Study potentially opens a new route to more selective cancer drug design

Researchers at the Icahn School of Medicine at Mount Sinai have identified a previously hidden druggable site in a cancer-related protein that could open the door toward the development of a new generation of more precise cancer drugs. The finding also reveals important limitations in today's artificial intelligence tools for drug discovery.

The study, published in the June 2 online issue of the Journal of the American Chemical Society [10.1021/jacs.6c05178], focused on PKMYT1, a type of protein known as a kinase that helps control how cells grow and divide. Because this process can go wrong in cancer, PKMYT1 has emerged as a promising target for new cancer drugs.

Most experimental drugs designed to block kinases work by targeting a region called the ATP-binding site-the part of the protein that uses the cell's energy supply to function. But many kinases share nearly identical ATP-binding sites, making it difficult for drugs to distinguish between the desired target and other kinases, which can lead to unwanted side effects.

Using a combination of AI-based protein prediction tools and laboratory experiments, the researchers discovered an entirely new "hidden" pocket in PKMYT1 where a molecule could bind-a site that current state-of-the-art AI systems missed.

Our study shows both the power and the limitations of AI in drug discovery. AI was very accurate when predicting known protein shapes, but it missed a completely unexpected binding pocket that we could only uncover experimentally. That hidden site may ultimately provide a new way to design more selective cancer drugs."

Avner Schlessinger, PhD, co-senior and co-corresponding author, Professor of Pharmacological Sciences, Director of the AI Small Molecule Drug Discovery Center, and Associate Director, Mount Sinai Center for Therapeutics Discovery, Icahn School of Medicine at Mount Sinai

The findings suggest that proteins such as PKMYT1 are far more flexible than previously appreciated, constantly shifting between different shapes rather than existing in a single fixed form. The study also found that even tiny chemical changes to a molecule could dramatically alter how and where it binds to the protein, say the investigators.

The research team used the AI system AlphaFold2 to predict possible structures of PKMYT1 and then performed virtual screening to identify molecules that might interact with it. They followed up with X-ray crystallography, biochemical testing, and cellular studies to confirm how the molecules behaved in various experimental systems.

Additional AI tools, including AlphaFold3 and Boltz-2, along with molecular dynamics simulations, were then used to test whether current computational approaches could predict the newly discovered binding mode.

"One of the most surprising findings was that a very small chemical modification caused the molecule to switch from binding in this hidden pocket to binding in a much more conventional way," says co-senior and co-corresponding author Michael Lazarus, PhD, Associate Professor of Pharmacological Sciences, and Associate Director of the Mount Sinai Center for Therapeutics Discovery, at the Icahn School of Medicine at Mount Sinai. "That tells us these proteins are incredibly dynamic and sensitive to subtle molecular changes. It also reinforces why experimental validation remains essential, even in the era of AI."

The investigators say the work could eventually help scientists develop more selective drugs that avoid some of the toxicity and specificity challenges associated with traditional kinase inhibitors. The findings may also help improve future AI systems by teaching them to better recognize hidden and dynamic protein states that are currently overlooked.

While additional research is needed, the findings provide an important early foundation for developing future therapies targeting this newly discovered site. The compounds identified in the study represent promising starting points for further optimization and testing in disease models.

Next, the team plans to develop more potent compounds that target the newly discovered site and investigate whether similar hidden pockets exist in other cancer-related kinases. They also hope to refine computational methods so AI systems can better predict these hard-to-detect protein shapes in the future.