Development of novel PEX5-PEX14 protein-protein interaction (PPI) inhibitors based on an oxopiperazine template

Protein-protein interactions (PPIs) constitute an important but challenging class of molecular targets for small molecules. The PEX5-PEX14 PPI has been shown to play a critical role in glycosome biogenesis and its disruption impairs the metabolism in Trpanosoma parasites, eventually leading to their death. Therefore, this PPI is a potential molecular target for new drugs against diseases caused by Trypanosoma infections. Here, we report a new class of peptidomimetic scaffolds to target the PEX5-PEX14 PPI. The molecular design was based on an oxopiperazine template for the α-helical mimetics. A structural simplification along with modifications of the central oxopiperazine scaffold and addressing the lipophilic interactions led to the development of peptidomimetics that inhibit PEX5-TbPEX14 PPI and display cellular activity against T. b. brucei. This approach provides an alternative approach towards the development of trypanocidal agents and may be generally useful for the design of helical mimetics as PPI inhibitors.

Elucidation of a Thermodynamical Feature Attributed to Substrate Binding to the Prokaryotic H+/Oligopeptide Cotransporter YdgR with Calorimetric Analysis: The Substrate Binding Driven by the Change in Entropy Implies the Release of Bound Water Molecules from the Binding Pocket

Here, we have elucidated the substrate recognition mechanism by a prokaryotic H⁺/oligopeptide cotransporter, YdgR, using isothermal titration calorimetry. Under acidic conditions (pH 6.0), the binding of a dipeptide, Val-Ala, to YdgR elicited endothermic enthalpy, which compensated for the increase in entropy due to dipeptide binding. A series of dipeptides were used in the binding titration. The dipeptides represent Val-X and X-Val, where X is Ala, Ser, Val, Tyr, or Phe. Most dipeptides revealed endothermic enthalpy, which was completely compensated by the increase in entropy due to dipeptide binding. The change in enthalpy due to binding correlated well with the change in entropy, whereas the Gibbs free energy involved in the binding of the dipeptide to YdgR remained unchanged irrespective of dipeptide sequences, implying that the binding reaction was driven by entropy, that is, the release of bound water molecules in the binding pocket. It is also important to clarify that, based on the prediction of water molecules in the ligand-binding pocket of YdgR, the release of three bound water molecules in the putative substrate binding pocket occurred through binding to YdgR. In the comparison of Val-X and X-Val dipeptides, the N-terminal region of the binding pocket might contain more bound water molecules than the C-terminal region. In light of these findings, we suggest that bound water molecules might play an important role in substrate recognition and binding by YdgR.

Picomolar FKBP inhibitors enabled by a single water-displacing methyl group in bicyclic [4.3.1] aza-amides

Methyl groups can have profound effects in drug discovery but the underlying mechanisms are diverse and incompletely understood. Here we report the stereospecific effect of a single, solvent-exposed methyl group in bicyclic [4.3.1] aza-amides, robustly leading to a 2 to 10-fold increase in binding affinity for FK506-binding proteins (FKBPs). This resulted in the most potent and efficient FKBP ligands known to date. By a combination of co-crystal structures, isothermal titration calorimetry (ITC), density-functional theory (DFT), and 3D reference interaction site model (3D-RISM) calculations we elucidated the origin of the observed affinity boost, which was purely entropically driven and relied on the displacement of a water molecule at the protein-ligand-bulk solvent interface. The best compounds potently occupied FKBPs in cells and enhanced bone morphogenic protein (BMP) signaling. Our results show how subtle manipulation of the solvent network can be used to design atom-efficient ligands for difficult, solvent-exposed binding pockets.

Deep learning model predicts water interaction sites on the surface of proteins using limited-resolution data

We develop a residual deep learning model, hotWater (https://pypi.org/project/hotWater/), to identify key water interaction sites on proteins for binding models and drug discovery. This is tested on new crystal structures, as well as cryo-EM and NMR structures from the PDB and in crystallographic refinement with promising results.