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

Autor: Dao FY; Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; School of Biological Sciences, Nanyang Technological University, Singapore 639798, Singapore., Liu ML; Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China., Su W; Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China., Lv H; Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; Department of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland; SIB Swiss Institute of Bioinformatics, Zurich, Switzerland., Zhang ZY; Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China., Lin H; Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China. Electronic address: hlin@uestc.edu.cn., Liu L; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China. Electronic address: liliu2010imu@163.com.
Jazyk: angličtina
Zdroj: International journal of biological macromolecules [Int J Biol Macromol] 2023 Feb 15; Vol. 228, pp. 706-714. Date of Electronic Publication: 2022 Dec 28.
DOI: 10.1016/j.ijbiomac.2022.12.250
Abstrakt: 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.
Competing Interests: Conflict of interest The authors declare that they have no competing interests.
(Copyright © 2022 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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