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Protein-protein Interfaces

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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: Jul 8, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

A significance score for protein-protein interaction models through random docking.

Katherine Maia McCoy¹, Margaret E Ackerman^1,2, Gevorg Grigoryan^1,3

¹Department of Biological Sciences, Dartmouth College, Hanover, New Hampshire, USA.

Protein Science : a Publication of the Protein Society

|December 11, 2023

Summary

We developed a new metric, the random-docking (RD) p-value, to compare protein-protein interaction (PPI) models. This score normalizes model accuracy across different protein complexes, enabling global comparisons and better assessment of prediction methods.

Keywords:

CAPRI interface protein interaction prediction protein structure protein-binding model assessment protein-protein interactions significance score

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Area of Science:

Structural biology
Computational biology
Bioinformatics

Background:

Comparing protein-protein interaction (PPI) model accuracy across diverse complexes is challenging due to variations in size and shape.
Existing metrics lack normalization, hindering absolute accuracy comparisons on a global scale.

Purpose of the Study:

To develop a statistically robust metric for evaluating protein-protein interaction (PPI) docking models.
To enable accurate and comparable assessment of PPI model performance across different protein complexes.

Main Methods:

Developed a random-docking (RD) p-value metric.
Evaluated model accuracy by comparing it against a reference distribution generated by random docking.
Utilized data from Critical Assessment of PRediction of Interactions (CAPRI) rounds (2017-2020).

Main Results:

The RD p-value normalizes scores, making models globally comparable across complexes of varying sizes and shapes.
The ease of achieving specific accuracy metrics (RMSD, DOCKQ) varies significantly between protein targets.
RD p-values can incorporate prior interface information to distinguish true prediction improvements from basic modeling constraints.

Conclusions:

The RD p-value offers a standardized and reliable method for assessing the accuracy of protein-protein interaction (PPI) models.
This metric facilitates more meaningful comparisons of docking model performance in structural biology.
Efficient code for computing RD p-values is publicly available.