Molecular simulation AI tool reveals unsolved structure of transporter protein

In a groundbreaking study, researchers have revealed a previously unknown conformational state of a crucial transport protein, OxlT, which plays a critical role in preventing kidney stone formation. This discovery, made using advanced computational methods, offers new insights into protein function and potential therapeutic targets.

Proteins are the building blocks of life and perform essential functions in every living organism. Transporter proteins, like OxlT, are particularly important because they transport vital substances across cell membranes. OxlT, present in the oxalate-degrading bacteria Oxalobacter formigenes, plays a key role in managing oxalate levels in the human body.

Excess oxalate can lead to kidney stones, a painful and common health problem. Understanding the function of OxlT is crucial, but until now, scientists lacked comprehensive knowledge of its different structural states, particularly the inward-open conformation, a critical part of its transport mechanism.

This study, led by Jun Ohnuki and colleagues, used advanced computational techniques to simulate the dynamics of the OxlT protein. They used Gaussian Accelerated Molecular Dynamics (GaMD) and AlphaFold2, a state-of-the-art machine learning tool, to explore the structure and function of OxlT. The article titled “Accelerated Molecular Dynamics and AlphaFold Uncover a Missing Conformational State of Transporter Protein OxlT” is published in The Journal of Physical Chemistry Letters.

The team successfully predicted the elusive inward-open conformation of OxlT, an important step in understanding its full functional cycle. This conformation revealed that OxlT prefers to bind formate rather than oxalate in this state, a crucial aspect of its role in oxalate handling.

Additionally, the research identified specific amino acid residues essential for this conformational transition, a finding that could have broader implications for understanding protein dynamics.

The implications of this research extend beyond a single protein. The methodology and insights gained from this study provide a model for exploring the dynamics of other proteins, particularly transport proteins, which are often targets for therapeutic drugs.

Understanding these proteins at a detailed level can lead to the development of more effective treatments for various conditions. Additionally, this research illustrates the power of combining computational biology and machine learning, a rapidly evolving field that promises to unlock many of biology’s most difficult mysteries.

By filling a crucial gap in our understanding of the OxlT protein, this study not only contributes to potential advances in kidney stone prevention, but also paves the way for future breakthroughs in biomedical research.

The research team includes Jun Ohnuki, Titouan Jaunet-Lahary and Kei-ichi Okazaki from the Computational Sciences Research Center at the NINS Institute of Molecular Sciences (IMS). Atsuko Yamashita, from the Graduate School of Medicine, Dentistry and Pharmaceutical Sciences at Okayama University, completes the team.