Affiliation:
1. Department of Computer Science and Engineering, The Chinese University of Hong Kong , Sha Tin, Hong Kong SAR 999077, China
Abstract
Abstract
Motivation
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.
Results
On both the cross-fold and cross-dataset validation, our method outperforms the state-of-the-art methods. Notably, AcrNET improves the prediction performance by at least 15% regarding the F1 score for the cross-dataset test problem comparing with state-of-art Deep Learning method. 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 ESM-1b, which was pre-trained on 250 million protein sequences, AcrNET overcomes the data scarcity problem. Extensive experiments and analysis suggest that the Transformer model feature, evolutionary feature, and local structure feature complement each other, which indicates the critical properties of anti-CRISPR proteins. AlphaFold prediction, further motif analysis, and docking experiments further demonstrate that AcrNET can capture the evolutionarily conserved pattern and the interaction between anti-CRISPR and the target implicitly.
Availability and implementation
Web server: https://proj.cse.cuhk.edu.hk/aihlab/AcrNET/. Training code and pre-trained model are available at.
Funder
Chinese University of Hong Kong
Innovation and Technology Fund
Publisher
Oxford University Press (OUP)
Subject
Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability
Reference49 articles.
1. Fitting a mixture model by expectation maximization to discover motifs in bipolymers;Bailey,1994
2. The universal protein resource (uniprot);Bairoch;Nucleic Acids Res,2007
3. Bacteriophage genes that inactivate the CRISPR/CAS bacterial immune system;Bondy-Denomy;Nature,2013
4. Lzerd webserver for pairwise and multiple protein–protein docking;Christoffer;Nucleic Acids Res,2021