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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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Protein domains are small structurally independent units that are part of a single amino acid chain. Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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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.
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Related Experiment Video

Updated: Aug 30, 2025

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

Recent Advances in Machine Learning Variant Effect Prediction Tools for Protein Engineering.

Jesse Horne¹, Diwakar Shukla²

¹Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Champaign, Illinois 61801, United States.

Industrial & Engineering Chemistry Research

|September 2, 2022

Summary

This summary is machine-generated.

Predicting the functional impact of protein mutations is crucial for protein engineering. Recent machine learning advances, including neural networks and natural language embeddings, are improving variant effect prediction (VEP) accuracy.

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Area of Science:

Biochemistry
Bioinformatics
Computational Biology

Background:

Proteins perform diverse biological roles, but predicting how sequence changes affect function is challenging.
Protein engineering aims to design proteins with specific properties, requiring accurate models of sequence-structure-function relationships.
Epistatic interactions between mutations complicate the mapping of genetic modifications to protein function.

Purpose of the Study:

To review recent advances in variant effect prediction (VEP) for protein engineering.
To highlight machine learning (ML) techniques enhancing the prediction of mutation effects on protein function.
To discuss challenges in estimating biomolecular functional differences.

Main Methods:

Review of machine learning approaches for VEP.
Focus on convolutional neural networks (CNNs), graph neural networks (GNNs), and natural language embeddings.
Integration of ML techniques from the broader research community.

Main Results:

ML methods show significant progress in modeling mutation-function relationships.
CNNs, GNNs, and natural language embeddings are key techniques for VEP.
Improved VEP accelerates protein engineering tasks.

Conclusions:

Advances in ML are crucial for overcoming challenges in VEP.
These methods enhance the ability to predict and engineer protein functions.
VEP tools are becoming indispensable for modern protein engineering.