New tool to map protease specificity may pave the way for improved treatments

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FMI researchers have developed a new tool that maps how proteases—enzymes that process proteins—cut their targets. This innovation offers new insight into the highly selective nature of proteases, which were previously seen as indiscriminate degraders. The work could change how we approach drug design, particularly for conditions such as diabetes and obesity, by creating more stable, targeted treatments.

In a shift from their usual studies on RNA and developmental timing, researchers in the Grosshans lab at the FMI have created a new technique, named qPISA—or "quantitative Protease specificity Inference from Substrate Analysis," that aims to provide a deeper understanding of proteases, a class of enzymes that processes proteins.

While proteases are often thought of as indiscriminate degraders, many show a remarkable degree of specificity, targeting only certain proteins, says study lead author Helge Grosshans. The research is published in the journal Molecular Systems Biology.

Understanding this specificity has long been a challenge, but qPISA offers a solution. The approach allows researchers to identify specific patterns, or "cleavage motifs," in proteins that proteases target. Understanding these cleavage motifs could aid in the design of drugs that either boost or dampen protease activity, depending on the need.

The researchers focused on DPP4, a protease that in people plays an essential role in regulating blood sugar. DPP4 achieves this by breaking down a peptide hormone called GLP-1. Drugs that inhibit DPP4 or mimic GLP-1 are currently widely used to treat type 2 diabetes and obesity.

Using qPISA, the team could predict the stability—or half-life—of DPP4's protein targets. This knowledge allowed them to explore ways to modify GLP-1 to extend its activity without losing effectiveness. This is a significant step forward in potentially enhancing diabetes and obesity treatments by making GLP-1 more stable in the body, Grosshans says.

The team also investigated a related enzyme, known as DPF-3, from the worm Caenorhabditis elegans. Using cryo-electron microscopy, they determined the structure of DPF-3 and found that it shares extensive similarities with human DPP4. Nonetheless, analysis by qPISA revealed a distinct specificity signature, resulting from complex interactions of the protease with its substrate.

The work, Grosshans says, "advances our understanding of protease selectivity and could potentially lead to more effective, long-lasting treatments for type 2 diabetes and obesity."

More information: Rajani Kanth Gudipati et al, Deep quantification of substrate turnover defines protease subsite cooperativity, Molecular Systems Biology (2024). DOI: 10.1038/s44320-024-00071-4

Journal information: Molecular Systems Biology

Provided by Friedrich Miescher Institute for Biomedical Research