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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,...

Conserved Binding Sites

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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Predicting Molecular Geometry

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Ligand Binding Sites

Ligand Binding Sites

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: Nov 14, 2025

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

Protein interaction interface region prediction by geometric deep learning.

Bowen Dai¹, Chris Bailey-Kellogg¹

¹Computer Science Department, Dartmouth, Hanover, NH 03755, USA.

Bioinformatics (Oxford, England)

|March 11, 2021

Summary

This summary is machine-generated.

A new computational method, PInet (Protein Interface Network), accurately predicts protein interaction interfaces by combining geometric deep learning with physical principles. This approach improves upon existing methods for understanding protein complex structures.

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Last Updated: Nov 14, 2025

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

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Identifying Protein-protein Interaction Sites Using Peptide Arrays

Identifying Protein-protein Interaction Sites Using Peptide Arrays

Published on: November 18, 2014

Area of Science:

Computational biology
Structural bioinformatics
Machine learning in structural biology

Background:

Protein-protein interactions are crucial for molecular processes.
Experimental determination of protein complex structures is challenging and does not scale.
Current computational methods for predicting protein interactions lack sufficient precision and recall.

Purpose of the Study:

To develop an improved computational method for predicting protein interaction interfaces.
To leverage both data-driven and physics-driven approaches for enhanced prediction accuracy.
To provide a scalable solution for analyzing protein interactions.

Main Methods:

Developed a Geometric Deep Neural Network named PInet (Protein Interface Network).
PInet processes protein structures as point clouds to identify interaction regions.
The model learns geometrical and physicochemical surface complementarity for prediction.

Main Results:

PInet achieves state-of-the-art or superior performance in predicting protein interface regions on benchmark datasets.
The method simultaneously predicts interfaces on both interacting proteins.
Predictions are interpretable in terms of underlying physical complementarity.

Conclusions:

PInet offers a powerful and accurate tool for predicting protein-protein interaction interfaces.
The unified approach of PInet advances computational structural biology.
The method's performance highlights the potential of geometric deep learning in this field.