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本研究采用高通量相互作用检测技术SORTCERY，测定了数千组蛋白质-肽段的结合亲和力，并利用该数据为三类Bcl-2蛋白家族成员的肽段结合景观模型完成参数校准。我们依托所构建的模型，设计出仅对Bcl-xL、Mcl-1或Bfl-1中的单一蛋白展现高亲和力与特异性结合的肽段；同时还设计了另一类肽段，可选择性结合上述三种蛋白中的两种。本次提供的原始数据为可作为分析流程输入的多重fastq文件。更多详细信息可参阅相关研究论文，或访问下述GitHub仓库：https://github.com/KeatingLab/sortcery_design