Block-based characterization of protease specificity from substrate sequence profile

Sensitive Identification of Known and Unknown Protease Activities by Unsupervised Linear Motif Deconvolution

The cleavage-site specificities for many proteases are not well understood, restricting the utility of supervised classification methods. We present an algorithm and web interface to overcome this limitation through the unsupervised detection of overrepresented patterns in protein sequence data, providing insight into the mixture of protease activities contributing to a complex system. Here, we apply the RObust LInear Motif Deconvolution (RoLiM) algorithm to confidently detect substrate cleavage patterns for SARS-CoV-2 MPro protease in the N-terminome data of an infected human cell line. Using mass spectrometry-based peptide data from a case-control comparison of 341 primary urothelial bladder cancer cases and 110 controls, we identified distinct sequence motifs indicative of increased matrix metallopeptidase activity in urine from cancer patients. The evaluation of N-terminal peptides from patient plasma post-chemotherapy detected novel granzyme B/corin activity. RoLiM will enhance the unbiased investigation of peptide sequences to establish the composition of known and uncharacterized protease activities in biological systems. RoLiM is available at http://langelab.org/rolim/.

Mapping specificity, cleavage entropy, allosteric changes and substrates of blood proteases in a high-throughput screen

Proteases are among the largest protein families and critical regulators of biochemical processes like apoptosis and blood coagulation. Knowledge of proteases has been expanded by the development of proteomic approaches, however, technology for multiplexed screening of proteases within native environments is currently lacking behind. Here we introduce a simple method to profile protease activity based on isolation of protease products from native lysates using a 96FASP filter, their analysis in a mass spectrometer and a custom data analysis pipeline. The method is significantly faster, cheaper, technically less demanding, easy to multiplex and produces accurate protease fingerprints. Using the blood cascade proteases as a case study, we obtain protease substrate profiles that can be used to map specificity, cleavage entropy and allosteric effects and to design protease probes. The data further show that protease substrate predictions enable the selection of potential physiological substrates for targeted validation in biochemical assays.

Characterizing proteases in their native environment is still challenging. Here, the authors develop a proteomics workflow for analyzing protease-specific peptides from cell lysates in 96-well format, providing mechanistic insights into blood proteases and enabling the prediction of protease substrates.

Revealing favorable and unfavorable residues in cooperative positions in protease cleavage sites

Proteases play critical roles in a wide variety of fundamental biological functions, and numerous protease inhibitors have been developed to treat various diseases including cancer. A wide range of experimental and computational methods have been developed to investigate the specificity and catalytic mechanisms of proteases. However, these methods only focused on the preferences of a single position around a cleavage site in a substrate, rarely on the compositionality of the subsites. We present new methods to quantify the specificity of proteases by considering the combinatorial patterns of amino acid residuals of cleavage sites in substrates. By incorporating the preference at positions, we modeled three types of favorable combinations of residues in cleavage sites. Moreover, by constructing a relationship weight matrix of residues between two positions, we can easily identify unfavorable combinations of residues at the positions. Applying these methods to a set of known cleavage sites of proteases, we revealed numerous favorable and unfavorable residues in cooperative positions in the protease cleavage sites. The results can help understand the specificity and catalytic mechanisms of proteases. To our knowledge, this is the first study that quantifies unfavorable combinations of amino acids between two sites. Furthermore, this method is not limited to the study of proteases and cleavage sites, and can be generalized to uncover the relationships of residues at meaningful sites in other proteins.