Revealing Unexplored Sequence-Function Space Using Sequence Similarity Networks

The
rapidly expanding number of protein sequences found in public
databases can improve our understanding of how protein functions evolve.
However, our current knowledge of protein function likely represents
a small fraction of the diverse repertoire that exists in nature.
Integrative computational methods can facilitate the discovery of
new protein functions and enzymatic reactions through the observation
and investigation of the complex sequence-structure–function
relationships within protein superfamilies. Here, we highlight the
use of sequence similarity networks (SSNs) to identify previously
unexplored sequence and function space. We exemplify this approach
using the nitroreductase (NTR) superfamily. We demonstrate that SSN
investigations can provide a rapid and effective means to classify
groups of proteins, therefore exposing experimentally unexplored sequences
that may exhibit novel functionality. Integration of such approaches
with systematic experimental characterization will expand our understanding
of the functional diversity of enzymes and their associated physiological
roles.