An unexpected switch in peptide binding mode: from simulation to substrate specificity

<p>A ten microsecond molecular dynamics simulation of a kallikrein-related peptidase 7 peptide complex revealed an unexpected change in binding mode. After more than two microseconds unrestrained sampling we observe a spontaneous transition of the binding pose including a 180° rotation around the P1 residue. Subsequently, the substrate peptide occupies the prime side region rather than the cognate non-prime side in a stable conformation. We characterize the unexpected binding mode in terms of contacts, solvent-accessible surface area, molecular interactions and energetic properties. We compare the new pose to inhibitor-bound structures of kallikreins with occupied prime side and find that a similar orientation is adopted. Finally, we apply <i>in silico</i> mutagenesis based on the alternative peptide binding position to explore the prime side specificity of kallikrein-related peptidase 7 and compare it to available experimental data. Our study provides the first microsecond time scale simulation data on a kallikrein protease and shows previously unexplored prime side interactions. Therefore, we expect our study to advance the rational design of inhibitors targeting kallikrein-related peptidase 7, an emerging drug target involved in several skin diseases as well as cancer.</p

Toward novel inhibitors against KdsB: a highly specific and selective broad-spectrum bacterial enzyme

<p>KdsB (3-deoxy-manno-octulosonate cytidylyltransferase) is a highly specific and selective bacterial enzyme that catalyzes KDO (3-Deoxy-D-mano-oct-2-ulosonic acid) activation in KDO biosynthesis pathway. Failure in KDO biosynthesis causes accumulation of lipid A in the bacterial outer membrane that leads to cell growth arrest. This study reports a combinatorial approach comprising virtual screening of natural drugs library, molecular docking, computational pharmacokinetics, molecular dynamics simulation, and binding free energy calculations for the identification of potent lead compounds against the said enzyme. Virtual screening demonstrated 1460 druglike compounds in a total of 4800, while molecular docking illustrated Ser13, Arg14, and Asp236 as the anchor amino acids for recognizing and binding the inhibitors. Functional details of the enzyme in complex with the best characterized compound-226 were explored through two hundred nanoseconds of MD simulation. The ligand after initial adjustments jumps into the active cavity, followed by the deep cavity, and ultimately backward rotating movement toward the initial docked site of the pocket. During the entire simulation period, Asp236 remained in contact with the ligand and can be considered as a major catalytic residue of the enzyme. Radial distribution function confirmed that toward the end of the simulation, strengthening of ligand-receptor occurred with ligand and enzyme active residues in close proximity. Binding free energy calculations via MM(PB/GB)SA and Waterswap reaction coordinates, demonstrated the high affinity of the compound for enzyme active site residues. These findings can provide new avenues for designing potent compounds against notorious bacterial pathogens.</p

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

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

Workflow followed in degradome mapping:

<p>Sequence logos of protease cleavage sites are extracted and combined to a vector containing probabilities of amino acids and each subpocket position (1a). Thereby, quantitative distances between protease substrate readout can be calculated by scalar projection of one protease vector on the other. To illustrate the behavior of our metric, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003353#pcbi-1003353-g001" target="_blank">figure 1b</a> shows distances for an exemplary set of protease and their respective sequence logos.</p

Protease specificity tree over the whole binding site region:

<p>The degradome is mapped to a protease specificity tree based on substrate similarity over S4-S4′. 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 total cleavage entropies in a color spectrum from red (specific) over yellow to green (unspecific). The protease specificity tree shows striking similarities in substrate readout of proteases based on different catalytic mechanism.</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 benzamidine to the substrate-driven protease specificity tree:

<p>Known targets from the ChEMBL database (outer ring blue) cluster on top of the protease specificity tree based on S4-S1 substrate readout compared to unknown targets (outer ring light grey) and targets without ChEMBL identifier (outer ring white). Proteases are colored according to their catalytic type: serine proteases (cyan), metallo proteases (pink), cysteine proteases (dark grey) and aspartic proteases (blue). Targets of benzamidine are members of the chymotrypsin fold preferring positively charged amino acids at P1. Off-target binding of benzamidine to proteases positioned in vicinity of the already known targets (e.g. granzyme A) is proposed.</p