Benchmark of Generic Shapes for Macrocycles

Novel SK channel positive modulators prevent ferroptosis and excitotoxicity in neuronal cells

Small conductance calcium-activated potassium (SK) channel activity has been proposed to play a role in the pathology of several neurological diseases. Besides regulating plasma membrane excitability, SK channel activation provides neuroprotection against ferroptotic cell death by reducing mitochondrial Ca2+ uptake and reactive oxygen species (ROS). In this study, we employed a multifaceted approach, integrating structure-based and computational techniques, to strategically design and synthesize an innovative class of potent small-molecule SK2 channel modifiers through highly efficient multicomponent reactions (MCRs). The compounds' neuroprotective activity was compared with the well-studied SK positive modulator, CyPPA. Pharmacological SK channel activation by selected compounds confers neuroprotection against ferroptosis at low nanomolar ranges compared to CyPPA, that mediates protection at micromolar concentrations, as shown by an MTT assay, real-time cell impedance measurements and propidium iodide staining (PI). These novel compounds suppress increased mitochondrial ROS and Ca2+ level induced by ferroptosis inducer RSL3. Moreover, axonal degeneration was rescued by these novel SK channel activators in primary mouse neurons and they attenuated glutamate-induced neuronal excitability, as shown via microelectrode array. Meanwhile, functional afterhyperpolarization of the novel SK2 channel modulators was validated by electrophysiological measurements showing more current change induced by the novel modulators than the reference compound, CyPPA. These data support the notion that SK2 channel activation can represent a therapeutic target for brain diseases in which ferroptosis and excitotoxicity contribute to the pathology.

High-Quality Conformer Generation with CONFORGE: Algorithm and Performance Assessment

Knowledge of the putative bound-state conformation of a molecule is an essential prerequisite for the successful application of many computer-aided drug design methods that aim to assess or predict its capability to bind to a particular target receptor. An established approach to predict bioactive conformers in the absence of receptor structure information is to sample the low-energy conformational space of the investigated molecules and derive representative conformer ensembles that can be expected to comprise members closely resembling possible bound-state ligand conformations. The high relevance of such conformer generation functionality led to the development of a wide panel of dedicated commercial and open-source software tools throughout the last decades. Several published benchmarking studies have shown that open-source tools usually lag behind their commercial competitors in many key aspects. In this work, we introduce the open-source conformer ensemble generator CONFORGE, which aims at delivering state-of-the-art performance for all types of organic molecules in drug-like chemical space. The ability of CONFORGE and several well-known commercial and open-source conformer ensemble generators to reproduce experimental 3D structures as well as their computational efficiency and robustness has been assessed thoroughly for both typical drug-like molecules and macrocyclic structures. For small molecules, CONFORGE clearly outperformed all other tested open-source conformer generators and performed at least equally well as the evaluated commercial generators in terms of both processing speed and accuracy. In the case of macrocyclic structures, CONFORGE achieved the best average accuracy among all benchmarked generators, with RDKit's generator coming close in second place.

Multicomponent Macrocyclic IL-17a Modifier

IL-17a is a major inflammation target, with several approved antibodies in clinical use. Small-molecule IL-17a antagonists are an emerging hot topic, with the recent advancement of three compounds into clinical trials. Here, we describe the design, discovery, synthesis, and screening of macrocyclic compounds that bind to IL-17a. We found that all currently described IL-17a modifiers belong to the same pharmacophore model, likely resulting in a similar receptor binding mode on IL-17a. A pipeline of pharmacophore analysis, virtual screening, resynthesis, and protein biophysics resulted in a potent IL-17a macrocyclic modifier.

Dynamic Docking of Macrocycles in Bound and Unbound Protein Structures with DynaDock

Macrocycles are interesting molecules with unique features due to their conformationally constrained yet flexible ring structure. This characteristic poses a difficult challenge for computational modeling studies since they rely on accurate structural descriptions. In particular, molecular docking calculations suffer from the lack of ring flexibility during pose generation, which is often compensated by using pregenerated ligand conformer ensembles. Moreover, receptor structures are mainly treated rigidly, which limits the use of many docking tools. In this study, we optimized our previous molecular dynamics-based sampling and docking pipeline specifically designed for the accurate prediction of macrocyclic compounds. We developed a dihedral classification procedure for in-depth conformational analysis of the macrocyclic rings and extracted structural ensembles that were subsequently docked in both bound and unbound protein structures employing a fully flexible approach. Our results suggest that including a ring conformer close to the bound state in the starting ensemble increases the chance of successful docking. The bioactive conformations of a diverse set of ligands could be predicted with high and decent accuracy in bound and unbound protein structures, respectively, due to the incorporation of full molecular flexibility in our approach. The remaining unsuccessful docking calculations were mainly caused by large flexible substituents that bind to surface-exposed binding sites, rather than the macrocyclic ring per se and could be further improved by explicit molecular dynamics simulations of the docked complex.

Computational studies of molecular pre-organization through macrocyclization: Conformational distribution analysis of closely related non-macrocyclic and macrocyclic analogs

Macrocycles form an important compound class in medicinal chemistry due to their interesting structural and biological properties. To help design macrocycles, it is important to understand how the conformational preferences are affected upon macrocyclization of a lead compound. To address this, we collected a unique data set of protein-ligand complexes containing "non-macrocyclic" ("linear") ligands matched with macrocyclic analogs binding to the same protein in a similar pose. Out of the 39 co-crystallized ligands considered, 10 were linear and 29 were macrocyclic. To enable a more general analysis, 128 additional ligands from the publications associated with these protein data bank entries were added to the data set. Using in total 167 collected ligands, we investigated if the conformers in the macrocyclic conformational ensembles were more similar to the bioactive conformation in comparison to the conformers of their linear counterparts. Unexpectedly, in most cases the macrocycle conformational ensemble distributions were not very different from those of the linear compounds. Thus, care should be taken when designing macrocycles with the aim to focus their conformational preference towards the bioactive conformation. We also set out to investigate potential conformational flexibility differences between the two compound classes, computational energy window settings and evaluate a literature metric for approximating the conformational focusing on the bioactive conformation.

The X-ray structure of tubulysin analogue TGL in complex with tubulin and three possible routes for the development of next-generation tubulysin analogues

Microtubule-targeting agents (MTAs) are the most commonly used anti-cancer drugs. At least fourteen microtubule inhibitors and ten antibody drug conjugates (ADCs) linking MTAs are approved by FDA for clinical use in cancer therapy. In current research, we determined the crystal structure of tubulysin analogue TGL in complex with tubulin at a high resolution (2.65 Å). In addition, we summarized all of the previously published high-resolution crystal structures of ligands in the vinca site to provide structural insights for the rational design of the new vinca-site ligands. Moreover, based on the aligned results of the vinca site ligands, we provided three possible routes for designing new tubulysin analogues, namely macrocyclization between the N-14 side chain and the N-9 side chain, the hybird of tubulysin M and phomopsin A, and growing new aryl group at C-21. These designed structures will inspire the development of new MTAs or payloads in cancer therapy.