AI-Designed Allosteric Proteins that Morph on Demand for Use in Delivery Systems and Biosensors

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David Baker’s Latest Nature Paper: AI-Designed Allosteric Proteins that Morph on Demand for Use in Delivery Systems and Biosensors

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David Baker’s Latest Nature Paper: AI-Designed Allosteric Proteins that Morph on Demand for Use in Delivery Systems and Biosensors

In the 1880s, Thomas Edison’s invention of the light socket and switch revolutionized electrical circuits by introducing the concept of controlling them with a switch. Today, switches are ubiquitous in daily life, with activations triggered by touch, sound, or light in response to specific stimuli.

The functionality of a switch relies on its intricate internal design, involving multiple state transitions. Interestingly, natural proteins often exhibit various conformational states, allowing them to activate or form complexes in response to stimuli such as enzymes, cell receptors, and signaling molecules. Imagine designing a protein switch that could be activated under certain conditions, akin to a circuit switch. This would allow precise control over life processes on a microscopic scale.

On August 14, 2024, David Baker, a pioneer in protein design at the University of Washington, and his team published a groundbreaking study in the prestigious journal Nature titled “De novo design of allosterically switchable protein assemblies.”

This research leverages artificial intelligence (AI) to design entirely new proteins from scratch.

These novel proteins, which do not exist in nature, can reliably and accurately control assembly and disassembly through conformational changes.

The team’s designs include various dynamic protein arrangements, providing a roadmap for applications in triggered delivery systems, biosensors, cellular feedback control circuits, and more.

Allosteric regulation of protein function, where binding of an effector molecule induces conformational changes at distant functional sites, is central to metabolic and cellular signal transduction control. Designing proteins that respond to specific molecular signals and alter their structure to perform a function has long been a goal of protein engineering.

Historically, engineered allosteric proteins have relied on coupling with existing natural proteins, limiting their functional diversity. In contrast, de novo protein design expands the range of previously unexplored properties, opening new possibilities for precise control of protein functions.

In this study, inspired by the classic Monod-Wyman-Changeux (MWC) model of cooperativity, Baker’s team explored allosteric redesign by coupling rigid-body interfaces with switchable polypeptide hinge modules to guide the formation of alternative oligomeric states.

Using AI software previously developed by the team, they designed novel proteins capable of assembling and disassembling in response to stimuli. These include cyclic structures that activate biosensing applications and cage-like structures that could be used as delivery carriers for controlled drug release. In essence, this AI-designed protein switch functions as a “hinge” system: without an effector peptide, the proteins remain rod-like; upon binding with a specific ligand, they undergo a conformational change to a “V” shape, forming a cyclic structure.

The team validated the kinetics of these AI-designed proteins through in vitro experiments. Size-exclusion chromatography (SEC), mass spectrometry, and electron microscopy showed that the allosteric protein assemblies closely matched their design models both in the presence and absence of peptide effectors.

Notably, the cyclic structures exhibited additional properties, such as cooperativity. In these cooperative proteins, binding of one ligand molecule enhances the affinity of others, leading to rapid, switch-like responses essential for precise control. This phenomenon is seen in natural proteins like hemoglobin, which rapidly captures oxygen in the lungs.

Dr. Arvind Pillai, the paper’s first author, noted that the AI-designed proteins share little sequence similarity with any natural proteins. They can assemble and disassemble in response to stimuli, paving the way for future biotechnologies that could rival the complexity of natural systems.

In conclusion, this Nature study demonstrates that AI-designed proteins can create allosterically triggered delivery systems, protein nanomachines, and cellular feedback control circuits, surpassing previous efforts in simple protein assembly and disassembly. This research paves the way for more advanced control of engineered protein functions.

David Baker’s team plans to explore whether AI-designed proteins can interact with small molecules and catalyze reactions accurately, a challenging frontier in the field. Looking ahead, the team aims to evaluate the dynamics of these engineered proteins in broader biological contexts. Future work includes positioning these designed functions on the surfaces of cells in tissue cultures, providing valuable tools for feedback control in therapies such as adoptive cell therapy.

David Baker’s Latest Nature Paper: AI-Designed Allosteric Proteins that Morph on Demand for Use in Delivery Systems and Biosensors

References:

1. Nature Article Link
2. Nature News Article

(source:internet, reference only)

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