Quantitative profiling of protease specificity

• Published in: PLoS computational biology Vol. 17; no. 2; p. e1008101
• Main Authors: Ratnikov, Boris I., Cieplak, Piotr, Remacle, Albert G., Nguyen, Elise, Smith, Jeffrey W.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.02.2021; Public Library of Science (PLoS)
• Subjects: Amino Acid Sequence; Binding sites; Biology and Life Sciences; Catalytic Domain - genetics; Combinatorics; Computational Biology; Divergence; Efficiency; Enzymes; Gelatinase A; High-Throughput Nucleotide Sequencing; Information Theory; Matrix Metalloproteinase 2 - chemistry; Matrix Metalloproteinase 2 - genetics; Matrix Metalloproteinase 2 - metabolism; Matrix Metalloproteinase 9 - chemistry; Matrix Metalloproteinase 9 - genetics; Matrix Metalloproteinase 9 - metabolism; Models, Biological; Models, Molecular; Peptide Hydrolases - classification; Peptide Hydrolases - genetics; Peptide Hydrolases - metabolism; Peptide Library; Peptides; Physical Sciences; Probes; Protease; Protein Folding; Proteinase; Proteins; Proteolysis; Proteomics - methods; Proteomics - statistics & numerical data; Quantitative analysis; Research and Analysis Methods; Selectivity; Substrate Specificity - genetics; Substrate Specificity - physiology; Substrates
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1008101

Proteases are an important class of enzymes, whose activity is central to many physiologic and pathologic processes. Detailed knowledge of protease specificity is key to understanding their function. Although many methods have been developed to profile specificities of proteases, few have the diversity and quantitative grasp necessary to fully define specificity of a protease, both in terms of substrate numbers and their catalytic efficiencies. We have developed a concept of “selectome”; the set of substrate amino acid sequences that uniquely represent the specificity of a protease. We applied it to two closely related members of the Matrixin family–MMP-2 and MMP-9 by using substrate phage display coupled with Next Generation Sequencing and information theory-based data analysis. We have also derived a quantitative measure of substrate specificity, which accounts for both the number of substrates and their relative catalytic efficiencies. Using these advances greatly facilitates elucidation of substrate selectivity between closely related members of a protease family. The study also provides insight into the degree to which the catalytic cleft defines substrate recognition, thus providing basis for overcoming two of the major challenges in the field of proteolysis: 1) development of highly selective activity probes for studying proteases with overlapping specificities, and 2) distinguishing targeted proteolysis from bystander proteolytic events.