Sensitive Identification of Known and Unknown Protease Activities by Unsupervised Linear Motif Deconvolution.
Anal Chem
; 94(4): 2244-2254, 2022 02 01.
Article
in English
| MEDLINE | ID: covidwho-1625849
ABSTRACT
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/.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Coronavirus 3C Proteases
/
SARS-CoV-2
Type of study:
Diagnostic study
/
Experimental Studies
/
Observational study
Limits:
Humans
Language:
English
Journal:
Anal Chem
Year:
2022
Document Type:
Article
Affiliation country:
Acs.analchem.1c04937
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