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.
Institute of General, Inorganic and Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innsbruck, Austria.
Julian E
JE
Fuchs
Susanne
S
von Grafenstein
Roland G
RG
Huber
Christian
C
Kramer
Klaus R
KR
Liedl
D000596:Amino Acids; D001665:Binding Sites; D016000:Cluster Analysis; D055808:Drug Discovery / Q000379:methods*; D010447:Peptide Hydrolases / Q000737:chemistry* / Q000378:metabolism; D010455:Peptides / Q000737:chemistry* / Q000378:metabolism; D025341:Principal Component Analysis; D011506:Proteins / Q000737:chemistry / Q000378:metabolism; D040901:Proteomics / Q000379:methods*
D000596:Amino Acids; D010455:Peptides; D011506:Proteins; D010447:Peptide Hydrolases