  Enzyme is the major workhorse to carry out the diverse cellular functions. It
catalyzes the biological reactions with a high specificity, with its topology
playing a crucial role. For ecologically safe production of numerous
bioproducts including drugs and chemicals, we have been striving to design the
industrially useful enzyme molecules with highly improved catalytic capability.
As the sequence space is enormous for an enzyme, its quick and effective
exploration is quite improbable for the mutagenesis studies whose accuracy is
greatly reliant on the prior information of the mutated sites and the extent of
rigorous screening of the mutant libraries. Although directed evolution methods
significantly aid the construction of a functionally improved molecule, their
credibility depends on the successful excavation of the functionally similar
sequence space in the available databases, encompassing billions of proteins.
As deep learning methods aid us to extensively uncover the underlying network
of all the key catalytic positions without any experimental data, their
implementation has reliably increased the accuracy of directed evolution. The
chapter comprehensively explains data mining and deep learning methods to
further showcase their importance in enzyme engineering methods. The key
biological and algorithmic limitations of these deep learning methodologies are
lastly highlighted.
