Chia Bing Shao, Seah Yu Fen Samantha, Wang Bolun, Shen Kimberle, Srivastava Diya, Chew Wei Leong
Genome Institute of Singapore, Agency for Science, Technology and Research, 60 Biopolis Street, Singapore 138672, Singapore.
Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore.
ACS Synth Biol. 2025 Mar 21;14(3):636-647. doi: 10.1021/acssynbio.4c00686. Epub 2025 Feb 25.
CRISPR-Cas technology has revolutionized biology by enabling precise DNA and RNA edits with ease. However, significant challenges remain for translating this technology into clinical applications. Traditional protein engineering methods, such as rational design, mutagenesis screens, and directed evolution, have been used to address issues like low efficacy, specificity, and high immunogenicity. These methods are labor-intensive, time-consuming, and resource-intensive and often require detailed structural knowledge. Recently, computational strategies have emerged as powerful solutions to these limitations. Using artificial intelligence (AI) and machine learning (ML), the discovery and design of novel gene-editing enzymes can be streamlined. AI/ML models predict activity, specificity, and immunogenicity while also enhancing mutagenesis screens and directed evolution. These approaches not only accelerate rational design but also create new opportunities for developing safer and more efficient genome-editing tools, which could eventually be translated into the clinic.
CRISPR-Cas技术通过能够轻松实现精确的DNA和RNA编辑，给生物学带来了革命性变化。然而，将这项技术转化为临床应用仍面临重大挑战。传统的蛋白质工程方法，如理性设计、诱变筛选和定向进化，已被用于解决诸如低效率、特异性和高免疫原性等问题。这些方法劳动强度大、耗时且资源密集，并且通常需要详细的结构知识。最近，计算策略已成为解决这些局限性的有力解决方案。利用人工智能（AI）和机器学习（ML），新型基因编辑酶的发现和设计可以得到简化。AI/ML模型可预测活性、特异性和免疫原性，同时还能增强诱变筛选和定向进化。这些方法不仅加速了理性设计，还为开发更安全、更高效的基因组编辑工具创造了新机会，最终可能转化应用于临床。
