RoseNet: Predicting Energy Metrics of Double InDel Mutants Using Deep
  Learning
Sarah Coffland Katie Christensen Filip Jagodzinski Brian Hutchinson
An amino acid insertion or deletion, or InDel, can have profound and varying
functional impacts on a protein's structure. InDel mutations in the
transmembrane conductor regulator protein for example give rise to cystic
fibrosis. Unfortunately performing InDel mutations on physical proteins and
studying their effects is a time prohibitive process. Consequently, modeling
InDels computationally can supplement and inform wet lab experiments. In this
work, we make use of our data sets of exhaustive double InDel mutations for
three proteins which we computationally generated using a robotics inspired
inverse kinematics approach available in Rosetta. We develop and train a neural
network, RoseNet, on several structural and energetic metrics output by Rosetta
during the mutant generation process. We explore and present how RoseNet is
able to emulate the exhaustive data set using deep learning methods, and show
to what extent it can predict Rosetta metrics for unseen mutant sequences with
two InDels. RoseNet achieves a Pearson correlation coefficient median accuracy
of 0.775 over all Rosetta scores for the largest protein. Furthermore, a
sensitivity analysis is performed to determine the necessary quantity of data
required to accurately emulate the structural scores for computationally
generated mutants. We show that the model can be trained on minimal data (<50%)
and still retain a high level of accuracy.