EP-Pred: A Machine Learning Tool for Bioprospecting Promiscuous Ester Hydrolases

When bioprospecting for novel industrial enzymes, substrate promiscuity is a desirable
property that increases the reusability of the enzyme. Among industrial enzymes, ester hydrolases
have great relevance for which the demand has not ceased to increase. However, the search for
new substrate promiscuous ester hydrolases is not trivial since the mechanism behind this prop-
erty is greatly influenced by the active site’s structural and physicochemical characteristics. These
characteristics must be computed from the 3D structure, which is rarely available and expensive to
measure, hence the need for a method that can predict promiscuity from sequence alone. Here we
report such a method called EP-pred, an ensemble binary classifier, that combines three machine
learning algorithms: SVM, KNN, and a Linear model. EP-pred has been evaluated against the
Lipase Engineering Database together with a hidden Markov approach leading to a final set of ten
sequences predicted to encode promiscuous esterases. Experimental results confirmed the validity of
our method since all ten proteins were found to exhibit a broad substrate ambiguity.