Predicting protein functional motions using Machine Learning.

An unexplored area in bioengineering is the rational design of protein function by changes in
protein functional motions (Huang et al, 2016). This can lead to industrial applications in
different areas, e.g. production of biofilms and commodity chemicals. A major challenge is
computational cost: analysis of protein motions requires dealing with large simulated dataset
that are generally processed by experts in a 'one-protein-at-a-time' fashion. Machine Learning
provides extensive possibilities to automate most of this process, which would lead to scaling
in larger number of proteins and mutant variants to inform better synthetic biology design.