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Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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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.
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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Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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Protein-protein Interfaces02:04

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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Protein Organization01:24

Protein Organization

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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
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Related Experiment Video

Updated: May 28, 2025

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
09:51

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

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Integrating protein language models and automatic biofoundry for enhanced protein evolution.

Qiang Zhang1,2, Wanyi Chen1,3,4, Ming Qin1,5

  • 1Zhejiang University, Hangzhou, Zhejiang, 310058, China.

Nature Communications
|February 11, 2025
PubMed
Summary

This study introduces an automated protein engineering platform using machine learning and biofoundry. It rapidly improves enzyme activity, accelerating protein evolution for industrial use.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Protein Engineering

Background:

  • Traditional protein engineering methods like directed evolution are slow and labor-intensive.
  • Machine learning and automated biofoundries offer new avenues for optimizing protein engineering.
  • Automated protein engineering platforms can accelerate the discovery of novel protein functions.

Purpose of the Study:

  • To develop a closed-loop, automated protein engineering platform.
  • To integrate a protein language model (ESM-2) with an automated biofoundry for rapid protein evolution.
  • To enhance the speed and accuracy of protein engineering for industrial applications.

Main Methods:

  • A closed-loop Design-Build-Test-Learn system was implemented.
  • The protein language model ESM-2 was used for zero-shot prediction of protein variants.
  • An automated biofoundry constructed and evaluated variants.
  • A multi-layer perceptron trained a fitness predictor for subsequent variant selection.

Main Results:

  • Four rounds of automated evolution were completed in 10 days using tRNA synthetase as a model enzyme.
  • Enzyme activity was improved by up to 2.4-fold.
  • The platform demonstrated significantly enhanced speed and accuracy in protein evolution.

Conclusions:

  • The developed platform streamlines protein engineering through automation and machine learning.
  • This approach accelerates the discovery of improved enzymes for various applications.
  • Automated protein evolution holds significant promise for industrial biotechnology.