Substrate-Driven Mapping of the Degradome by Comparison of Sequence Logos

Sequence logos are frequently used to illustrate substrate preferences and specificity of proteases. Here, we employed the compiled substrates of the MEROPS database to introduce a novel metric for comparison of protease substrate preferences. The constructed similarity matrix of 62 proteases can be used to intuitively visualize similarities in protease substrate readout via principal component analysis and construction of protease specificity trees. Since our new metric is solely based on substrate data, we can engraft the protease tree including proteolytic enzymes of different evolutionary origin. Thereby, our analyses confirm pronounced overlaps in substrate recognition not only between proteases closely related on sequence basis but also between proteolytic enzymes of different evolutionary origin and catalytic type. To illustrate the applicability of our approach we analyze the distribution of targets of small molecules from the ChEMBL database in our substrate-based protease specificity trees. We observe a striking clustering of annotated targets in tree branches even though these grouped targets do not necessarily share similarity on protein sequence level. This highlights the value and applicability of knowledge acquired from peptide substrates in drug design of small molecules, e.g., for the prediction of off-target effects or drug repurposing. Consequently, our similarity metric allows to map the degradome and its associated drug target network via comparison of known substrate peptides. The substrate-driven view of protein-protein interfaces is not limited to the field of proteases but can be applied to any target class where a sufficient amount of known substrate data is available.Author Summary: We present a novel approach to intuitively map the degradome, the set of proteolytic enzymes, based on their substrates rather than on the protease sequences. Information stored in cleavage site sequence logos is extracted and transferred into a metric for similarity in protease substrate recognition. By capturing similarity in substrate readout, we inherently focus on the biomolecular recognition process between protease and substrate. Furthermore, we are able to include proteases of different evolutionary origin into our analysis, because no assumption on homology has to made. In a second step, we show how knowledge from peptide substrates can directly be transferred into small molecule recognition. By mining protease inhibition data in the ChEMBL database we show, how our substrate-driven protease specificity trees group known targets of protease inhibitors. Thus, our substrate-based maps of the degradome can be utilized in the prediction of off-target effects or drug repurposing. As our approach is not limited to the protease universe, our similarity metric can be expanded to any kind of protein-protein interface given sufficient substrate data.