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
Zhejiang Lab Hangzhou China.
Department of Laboratory Medicine, The First Affiliated Hospital Zhejiang University School of Medicine Hangzhou China.
Imeta. 2024 Jun 15;3(4):e214. doi: 10.1002/imt2.214. eCollection 2024 Aug.
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 = 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 . 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.
快速准确的诊断测试对于改善患者治疗效果和对抗传染病至关重要。基于成簇规律间隔短回文重复序列（CRISPR）Cas12a的检测系统已成为现场核酸检测的一种有前景的解决方案。尽管如此，为基于Cas12a的检测有效设计CRISPR RNA（crRNA）仍然具有挑战性且耗时。在本研究中，我们提出了一种用于Cas12a介导诊断的基于深度学习的增强型crRNA设计系统，称为EasyDesign。该系统采用了优化的卷积神经网络（CNN）预测模型，该模型在一个包含11496个经实验验证的基于Cas12a的检测案例的综合数据集上进行训练，这些案例涵盖了广泛的流行病原体，斯皮尔曼相关系数为0.812。我们进一步评估了该模型在为训练数据中未包含的四种病原体设计crRNA时的性能：猴痘病毒、肠道病毒71型、柯萨奇病毒A16型和[此处原文缺失一种病原体名称]。结果表明，与传统实验筛选相比，该模型具有卓越的预测性能。此外，我们开发了一个交互式网络服务器（https://crispr.zhejianglab.com/），将EasyDesign与重组酶聚合酶扩增（RPA）引物设计相结合，提高了用户的可及性。通过这个基于网络的平台，我们成功地为六种人乳头瘤病毒（HPV）亚型设计了最佳的Cas12a crRNA。值得注意的是，每种HPV亚型预测的前五个crRNA在CRISPR检测中均表现出强烈的荧光信号，这表明该平台可以有效地促进临床样本检测。总之，EasyDesign为基于Cas12a的检测中的crRNA设计提供了一种快速可靠的解决方案，可作为临床诊断和研究应用的宝贵工具。
