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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...

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Related Experiment Video

Updated: Jun 26, 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

Deep learning for the PSIPRED Protein Analysis Workbench.

Daniel W A Buchan¹, Lewis Moffat¹, Andy Lau¹

¹UCL Bioinformatics Group, Department of Computer Science, University College London, London, WC1E 6BT, UK.

Nucleic Acids Research

|May 15, 2024

Summary

This summary is machine-generated.

The PSIRED Workbench, a bioinformatics tool, now features new deep learning methods for protein analysis. Server usage trends are discussed post-AlphaFold2, with future developments outlined.

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Area of Science:

Bioinformatics
Computational Biology
Structural Bioinformatics

Background:

The PSIRED Workbench is a widely used bioinformatics web service.
It provides various machine learning-based analyses for protein structure and function characterization.

Purpose of the Study:

To update users on recent developments and additions to the PSIRED Workbench.
To highlight new Deep Learning-based methods integrated into the service.
To discuss server usage trends and future plans.

Main Methods:

Focus on the integration of new Deep Learning models.
Analysis of server usage data.
Overview of planned future developments.

Main Results:

The PSIRED Workbench has been updated with new Deep Learning capabilities.
Server usage patterns have shifted following the release of AlphaFold2.
Upcoming developments are planned to further enhance the service.

Conclusions:

The PSIRED Workbench continues to evolve, incorporating advanced Deep Learning techniques.
The platform remains a valuable resource for protein structure and function analysis.
Future updates will ensure its continued relevance in the bioinformatics field.