Deep profiling of protease substrate specificity enabled by dual random and scanned human proteome substrate phage libraries

Abstract

AbstractProteolysis is a major post-translational regulator of biology both inside and outside of cells. Broad identification of optimal cleavage sites and natural substrates of proteases is critical for drug discovery and to understand protease biology. Here we present a method that employs two genetically encoded substrate phage display libraries coupled with next generation sequencing (SPD-NGS) that allows up to 10,000-fold deeper sequence coverage of the typical 6 to 8 residue protease cleavage sites compared to state-of-the-art synthetic peptide libraries or proteomics. We applied SPD-NGS to two classes of proteases, the intracellular caspases 2, 3, 6, 7 and 8, and the ectodomains of the membrane sheddases, ADAMs 10 and 17. The first library (Lib 10AA) was used to determine substrate cleavage motifs. Lib 10AA contains a highly diverse randomized 10-mer substrate peptide sequences (109 unique members) that was displayed mono-valently on filamentous phage and bound to magnetic beads via an N-terminal biotin. The protease was allowed to cleave the SPD beads, and the released phage subjected to up to three total rounds of positive selection followed by next generation sequencing (NGS). This allowed us to identify from 104 to 105 unique cleavage sites over a 1000-fold dynamic range of NGS counts (ranging from 3-4000), and produced consensus and optimal cleavage motifs based positional sequencing scoring matrices that closely matched synthetic peptide data. A second SPD-NGS library (Lib hP) was constructed that allowed us to identify candidate human proteome sequences. Lib hP displayed virtually the entire human proteome tiled in contiguous 49AA sequences with 25AA overlaps (nearly 1 million members). After three rounds of positive selection we identified up to 104 natural linear cut sites depending on the protease and captured most of the examples previously identified by proteomics (ranging from 30 to 1500) and predicted 10 to 100-fold more. Structural bioinformatics was used to facilitate the identification of candidate natural protein substrates. SPD-NGS is rapid, reproducible, simple to perform and analyze, inexpensive, renewable, with unprecedented depth of coverage for substrate sequences. SPD-NGS is an important tool for protease biologists interested protease specificity for specific assays and inhibitors and to facilitate identification of natural protein substrates.