AcrNET: predicting anti-CRISPR with deep learning

Yunxiang Li, Yumeng Wei, Sheng Xu, Qingxiong Tan, Licheng Zong, Jiuming Wang, Yixuan Wang, Jiayang Chen, Liang Hong, Yu Li

Abstract: As an important group of proteins discovered in phages, anti-CRISPR inhibits the activity of the immune system of bacteria (i.e. CRISPR-Cas), offering promise for gene editing and phage therapy. However, the prediction and discovery of anti-CRISPR are challenging due to their high variability and fast evolution. Existing biological studies rely on known CRISPR and anti-CRISPR pairs, which may not be practical considering the huge number. Computational methods struggle with prediction performance. To address these issues, we propose a novel deep neural network for anti-CRISPR analysis (AcrNET), which achieves significant performance.