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Conservation of Protein Domains Over Different Proteins

Conservation of Protein Domains Over Different Proteins

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.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...

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Protein engineering in the deep learning era.

Bingxin Zhou^1,2, Yang Tan^2,3,4, Yutong Hu⁵

¹Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China.

|January 2, 2025

Summary

This summary is machine-generated.

Deep learning advances protein engineering for industry, health, and environment. This review details deep learning methods for protein understanding and engineering, guiding biologists and computer scientists toward new breakthroughs.

Keywords:

artificial intelligence geometric deep learning protein engineering protein language model synthetic biology

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Area of Science:

Biotechnology
Computational Biology
Artificial Intelligence

Background:

Deep learning (DL) has become a powerful tool in protein engineering.
Protein engineering addresses critical challenges in industrial production, healthcare, and environmental sustainability.

Purpose of the Study:

To review protein engineering problems through the lens of deep learning.
To provide a comprehensive guide for biologists and computer scientists in this interdisciplinary field.

Main Methods:

Discussion of representation methods for protein sequences and structures.
Summary of state-of-the-art protein language models and geometric deep learning.
Exploration of multi-modal biological data learning and encoding pipelines.

Main Results:

Detailed overview of deep learning approaches for protein understanding and engineering.
Identification of common downstream tasks and benchmark datasets.
Focus on applications like mutation site identification and property prediction for virtual screening.

Conclusions:

Deep learning offers advanced tools for protein engineering applications.
A standardized approach and consolidated data resources will accelerate progress.
Future integration of biology and computer science communities will unlock DL's full potential in protein engineering.