Quantum simulations of SARS-CoV-2 main protease Mpro enable high-quality scoring of diverse ligands.

The COVID-19 pandemic has led to unprecedented efforts to identify drugs that can reduce its associated morbidity/mortality rate. Computational chemistry approaches hold the potential for triaging potential candidates far more quickly than their experimental counterparts. These methods have been widely used to search for small molecules that can inhibit critical proteins involved in the SARS-CoV-2 replication cycle. An important target is the SARS-CoV-2 main protease Mpro, an enzyme that cleaves the viral polyproteins into individual proteins required for viral replication and transcription. Unfortunately, standard computational screening methods face difficulties in ranking diverse ligands to a receptor due to disparate ligand scaffolds and varying charge states. Here, we describe full density functional quantum mechanical (DFT) simulations of Mpro in complex with various ligands to obtain absolute ligand binding energies. Our calculations are enabled by a new cloud-native parallel DFT implementation running on computational resources from Amazon Web Services (AWS). The results we obtain are promising: the approach is quite capable of scoring a very diverse set of existing drug compounds for their affinities to M pro and suggest the DFT approach is potentially more broadly applicable to repurpose screening against this target. In addition, each DFT simulation required only ~ 1 h (wall clock time) per ligand. The fast turnaround time raises the practical possibility of a broad application of large-scale quantum mechanics in the drug discovery pipeline at stages where ligand diversity is essential.

A Novel Bifunctional Alkylphenol Anesthetic Allows Characterization of γ-Aminobutyric Acid, Type A (GABAA), Receptor Subunit Binding Selectivity in Synaptosomes.

Propofol, an intravenous anesthetic, is a positive modulator of the GABAA receptor, but the mechanistic details, including the relevant binding sites and alternative targets, remain disputed. Here we undertook an in-depth study of alkylphenol-based anesthetic binding to synaptic membranes. We designed, synthesized, and characterized a chemically active alkylphenol anesthetic (2-((prop-2-yn-1-yloxy)methyl)-5-(3-(trifluoromethyl)-3H-diazirin-3-yl)phenol, AziPm-click (1)), for affinity-based protein profiling (ABPP) of propofol-binding proteins in their native state within mouse synaptosomes. The ABPP strategy captured ∼4% of the synaptosomal proteome, including the unbiased capture of five α or ß GABAA receptor subunits. Lack of γ2 subunit capture was not due to low abundance. Consistent with this, independent molecular dynamics simulations with alchemical free energy perturbation calculations predicted selective propofol binding to interfacial sites, with higher affinities for α/ß than γ-containing interfaces. The simulations indicated hydrogen bonding is a key component leading to propofol-selective binding within GABAA receptor subunit interfaces, with stable hydrogen bonds observed between propofol and α/ß cavity residues but not γ cavity residues. We confirmed this by introducing a hydrogen bond-null propofol analogue as a protecting ligand for targeted-ABPP and observed a lack of GABAA receptor subunit protection. This investigation demonstrates striking interfacial GABAA receptor subunit selectivity in the native milieu, suggesting that asymmetric occupancy of heteropentameric ion channels by alkylphenol-based anesthetics is sufficient to induce modulation of activity.

A predicted binding site for cholesterol on the GABAA receptor.

Modulation of the GABA type A receptor (GABAAR) function by cholesterol and other steroids is documented at the functional level, yet its structural basis is largely unknown. Current data on structurally related modulators suggest that cholesterol binds to subunit interfaces between transmembrane domains of the GABAAR. We construct homology models of a human GABAAR based on the structure of the glutamate-gated chloride channel GluCl of Caenorhabditis elegans. The models show the possibility of previously unreported disulfide bridges linking the M1 and M3 transmembrane helices in the α and γ subunits. We discuss the biological relevance of such disulfide bridges. Using our models, we investigate cholesterol binding to intersubunit cavities of the GABAAR transmembrane domain. We find that very similar binding modes are predicted independently by three approaches: analogy with ivermectin in the GluCl crystal structure, automated docking by AutoDock, and spontaneous rebinding events in unbiased molecular dynamics simulations. Taken together, the models and atomistic simulations suggest a somewhat flexible binding mode, with several possible orientations. Finally, we explore the possibility that cholesterol promotes pore opening through a wedge mechanism.

The Structural Basis for Low Conductance in the Membrane Protein VDAC upon ß-NADH Binding and Voltage Gating.

The voltage-dependent anion channel (VDAC) forms the primary diffusion pore of the outer mitochondrial membrane. In its apo form, VDAC adopts an open conformation with high conductance. States of lower conductance can be induced by ligand binding or the application of voltage. Here, we clarify at the atomic level how ß-NADH binding leads to a low-conductance state and characterize the role of the VDAC N-terminal helix in voltage gating. A high-resolution NMR structure of human VDAC-1 with bound NADH, combined with molecular dynamics simulation show that ß-NADH binding reduces the pore conductance sterically without triggering a structural change. Electrophysiology recordings of crosslinked protein variants and NMR relaxation experiments probing different time scales show that increased helix dynamics is present in the open state and that motions of the N-terminal helices are involved in the VDAC voltage gating mechanism.

Physical Accuracy Leads to Biological Relevance: Best Practices For Simulating Ligand-Gated Ion Channels Interacting With General Anesthetics.

Efforts to detect binding modes of general anesthetics (GAs) for pentameric ligand-gated ion channels (pLGICs) are often complicated by a large number of indicated sites, as well as the challenges of ranking sites by affinity and determining which sites are occupied at clinical concentrations. Physics-based computational methods offer a powerful route for determining affinities of ligands to isolated binding sites, but preserving accuracy is essential. This chapter describes a step-by-step approach to multiple methods for identifying candidate sites and quantifying binding affinities and also discusses limitations and common pitfalls.

Role of the Fourth Transmembrane α Helix in the Allosteric Modulation of Pentameric Ligand-Gated Ion Channels.

The gating of pentameric ligand-gated ion channels is sensitive to a variety of allosteric modulators that act on structures peripheral to those involved in the allosteric pathway leading from the agonist site to the channel gate. One such structure, the lipid-exposed transmembrane α helix, M4, is the target of lipids, neurosteroids, and disease-causing mutations. Here we show that M4 interactions with the adjacent transmembrane α helices, M1 and M3, modulate pLGIC function. Enhanced M4 interactions promote channel function while ineffective interactions reduce channel function. The interface chemistry governs the intrinsic strength of M4-M1/M3 inter-helical interactions, both influencing channel gating and imparting distinct susceptibilities to the potentiating effects of a lipid-facing M4 congenital myasthenic syndrome mutation. Through aromatic substitutions, functional studies, and molecular dynamics simulations, we elucidate a mechanism by which M4 modulates channel function.

Pentameric Ligand-gated Ion Channels : Insights from Computation.

Pentameric ligand-gated ion channels (pLGICs) conduct upon the binding of an agonist and are fundamental to neurotransmission. New insights into the complex mechanisms underlying pLGIC gating, ion selectivity, and modulation have recently been gained via a series of crystal structures in prokaryotes and C .elegans, as well as computational studies relying on these structures. Here we review contributions from a variety of computational approaches, including normal mode analysis, automated docking, and fully atomistic molecular dynamics simulation. Examples from our own research, particularly concerning interactions with general anesthetics and lipids, are used to illustrate predictive results complementary to crystallographic studies.