Conformational sampling and docking with AlphaFold
Title: Conformational sampling and docking with AlphaFold
SNIC Project: Berzelius-2022-85
Project Type: LiU Berzelius
Principal Investigator: Björn Wallner <bjorn.wallner@liu.se>
Affiliation: Linköpings universitet
Duration: 2022-05-01 – 2022-11-01
Classification: 10601
Keywords:

Abstract

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 want to utilise the AlphaFold AI software for protein structure prediction developed by DeepMind to study protein-protein interactions involving multiple dynamic and disordered partners. We have during the last six month adapted AlphaFold to better sample the conformational space and it is now possible to sample more conformational states to capture the dynamics and functional nature of proteins. We will continue developing this protocol. In the coming month we will also explore the possibilities to retrain AlphaFold to tailor it our specific application areas. A complete retrain of AlphaFold is beyond the scope of this small allocation (it would require the complete Berzelius for a couple of weeks at least). Thus, what we are aiming for here is if we can picky-back on the learned representations in AlphaFold and retrain for instance the structural module to improve the selection of different conformational states.