Institut de la Vision, Sorbonne Université, INSERM, CNRS, 17 rue Moreau, 75012, Paris, France.
Graduate School of Informatics, Kyoto University, Yoshida Hon-machi, Sakyo-ku, Kyoto, 606-8501, Japan.
Nat Commun. 2023 Dec 26;14(1):8504. doi: 10.1038/s41467-023-43967-9.
Forward genetic screens of mutated variants are a versatile strategy for protein engineering and investigation, which has been successfully applied to various studies like directed evolution (DE) and deep mutational scanning (DMS). While next-generation sequencing can track millions of variants during the screening rounds, the vast and noisy nature of the sequencing data impedes the estimation of the performance of individual variants. Here, we propose ACIDES that combines statistical inference and in-silico simulations to improve performance estimation in the library selection process by attributing accurate statistical scores to individual variants. We tested ACIDES first on a random-peptide-insertion experiment and then on multiple public datasets from DE and DMS studies. ACIDES allows experimentalists to reliably estimate variant performance on the fly and can aid protein engineering and research pipelines in a range of applications, including gene therapy.
正向遗传筛选突变变体是一种用于蛋白质工程和研究的通用策略，已成功应用于定向进化 (DE) 和深度突变扫描 (DMS) 等各种研究中。虽然下一代测序可以在筛选轮次中跟踪数百万个变体，但测序数据的广泛和嘈杂性质阻碍了对个别变体性能的估计。在这里，我们提出了 ACIDES，它结合了统计推断和计算机模拟，通过为个别变体赋予准确的统计分数来提高文库选择过程中的性能估计。我们首先在随机肽插入实验中测试了 ACIDES，然后在 DE 和 DMS 研究的多个公共数据集上进行了测试。ACIDES 允许实验人员实时可靠地估计变体性能，并且可以在包括基因治疗在内的各种应用中为蛋白质工程和研究管道提供帮助。
