Modelling of transient protein-protein interactions relevant for human health

NAISS 2023/6-115


NAISS Medium Storage

Principal Investigator:

Björn Wallner


Linköpings universitet

Start Date:


End Date:


Primary Classification:

10203: Bioinformatics (Computational Biology) (applications to be 10610)

Secondary Classification:

10610: Bioinformatics and Systems Biology (methods development to be 10203)

Tertiary Classification:

10601: Structural Biology




Proteins are key players in virtually all biological events and accomplish their function as part of larger protein complexes. Unfortunately, compared to structure determination of individual proteins, structural characterization protein-protein interactions is much more difficult and is a major challenge in structural biology. For transient interactions it is even more difficult since the interactions only last for a short period of time they are hard to even detect, and thus even harder to study in molecular detail. Yet these transient interactions are key for regulating complex signalling networks that determine normal cell fate or lead to diseases including such as cancer, autoimmunity, cardiovascular and neurological diseases. In this proposal, we suggest an integrative approach where we combine new technologies in proteomics with structural bioinformatics and computational modelling to study and understand highly dynamic transient interactions. We will develop computational tools to study protein-protein interactions involving disorder and tools that can be used to turn binary interaction maps into 3D models for further investigation and characterisation. In the applied part of the project we aim to analyze the interaction networks of four IDPs and their role in human health and disease: the oncoprotein MYC, the recently identified DIORA1 in autoimmune disease, TP53BP1 in breast cancer, and Apollo in DNA repair. In addition, we will also use the storage to run AlphaFold2 multiple sequence alignment, and interference. We also have workflow where we run the multiple sequence alignments (which do not benefit from GPU) on tetralith, and transfer them to Berzelius, for the inference on GPU.