Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Protein language models-assisted optimization of a uracil-N-glycosylase variant enables programmable T-to-G and T-to-C base editing.

Molecular cell - 4 Apr 2024

He Yan, Zhou Xibin, Chang Chong, Chen Ge, Liu Weikuan, Li Geng, Fan Xiaoqi, Sun Mingsun, Miao Chensi, Huang Qianyue, Ma Yunqing, Yuan Fajie, Chang Xing

Abstract excerpt

Current base editors (BEs) use DNA deaminases, including cytidine deaminase in cytidine BE (CBE) or adenine deaminase in adenine BE (ABE), to facilitate transition nucleotide substitutions. Combining CBE or ABE with glycosylase enzymes can induce limited transversion mutations. Nonetheless, a critical demand remains for BEs capable of generating alternative mutation types, such as T>G corrections. In this study,...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Gene Editing
Share to UNG gene

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Mutation

Share to Uracil-DNA Glycosidase

Source

Identifiers

PMID: 38377993
DOI: 10.1016/j.molcel.2024.01.021

Genes

UNG

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 2 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Protein language models-assisted optimization of a uracil-N-glycosylase variant enables programmable T-to-G and T-to-C base editing.PMID 38377993Read ↗

Protein language models-assisted engineering of Uracil-N glycosylase enables programmable T-to-G and T-to-C base editingSemantic proximity Read ↗

Machine Learning and Directed Evolution of Base Editing EnzymesSemantic proximity Read ↗

Machine Learning-Driven Optimization of Specific, Compact, and Efficient Base Editors via Single-Round DiversificationSemantic proximity Read ↗

High-precision cytosine base editors by evolving nucleic-acid-recognition hotspots in deaminase.Semantic proximity Read ↗

Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering.Semantic proximity Read ↗

Rapid customization of base editors via machine learning-powered combinatorial mutagenesis.Semantic proximity Read ↗

A general temperature-guided language model to design proteins of enhanced stability and activity.Semantic proximity Read ↗

A comprehensive benchmark for multiple highly efficient base editors with broad targeting scopeSemantic proximity Read ↗

A-to-G/C/T and C-to-T/G/A dual-function base editor for creating multi-nucleotide variants.Semantic proximity Read ↗

Predicting prime editing efficiency and product purity by deep learning.Semantic proximity Read ↗

Human cell based directed evolution of adenine base editors with improved efficiency.Semantic proximity Read ↗

Adapting protein language models for structure-conditioned designSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.