小柯机器人

近日，奥地利分子病理学研究所Alexander Stark及其课题组开发出DeepSTARR，可从DNA序列预测增强子的活性，并能从头设计合成增强子。2022年5月12日，《自然—遗传学》杂志在线发表了这项成果。

研究人员建立了一个深度学习模型，DeepSTARR，直接从黑腹果蝇S2细胞的DNA序列中定量预测了数千个发育和管家型增强子的活性。该模型学习了相关的转录因子（TF）模体和高阶语法规则。研究人员在实验中验证了这些规则，并通过测试4万多个野生型和突变型果蝇和人类增强子，证明它们可以推广到人类。最后，研究人员设计并在功能上验证了具有理想活性的合成增强子。

据介绍，增强子序列控制着基因的表达，包括不同TF的结合位点（模体）。尽管进行了广泛的遗传和计算研究，但对DNA序列和调控活性之间的关系知之甚少，而从头设计增强子一直是个挑战。

附：英文原文

Title: DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers

Author: de Almeida, Bernardo P., Reiter, Franziska, Pagani, Michaela, Stark, Alexander

Issue&Volume: 2022-05-12

Abstract: Enhancer sequences control gene expression and comprise binding sites (motifs) for different transcription factors (TFs). Despite extensive genetic and computational studies, the relationship between DNA sequence and regulatory activity is poorly understood, and de novo enhancer design has been challenging. Here, we built a deep-learning model, DeepSTARR, to quantitatively predict the activities of thousands of developmental and housekeeping enhancers directly from DNA sequence in Drosophila melanogaster S2 cells. The model learned relevant TF motifs and higher-order syntax rules, including functionally nonequivalent instances of the same TF motif that are determined by motif-flanking sequence and intermotif distances. We validated these rules experimentally and demonstrated that they can be generalized to humans by testing more than 40,000 wildtype and mutant Drosophila and human enhancers. Finally, we designed and functionally validated synthetic enhancers with desired activities de novo. A deep-learning model called DeepSTARR quantitatively predicts enhancer activity on the basis of DNA sequence. The model learns relevant motifs and syntax rules, allowing for the design of synthetic enhancers with specific strengths.

DOI: 10.1038/s41588-022-01048-5

Source: https://www.nature.com/articles/s41588-022-01048-5

Nature Genetics：《自然—遗传学》，创刊于1992年。隶属于施普林格·自然出版集团，最新IF：41.307
官方网址：https://www.nature.com/ng/
投稿链接：https://mts-ng.nature.com/cgi-bin/main.plex