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Related Experiment Video

Updated: Apr 11, 2026

Synthesis of an Intein-mediated Artificial Protein Hydrogel

Synthesis of an Intein-mediated Artificial Protein Hydrogel

Published on: January 27, 2014

How artificial intelligence is reengineering protein engineering.

Jennifer Listgarten^1,2,3, Hanlun Jiang¹

¹Department of Electrical Engineering and Computer Science, UC Berkeley, Berkeley, CA, USA.

Science (New York, N.Y.)

|April 9, 2026

Summary

This summary is machine-generated.

Artificial intelligence (AI) accelerates protein engineering by enabling efficient design of proteins with specific functions. AI models generate novel protein sequences and structures, optimizing applications in medicine, agriculture, and industry.

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Author Spotlight: Advancing Protein Engineering – Harnessing Evolution Through PRANCE and Lab Automation

Author Spotlight: Advancing Protein Engineering – Harnessing Evolution Through PRANCE and Lab Automation

Published on: January 12, 2024

Area of Science:

Biochemistry and Molecular Biology
Computational Biology
Bioengineering

Background:

Protein engineering has advanced significantly, integrating computational modeling and high-throughput experiments.
Applications span therapeutics, diagnostics, agriculture, and manufacturing.
Artificial intelligence (AI) is revolutionizing protein engineering by enhancing sequence space exploration.

Purpose of the Study:

To review recent advancements in AI-driven protein engineering.
To provide a unified statistical perspective on modern AI approaches in this field.
To highlight AI's role in designing proteins with specific desired properties.

Main Methods:

Generative modeling for protein sequences, backbone structures, and atomic coordinates.
Tailoring AI models for designing proteins with specific functionalities.
AI-based methods for extracting protein representations and scoring candidate sequences.
Development of synthesis-aware library design techniques.

Main Results:

AI enables more efficient searching of high-dimensional sequence space for desired protein properties.
Notable advances include generative models for various protein aspects (sequences, structures, atoms).
AI facilitates tailored design of proteins for specific applications and improved library design.

Conclusions:

AI significantly propels protein engineering, offering powerful tools for protein design and optimization.
A statistical interpretation provides a unifying framework for understanding modern AI approaches.
AI advancements are expanding the scope and efficiency of protein engineering across diverse industries.