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Why do Sequence Signatures Predict Enzyme Mechanism? Homology versus Chemistry.

Beattie KE, De Ferrari L, Mitchell JB - Evol. Bioinform. Online (2015)

Bottom Line: The non-catalytic signatures gave indistinguishable results 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.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Sciences Research Complex and EaStCHEM School of Chemistry, Purdie Building, University of St Andrews, North Haugh, St Andrews, Scotland, UK.

ABSTRACT
First, we identify InterPro sequence signatures representing evolutionary relatedness and, second, signatures identifying specific chemical machinery. Thus, we predict the chemical mechanisms of enzyme-catalyzed reactions from catalytic and non-catalytic subsets of InterPro signatures. We first scanned our 249 sequences using InterProScan and then used the MACiE database to identify those amino acid residues that are important for catalysis. The sequences were mutated in silico to replace these catalytic residues with glycine and then again scanned using InterProScan. Those signature matches from the original scan that 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 non-catalytic signatures gave indistinguishable results 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.

No MeSH data available.


Classification performance of catalytic and non-catalytic signatures. The micro-averaged precision and sensitivity achieved by using the catalytic and non-catalytic sets and the proportions of our InterPro signatures belonging to each group.
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f2-ebo-11-2015-267: Classification performance of catalytic and non-catalytic signatures. The micro-averaged precision and sensitivity achieved by using the catalytic and non-catalytic sets and the proportions of our InterPro signatures belonging to each group.

Mentions: MACiE mechanisms were predicted using the following: (1) only catalytic signatures, (2) only non-catalytic signatures, and (3) all available signatures, where catalytic signatures are those which disappeared under the in silico mutation procedure described earlier, see Table 2 and Figure 2. Again the numbers of attributes in each group were unbalanced: 519 non-catalytic and 78 catalytic signatures. The non-catalytic group gave a precision of 0.991 and a sensitivity of 0.970, which were indistinguishable from the results for the full combined set of signatures. The catalytic signatures alone gave less impressive predictivity, with precision and sensitivity of 0.791 and 0.735, respectively. Although the performance of the catalytic signatures was thus weaker, they formed only 14% of the total signatures in comparison to 93% for the non-catalytic signatures (this does not sum to 100%, as some signatures can be in both sets for different proteins).


Why do Sequence Signatures Predict Enzyme Mechanism? Homology versus Chemistry.

Beattie KE, De Ferrari L, Mitchell JB - Evol. Bioinform. Online (2015)

Classification performance of catalytic and non-catalytic signatures. The micro-averaged precision and sensitivity achieved by using the catalytic and non-catalytic sets and the proportions of our InterPro signatures belonging to each group.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4696837&req=5

f2-ebo-11-2015-267: Classification performance of catalytic and non-catalytic signatures. The micro-averaged precision and sensitivity achieved by using the catalytic and non-catalytic sets and the proportions of our InterPro signatures belonging to each group.
Mentions: MACiE mechanisms were predicted using the following: (1) only catalytic signatures, (2) only non-catalytic signatures, and (3) all available signatures, where catalytic signatures are those which disappeared under the in silico mutation procedure described earlier, see Table 2 and Figure 2. Again the numbers of attributes in each group were unbalanced: 519 non-catalytic and 78 catalytic signatures. The non-catalytic group gave a precision of 0.991 and a sensitivity of 0.970, which were indistinguishable from the results for the full combined set of signatures. The catalytic signatures alone gave less impressive predictivity, with precision and sensitivity of 0.791 and 0.735, respectively. Although the performance of the catalytic signatures was thus weaker, they formed only 14% of the total signatures in comparison to 93% for the non-catalytic signatures (this does not sum to 100%, as some signatures can be in both sets for different proteins).

Bottom Line: The non-catalytic signatures gave indistinguishable results 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.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Sciences Research Complex and EaStCHEM School of Chemistry, Purdie Building, University of St Andrews, North Haugh, St Andrews, Scotland, UK.

ABSTRACT
First, we identify InterPro sequence signatures representing evolutionary relatedness and, second, signatures identifying specific chemical machinery. Thus, we predict the chemical mechanisms of enzyme-catalyzed reactions from catalytic and non-catalytic subsets of InterPro signatures. We first scanned our 249 sequences using InterProScan and then used the MACiE database to identify those amino acid residues that are important for catalysis. The sequences were mutated in silico to replace these catalytic residues with glycine and then again scanned using InterProScan. Those signature matches from the original scan that 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 non-catalytic signatures gave indistinguishable results 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.

No MeSH data available.