05/16/2025
Cleveland Clinic researchers launch novel database for proteolysis research
Cleveland Clinic researchers have designed a database to address an unmet need in proteolysis research: a tool that allows researchers to easily see how specific proteins break down within a disorder or disease.
The database is called DICED, an acronym for “database of identified cleavage sites endemic to diseases states.” It was developed by a team led by Suneel Apte, MBBS, DPhil, whose lab focuses on enzymes called proteases and their function in human health. Dr. Apte shared his thoughts on the development process behind DICED and his hopes for how it will empower other research teams.
Where did you get the idea for DICED?
Proteases are responsible for tissue breakdown in disorders such as arthritis and aortic aneurysms. We utilize a mass spectrometry-based technique called N-terminomics to identify protease cleavage sites, which generates huge amounts of data. Since it was challenging to compare multiple datasets simultaneously, we developed DICED as a tool that could also help determine, for example, if the same protease was active in different disorders. My lab worked alongside Cleveland Clinic’s Center for Computational Life Sciences, knowing that they had the necessary expertise to develop such a database, including Daniel Blankenberg, PhD and Jayadev Joshi, PhD, who acted as the design architect. My lab members Sumit Bhutada, PhD and Daniel Martin, PhD generated all the datasets that DICED accesses.
How does DICED support medical research?
The database is meant for scientists and physicians who need to find similarities at the protein level between certain disorders. Once we have determined the breakdown process in several diseases, DICED will help us answer several questions, such as: Which destructive events are common across unrelated diseases? Which proteases act in the same or distinct conditions? The fragment end sequences available through the database will help design specific antibodies that could target or produce disease-specific biomarkers.
How did your team make DICED easy to use?
My own knowledge gaps served as a starting point for figuring out our design. Our priorities were to use common search terms, such as protein name or gene symbol, and present the data in ways that would be easy to view and handle. The design process took many group meetings where we tinkered with several possibilities. It was an iterative process that took two years, yet a very enjoyable one, with all kinds of new things learnt.
