A complete structural knowledge of all proteins and their interactions
in a cellular compartment would provide an unprecedented detailed
picture of the compartment. Such a model would make it possible not
only to ask what functions and regulation could occur but also what
could not. A few years ago it would be completely unrealistic to even obtain the
structure of all proteins and even more unrealistic to
obtain details about their interactions. But there has recently been a
revolution in structure prediction for both individual proteins and
complexes. The basis for this is the development of contact predictions
methods using direct coupling information. Here, we apply for
computational resources to continue the development of our tools
using deep learning approaches and to apply them for proteomic level
structure prediction projects.
In summary we develop methods that combine deep learning methods with molecular simulations techniques in a unique way to obtain 3D-structure of proteins and complexes.