The motion of a protein is very much connected to the function of a protein. Being able to predict the motion of a protein is therefore an important step in drug design.
The thesis builds on the work from Ines Putz, applying deep neural networks from the contact prediction field to breaking contact prediction during protein motion. Elastic Network Models are useful in determining the coarse-grained motion of proteins. During structural transitions, certain residue pairs that were spatially close become separated, so called breaking contacts. Incorporating this information improves the prediction of protein motion. In this thesis, we want to apply state of the art machine learning methods to breaking contact prediction.