Predictive models of protease specificity based on quantitative protease-activity profiling data
| Autor: | Gennady V. Ponomarev, Mikhail S. Gelfand, Piotr Cieplak, Gennady G. Fedonin, Evgenii V. Matveev, Marat D. Kazanov, Boris I. Ratnikov, Alexey Eroshkin |
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| Rok vydání: | 2019 |
| Předmět: |
Proteases
Proteolysis medicine.medical_treatment Biophysics Computational biology Biochemistry Article Analytical Chemistry 03 medical and health sciences 0302 clinical medicine Activity profiling medicine Humans Molecular Biology 030304 developmental biology 0303 health sciences Protease Training set medicine.diagnostic_test Chemistry Computational Biology Experimental data Position weight matrix Algorithms 030217 neurology & neurosurgery Predictive modelling Peptide Hydrolases |
| Zdroj: | Biochim Biophys Acta Proteins Proteom |
| ISSN: | 1570-9639 |
| DOI: | 10.1016/j.bbapap.2019.07.006 |
| Popis: | 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 towards 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. |
| Databáze: | OpenAIRE |
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