National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA.
Department of Microbiology and Immunology, University of California San Francisco, San Francisco, CA, USA.
Nat Commun. 2020 Jul 29;11(1):3784. doi: 10.1038/s41467-020-17652-0.
The CRISPR-Cas are adaptive bacterial and archaeal immunity systems that have been harnessed for the development of powerful genome editing and engineering tools. In the incessant host-parasite arms race, viruses evolved multiple anti-defense mechanisms including diverse anti-CRISPR proteins (Acrs) that specifically inhibit CRISPR-Cas and therefore have enormous potential for application as modulators of genome editing tools. Most Acrs are small and highly variable proteins which makes their bioinformatic prediction a formidable task. We present a machine-learning approach for comprehensive Acr prediction. The model shows high predictive power when tested against an unseen test set and was employed to predict 2,500 candidate Acr families. Experimental validation of top candidates revealed two unknown Acrs (AcrIC9, IC10) and three other top candidates were coincidentally identified and found to possess anti-CRISPR activity. These results substantially expand the repertoire of predicted Acrs and provide a resource for experimental Acr discovery.
CRISPR-Cas 是适应性细菌和古细菌免疫系统，已被用于开发强大的基因组编辑和工程工具。在持续的宿主-寄生虫军备竞赛中，病毒进化出多种抗防御机制，包括多种抗 CRISPR 蛋白（Acrs），这些蛋白特异性地抑制 CRISPR-Cas，因此具有作为基因组编辑工具调节剂的巨大应用潜力。大多数 Acrs 是小型且高度可变的蛋白质，这使得它们的生物信息学预测成为一项艰巨的任务。我们提出了一种用于全面 Acr 预测的机器学习方法。该模型在针对未见测试集进行测试时表现出很高的预测能力，并被用于预测 2500 个候选 Acr 家族。对顶级候选物的实验验证揭示了两种未知的 Acr（AcrIC9、IC10），另外三个顶级候选物被偶然发现并被发现具有抗 CRISPR 活性。这些结果大大扩展了预测的 Acr 库，并为实验 Acr 发现提供了资源。
