Colloquium Series: Samuel Kou

Our upcoming event for the Statistics Colloquium Series is scheduled for Monday, April 13 from 12:00 – 1:00pm (ET) and will be an in-person presentation at Maxwell-Dworkin 134A/B. Lunch will be provided to guests following the talk. This week's speaker will be faculty member Sam Kou from our Statistics department.

Generate diverse protein conformations through statistical learning and AlphaFold

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 iterative statistical sampling/learning 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 an iterative sequential sampling approach, which allows for the incorporation of protein residue co-evolutionary information in the structure prediction, broadening the conformational possibilities that AlphaFold can investigate. We will illustrate the capabilities of the statistical sampling approach through examples.