Biology subjects

Villiger, L.

Publications and source records attributed to Villiger, L..

2 recordsLinked to original sources

bioRxiv2020

Replacing the SpCas9 HNH domain by deaminases generates compact base editors with an alternative targeting scope

Base editors are RNA-guided deaminases that enable site-specific nucleotide transitions. The targeting scope of these Cas-deaminase fusion proteins critically depends on the availability of a protospacer adjacent motif (PAM) at the target locus and is limited to a window within the CRISPR-Cas R-loop, where single stranded (ss)DNA is accessible to the deaminase. Here, we reason that the Cas9-HNH nuclease domain sterically constrains ssDNA accessibility, and demonstrate that omission of this domain expands the editing window. By exchanging the HNH nuclease domain with an adenosine deaminase we furthermore engineer adenine base editor variants (HNHx-ABE) with PAM-proximally shifted editing windows. This work expands the targeting scope of base editors, and provides base editor variants that are substantially smaller. It moreover informs of potential future directions in Cas9 protein engineering, where the HNH domain could be replaced by other enzymes that act on ssDNA.

bioengineering↗

bioRxiv2020

Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens

Base editors are chimeric ribonucleoprotein complexes consisting of a DNA-targeting CRISPR-Cas module and a single-stranded DNA deaminase. They enable conversion of C•G into T•A base pairs and vice versa on genomic DNA. While base editors have vast potential as genome editing tools for basic research and gene therapy, their application has been hampered by a broad variation in editing efficiencies on different genomic loci. Here we perform an extensive analysis of adenine- and cytosine base editors on thousands of lentivirally integrated genetic sequences and establish BE-DICT, an attention-based deep learning algorithm capable of predicting base editing outcomes with high accuracy. BE-DICT is a versatile tool that in principle can be trained on any novel base editor variant, facilitating the application of base editing for research and therapy.Competing Interest StatementThe authors have declared no competing interest.View Full Text

genomics↗