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/.