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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: Nov 2, 2025

An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

How Deep Learning Tools Can Help Protein Engineers Find Good Sequences.

Margarita Osadchy¹, Rachel Kolodny¹

¹Department of Computer Science, Jacobs Building, University of Haifa, 199 Aba Houshi Road, Mount Carmel, Haifa, Israel 3498838.

The Journal of Physical Chemistry. B

|June 9, 2021

Summary

This summary is machine-generated.

Deep learning advances protein engineering by creating predictive models and generative methods. These tools efficiently discover rare, protein-like sequences with desired properties.

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Area of Science:

Computational biology
Biotechnology
Machine learning

Background:

Deep learning offers powerful computational tools for complex challenges.
Protein engineering requires predicting protein properties and designing novel sequences.
Identifying specific protein sequences is challenging due to vast sequence space.

Purpose of the Study:

To review deep learning applications in protein engineering.
To highlight methods for predicting protein properties and generating sequences.
To address the challenge of finding rare, functional protein sequences.

Main Methods:

Utilizing deep neural networks as oracles for predicting protein properties.
Employing generative models for sampling protein-like sequences.
Integrating predictive models with sampling methods for efficient sequence design.

Main Results:

Deep networks can accurately predict protein properties from amino acid sequences.
Generative models enable sampling from learned distributions of protein properties.
Combined approaches enhance the efficiency of discovering rare, desired protein sequences.

Conclusions:

Deep learning significantly improves protein property prediction and sequence design.
Generative models are key for sampling and optimizing protein sequences.
These advancements accelerate the discovery of novel proteins for various applications.