Additional corresponding author: Dr. Piotr Cieplak,E-mail: email@example.com
Sequence-derived structural features driving proteolytic processing
Article first published online: 11 DEC 2013
© 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Volume 14, Issue 1, pages 42–50, January 2014
How to Cite
Belushkin, A. A., Vinogradov, D. V., Gelfand, M. S., Osterman, A. L., Cieplak, P. and Kazanov, M. D. (2014), Sequence-derived structural features driving proteolytic processing. Proteomics, 14: 42–50. doi: 10.1002/pmic.201300416
Colour Online: See the article online to view Figs. 1–4 and 7 in colour.
- Issue published online: 13 JAN 2014
- Article first published online: 11 DEC 2013
- Accepted manuscript online: 13 NOV 2013 05:45PM EST
- Manuscript Accepted: 28 OCT 2013
- Manuscript Revised: 22 OCT 2013
- Manuscript Received: 23 SEP 2013
- Ministry of Education and Science of the Russian Federation. Grant Numbers: 8049, 8135
- NIH. Grant Number: R01GM098835
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Table S1. Distribution of cleavage sites among types of protein secondary structure predicted by six different bioinformatics methods. (a) Number of cleavage sites. (b) Total number of amino acids of particular secondary structure type. (c) Cleavage rate per peptide bond.
Table S2. Comparison of frequencies of cleavage sites between central and peripheral parts of b-strands predicted by six different bioinformatics methods.
Table S3. Significance of associations (-log(p-value)) of proteolytic events and sequence-derived protein structural features predicted by different bioinformatics methods.
Table S4. Prediction capabilities of sequence-derived structural features estimated by (a) Area under ROC Curve (AUC), (b) F-score, (c) Sensitivity, (d) Specificity metrics. AUC values, which are less than 0.5, are inverted (1-AUC) and maked by dark background. Binary features contain additional column with metrics calculated for their confidence values.
Table S5. Area under ROC Curve estimates calculated separately for substrates of a four protease types.
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