Dixit Shriniket, Kumar Anant, Srinivasan Kathiravan, Vincent P M Durai Raj, Ramu Krishnan Nadesh
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.
School of Bioscience and Technology, Vellore Institute of Technology, Vellore, India.
Front Bioeng Biotechnol. 2024 Jan 8;11:1335901. doi: 10.3389/fbioe.2023.1335901. eCollection 2023.
Clustered regularly interspaced short palindromic repeat (CRISPR)-based genome editing (GED) technologies have unlocked exciting possibilities for understanding genes and improving medical treatments. On the other hand, Artificial intelligence (AI) helps genome editing achieve more precision, efficiency, and affordability in tackling various diseases, like Sickle cell anemia or Thalassemia. AI models have been in use for designing guide RNAs (gRNAs) for CRISPR-Cas systems. Tools like DeepCRISPR, CRISTA, and DeepHF have the capability to predict optimal guide RNAs (gRNAs) for a specified target sequence. These predictions take into account multiple factors, including genomic context, Cas protein type, desired mutation type, on-target/off-target scores, potential off-target sites, and the potential impacts of genome editing on gene function and cell phenotype. These models aid in optimizing different genome editing technologies, such as base, prime, and epigenome editing, which are advanced techniques to introduce precise and programmable changes to DNA sequences without relying on the homology-directed repair pathway or donor DNA templates. Furthermore, AI, in collaboration with genome editing and precision medicine, enables personalized treatments based on genetic profiles. AI analyzes patients' genomic data to identify mutations, variations, and biomarkers associated with different diseases like Cancer, Diabetes, Alzheimer's, etc. However, several challenges persist, including high costs, off-target editing, suitable delivery methods for CRISPR cargoes, improving editing efficiency, and ensuring safety in clinical applications. This review explores AI's contribution to improving CRISPR-based genome editing technologies and addresses existing challenges. It also discusses potential areas for future research in AI-driven CRISPR-based genome editing technologies. The integration of AI and genome editing opens up new possibilities for genetics, biomedicine, and healthcare, with significant implications for human health.
基于成簇规律间隔短回文重复序列(CRISPR)的基因组编辑(GED)技术为理解基因和改善医学治疗带来了令人兴奋的可能性。另一方面,人工智能(AI)有助于基因组编辑在应对各种疾病(如镰状细胞贫血或地中海贫血)时实现更高的精准度、效率和可承受性。人工智能模型已被用于为CRISPR-Cas系统设计引导RNA(gRNA)。诸如DeepCRISPR、CRISTA和DeepHF等工具能够为指定的靶序列预测最佳引导RNA(gRNA)。这些预测考虑了多个因素,包括基因组背景、Cas蛋白类型、所需的突变类型、靶向/脱靶评分、潜在的脱靶位点以及基因组编辑对基因功能和细胞表型的潜在影响。这些模型有助于优化不同的基因组编辑技术,如碱基编辑、引导编辑和表观基因组编辑,这些都是在不依赖同源定向修复途径或供体DNA模板的情况下对DNA序列引入精确且可编程变化的先进技术。此外,人工智能与基因组编辑和精准医学协作,能够实现基于基因图谱的个性化治疗。人工智能分析患者的基因组数据,以识别与癌症、糖尿病、阿尔茨海默病等不同疾病相关的突变、变异和生物标志物。然而,仍存在一些挑战,包括高成本、脱靶编辑、CRISPR载体合适的递送方法、提高编辑效率以及确保临床应用中的安全性。本综述探讨了人工智能对改进基于CRISPR的基因组编辑技术的贡献,并解决了现有挑战。它还讨论了人工智能驱动的基于CRISPR的基因组编辑技术未来研究的潜在领域。人工智能与基因组编辑的整合为遗传学、生物医学和医疗保健开辟了新的可能性,对人类健康具有重大意义。