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Enzyme mechanism prediction: a template matching problem on InterPro signature subspaces

Overview of attention for article published in BMC Research Notes, December 2015
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Title
Enzyme mechanism prediction: a template matching problem on InterPro signature subspaces
Published in
BMC Research Notes, December 2015
DOI 10.1186/s13104-015-1730-7
Pubmed ID
Authors

Hamse Y. Mussa, Luna De Ferrari, John B. O. Mitchell

Abstract

We recently reported that one may be able to predict with high accuracy the chemical mechanism of an enzyme by employing a simple pattern recognition approach: a k Nearest Neighbour rule with k = 1 (k1NN) and 321 InterPro sequence signatures as enzyme features. The nearest-neighbour rule is known to be highly sensitive to errors in the training data, in particular when the available training dataset is small. This was the case in our previous study, in which our dataset comprised 248 enzymes annotated against 71 enzymatic mechanism labels from the MACiE database. In the current study, we have carefully re-analysed our dataset and prediction results to "explain" why a high variance k1NN rule exhibited such remarkable classification performance. We find that enzymes with different chemical mechanism labels in this dataset reside in barely overlapping subspaces in the feature space defined by the 321 features selected. These features contain the appropriate information needed to accurately classify the enzymatic mechanisms, rendering our classification problem a basic look-up exercise. This observation dovetails with the low misclassification rate we reported. Our results provide explanations for the "anomaly"-a basic nearest-neighbour algorithm exhibiting remarkable prediction performance for enzymatic mechanism despite the fact that the feature space was large and sparse. Our results also dovetail well with another finding we reported, namely that InterPro signatures are critical for accurate prediction of enzyme mechanism. We also suggest simple rules that might enable one to inductively predict whether a novel enzyme possesses any of our 71 predefined mechanisms.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer > Senior Lecturer 1 50%
Student > Postgraduate 1 50%
Readers by discipline Count As %
Unspecified 1 50%
Agricultural and Biological Sciences 1 50%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 December 2015.
All research outputs
#3,172,847
of 6,650,505 outputs
Outputs from BMC Research Notes
#884
of 1,806 outputs
Outputs of similar age
#141,375
of 279,711 outputs
Outputs of similar age from BMC Research Notes
#71
of 156 outputs
Altmetric has tracked 6,650,505 research outputs across all sources so far. This one is in the 29th percentile – i.e., 29% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,806 research outputs from this source. They receive a mean Attention Score of 4.1. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
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