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 complex 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 detect and, thus, even more challenging to study in molecular detail. Yet these transient interactions are key for regulating complex signaling networks that determine normal cell fate or lead to diseases such as cancer, autoimmunity, cardiovascular and neurological diseases. In this proposal, we want to utilize the AlphaFold AI software for protein structure prediction developed by DeepMind to study protein-protein interactions involving multiple dynamic and disordered partners. Over the last year, we have adapted AlphaFold to better sample the conformational space. It is now possible to sample more conformational states to capture the dynamics and functional nature of proteins. We will continue developing this protocol and adapting it to newer versions of AlphaFold (AF3). In particular, we like to explore strategies to improve the sampling of less abundant conformational states.