Proteins are the microscopic workhorses of life, carrying out countless jobs within our cells. For years, scientists have dreamed of designing new proteins to perform specific tasks, like fixing genetic typos that cause disease or improving crop resilience. However, creating these custom proteins has been a slow, expensive, and often frustrating process.

Now, a team led by Prof. GAO Caixia from the Institute of Genetics and Developmental Biology of the Chinese Academy of Sciences has developed a groundbreaking new method that changes the game. Published in the journal Cell, their work introduces an AI-powered strategy called AiCE, which stands for AI-informed Constraints for protein Engineering. It’s a clever approach that makes protein design faster, more efficient, and remarkably accessible.

Imagine you have a complex 3D blueprint of a protein but need to know which building blocks—amino acids—to use. AiCE taps into existing AI models that are good at solving this puzzle, a process known as "inverse folding." But here's the twist: instead of building a whole new, power-hungry AI, the researchers taught their system to work smarter, not harder. They integrated two crucial sets of rules: one based on the protein's physical structure and another based on its evolutionary history.

AiCEsingle Module

This innovative method was first tested with a module called AiCEsingle, designed to predict the best single changes to a protein's amino acid sequence. When benchmarked against dozens of datasets, AiCEsingle proved to be significantly more accurate than other AI methods. Simply adding the structural rules alone boosted its accuracy by an impressive 37%.

Building on this success, the team tackled an even bigger challenge: predicting multiple beneficial changes at once. This is tricky because some changes that work well on their own can clash when combined. They developed AiCEmulti, a tool that cleverly uses evolutionary data to foresee these conflicts. This allows it to identify powerful combinations of mutations with minimal computational effort.

Using the AiCE framework, the researchers successfully supercharged eight different proteins, creating powerful new tools with immediate real-world applications. They engineered highly precise "base editors," molecules that can perform surgery on DNA to correct genetic defects, a key step forward for precision medicine. This has led to the creation of an editor that works with pinpoint accuracy, another with much higher fidelity, and even one that can edit the DNA inside our cellular power plants, the mitochondria, with 13 times greater activity.

AiCE represents a major leap forward, offering a simple yet powerful strategy for protein engineering. By cleverly enhancing existing AI tools, this research opens the door for scientists everywhere to design the next generation of proteins that could transform medicine, agriculture, and beyond.

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