Researchers at the Center for Algorithmic and Robotized Synthesis within the Institute for Basic Science have taken a significant step forward in understanding the stability of proteins by leveraging the power of AI. The research team used AlphaFold2 to explore how mutations affect protein stability-;a crucial factor in ensuring proteins function correctly and do not cause diseases like Alzheimer's. DeepMind's AlphaFold algorithm, which can accurately predict a protein's structure from its gene, has been a game-changer across the field of biology, making structural biology accessible to everyone.
Despite this immense success, two fundamental questions remain unanswered: Will the predicted structures fold correctly and stay folded? And a general question about AI algorithms: how does AlphaFold actually work? A critical limitation of AlphaFold is that it was trained on a set of stable proteins that stay folded at physiological temperatures. As a result, it predicts the most likely folded structure without knowing if it will certainly fold or will be unstable. Knowing and predicting protein stability is crucial because unstable proteins can misfold, leading to dysfunction and potentially serious diseases, so the cells must spend a lot of energy to get rid of them.
Furthermore, most proteins are only marginally stable , making them highly susceptible to mutations that can cause them to unfold. Thus, protein engineering is much about careful navigation in a minefield of dysfunctio.