Biology is a wondrous yet delicate tapestry. At the heart is DNA, the master weaver that encodes proteins, responsible for orchestrating the many biological functions that sustain life within the human body. However, our body is akin to a finely tuned instrument, susceptible to losing its harmony. After all, we’re faced with an ever-changing and relentless natural world: pathogens, viruses, diseases, and cancer. 

Imagine if we could expedite the process of creating vaccines or drugs for newly emerged pathogens. What if we had gene editing technology capable of automatically producing proteins to rectify DNA errors that cause cancer? The quest to identify proteins that can strongly bind to targets or speed up chemical reactions is vital for drug development, diagnostics, and numerous industrial applications, yet it is often a protracted and costly endeavor.

To advance our capabilities in protein engineering, MIT CSAIL researchers came up with “FrameDiff,” a computational tool for creating new protein structures beyond what nature has produced. The machine learning approach generates “frames” that align with the inherent properties of protein structures, enabling it to construct novel proteins independently of preexisting designs, facilitating unprecedented protein structures.

“In nature, protein design is a slow-burning process that takes millions of years. Our technique aims to provide an answer to tackling human-made problems that evolve much faster than nature’s pace,” says MIT CSAIL PhD student Jason Yim, a lead author on a new paper about the work. “The aim, with respect to this new capacity of generating synthetic protein structures, opens up a myriad of enhanced capabilities, such as better binders. This means engineering proteins that can attach to other molecules more efficiently and selectively, with widespread implications related to targeted drug delivery and biotechnology, where i