  There is a crescent use of enzymes in multiple industries and sciences,
ranging from materials and fuel synthesis to pharmaceutical and food
production. Their applicability in this variety of fields depends not only on
their biochemical function but also on their physicochemical properties. In the
present work, we describe how the coincidence methodology can be employed to
construct similarity networks of seventy well-studied enzymes of the Glycoside
Hydrolase Family 13 and to identify communities of physicochemically related
enzymes. More specifically, each of the selected enzymes is mapped into a
network node, while the links between pairs of enzymes are determined by the
coincidence similarity between selected physicochemical features of interest.
The obtained networks have modularity and number of isolated nodes optimized
respectively to two parameters involved in the coincidence methodology,
resulting in highly modular networks. In order to investigate the effect of the
considered physicochemical features on the enzymes relationships, the
coincidence-based method also is applied to create a meta-network, in which the
enzymes similarity networks obtained by the combination of every possible
feature becomes nodes of a feature combination network, and the coincidence
similarity between those networks defines the respective links. The obtained
feature combination network systematically and comprehensively indicates the
impact of the selected physicochemical features on enzyme similarity. Several
interesting results are reported and discussed, including the identification of
subgroups of enzymes with similar physicochemical features within catalytical
classes, providing important information for the selection and design of
enzymes for targeted biotechnological applications.
