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LevSeq: Rapid Generation of Sequence-Function Data for Directed Evolution and Machine Learning.

Yueming Long1, Ariane Mora1, Francesca-Zhoufan Li2

  • 1Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California91125, United States.

ACS Synthetic Biology
|December 24, 2024
PubMed
Summary
This summary is machine-generated.

We developed Long-read every variant Sequencing (LevSeq) to rapidly generate protein sequence-function data. This method aids data-driven protein engineering and machine learning-guided protein engineering (MLPE) by improving library quality control.

Keywords:
Directed EvolutionMachine LearningMutagenesis LibrariesNanopore SequencingProtein EngineeringSequence-Function Data

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Area of Science:

  • Biochemistry
  • Molecular Biology
  • Bioinformatics

Background:

  • Sequence-function data is crucial for understanding protein landscapes but is seldom generated during directed evolution.
  • Existing methods for obtaining comprehensive sequence-function data are often time-consuming and resource-intensive.

Purpose of the Study:

  • To introduce Long-read every variant Sequencing (LevSeq), a novel pipeline for rapid generation of protein sequence-function data.
  • To enable data-driven protein engineering and machine learning-guided protein engineering (MLPE).

Main Methods:

  • LevSeq utilizes a dual barcoding strategy combined with nanopore sequencing.
  • The pipeline integrates seamlessly into existing protein engineering workflows.
  • Open-source software is provided for data analysis and visualization.

Main Results:

  • LevSeq rapidly generates sequence-function data for entire protein-coding genes.
  • The method facilitates quality control of mutagenesis libraries, reducing costs and time.
  • Simulation studies confirm LevSeq's accuracy in variant detection under diverse conditions.
  • LevSeq demonstrated utility in engineering protoglobins for novel chemistry.

Conclusions:

  • LevSeq empowers data-driven protein engineering by providing essential sequence-function data.
  • Widespread adoption and data sharing will advance the understanding of protein sequence-function relationships.
  • The pipeline enhances efficiency and reduces resource expenditure in protein engineering efforts.