This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Conformational changes in proteins are vital to their function yet remain challenging for state-of-the-art artificial intelligence, such as AlphaFold3, to predict. Researchers at the Institute for Molecular Science (IMS), and the Graduate University for Advanced Studies, SOKENDAI introduced a repulsive force between predicted structures, allowing AlphaFold3 to sample the multiple conformational states that its default settings rarely capture.
Proteins are chain-like molecules made of amino acids that fold into three-dimensional structures determined by the sequence of those amino acids. In response to cues such as the binding of a ligand (a molecule that attaches to the protein), they switch between different shapes of that structure, known as conformational states, to carry out functions such as synthesizing or transporting substances. Predicting the folded structure from the amino acid sequence alone had been a long-standing challenge in protein science until researchers at Google DeepMind developed AlphaFold, an AI that achieves highly accurate structure prediction.
For this achievement, researchers John Jumper and Demis Hassabis shared the 2024 Nobel Prize in Chemistry. However, while proteins function by switching between multiple conformational states, AlphaFold is known to predict only a single conformation for many proteins, limiting its applicability to the life sciences, including drug design. The research group of Jun Ohnuki and Kei-ichi Okazaki at the Institute for Molecular Science (IMS), National Institutes of Natural Sciences, and the Graduate University for Advanced Studies, SOKENDAI, set out to develop a novel AlphaFold-based method for sampling conformational changes.
The group has now developed a sampling scheme that introduces a repulsive force between predicted structures in AlphaFold and can predict protein conformational changes that had been difficult for AlphaFold with its default settings. The results are published in JACS Au. The latest version, AlphaFold 3 (AF3), uses a diffusion generative model, a powerful class of AI also used for image generation, for structure prediction.
The diffusion generative model first creates an initial state in which the protein's atoms are scattered randomly by noise, then removes that noise, moving the atoms toward positions of higher probability. In the language of physics, positions of higher probability correspond to positions of lower energy. In other words, the diffusion generative model moves atoms down the energy gradient and thereby finds a low-energy folded structure.
The reason AF3 predicts only one particular conformation is that this conformation lies at a lower energy than the others. The researchers therefore repeated the AF3 structure prediction multiple times and introduced a bias energy term that raises the energy whenever a new prediction approaches the atomic coordinates of a previously predicted structure. With this bias built into the AF3 diffusion model, a repulsive force acts during structure prediction so that the model avoids approaching earlier predictions, enhancing the sampling of other conformational states.
Extract — continue reading at the source.
