AcrNET: Predicting Anti-CRISPR with Deep Learning

As an important group of proteins discovered in phages, anti-CRISPR inhibits the activity of the immune system of bacteria (i.e., CRISPR-Cas), showing great potential for gene editing and phage therapy. However, the prediction and discovery of anti-CRISPR are challenging for its high variability and fast evolution. Existing biological studies often depend on known CRISPR and anti-CRISPR pairs, which may not be practical considering the huge number of pairs in reality. Computational methods usually struggle with prediction performance. To tackle these issues, we propose a novel deep neural network for anti-CRISPR analysis (AcrNET), which achieves impressive performance. On both the cross-fold and cross-dataset validation, our method outperforms the previous state-of-the-art methods significantly. Impressively, AcrNET improves the prediction performance by at least 15% regarding the F1 score for the cross-dataset test. Moreover, AcrNET is the first computational method to predict the detailed anti-CRISPR classes, which may help illustrate the anti-CRISPR mechanism. Taking advantage of a Transformer protein language model pre-trained on 250 million protein sequences, AcrNET overcomes the data scarcity problem. Extensive experiments and analysis suggest that Transformer model feature, evolutionary feature, and local structure feature complement each other, which indicates the critical properties of anti-CRISPR proteins. Combined with AlphaFold prediction, further motif analysis and docking experiments demonstrate that AcrNET captures the evolutionarily conserved pattern and the interaction between anti-CRISPR and the target implicitly. With the impressive prediction capability, AcrNET can serve as a valuable tool for anti-CRISPR study and new anti-CRISPR discovery, with a free webserver at https://proj.cse.cuhk.edu.hk/aihlab/AcrNET/.