Conformational Effects on the Passive Membrane Permeability of Synthetic Macrocycles.

Macrocyclic compounds (MCs) can have complex conformational properties that affect pharmacologically important behaviors such as membrane permeability. We measured the passive permeability of 3600 diverse nonpeptidic MCs and used machine learning to analyze the results. Incorporating selected properties based on the three-dimensional (3D) conformation gave models that predicted permeability with Q2 = 0.81. A biased spatial distribution of polar versus nonpolar regions was particularly important for good permeability, consistent with a mechanism in which the initial insertion of nonpolar portions of a MC helps facilitate the subsequent membrane entry of more polar parts. We also examined effects on permeability of 800 substructural elements by comparing matched molecular pairs. Some substitutions were invariably beneficial or invariably deleterious to permeability, while the influence of others was highly contextual. Overall, the work provides insights into how the permeability of MCs is influenced by their 3D conformational properties and suggests design hypotheses for achieving macrocycles with high membrane permeability.

Defining and navigating macrocycle chemical space.

Macrocyclic compounds (MCs) are of growing interest for inhibition of challenging drug targets. We consider afresh what structural and physicochemical features could be relevant to the bioactivity of this compound class. Using these features, we performed Principal Component Analysis to map oral and non-oral macrocycle drugs and clinical candidates, and also commercially available synthetic MCs, in structure-property space. We find that oral MC drugs occupy defined regions that are distinct from those of the non-oral MC drugs. None of the oral MC regions are effectively sampled by the synthetic MCs. We identify 13 properties that can be used to design synthetic MCs that sample regions overlapping with oral MC drugs. The results advance our understanding of what molecular features are associated with bioactive and orally bioavailable MCs, and illustrate an approach by which synthetic chemists can better evaluate MC designs. We also identify underexplored regions of macrocycle chemical space.

Recapitulating the Binding Affinity of Nrf2 for KEAP1 in a Cyclic Heptapeptide, Guided by NMR, X-ray Crystallography, and Machine Learning.

Macrocycles, including macrocyclic peptides, have shown promise for targeting challenging protein-protein interactions (PPIs). One PPI of high interest is between Kelch-like ECH-Associated Protein-1 (KEAP1) and Nuclear Factor (Erythroid-derived 2)-like 2 (Nrf2). Guided by X-ray crystallography, NMR, modeling, and machine learning, we show that the full 20 nM binding affinity of Nrf2 for KEAP1 can be recapitulated in a cyclic 7-mer peptide, c[(D)-ß-homoAla-DPETGE]. This compound was identified from the Nrf2-derived linear peptide GDEETGE (KD = 4.3 µM) solely by optimizing the conformation of the cyclic compound, without changing any KEAP1 interacting residue. X-ray crystal structures were determined for each linear and cyclic peptide variant bound to KEAP1. Despite large variations in affinity, no obvious differences in the conformation of the peptide binding residues or in the interactions they made with KEAP1 were observed. However, analysis of the X-ray structures by machine learning showed that locations of strain in the bound ligand could be identified through patterns of subangstrom distortions from the geometry observed for unstrained linear peptides. We show that optimizing the cyclic peptide affinity was driven partly through conformational preorganization associated with a proline substitution at position 78 and with the geometry of the noninteracting residue Asp77 and partly by decreasing strain in the ETGE motif itself. This approach may have utility in dissecting the trade-off between conformational preorganization and strain in other ligand-receptor systems. We also identify a pair of conserved hydrophobic residues flanking the core DxETGE motif which play a conformational role in facilitating the high-affinity binding of Nrf2 to KEAP1.