De novo design of ligand binding and sensing with a physics based generative approach
TL;DR
A physics-based generative approach for the de novo creation of proteins that bind small molecules and metal ions is introduced, allowing for precise architectural control of the protein–ligand complex and facilitating the development of biosensors based on either ligand-triggered protein reassociation via split-protein reassembly or ligand-induced protein folding.
Abstract
The de novo design of ligand-binding proteins has tremendous potential to revolutionize biosensor technology, yet converting these designs into functional sensors remains a major challenge due to the need for ligand-induced conformational changes or modulation of protein–protein interactions. Here, we introduce a physics-based generative approach for the de novo creation of proteins that bind small molecules and metal ions. Our method achieves customizable ligand-binding pocket formation in parallel with simulated protein folding, allowing for precise architectural control of the protein–ligand complex and facilitating the development of biosensors based on either ligand-triggered protein reassociation via split-protein reassembly or ligand-induced protein folding. We demonstrate the versatility of our computational method through successful designs targeting five small molecules, including the very small neurotransmitters serotonin and dopamine, and two metal ions. Biophysical characterization confirmed correct ligand binding, and crystal structures closely matched computational models. We demonstrated the biosensor engineering potential of these designs by constructing serotonin and dopamine sensors using a split protein strategy and explored several approaches to enhance sensor activity. Additionally, we developed a zinc sensor through a zinc-induced protein folding mechanism. Overall, our physics-based generative approach provides a robust framework for the de novo design of ligand-binding proteins, opening new avenues for the development of ligand-responsive biosensors.