Deep learning enhancing guide RNA design for CRISPR/Cas12a‐based diagnostics

• Published in: iMeta Vol. 3; no. 4; pp. e214 - n/a
• Main Authors: Huang, Baicheng, Guo, Ling, Yin, Hang, Wu, Yue, Zeng, Zihan, Xu, Sujie, Lou, Yufeng, Ai, Zhimin, Zhang, Weiqiang, Kan, Xingchi, Yu, Qian, Du, Shimin, Li, Chao, Wu, Lina, Huang, Xingxu, Wang, Shengqi, Wang, Xinjie
• Format: Journal Article
• Language: English
• Published: Australia John Wiley and Sons Inc 01.08.2024; Wiley
• Subjects: Cas12a; convolutional neural network; CRISPR; crRNA design; deep learning; diagnostic; crRNA design; CRISPR; deep learning; Cas12a; diagnostic; convolutional neural network
• ISSN: 2770-596X; 2770-5986; 2770-596X
• DOI: 10.1002/imt2.214

Rapid and accurate diagnostic tests are fundamental for improving patient outcomes and combating infectious diseases. The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) Cas12a‐based detection system has emerged as a promising solution for on‐site nucleic acid testing. Nonetheless, the effective design of CRISPR RNA (crRNA) for Cas12a‐based detection remains challenging and time‐consuming. In this study, we propose an enhanced crRNA design system with deep learning for Cas12a‐mediated diagnostics, referred to as EasyDesign. This system employs an optimized convolutional neural network (CNN) prediction model, trained on a comprehensive data set comprising 11,496 experimentally validated Cas12a‐based detection cases, encompassing a wide spectrum of prevalent pathogens, achieving Spearman's ρ = 0.812. We further assessed the model performance in crRNA design for four pathogens not included in the training data: Monkeypox Virus, Enterovirus 71, Coxsackievirus A16, and Listeria monocytogenes. The results demonstrated superior prediction performance compared to the traditional experiment screening. Furthermore, we have developed an interactive web server (https://crispr.zhejianglab.com/) that integrates EasyDesign with recombinase polymerase amplification (RPA) primer design, enhancing user accessibility. Through this web‐based platform, we successfully designed optimal Cas12a crRNAs for six human papillomavirus (HPV) subtypes. Remarkably, all the top five predicted crRNAs for each HPV subtype exhibited robust fluorescent signals in CRISPR assays, thereby suggesting that the platform could effectively facilitate clinical sample testing. In conclusion, EasyDesign offers a rapid and reliable solution for crRNA design in Cas12a‐based detection, which could serve as a valuable tool for clinical diagnostics and research applications. Advanced crRNA Design System: Developed EasyDesign, utilizing a convolutional neural network (CNN) trained with over 11,000 diagnostic‐target pairs to enable the creation of highly sensitive crRNAs for Cas12a‐based nucleic acid diagnostics. Proven Predictive Capabilities: EasyDesign demonstrates superior predictive performance for crRNA‐mediated Cas12a detection, validated through its successful application in designing diagnostics for a variety of viruses in clinical settings. User‐friendly web platform: Features an intuitive web platform that streamlines the creation of CRISPR/Cas12a‐based diagnostic tools, thereby enhancing accessibility and usability for both researchers and clinicians. Highlights Advanced crRNA design system: Developed EasyDesign, utilizing a convolutional neural network (CNN) trained with over 11,000 diagnostic‐target pairs to enable the creation of highly sensitive crRNAs for Cas12a‐based nucleic acid diagnostics. Proven predictive capabilities: EasyDesign demonstrates superior predictive performance for crRNA‐mediated Cas12a detection, validated through its successful application in designing diagnostics for a variety of viruses in clinical settings. User‐friendly web platform: Features an intuitive web platform that streamlines the creation of CRISPR/Cas12a‐based diagnostic tools, thereby enhancing accessibility and usability for both researchers and clinicians.