Peptide design by optimization on a data-parameterized protein interaction landscape

We applied the high-throughput interaction assay SORTCERY to measure thousands of protein-peptide binding affinities and used the data to parameterize models of the peptide-binding landscape for three members of the Bcl-2 family of proteins. We applied the models to design peptides that bound with high affinity and specificity to just one of Bcl-xL, Mcl-1, or Bfl-1. We designed additional peptides that bound selectively to two out of three of these proteins. The raw data provided are the multiplexed fastq files that serve as inputs to our analysis pipeline. Additional detail is available in our corresponding publication and at the following github repository. https://github.com/KeatingLab/sortcery_design Overall design: DNA samples from yeast-surface display peptide libraries were deep sequenced after sorting with fluorescence activated cell sorting.本研究采用高通量相互作用测定法SORTCERY，对数千组蛋白质-肽结合亲和力进行了检测，并利用所得数据为Bcl-2蛋白家族的三个成员构建肽结合态势模型并完成参数化。我们依托该模型设计出了仅对Bcl-xL、Mcl-1或Bfl-1中的单一靶标具备高亲和力与特异性结合能力的肽段；此外还设计了可选择性结合上述三种蛋白中任意两种的肽段。本次提供的原始数据为可作为本研究分析流程输入文件的多重fastq格式文件。更多详细信息可参阅我们的相关研究论文，以及下述GitHub仓库：https://github.com/KeatingLab/sortcery_design。整体实验设计：针对酵母表面展示肽库的DNA样本，经荧光激活细胞分选后完成了深度测序。