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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...

Protein-Protein Interfaces

Protein-Protein Interfaces

Protein Networks

Protein Networks

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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...

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Related Experiment Video

Updated: Jan 18, 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

Machine learning to predict de novo protein-protein interactions.

Pablo Gainza¹, Richard D Bunker¹, Sharon A Townson¹

¹Monte Rosa Therapeutics, Klybeckstrasse 191, 4057 Basel, Switzerland.

Trends in Biotechnology

|May 27, 2025

Summary

Machine learning advances structural biology, enhancing protein-protein interaction (PPI) prediction. Novel methods enable de novo PPI prediction, including those not found in nature, aiding drug discovery and protein engineering.

Keywords:

machine learning molecular glues molecular surfaces protein interaction design protein–protein interactions (PPIs)

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Last Updated: Jan 18, 2026

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Published on: January 26, 2024

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Published on: March 3, 2015

Area of Science:

Structural biology
Computational biology
Machine learning

Background:

Machine learning (ML) has significantly improved the prediction of protein-protein interactions (PPIs).
Predicting novel PPIs, especially those not observed in nature (de novo), remains a challenge.

Purpose of the Study:

To review recent advancements in computational methods for predicting protein-protein interactions (PPIs).
To highlight novel machine learning approaches for de novo PPI prediction.
To explore the biotechnological applications of these predictive capabilities.

Main Methods:

Review of recent literature on machine learning algorithms for PPI prediction.
Focus on methods utilizing co-folding, atomic graphs, and molecular surface learning.
Discussion of de novo prediction strategies, including those induced by small molecules.

Main Results:

Novel ML algorithms, such as those based on co-folding and atomic graphs, show promise for PPI prediction.
Methods learning from molecular surfaces can predict previously unknown interactions, including small molecule-induced ones.
Emerging applications include predicting antibody-antigen complexes and molecular glue-induced PPIs.

Conclusions:

Computational prediction of PPIs, particularly de novo prediction, is rapidly advancing.
These advancements hold significant potential for drug discovery and protein engineering.
Future research directions include further development of ML models and exploration of new biotechnological applications.