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Related Concept Videos

Conservation of Protein Domains Over Different Proteins02:26

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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Related Experiment Video

Updated: Jul 29, 2025

Author Spotlight: Advancing Protein Engineering – Harnessing Evolution Through PRANCE and Lab Automation
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In vitro continuous protein evolution empowered by machine learning and automation.

Tianhao Yu1, Aashutosh Girish Boob2, Nilmani Singh3

  • 1Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Carl R. Woese Institute for Genomic Biology, Urbana, IL, USA; NSF Molecule Maker Lab Institute, Urbana, IL, USA.

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|May 24, 2023
PubMed
Summary
This summary is machine-generated.

Directed evolution for protein engineering is enhanced by machine learning (ML) and automation. This perspective proposes a closed-loop system integrating ML and automation for efficient in vitro protein evolution.

Keywords:
automationclosed-loopcontinuous evolutiondirected evolutionmachine learning

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Area of Science:

  • Biotechnology
  • Protein Engineering
  • Computational Biology

Background:

  • Directed evolution is a powerful protein engineering tool but is labor-intensive.
  • Machine learning (ML) offers in silico variant evaluation to streamline evolution.
  • Laboratory automation enables high-throughput data acquisition for ML model development.

Purpose of the Study:

  • To propose a closed-loop in vitro continuous protein evolution framework.
  • To integrate machine learning and laboratory automation for enhanced protein engineering.
  • To provide an overview of recent advancements in ML-guided directed evolution.

Main Methods:

  • Leveraging machine learning for in silico screening of protein variants.
  • Utilizing laboratory automation for high-throughput data generation.
  • Implementing a closed-loop system for continuous in vitro evolution.

Main Results:

  • The proposed framework integrates ML and automation for efficient protein evolution.
  • High-throughput data acquisition supports robust ML model development.
  • In silico evaluation guides experimental design, reducing time and cost.

Conclusions:

  • Combining ML and automation offers a powerful approach to protein engineering.
  • Closed-loop systems accelerate the discovery and optimization of proteins.
  • This integrated strategy enhances the efficiency and scope of directed evolution.