structures; designed peptides were structurally distinct from known binders.

Conclusions:

Demonstrated the efficacy of a landscape modeling approach for challenging protein design problems.
The designed peptides show potential as diagnostic tools or targeted cancer therapeutics.