Our work with the Qiu group is now online in PLoS Computational Biology! The activity of enzymatic proteins, which are called proteases, drives numerous important processes in health and disease: including cancer, immunity, and infectious disease.
Many labs have developed useful diagnostics by designing sensors that measure the activity of these proteases. However, if we want to detect multiple proteases at the same time, it becomes impractical to design sensors that only detect one protease. This is due to a phenomenon called protease promiscuity, which means that proteases will activate multiple different sensors.
Computational methods have been created to solve this problem, but the challenge is that these often require large amounts of training data. Further, completely different proteases may be detected by the same subset of sensors.
In this work, we design a computational method to overcome this problem by clustering similar proteases into “subfamilies”, which increases estimation accuracy. Further, our method tests multiple combinations of sensors to maintain accuracy while minimizing the number of sensors used.
Together, we envision that this work will increase the amount of useful information we can extract from biological samples, which may lead to better clinical diagnostics.