Precursors for cytochrome P450 profiling breath tests from an in silico screening approach

The family of cytochrome P450 enzymes (CYPs) is a major player in the metabolism of drugs and xenobiotics. Genetic polymorphisms and transcriptional regulation give a complex patient-individual CYP activity profile for each human being. Therefore, personalized medicine demands easy and non-invasive measurement of the CYP phenotype. Breath tests detect volatile organic compounds (VOCs) in the patients’ exhaled air after administration of a precursor molecule. CYP breath tests established for individual CYP isoforms are based on the detection of 13CO2 or 14CO2 originating from CYP-catalyzed oxidative degradation reactions of isotopically labeled precursors. We present an in silico work-flow aiming at the identification of novel precursor molecules, likely to result in VOCs other than CO2 upon oxidative degradation as we aim at label-free precursor molecules. The ligand-based work-flow comprises five parts: (1) CYP profiling was encoded as a decision tree based on 2D molecular descriptors derived from established models in the literature and validated against publicly available data extracted from the DrugBank. (2) Likely sites of metabolism were identified by reactivity and accessibility estimation for abstractable hydrogen radical. (3) Oxidative degradation reactions (O- and N-dealkylations) were found to be most promising in the release of VOCs. Thus, the CYP-catalyzed oxidative degradation reaction was encoded as SMIRKS (a programming language style to implement reactions based on the SMARTS description) to enumerate possible reaction products. (4) A quantitative structure property relation (QSPR) model aiming to predict the Henry constant H was derived from data for 488 organic compounds and identifies potentially VOCs amongst CYP reaction products. (5) A blacklist of naturally occurring breath components was implemented to identify marker molecules allowing straightforward detection within the exhaled air.peer-reviewe

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

<div><p>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.</p></div

Protease specificity tree over the non-prime binding site region S4-S1:

<p>The degradome is mapped to a protease specificity tree based on local substrate similarity over S4-S1 pockets. Proteases are colored according to their catalytic type: serine proteases (cyan), metallo proteases (pink), cysteine proteases (dark grey), aspartic proteases (blue). The outer ring shows cleavage entropies for the range S4-S1 in a color spectrum from red (specific) over yellow to green (unspecific). The reduced scattering of catalytic types when compared to the protease specificity tree for the whole binding site indicates a grouping of evolutionary close members.</p

Mapping of known targets of BI 201335 to the protease specificity tree:

<p>Known targets in ChEMBL (outer ring blue) cluster on the right side of the protease specificity tree, calculated over the whole S4-S4′ region, compared to unknown targets (outer ring light grey). Proteases without a ChEMBL identifier are colored white in the outer ring. Known targets include all catalytic mechanisms of proteases: serine proteases (cyan), metallo proteases (pink), cysteine proteases (dark grey) and aspartic proteases (blue). This highlights the promiscuous binding of a single ligand to several proteases.</p

Protease specificity tree based on S1 amino acids:

<p>The degradome is mapped to a protease specificity tree based on S1 amino acid frequencies in substrates. Proteases are colored according to their catalytic type: serine proteases (cyan), metallo proteases (pink), cysteine proteases (dark grey), aspartic proteases (blue). The outer ring shows subpocket cleavage entropies for the S1 pocket in a color spectrum from red (specific) over yellow to green (unspecific). A grouping of proteases recognizing aspartic acid, basic amino acids as well as hydrophobic or unspecific proteases is observed.</p

Mapping of known targets of 2-[(4-methoxybenzyl)sulfanyl]-6-methylpyrimidin-4-ol to the protease specificity tree:

<p>Known targets (outer ring blue) cluster in the right part of the tree calculated over the whole S4-S4′ binding site region covering several metallo proteases. Unknown targets (outer ring white) and proteases without ChEMBL identifier (outer ring light grey) are found on the left side of the protease tree. This ligand is only known to bind to metallo proteases (pink), whilst serine proteases (cyan), cysteine proteases (dark grey) and aspartic proteases (blue) are not inhibited.</p

Principal component analysis of the protease similarity matrix:

<p>Eigenvectors of the protease similarity matrix are used to map the degradome in lower dimensionality. Plotting principal component 1 (PC1) versus principal component two (PC2) and coloring according to cleavage entropy in a spectrum from red (specific) via yellow to green (unspecific) (2a) shows that both primary principal components mainly contain information on protease specificity. Coloring according to catalytic types (2b, serine protease: cyan, metallo protease: pink, cysteine protease: dark grey, aspartic protease: blue, protease complex: white) shows that PC2 separates serine proteases from other degradome members. PC3 does not correlate to substrate promiscuity (2c), but rather splits up metallo proteases (2d). Similarly, PC6 does not correlate to overall substrate readout (2e), but groups catalytic types of proteases only via their substrate preferences in combination with PC3 (2f): Metallo proteases are grouped to the left, cysteine proteases on top, aspartic proteases on the bottom, serine proteases in the center.</p