Generate diverse protein conformations through AlphaFold

Abstract

The introduction of AlphaFold has revolutionized the task of protein structure prediction from a given sequence of amino acids; the groundbreaking contribution of AlphaFold was recognized by the 2024 Nobel Prize in Chemistry. As a deep-learning based method, AlphaFold was trained from the publicly available Protein Data Bank (PDB), a database of known protein structures. An inherent limitation of AlphaFold is that its prediction can only give a static structure, whereas in reality, the structures of proteins are dynamic and can change in response to their environment or binding partners, with significant biological consequences. In this talk, we focus on enhancing and diversifying protein structure prediction using AlphaFold. Through a principled statistical sampling framework, we significantly expand AlphaFold’s capabilities, enabling it to explore a broader conformational space. Key methodologies involve modifying the multiple sequence alignment (MSA) and template inputs to encourage AlphaFold to explore different conformations, thereby increasing structural diversity. This is achieved in particular through a sequential sampling approach that directly incorporates the coevolutionary information of protein residues, which allows for the creation of multiple diverse MSAs, broadening the conformational possibilities that AlphaFold can investigate. We will illustrate the capabilities of the sequential sampling approach through examples.