Department of Computer Science, Dartmouth College, Hanover, New Hampshire, USA.
Protein Sci. 2023 Apr;32(4):e4607. doi: 10.1002/pro.4607.
We propose a high-throughput method for quantitatively measuring hundreds of protein-peptide binding affinities in parallel. In this assay, a solution of protein is dialyzed into a buffer containing a pool of potential binding peptides, such that upon equilibration the relative abundance of a peptide species is mathematically related to that peptide's dissociation constant, K . We use isobaric multiplexed quantitative proteomics to simultaneously determine the relative abundance, and hence the K and its associated error, for an entire peptide library. We apply this technique, which we call PEDAL (parallel equilibrium dialysis for affinity learning), to determine accurate K 's between a PDZ domain and hundreds of peptides, spanning an affinity range of multiple orders of magnitude in a single experiment. PEDAL is a convenient, fast, and low-cost method for measuring large numbers of protein-peptide affinities in parallel, providing a rare combination of true in-solution binding equilibria with the ability to multiplex.
我们提出了一种高通量方法,可平行定量测量数百种蛋白质-肽结合亲和力。在该测定法中,将蛋白质溶液透析到包含潜在结合肽池的缓冲液中,使得在平衡时,肽种类的相对丰度与该肽的解离常数 K 呈数学关系。我们使用等压多重定量蛋白质组学技术,可同时确定整个肽文库的相对丰度,从而确定 K 及其相关误差。我们将此技术(我们称之为 PEDAL(用于亲和力学习的平行平衡透析))应用于确定 PDZ 结构域与数百种肽之间的精确 K ',涵盖了单个实验中多个数量级的亲和力范围。PEDAL 是一种方便,快速且低成本的方法,可平行测量大量蛋白质-肽亲和力,具有真正的溶液结合平衡与多路复用能力的罕见组合。