Peptidic Macrocycles - Conformational Sampling and Thermodynamic Characterization

Macrocycles are of considerable interest as highly specific drug candidates, yet they challenge standard conformer generators with their large number of rotatable bonds and conformational restrictions. Here, we present a molecular dynamics-based routine that bypasses current limitations in conformational sampling and extensively profiles the free energy landscape of peptidic macrocycles in solution. We perform accelerated molecular dynamics simulations to capture a diverse conformational ensemble. By applying an energetic cutoff, followed by geometric clustering, we demonstrate the striking robustness and efficiency of the approach in identifying highly populated conformational states of cyclic peptides. The resulting structural and thermodynamic information is benchmarked against interproton distances from NMR experiments and conformational states identified by X-ray crystallography. Using three different model systems of varying size and flexibility, we show that the method reliably reproduces experimentally determined structural ensembles and is capable of identifying key conformational states that include the bioactive conformation. Thus, the described approach is a robust method to generate conformations of peptidic macrocycles and holds promise for structure-based drug design.


Figure S1
Conformational space of cyclo-(Pro-Ser-leu-Asp-Val) captured in 1µs cMD simulation. (A) The cMD ensemble is color-coded according to the free energies and depicted as projection onto the first two eigenvectors of the aMD dihedral PCA. (B) Occupied ω  torsional angles, the simulation is started in the cis state and does not exhibit any snapshots in the trans state.

Table S1
Applied boosting parameters for aMD simulation of each system.

Figure S2
Cartesian PCA of cyclo-(Pro-Ser-leu-Asp-Val). The aMD ensemble is color-coded according to the reweighted free energies and depicted as projection onto the first two PCA eigenvectors.

Figure S4
Reweighted distribution of ω Val-5 in cyclo-(Pro-Ser-leu-Asp-Val). Reweighting all snapshots of the aMD ensemble results in a trans (red) to cis (blue) state ratio of 25/75.

Figure S5
Comparison to bioactive conformation of cilengitide. The starting structure (red) and cluster representative c16 (orange) (see Figure 5) are superposed with the target-bound conformation of cilengitide. The RMSD to the bioactive conformation are 1.0 Å for the starting structure and 0.6 Å for the cluster representative from the aMD simulation.

Table S2
Dihedral angles of cyclo-(Pro-Ser-leu-Asp-Val) cluster representatives depicted in Figure 4 Cluster-ID  Figure S6 Conformational space of cyclo-(Arg-Arg-Trp-Trp-Arg-Phe). The aMD ensemble is color-coded according to the reweighted free energies and depicted as projection onto the first two PCA eigenvectors.

Table S4
Sum of dihedral entropies. The entropy is calculated for each backbone dihedral and summed up to quantify and compare the global flexibility of the studied peptidic macrocycles. Error estimations derive from block averaging using a block size of 50,000 frames Table S5 Dihedral angles of the cyclo-(Arg-Arg-Trp-Trp-Arg-Phe cluster representatives as depicted in Figure 11.