To the lay person, “protein folding” doesn’t sound like much to get excited about.  But when you stop to consider – or, for the lay person, come to understand for the first time – that nearly every fundamental biological process necessary for life is carried out by proteins,  including creating and maintaining the shapes of cells and tissues, and that they do these things “by folding themselves into precise 3D structures that govern how they interact with other molecules,” then protein folding becomes a pretty big deal, to put it mildly.

For that reason, it’s also a big deal when, as recently reported by Harvard Medical School, researchers turn to deep learning to help them predict the 3D structure of a protein based on its amino acid sequence.

According to the report, systems biologist Mohammed AlQuraishi has detailed a new approach for computationally determining protein structure—achieving accuracy comparable to current state-of-the-art methods but at speeds upward of a million times faster.

“Protein folding has been one of the most important problems for biochemists over the last half century, and this approach represents a fundamentally new way of tackling that challenge,” said AlQuraishi, instructor in systems biology in the Blavatnik Institute at HMS and a fellow in the Laboratory of Systems Pharmacology. “We now have a whole new vista from which to explore protein folding, and I think we’ve just begun to scratch the surface.”

Even more remarkable, observers say, is that tapping AI enabled just one researcher to make this breakthrough.

Said Peter Sorger, Otto Krayer Professor of Systems Pharmacology,  “one remarkable feature of AlQuraishi’s work is that a single research fellow, embedded in the rich research ecosystem of Harvard Medical School and the Boston biomedical community, can compete with companies such as Google in one of the hottest areas of computer science.”

Because a protein’s shape determines its function and the extent of its dysfunction in disease, efforts to illuminate protein structures are central to all of molecular biology—and in particular, therapeutic science and the development of lifesaving and life-altering medicines.

In recent years, computational methods have made significant strides in predicting how proteins fold based on knowledge of their amino acid sequence. If fully realized, these methods have the potential to transform virtually all facets of biomedical research. Current approaches, however, are limited in the scale and scope of the proteins that can be determined.

“We now have a whole new vista from which to explore protein folding, and I think we’ve just begun to scratch the surface,” AlQuraishi said.