Detecting cancer in the earliest stages could dramatically reduce cancer deaths because cancers are usually easier to treat when caught early. To help achieve that goal, MIT and Microsoft researchers are using artificial intelligence to design molecular sensors for early detection.
The researchers developed an AI model to design peptides (short proteins) that are targeted by enzymes called proteases, which are overactive in cancer cells.
Nanoparticles coated with these peptides can act as sensors that give off a signal if cancer-linked proteases are present anywhere in the body.
Depending on which proteases are detected, doctors would be able to diagnose the particular type of cancer that is present. These signals could be detected using a simple urine test that could even be done at home.
“We’re focused on ultra-sensitive detection in diseases like the early stages of cancer, when the tumour burden is small, or early on in recurrence after surgery,” says Sangeeta Bhatia, Professor at MIT.
Amplifying cancer signals
In their new study, the researchers moved beyond the traditional trial-and-error process by developing a novel AI system, named CleaveNet, to design peptide sequences that could be cleaved efficiently and specifically by target proteases of interest.
Users can prompt CleaveNet with design criteria, and CleaveNet will generate candidate peptides likely to fit those criteria.
In this way, CleaveNet enables users to tune the efficiency and specificity of peptides generated by the model, opening a path to improving the sensors’ diagnostic power.
“If we know that a particular protease is really key to a certain cancer, and we can optimise the sensor to be highly sensitive and specific to that protease, then that gives us a great diagnostic signal,” Ava Amini, Principal Researcher at Microsoft Research and a former graduate student in Bhatia’s lab says.
“We can leverage the power of computation to try to specifically optimise for these efficiency and selectivity metrics.”
For a peptide that contains 10 amino acids, there are about 10 trillion possible combinations.
Using AI to search that immense space allows for prediction, testing, and identification of useful sequences much faster than humans would be able to find them, while also considerably reducing experimental costs.
Predicting enzyme activity
Bhatia’s lab is currently part of an ARPA-H-funded project to create reporters for an at-home diagnostic kit that could potentially detect and distinguish between 30 different types of cancer, in early stages of disease, based on measurements of protease activity.
These sensors could include detection of not only MMP-mediated cleavage, but also other enzymes such as serine proteases and cysteine proteases.
Peptides designed using CleaveNet could also be incorporated into cancer therapeutics such as antibody treatments.
Using a specific peptide to attach a therapeutic, such as a cytokine or small molecule drug, to a targeting antibody could enable the medicine to be released only when the peptides are exposed to proteases in the tumour environment, improving efficacy and reducing side effects.
Beyond direct applications in diagnostics and therapeutics, combining efforts from the ARPA-H work with this modelling framework could enable the creation of a comprehensive “protease activity atlas” that spans multiple protease classes and cancers.
Such a resource could further accelerate research in early cancer detection, protease biology, and AI models for peptide design.