753-基于蛋白质语言模型定制 CRISPR-Cas PAM 特异性

这篇来自《自然-生物技术》的研究介绍了一种名为 Protein2PAM 的深度学习模型，旨在通过蛋白质序列直接预测并定制 CRISPR-Cas 系统的 PAM 识别特异性。研究人员构建了涵盖超 4.5 万个条目的 CRISPR-Cas 图谱作为训练集，显著提升了预测的精准度与效率。该模型能够识别决定 Cas9 蛋白功能的关键残基，且无需依赖结构信息即可辅助设计新型酶变体。通过对 Nme1Cas9 进行计算演化，团队成功开发出编辑范围更广且活性大幅提升的变体，攻克了传统工程化方法耗时耗力的难题。这一突破为个性化基因组编辑提供了更灵活的工具，使原本受限的基因序列变得可靶向。References: Nayfach S, Bhatnagar A, Novichkov A, et al. Customizing CRISPR–Cas PAM specificity with protein language models[J]. Nature Biotechnology, 2026: 1-10.前往小宇宙评论区与主播互动

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753-基于蛋白质语言模型定制 CRISPR-Cas PAM 特异性

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