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Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

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Related Experiment Video

Updated: Jan 16, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Peptide design through binding interface mimicry with PepMimic.

Xiangzhe Kong^1,2, Rui Jiao^1,2, Haowei Lin^3,4

¹Department of Computer Science and Technology, Tsinghua University, Beijing, China.

Nature Biomedical Engineering

|October 1, 2025

Summary

This summary is machine-generated.

An AI algorithm, PepMimic, creates peptide binders for targeted therapy by mimicking binding interfaces. This approach yields high-affinity peptides, outperforming random screening and showing potential for diagnostics and therapeutics.

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Last Updated: Jan 16, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Split-and-pool Synthesis and Characterization of Peptide Tertiary Amide Library

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Published on: June 20, 2014

Peptide-based Identification of Functional Motifs and their Binding Partners

Peptide-based Identification of Functional Motifs and their Binding Partners

Published on: June 30, 2013

Area of Science:

Biotechnology
Artificial Intelligence
Drug Discovery

Background:

Peptides offer advantages for targeted therapy, including oral bioavailability, cellular permeability, and high specificity.
Conventional small molecules and biologics have limitations in targeted therapy.
Developing novel peptide binders is crucial for advancing targeted therapies.

Purpose of the Study:

To develop an artificial intelligence (AI) algorithm, PepMimic, for designing peptide binders.
To mimic binding interfaces of known targets and binders to create short peptide binders.
To explore the potential of AI-designed peptides for diagnostic imaging and targeted therapeutics.

Main Methods:

Developed PepMimic, an AI algorithm to design peptide binders by mimicking binding interfaces.
Applied PepMimic to drug targets including PD-L1, CD38, BCMA, HER2, and CD4.
Validated peptide binders using surface plasmon resonance imaging and in vivo mouse models.

Main Results:

PepMimic successfully designed peptide binders with dissociation constant (KD) values as low as 10^-9 M.
AI-designed peptides demonstrated significantly higher binding affinity compared to random library screening.
Extensive validation in breast, myeloma, and lung tumor mouse models confirmed effective membrane binding.

Conclusions:

PepMimic is a powerful AI tool for designing high-affinity peptide binders.
AI-generated peptides show significant potential for clinical diagnostic imaging and targeted therapeutic applications.
This approach advances peptide-based drug discovery and development.