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

被引:27
|
作者
Zhou, Jie [1 ]
Li, Shantao [2 ]
Leung, Kevin K. [1 ]
O'Donovan, Brian [3 ]
Zou, James Y. [2 ,4 ]
DeRisi, Joseph L. [3 ,4 ]
Wells, James A. [1 ,4 ,5 ]
机构
[1] Univ Calif San Francisco, Dept Pharmaceut Chem, San Francisco, CA 94158 USA
[2] Stanford Univ, Dept Biomed Data Sci, Stanford, CA 94305 USA
[3] Univ Calif San Francisco, Dept Biochem & Biophys, San Francisco, CA 94158 USA
[4] Chan Zuckerberg Biohub, San Francisco, CA 94158 USA
[5] Univ Calif San Francisco, Dept Cellular & Mol Pharmacol, San Francisco, CA 94158 USA
基金
美国国家科学基金会;
关键词
protease specificity; human proteome; substrate phage library; NGS; SEQUESTRATION SCREENING YESS; PROTEOLYSIS; ACTIVATION;
D O I
10.1073/pnas.2009279117
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Proteolysis is a major posttranslational regulator of biology 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 sixto eight-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, and the ectodomains of the sheddases, ADAMs 10 and 17. The first library (Lib 10AA) allowed us to identify 10(4) to 10(5) unique cleavage sites over a 1,000fold dynamic range of NGS counts and produced consensus and optimal cleavage motifs based position-specific scoring matrices. A second SPD-NGS library (Lib hP), which displayed virtually the entire human proteome tiled in contiguous 49 amino acid sequences with 25 amino acid overlaps, enabled us to identify candidate human proteome sequences. We identified up to 104 natural linear cut sites, depending on the protease, and captured most of the examples previously identified by proteomics 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, and renewable, with unprecedented depth of coverage for substrate sequences, and is an important tool for protease biologists interested in protease specificity for specific assays and inhibitors and to facilitate identification of natural protein substrates.
引用
收藏
页码:25464 / 25475
页数:12
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