  The genetic blueprint for the essential functions of life is encoded in DNA,
which is translated into proteins -- the engines driving most of our metabolic
processes. Recent advancements in genome sequencing have unveiled a vast
diversity of protein families, but compared to the massive search space of all
possible amino acid sequences, the set of known functional families is minimal.
One could say nature has a limited protein "vocabulary." The major question for
computational biologists, therefore, is whether this vocabulary can be expanded
to include useful proteins that went extinct long ago, or maybe never evolved
in the first place. We outline a computational approach to solving this
problem. By merging evolutionary algorithms, machine learning (ML), and
bioinformatics, we can facilitate the development of completely novel proteins
which have never existed before. We envision this work forming a new sub-field
of computational evolution we dub evolutionary algorithms simulating molecular
evolution (EASME).
