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Dryad

Quantitative profiling of protease specificity

Cite this dataset

Cieplak, Piotr et al. (2021). Quantitative profiling of protease specificity [Dataset]. Dryad. https://doi.org/10.5061/dryad.ns1rn8pq1

Abstract

Proteases comprise 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 methodologies 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”, which defines the set of substrates that uniquely represents 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 numbers and relative catalytic efficiencies of substrates. Using these advances greatly facilitates uncovering selectivity between closely related members of protease families and provides insight into to the degree of contribution of catalytic cleft specificity to protein substrate recognition, thus providing basis to overcoming two of the major challenges in the field of proteolysis: 1) development of highly selective activity probes and inhibitors for studying proteases with overlapping specificities, and 2) distinguishing targeted proteolysis from bystander proteolytic events.

Methods

This dataset was preprocessed data from next generation sequencing of phage display data.

The data has been preprocessed by linux shell scripts and Fortran programs.

Usage notes

See information included in the text of our manuscript being submitted to Plos Computational Biology.

There are no missing values.

Funding

National Institute of General Medical Sciences, Award: GM107523