Projects per year
Abstract
We identify, firstly, InterPro sequence signatures representing evolutionary
relatedness and, secondly, signatures identifying specific chemical machinery. Thus, we predict the chemical mechanisms of enzyme catalysed reactions from “catalytic” and “non-catalytic” subsets of InterPro signatures. We first scanned our 249 sequences with InterProScan and then used the MACiE database to identify those amino acid residues which are important for catalysis. The sequences were mutated in silico to replace these catalytic residues with glycine, and then again scanned with InterProScan. Those signature matches from the original scan which disappeared on mutation were called “catalytic”. Mechanism was predicted using all signatures, only the 78 “catalytic” signatures, or only the 519 “non-catalytic” signatures. The noncatalytic signatures gave results indistinguishable from those for the whole feature set, with precision of 0.991 and sensitivity of 0.970. The catalytic signatures alone gave less impressive predictivity, with precision and sensitivity of 0.791 and 0.735, respectively. These results show that our successful prediction of enzyme mechanism
is mostly by homology rather than by identifying catalytic machinery.
relatedness and, secondly, signatures identifying specific chemical machinery. Thus, we predict the chemical mechanisms of enzyme catalysed reactions from “catalytic” and “non-catalytic” subsets of InterPro signatures. We first scanned our 249 sequences with InterProScan and then used the MACiE database to identify those amino acid residues which are important for catalysis. The sequences were mutated in silico to replace these catalytic residues with glycine, and then again scanned with InterProScan. Those signature matches from the original scan which disappeared on mutation were called “catalytic”. Mechanism was predicted using all signatures, only the 78 “catalytic” signatures, or only the 519 “non-catalytic” signatures. The noncatalytic signatures gave results indistinguishable from those for the whole feature set, with precision of 0.991 and sensitivity of 0.970. The catalytic signatures alone gave less impressive predictivity, with precision and sensitivity of 0.791 and 0.735, respectively. These results show that our successful prediction of enzyme mechanism
is mostly by homology rather than by identifying catalytic machinery.
Original language | English |
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Pages (from-to) | 267-274 |
Journal | Evolutionary Bioinformatics |
Volume | 2015 |
Issue number | 11 |
DOIs | |
Publication status | Published - 29 Dec 2015 |
Keywords
- Sequence signatures
- InterPro
- Enzyme catalysis
- Reaction mechanism
- Active site
- Evolution
- Homology
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Dive into the research topics of 'Why do sequence signatures predict enzyme mechanism? Homology versus Chemistry'. Together they form a unique fingerprint.Projects
- 1 Finished
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Machine Learning Approaches to Predict: Machine Learning Approaches to Predict Enzyme Function
Mitchell, J. B. O. (PI) & De Ferrari, L. (Researcher)
1/09/11 → 31/12/14
Project: Standard
Datasets
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Data underpinning: Why do sequence signatures predict enzyme mechanism? Homology versus Chemistry
Beattie, K. (Creator), De Ferrari, L. (Creator) & Mitchell, J. B. O. (Creator), SAGE Publications Ltd STM, 2015
Dataset