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Biochim Biophys Acta Proteins Proteom. 2019 Nov;1867(11):140253. doi: 10.1016/j.bbapap.2019.07.006. Epub 2019 Jul 19.

Predictive models of protease specificity based on quantitative protease-activity profiling data.

Author information

1
Central Research Institute of Epidemiology, Moscow 111123, Russia; A.A.Kharkevich Institute of Information Transmission Problems, Moscow 127051, Russia; Moscow Institute of Physics and Technology, Dolgoprudny 141700, Russia.
2
Sanford-Burnham-Prebys Medical Discovery Institute, La Jolla, CA 92037, USA.
3
Volgograd State University, Volgograd 400062, Russia.
4
A.A.Kharkevich Institute of Information Transmission Problems, Moscow 127051, Russia.
5
A.A.Kharkevich Institute of Information Transmission Problems, Moscow 127051, Russia; Skolkovo Institute of Science and Technology, Moscow 121205, Russia; National Research University Higher School of Economics, Moscow 101000, Russia.
6
A.A.Kharkevich Institute of Information Transmission Problems, Moscow 127051, Russia; Skolkovo Institute of Science and Technology, Moscow 121205, Russia; Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology, Moscow 117997, Russia. Electronic address: mkazanov@gmail.com.

Abstract

Bioinformatics-based prediction of protease substrates can help to elucidate regulatory proteolytic pathways that control a broad range of biological processes such as apoptosis and blood coagulation. The majority of published predictive models are position weight matrices (PWM) reflecting specificity of proteases toward target sequence. These models are typically derived from experimental data on positions of hydrolyzed peptide bonds and show a reasonable predictive power. New emerging techniques that not only register the cleavage position but also measure catalytic efficiency of proteolysis are expected to improve the quality of predictions or at least substantially reduce the number of tested substrates required for confident predictions. The main goal of this study was to develop new prediction models based on such data and to estimate the performance of the constructed models. We used data on catalytic efficiency of proteolysis measured for eight major human matrix metalloproteinases to construct predictive models of protease specificity using a variety of regression analysis techniques. The obtained results suggest that efficiency-based (quantitative) models show a comparable performance with conventional PWM-based algorithms, while less training data are required. The derived list of candidate cleavage sites in human secreted proteins may serve as a starting point for experimental analysis.

KEYWORDS:

Matrix metalloproteinases; Position weight matrix; Proteolysis; Proteolytic site; Regression analysis

PMID:
31330204
PMCID:
PMC6745255
[Available on 2020-11-01]
DOI:
10.1016/j.bbapap.2019.07.006
[Indexed for MEDLINE]

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