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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 polypeptide...

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,...

Protein Organization

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence.

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Related Experiment Video

Updated: May 8, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

How good is automated protein docking?

Dima Kozakov¹, Dmitri Beglov, Tanggis Bohnuud

¹Department of Biomedical Engineering, Boston University, Boston, Massachusetts, 02215.

|September 3, 2013

Summary

This summary is machine-generated.

The ClusPro protein docking server demonstrates reliable performance, achieving high-quality predictions comparable to human experts in the Critical Assessment of Prediction of Interactions (CAPRI) challenges. Its top-ranked models consistently identify accurate protein complex conformations.

Keywords:

CAPRI docking experiment method development protein-protein docking structure refinement user community web-based server

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Last Updated: May 8, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

Area of Science:

Computational biology
Structural bioinformatics
Protein-protein interactions

Background:

The ClusPro protein docking server has been a participant in the Critical Assessment of Prediction of Interactions (CAPRI) since 2004.
Evaluating automated protein-protein docking server performance against human expert predictions is crucial for assessing computational methods.

Purpose of the Study:

To evaluate the performance of the ClusPro 2.0 protein docking server in recent CAPRI rounds (22-27).
To compare the accuracy and reliability of ClusPro 2.0 against human predictors and assess its ranking capabilities.

Main Methods:

Analysis of ClusPro 2.0 performance on CAPRI targets 46-58 across Rounds 22-27.
Comparison of ClusPro 2.0's automated docking results with human expert submissions.
Assessment of model quality and ranking accuracy based on cluster size.

Main Results:

ClusPro 2.0 reliably produced acceptable or medium accuracy models for moderately difficult targets.
The server's automated docking quality closely matched that of top human predictors.
The top-ranked ClusPro model was acceptable or better for all evaluated targets, with accurate ranking for five out of six complexes.

Conclusions:

ClusPro 2.0 demonstrates robust performance in protein-protein docking, comparable to human experts.
Ranking protein complex models based on cluster size is a reliable method for identifying near-native conformations.
Automated servers like ClusPro offer reproducible and efficient predictions within strict time limits.