José Luis Villanueva-Cañas
Bioinformatics Specialist at Hospital Clínic de Barcelona
I find this to be a highly disruptive article with a very solid methodology, both computationally and in its subsequent experimental evaluation and validation in genome-editing models, in both human cells and plants.
Traditionally, protein design relies on variations of proteins that have been observed in nature in other organisms. On the one hand, this means that the changes introduced are backed by millions of years of evolution (they work!), but on the other hand, it somewhat limits the design to small variations of what already exists—the famous “evolutionary space” they mention. The methodology they use expands this concept and enables a major leap forward, creating the ability to design proteins that differ significantly from what is observed in nature—and a significant proportion of them actually function.
The authors note that the strategy they developed is a promising tool for exploring proteins not produced by evolution. However, the evolutionary space we can observe is reconstructed from living organisms today. Therefore, it is only a fraction of what has appeared in nature at some point, since we cannot observe everything that has existed but has become extinct without leaving a trace; nor everything that evolution has discarded for some reason, whether direct or indirect. A protein’s structure depends on multiple factors, not just its primary activity—which is what is measured here (the article focuses on measuring editing activity). Some factors could include the protein’s stability, the cost of producing it, its interactions with other proteins, and chance… so we must be cautious when holistically evaluating the proteins generated using this methodology.
In fact, some of the generated nucleases with the highest activity (v5 or v7) are also those with the most off-target editing sites (off-targets, which is a negative factor, as it reduces specificity), reflecting the importance of balancing or incorporating these other factors into the design or evaluation process.
Furthermore, this strategy may not be effective for less conserved proteins—that is, those restricted to certain organisms or branches of the tree of life—because there is not as much information available about their structure or function, as the authors already point out.
The most significant aspect of the study is that it establishes a design strategy or platform that serves as a template for creating other complex proteins that evolution has not produced. This study opens new doors both for genome editing—by providing a more refined tool for generating new proteins with the help of AI—and for the creation of new synthetic nucleases, which are genome-editing tools in their own right. It is a clear example of the effective use of AI tools in science.