Deep learning predicts the efficiency of guiding reRNAs for enhanced transposon associated TnpB
Transposon (IS200/IS605)-encoded TnpB proteins are predecessors of class 2 type V CRISPR effectors and eukaryotic RNA-guided DNA endonucleases (Fanzors). Composed of the minimal structural and functional core of class 2 type V endonucleases, TnpB emerged as one of the most compact genome editors identified. Here, we optimized Deinococcus radiodurans (ISDra2) TnpB for use in mammalian cells (enhanced(e)TnpB), resulting in an average 1.9-fold increase in editing efficiency, and generated a novel TnpB variant, eTnpB-K76A, capable of recognizing alternative TAMs. We further generated an extensive dataset on eTnpB editing rates at 10,211 target sequences, which enabled us to delineate rules for on- and off-target editing and to devise a deep learning model, termed TEEP (TnpB editing efficiency predictor), capable of predicting reRNA activity with high accuracy (r > 0.8). Leveraging TEEP, we designed reRNAs for targeting murine Pcsk9 and Dnmt1 and reached editing rates of 75.3 % in the liver and 14.1 % in the brain using single adeno-associated virus (AAV) vectors. The generation of eTnpB and eTnpB-K76A, in conjunction with TEEP, will facilitate the widespread application of ultracompact TnpB for research and therapeutics.