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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,...

Assembly of Signaling Complexes

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Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
Interaction domains in cell signaling
Interaction domains recognize exposed features of their binding partners containing post-translationally modified sequences,...

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Updated: Dec 31, 2025

Identifying Protein-protein Interaction Sites Using Peptide Arrays

Identifying Protein-protein Interaction Sites Using Peptide Arrays

Published on: November 18, 2014

InterPep2: global peptide-protein docking using interaction surface templates.

Isak Johansson-Åkhe¹, Claudio Mirabello¹, Björn Wallner¹

¹Division of Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden.

Bioinformatics (Oxford, England)

|January 10, 2020

Summary

This summary is machine-generated.

This study introduces InterPep2, a tool for predicting peptide-protein interactions. InterPep2 improves accuracy by using diverse templates and a random forest model, offering a valuable resource for structural biology.

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Last Updated: Dec 31, 2025

Identifying Protein-protein Interaction Sites Using Peptide Arrays

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Published on: November 18, 2014

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Area of Science:

Structural biology
Computational biology
Biophysics

Background:

Peptide-protein interactions are crucial for biological processes like gene regulation.
Experimental structure determination is challenging due to peptide ligand flexibility.
Existing prediction methods have limitations in performance and accessibility.

Purpose of the Study:

To develop and evaluate InterPep2, a freely available method for predicting peptide-protein interaction structures.
To enhance prediction accuracy using combined template sources and a machine learning approach.
To assess the performance of InterPep2 against state-of-the-art methods.

Main Methods:

Utilized templates from both peptide-protein and protein-protein interactions.
Employed a random forest model trained to predict DockQ-score using sequence and structural features.
Tested InterPep2-Refined on bound and unbound peptide-protein complexes.

Main Results:

InterPep2-Refined correctly positioned peptides within 4.0 Å LRMSD for 67 out of 252 bound complexes.
The method demonstrated superior self-assessment of prediction quality.
Performance on unbound complexes was on par with leading methods, with 15 correctly modeled complexes.
Combining InterPep2 with PIPER-FlexPepDock yielded 22% more near-native predictions.

Conclusions:

InterPep2 provides an improved and accessible method for predicting peptide-protein interaction structures.
The tool demonstrates robust performance and reliable quality assessment.
Combining template-based and ab initio methods further enhances prediction accuracy.