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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

Machine learning-driven optimization of specific, compact, and efficient base editors via single-round

Mykyta Ielanskyi1, Meng Wang2, Lewis Scott3

  • 1ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, 4040 Linz, Austria.

Nucleic Acids Research
|June 8, 2026
PubMed
Summary
This summary is machine-generated.

Researchers developed novel deaminases for base editing using DNA shuffling and generative models. These new enzymes show high on-base activity and reduced off-base editing, improving precision in genetic research.

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Efficient PAM-Less Base Editing for Zebrafish Modeling of Human Genetic Disease with zSpRY-ABE8e
07:31

Efficient PAM-Less Base Editing for Zebrafish Modeling of Human Genetic Disease with zSpRY-ABE8e

Published on: February 17, 2023

Area of Science:

  • Molecular Biology
  • Biotechnology
  • Genetics Engineering

Background:

  • Base editing offers precise single-nucleotide changes but current deaminases have limitations like off-targeting and off-base editing.
  • Existing deaminases are derived from large eukaryotic enzymes or evolved variants of E. coli TadA (ecTadA), each with distinct drawbacks.
  • Limitations include Cas-independent DNA targeting and off-base editing, hindering therapeutic and research applications.

Purpose of the Study:

  • To overcome limitations of current base editor deaminases by developing novel, highly specific enzymes.
  • To engineer improved deaminases with reduced off-target and off-base editing activities.
  • To explore sequence space efficiently using generative models and DNA shuffling for deaminase discovery.

Main Methods:

  • DNA shuffling of newly identified TadA orthologs to create diverse variant pools.
  • Training generative models on performance data from variant pools to predict enzyme efficiency.
  • Utilizing information-theoretic insights to guide sequence space exploration for novel deaminase generation.

Main Results:

  • Generated millions of training sequences for measuring base editor efficiency.
  • Created a set of novel, sequence-distinct cytosine and adenosine deaminases after one round of diversification.
  • Model-created deaminases outperformed those from traditional directed evolution, showing high on-base activity and lower off-base activity.

Conclusions:

  • Novel compact deaminases were identified with high on-base activity comparable to leading base editors.
  • Demonstrably lower off-base activity was observed in the newly engineered deaminases.
  • The developed approach offers a powerful strategy for generating improved deaminases for base editing applications.