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AcrPred: A hybrid optimization with enumerated machine learning algorithm to predict Anti-CRISPR proteins

Authors :
Fu-Ying Dao
Meng-Lu Liu
Wei Su
Hao Lv
Zhao-Yue Zhang
Hao Lin
Li Liu
Source :
International journal of biological macromolecules. 228
Publication Year :
2022

Abstract

CRISPR-Cas, as a tool for gene editing, has received extensive attention in recent years. Anti-CRISPR (Acr) proteins can inactivate the CRISPR-Cas defense system during interference phase, and can be used as a potential tool for the regulation of gene editing. In-depth study of Anti-CRISPR proteins is of great significance for the implementation of gene editing. In this study, we developed a high-accuracy prediction model based on two-step model fusion strategy, called AcrPred, which could produce an AUC of 0.952 with independent dataset validation. To further validate the proposed model, we compared with published tools and correctly identified 9 of 10 new Acr proteins, indicating the strong generalization ability of our model. Finally, for the convenience of related wet-experimental researchers, a user-friendly web-server AcrPred (Anti-CRISPR proteins Prediction) was established at http://lin-group.cn/server/AcrPred, by which users can easily identify potential Anti-CRISPR proteins.

Details

ISSN :
18790003
Volume :
228
Database :
OpenAIRE
Journal :
International journal of biological macromolecules
Accession number :
edsair.doi.dedup.....248164298d757f7776949c0542a2c0cb