Deep Learning in Protein Engineering: Designing Functional Soluble Proteins
What challenges does traditional protein design face?
Traditional protein design, often relying on physics-based methods like Rosetta, faces challenges in creating functional proteins with complex structures due to the need for parametric and symmetric restraints. Designing large, complex protein folds, especially those mimicking membrane proteins in soluble forms, remains difficult. Understanding and expanding the fold space to include soluble analogs of membrane proteins could unlock new functional capabilities in synthetic proteins.
How does AlphaFold2 transform protein design?
AlphaFold2 transforms protein design by accurately predicting protein structures and enabling the exploration of vast sequence spaces, leading to stable proteins with novel functions and intricate structures. It has expanded the fold space to include soluble analogs of membrane proteins, potentially advancing drug discovery and other applications.
What are the limitations in designing large protein folds?
Designing large protein folds faces challenges due to the need for accurate prediction and exploration of vast sequence spaces. Traditional physics-based methods struggle to create functional proteins with complex structures. Additionally, designing soluble analogs of membrane proteins, such as G protein-coupled receptors (GPCRs), is difficult due to their unique structural features. These limitations can be overcome with deep learning approaches like AlphaFold2 and ProteinMPNN, which enable accurate prediction and design of stable protein structures.