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Status: Unchecked ×Topic: CRISPR and Genetic Engineering ×Clear all

2 claims from 1 paper

1. Biochemistry, Genetics and Molecular Biology › CRISPR and Genetic Engineering

   Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering

   Cheng, Mao, Tang et al. · Cell Research · 2024

   The authors present ProMEP, a deep learning method that predicts mutation effects without alignments or labelled data, and use it to guide engineering of the gene-editing enzymes TnpB and TadA.

   Unchecked2 claims
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   1. UncheckedProMEP, a tool for predicting how amino acid changes affect proteins, is reported to match or beat other methods in accuracy and to run much faster.“ProMEP achieves state-of-the-art performance in mutational effect prediction and accomplishes a tremendous improvement in speed, enabling efficient and intelligent protein engineering.”
   2. UncheckedProMEP-guided mutants of TnpB and TadA edited genes more efficiently than the wild type or ABE8e, with fewer bystander and off-target effects for TadA.“The gene-editing efficiency of a 5-site mutant of TnpB reaches up to 74.04% (vs 24.66% for the wild type); and the base editing tool developed on the basis of a TadA 15-site mutant (in addition to the A106V/D108N double mutation that renders deoxyadenosine deaminase activity to TadA) exhibits an A-…”