Lab-in-the-loop framework enables rapid evolution of complex multi-mutant proteins
Phys.org
February 19, 2026
AI-Generated Deep Dive Summary
The field of protein engineering faces a monumental challenge due to the exponential growth of possible variants as complexity increases. For instance, a protein with just 100 amino acids can have an astronomical 20^100 possible combinations—more than the number of atoms in the observable universe. Traditional methods for exploring these vast sequence spaces are highly limited, often testing only hundreds of variants and focusing on narrow regions of the potential solutions. While recent advancements in machine learning (ML) have expanded the scope of computational searches, they still require tens of thousands of measurements and span 5–10 iterative rounds, which can be time-consuming and resource-intensive.
The article introduces a novel "lab-in-the-loop" framework that integrates machine learning with laboratory experiments to accelerate protein engineering. This approach leverages computational power to identify promising candidates efficiently, significantly reducing the number of experiments needed compared to traditional methods. By iteratively refining predictions through experimental feedback, the framework narrows down the search space and accelerates the discovery of high-performing protein variants. This method not only broadens exploration but also optimizes resource allocation by minimizing costly laboratory testing.
The significance of this innovation lies in its potential to revolutionize biotechnology and medicine. Proteins engineered using this framework could lead to breakthroughs in enzyme design, drug development, and industrial applications. By streamlining the discovery process, it enables scientists to tackle complex problems more effectively, such as creating custom enzymes for sustainable manufacturing or designing proteins with novel functions for medical treatments. This approach represents a major step forward in overcoming the limitations of traditional protein engineering methods, offering a faster and more efficient path to innovation.
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Originally published on Phys.org on 2/19/2026