FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology.

In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. In recent years, two computational techniques, machine learning (ML) and physics-based methods, have evolved substantially and are now frequently incorporated into the medicinal chemist's toolbox to enhance the efficiency of both hit optimization and candidate design. Both computational methods come with their own set of limitations, and they are often used independently of each other. ML's capability to screen extensive compound libraries expediently is tempered by its reliance on quality data, which can be scarce especially during early-stage optimization. Contrarily, physics-based approaches like free energy perturbation (FEP) are frequently constrained by low throughput and high cost by comparison; however, physics-based methods are capable of making highly accurate binding affinity predictions. In this study, we harnessed the strength of FEP to overcome data paucity in ML by generating virtual activity data sets which then inform the training of algorithms. Here, we show that ML algorithms trained with an FEP-augmented data set could achieve comparable predictive accuracy to data sets trained on experimental data from biological assays. Throughout the paper, we emphasize key mechanistic considerations that must be taken into account when aiming to augment data sets and lay the groundwork for successful implementation. Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. We believe that the physics-based augmentation of ML will significantly benefit drug discovery, as these techniques continue to evolve.

Identification and Characterization of Novel Small-Molecule SMOX Inhibitors.

The major intracellular polyamines spermine and spermidine are abundant and ubiquitous compounds that are essential for cellular growth and development. Spermine catabolism is mediated by spermine oxidase (SMOX), a highly inducible flavin-dependent amine oxidase that is upregulated during excitotoxic, ischemic, and inflammatory states. In addition to the loss of radical scavenging capabilities associated with spermine depletion, the catabolism of spermine by SMOX results in the production of toxic byproducts, including H2O2 and acrolein, a highly toxic aldehyde with the ability to form adducts with DNA and inactivate vital cellular proteins. Despite extensive evidence implicating SMOX as a key enzyme contributing to secondary injury associated with multiple pathologic states, the lack of potent and selective inhibitors has significantly impeded the investigation of SMOX as a therapeutic target. In this study, we used a virtual and physical screening approach to identify and characterize a series of hit compounds with inhibitory activity against SMOX. We now report the discovery of potent and highly selective SMOX inhibitors 6 (IC50 0.54 µM, Ki 1.60 µM) and 7 (IC50 0.23 µM, Ki 0.46 µM), which are the most potent SMOX inhibitors reported to date. We hypothesize that these selective SMOX inhibitors will be useful as chemical probes to further elucidate the impact of polyamine catabolism on mechanisms of cellular injury.

Small molecules targeting the NADH-binding pocket of VDAC modulate mitochondrial metabolism in hepatocarcinoma cells.

Voltage dependent anion channels (VDAC) control the flux of most anionic respiratory substrates, ATP, ADP, and small cations, crossing the outer mitochondrial membrane. VDAC closure contributes to the partial suppression of mitochondrial metabolism that favors the Warburg phenotype of cancer cells. Recently, it has been shown that NADH binds to a specific pocket in the inner surface of VDAC1, also conserved in VDAC2 and 3, closing the channel. We hypothesized that binding of small molecules to the NADH pocket, maintain VDAC in an open configuration by preventing closure induced by NADH and possible other endogenous regulators. We screened in silico, the South Carolina Compound Collection SC3 (~100,000 proprietary molecules), using shape-based queries of the NADH binding region of VDAC. After molecular docking of selected compounds, we physically screened candidates using mitochondrial membrane potential (ΔΨm), as an overall readout of mitochondrial metabolism. We identified SC18, as the most potent compound. SC18 bound to VDAC1, as assessed by a thermal shift assay. Short-term treatment with SC18 decreased ΔΨm in SNU-449 and HepG2 human hepatocarcinoma cells. Mitochondrial depolarization was similar in wild type, VDAC1/2, 1/3, and 2/3 double KO HepG2 cells indicating that the effect of SC18 was not VDAC isoform-dependent. In addition, SC18 decreased mitochondrial NADH and cellular ATP production; and increased basal respiration. Long-term exposure to SC18, decreased cell proliferation as determined by wound-healing and cell viability assays. In summary, SC18 is a novel VDAC-targeting small molecule that induces mitochondrial dysfunction and inhibits cell proliferation.

Selective targeting of CD38 hydrolase and cyclase activity as an approach to immunostimulation.

The ectoenzyme CD38 is highly expressed on the surface of mature immune cells, where they are a marker for cell activation, and also on the surface of multiple tumor cells such as multiple myeloma (MM). CD38-targeted monoclonal antibodies (MABs) such as daratumumab and isatuximab bind to CD38 and promote cancer cell death by stimulating the antitumor immune response. Although MABs are achieving unprecedented success in a percentage of cases, high rates of resistance limit their efficacy. Formation of the immunosuppressive intermediate adenosine is a major route by which this resistance is mediated. Thus there is an urgent need for small molecule agents that boost the immune response in T-cells. Importantly, CD38 is a dual-function enzyme, serving as a hydrolase and a nicotinamide adenine dinucleotide (NAD+) cyclase, and both of these activities promote immunosuppression. We have employed virtual and physical screening to identify novel compounds that are selective for either the hydrolase or the cyclase activity of CD38, and have demonstrated that these compounds activate T cells in vitro. We are currently optimizing these inhibitors for use in immunotherapy. These small molecule inhibitors of the CD38-hydrolase or cyclase activity can serve as chemical probes to determine the mechanism by which CD38 promotes resistance to MAB therapy, and could become novel and effective therapeutic agents that produce immunostimulatory effects. Our studies have identified the first small molecule inhibitors of CD38 specifically for use as immunostimulants.

Distinct GluN1 and GluN2 Structural Determinants for Subunit-Selective Positive Allosteric Modulation of N-Methyl-d-aspartate Receptors.

N-Methyl-d-aspartate receptors (NMDARs) are ionotropic ligand-gated glutamate receptors that mediate fast excitatory synaptic transmission in the central nervous system (CNS). Several neurological disorders may involve NMDAR hypofunction, which has driven therapeutic interest in positive allosteric modulators (PAMs) of NMDAR function. Here we describe modest changes to the tetrahydroisoquinoline scaffold of GluN2C/GluN2D-selective PAMs that expands activity to include GluN2A- and GluN2B-containing recombinant and synaptic NMDARs. These new analogues are distinct from GluN2C/GluN2D-selective compounds like (+)-(3-chlorophenyl)(6,7-dimethoxy-1-((4-methoxyphenoxy)methyl)-3,4-dihydroisoquinolin-2(1H)-yl)methanone (CIQ) by virtue of their subunit selectivity, molecular determinants of action, and allosteric regulation of agonist potency. The (S)-enantiomers of two analogues (EU1180-55, EU1180-154) showed activity at NMDARs containing all subunits (GluN2A, GluN2B, GluN2C, GluN2D), whereas the (R)-enantiomers were primarily active at GluN2C- and GluN2D-containing NMDARs. Determination of the actions of enantiomers on triheteromeric receptors confirms their unique pharmacology, with greater activity of (S) enantiomers at GluN2A/GluN2D and GluN2B/GluN2D subunit combinations than (R) enantiomers. Evaluation of the (S)-EU1180-55 and EU1180-154 response of chimeric kainate/NMDA receptors revealed structural determinants of action within the pore-forming region and associated linkers. Scanning mutagenesis identified structural determinants within the GluN1 pre-M1 and M1 regions that alter the activity of (S)-EU1180-55 but not (R)-EU1180-55. By contrast, mutations in pre-M1 and M1 regions of GluN2D perturb the actions of only the (R)-EU1180-55 but not the (S) enantiomer. Molecular modeling supports the idea that the (S) and (R) enantiomers interact distinctly with GluN1 and GluN2 pre-M1 regions, suggesting that two distinct sites exist for these NMDAR PAMs, each of which has different functional effects.

Discovery of novel aminopiperidinyl amide CXCR4 modulators through virtual screening and rational drug design.

The C-X-C chemokine receptor type 4 (CXCR4) is a potential therapeutic target for HIV infection, metastatic cancer, and inflammatory autoimmune diseases. In this study, we screened the ZINC chemical database for novel CXCR4 modulators through a series of in silico guided processes. After evaluating the screened compounds for their binding affinities to CXCR4 and inhibitory activities against the chemoattractant CXCL12, we identified a hit compound (ZINC 72372983) showing 100 nM affinity and 69% chemotaxis inhibition at the same concentration (100 nM). To increase the potency of our hit compound, we explored the protein-ligand interactions at an atomic level using molecular dynamics simulation which enabled us to design and synthesize a novel compound (Z7R) with nanomolar affinity (IC50 = 1.25 nM) and improved chemotaxis inhibition (78.5%). Z7R displays promising anti-inflammatory activity (50%) in a mouse edema model by blocking CXCR4-expressed leukocytes, being supported by our immunohistochemistry study.

Negative allosteric modulation of GluN1/GluN3 NMDA receptors.

NMDA receptors are ligand-gated ion channels that mediate excitatory neurotransmission. Most native NMDA receptors are tetrameric assemblies of two glycine-binding GluN1 and two glutamate-binding GluN2 subunits. Co-assembly of the glycine-binding GluN1 with glycine-binding GluN3 subunits (GluN3A-B) creates glycine activated receptors that possess strikingly different functional and pharmacological properties compared to GluN1/GluN2 NMDA receptors. The role of GluN1/GluN3 receptors in neuronal function remains unknown, in part due to lack of pharmacological tools with which to explore their physiological roles. We have identified the negative allosteric modulator EU1180-438, which is selective for GluN1/GluN3 receptors over GluN1/GluN2 NMDA receptors, AMPA, and kainate receptors. EU1180-438 is also inactive at GABA, glycine, and P2X receptors, but displays inhibition of some nicotinic acetylcholine receptors. Furthermore, we demonstrate that EU1180-438 produces robust inhibition of glycine-activated current responses mediated by native GluN1/GluN3A receptors in hippocampal CA1 pyramidal neurons. EU1180-438 is a non-competitive antagonist with activity that is independent of membrane potential (i.e. voltage-independent), glycine concentration, and extracellular pH. Non-stationary fluctuation analysis of neuronal current responses provided an estimated weighted mean unitary conductance of 6.1 pS for GluN1/GluN3A channels, and showed that EU1180-438 has no effect on conductance. Site-directed mutagenesis suggests that structural determinants of EU1180-438 activity reside near a short pre-M1 helix that lies parallel to the plane of the membrane below the agonist binding domain. These findings demonstrate that structural differences between GluN3 and other glutamate receptor subunits can be exploited to generate subunit-selective ligands with utility in exploring the roles GluN3 in neuronal function.

Accelerated Discovery of Potent Fusion Inhibitors for Respiratory Syncytial Virus.

A series of five benzimidazole-based compounds were identified using a machine learning algorithm as potential inhibitors of the respiratory syncytial virus (RSV) fusion protein. These compounds were synthesized, and compound 2 in particular exhibited excellent in vitro potency with an EC50 value of 5 nM. This new scaffold was then further refined leading to the identification of compound 44, which exhibited a 10-fold improvement in activity with an EC50 value of 0.5 nM.

NMDA receptor channel gating control by the pre-M1 helix.

The NMDA receptor (NMDAR) is an ionotropic glutamate receptor formed from the tetrameric assembly of GluN1 and GluN2 subunits. Within the flexible linker between the agonist binding domain (ABD) and the M1 helix of the pore-forming transmembrane helical bundle lies a two-turn, extracellular pre-M1 helix positioned parallel to the plasma membrane and in van der Waals contact with the M3 helix thought to constitute the channel gate. The pre-M1 helix is tethered to the bilobed ABD, where agonist-induced conformational changes initiate activation. Additionally, it is a locus for de novo mutations associated with neurological disorders, is near other disease-associated de novo sites within the transmembrane domain, and is a structural determinant of subunit-selective modulators. To investigate the role of the pre-M1 helix in channel gating, we performed scanning mutagenesis across the GluN2A pre-M1 helix and recorded whole-cell macroscopic and single channel currents from HEK293 cell-attached patches. We identified two residues at which mutations perturb channel open probability, the mean open time, and the glutamate deactivation time course. We identified a subunit-specific network of aromatic amino acids located in and around the GluN2A pre-M1 helix to be important for gating. Based on these results, we are able to hypothesize about the role of the pre-M1 helix in other NMDAR subunits based on sequence and structure homology. Our results emphasize the role of the pre-M1 helix in channel gating, implicate the surrounding amino acid environment in this mechanism, and suggest unique subunit-specific contributions of pre-M1 helices to GluN1 and GluN2 gating.

One Among Millions: The Chemical Space of Nucleic Acid-Like Molecules.

Biology encodes hereditary information in DNA and RNA, which are finely tuned to their biological functions and modes of biological production. The central role of nucleic acids in biological information flow makes them key targets of pharmaceutical research. Indeed, other nucleic acid-like polymers can play similar roles to natural nucleic acids both in vivo and in vitro; yet despite remarkable advances over the last few decades, much remains unknown regarding which structures are compatible with molecular information storage. Chemical space describes the structures and properties of molecules that could exist within a given molecular formula or other classification system. Using structure generation methods, we explore nucleic acid analogues within the formula ranges BC3-7H5-15O2-4 and BC3-6H5-15N1-2O0-4, where B is a recognition element (e.g., a nucleobase). Other restrictions included two obligatory points of attachment for inclusion into a linear polymer and substructures predicting chemical stability. These sets contain 86,007 (CHO) and 75,309 (CHNO) compositionally isomeric structures, representing 706,568 CHO and 454,422 CHNO stereoisomers, that diversely and densely occupy this space. These libraries point toward there being large spaces of unexplored chemistry relevant to pharmacology and biochemistry and efforts to understand the origins of life.

Error Tolerance of Machine Learning Algorithms across Contemporary Biological Targets.

Machine learning continues to make strident advances in the prediction of desired properties concerning drug development. Problematically, the efficacy of machine learning in these arenas is reliant upon highly accurate and abundant data. These two limitations, high accuracy and abundance, are often taken together; however, insight into the dataset accuracy limitation of contemporary machine learning algorithms may yield insight into whether non-bench experimental sources of data may be used to generate useful machine learning models where there is a paucity of experimental data. We took highly accurate data across six kinase types, one GPCR, one polymerase, a human protease, and HIV protease, and intentionally introduced error at varying population proportions in the datasets for each target. With the generated error in the data, we explored how the retrospective accuracy of a Naïve Bayes Network, a Random Forest Model, and a Probabilistic Neural Network model decayed as a function of error. Additionally, we explored the ability of a training dataset with an error profile resembling that produced by the Free Energy Perturbation method (FEP+) to generate machine learning models with useful retrospective capabilities. The categorical error tolerance was quite high for a Naïve Bayes Network algorithm averaging 39% error in the training set required to lose predictivity on the test set. Additionally, a Random Forest tolerated a significant degree of categorical error introduced into the training set with an average error of 29% required to lose predictivity. However, we found the Probabilistic Neural Network algorithm did not tolerate as much categorical error requiring an average of 20% error to lose predictivity. Finally, we found that a Naïve Bayes Network and a Random Forest could both use datasets with an error profile resembling that of FEP+. This work demonstrates that computational methods of known error distribution like FEP+ may be useful in generating machine learning models not based on extensive and expensive in vitro-generated datasets.

Dual inhibitors of LSD1 and spermine oxidase.

We have previously described the synthesis and evaluation of 3,5-diamino-1,2,4-triazole analogues as inhibitors of the flavin-dependent histone demethylase LSD1. These compounds are potent inhibitors of LSD1 without activity against monoamine oxidases A and B, and promote the elevation of H3K4me2 levels in tumor cells in vitro. We now report that the cytotoxicity of these analogues in pancreatic tumor cells correlates with the overexpression of LSD1 in each tumor type. In addition, we show that a subset of these 3,5-diamino-1,2,4-triazole analogues inhibit a related flavin-dependent oxidase, the polyamine catabolic enzyme spermine oxidase (SMOX) in vitro.

Staurosporine, an inhibitor of hormonally up-regulated neu-associated kinase.

HUNK is a protein kinase that is implicated in HER2-positive (HER2+) breast cancer progression and resistance to HER2 inhibitors. Though prior studies suggest there is therapeutic potential for targeting HUNK in HER2+ breast cancer, pharmacological agents that target HUNK are yet to be identified. A recent study showed that the broad-spectrum kinase inhibitor staurosporine binds to the HUNK catalytic domain, but the effect of staurosporine on HUNK enzymatic activity was not tested. We now show that staurosporine inhibits the kinase activity of a full length HUNK protein. Our findings further suggest that inhibiting HUNK with staurosporine has a strong effect on suppressing cell viability of HER2/neu mammary and breast cancer cells, which express high levels of HUNK protein and are dependent on HUNK for survival. Significantly, we use in vitro and in vivo methods to show that staurosporine synergizes with the HER2 inhibitor lapatinib to restore sensitivity toward HER2 inhibition in a HER2 inhibitor resistant breast cancer model. Collectively, these studies indicate that pharmacological inhibition of HUNK kinase activity has therapeutic potential for HER2+ breast cancers, including HER2+ breast cancers that have developed drug resistance.

A Machine Learning Approach for Predicting HIV Reverse Transcriptase Mutation Susceptibility of Biologically Active Compounds.

HIV resistance emerging against antiretroviral drugs represents a great threat to the continued prolongation of the lifespans of HIV-infected patients. Therefore, methods capable of predicting resistance susceptibility in the development of compounds are in great need. By targeting the major reverse transcription residues Y181, K103, and L100, we used the biological activities of compounds against these enzymes and the wild-type reverse transcriptase to create Naïve Bayes Networks. Through this machine learning approach, we could predict, with high accuracy, whether a compound would be susceptible to a loss of potency due to resistance. Also, we could perfectly predict retrospectively whether compounds would be susceptible to both a K103 mutant RT and a Y181 mutant RT. In the study presented here, our method outperformed a traditional molecular mechanics approach. This method should be of broad interest beyond drug discovery efforts, and serves to expand the utility of machine learning for the prediction of physical, chemical, or biological properties using the vast information available in the literature.

A structurally derived model of subunit-dependent NMDA receptor function.

KEY POINTS: The kinetics of NMDA receptor (NMDAR) signalling are a critical aspect of the physiology of excitatory synaptic transmission in the brain. Here we develop a mechanistic description of NMDAR function based on the receptor tetrameric structure and the principle that each agonist-bound subunit must undergo some rate-limiting conformational change after agonist binding, prior to channel opening. By fitting this mechanism to single channel data using a new MATLAB-based software implementation of maximum likelihood fitting with correction for limited time resolution, rate constants were derived for this mechanism that reflect distinct structural changes and predict the properties of macroscopic and synaptic NMDAR currents. The principles applied here to develop a mechanistic description of the heterotetrameric NMDAR, and the software used in this analysis, can be equally applied to other heterotetrameric glutamate receptors, providing a unifying mechanistic framework to understanding the physiology of glutamate receptor signalling in the brain. ABSTRACT: NMDA receptors (NMDARs) are tetrameric complexes comprising two glycine-binding GluN1 and two glutamate-binding GluN2 subunits. Four GluN2 subunits encoded by different genes can produce up to 10 different di- and triheteromeric receptors. In addition, some neurological patients contain a de novo mutation or inherited rare variant in only one subunit. There is currently no mechanistic framework to describe tetrameric receptor function that can be extended to receptors with two different GluN1 or GluN2 subunits. Here we use the structural features of glutamate receptors to develop a mechanism describing both single channel and macroscopic NMDAR currents. We propose that each agonist-bound subunit undergoes some rate-limiting conformational change after agonist binding, prior to channel opening. We hypothesize that this conformational change occurs within a triad of interactions between a short helix preceding the M1 transmembrane helix, the highly conserved M3 motif encoded by the residues SYTANLAAF, and the linker preceding the M4 transmembrane helix of the adjacent subunit. Molecular dynamics simulations suggest that pre-M1 helix motion is uncorrelated between subunits, which we interpret to suggest independent subunit-specific conformational changes may influence these pre-gating steps. According to this interpretation, these conformational changes are the main determinants of the key kinetic properties of NMDA receptor activation following agonist binding, and so these steps sculpt their physiological role. We show that this structurally derived tetrameric model describes both single channel and macroscopic data, giving a new approach to interpreting functional properties of synaptic NMDARs that provides a logical framework to understanding receptors with non-identical subunits.

An NMDAR positive and negative allosteric modulator series share a binding site and are interconverted by methyl groups.

N-methyl-d-aspartate receptors (NMDARs) are an important receptor in the brain and have been implicated in multiple neurological disorders. Many non-selective NMDAR-targeting drugs are poorly tolerated, leading to efforts to target NMDAR subtypes to improve the therapeutic index. We describe here a series of negative allosteric NMDAR modulators with submaximal inhibition at saturating concentrations. Modest changes to the chemical structure interconvert negative and positive modulation. All modulators share the ability to enhance agonist potency and are use-dependent, requiring the binding of both agonists before modulators act with high potency. Data suggest that these modulators, including both enantiomers, bind to the same site on the receptor and share structural determinants of action. Due to the modulator properties, submaximal negative modulators in this series may spare NMDAR at the synapse, while augmenting the response of NMDAR in extrasynaptic spaces. These modulators could serve as useful tools to probe the role of extrasynaptic NMDARs.

The Bioactive Protein-Ligand Conformation of GluN2C-Selective Positive Allosteric Modulators Bound to the NMDA Receptor.

N-methyl-d-aspartate (NMDA) receptors are ligand-gated, cation-selective channels that mediate a slow component of excitatory synaptic transmission. Subunit-selective positive allosteric modulators of NMDA receptor function have therapeutically relevant effects on multiple processes in the brain. A series of pyrrolidinones, such as PYD-106, that selectively potentiate NMDA receptors that contain the GluN2C subunit have structural determinants of activity that reside between the GluN2C amino terminal domain and the GluN2C agonist binding domain, suggesting a unique site of action. Here we use molecular biology and homology modeling to identify residues that line a candidate binding pocket for GluN2C-selective pyrrolidinones. We also show that occupancy of only one site in diheteromeric receptors is required for potentiation. Both GluN2A and GluN2B can dominate the sensitivity of triheteromeric receptors to eliminate the actions of pyrrolidinones, thus rendering this series uniquely sensitive to subunit stoichiometry. We experimentally identified NMR-derived conformers in solution, which combined with molecular modeling allows the prediction of the bioactive binding pose for this series of GluN2C-selective positive allosteric modulators of NMDA receptors. These data advance our understanding of the site and nature of the ligand-protein interaction for GluN2C-selective positive allosteric modulators for NMDA receptors.

The Structure-Activity Relationship of a Tetrahydroisoquinoline Class of N-Methyl-d-Aspartate Receptor Modulators that Potentiates GluN2B-Containing N-Methyl-d-Aspartate Receptors.

We have identified a series of positive allosteric NMDA receptor (NMDAR) modulators derived from a known class of GluN2C/D-selective tetrahydroisoquinoline analogues that includes CIQ. The prototypical compound of this series contains a single isopropoxy moiety in place of the two methoxy substituents present in CIQ. Modifications of this isopropoxy-containing scaffold led to the identification of analogues with enhanced activity at the GluN2B subunit. We identified molecules that potentiate the response of GluN2B/GluN2C/GluN2D, GluN2B/GluN2C, and GluN2C/GluN2D-containing NMDARs to maximally effective concentrations of agonist. Multiple compounds potentiate the response of NMDARs with submicromolar EC50 values. Analysis of enantiomeric pairs revealed that the S-(-) enantiomer is active at the GluN2B, GluN2C, and/or GluN2D subunits, whereas the R-(+) enantiomer is only active at GluN2C/D subunits. These results provide a starting point for the development of selective positive allosteric modulators for GluN2B-containing receptors.

Analysis of the three-dimensional structure of the African horse sickness virus VP7 trimer by homology modelling.

VP7 is the major core protein of orbiviruses and is essential for virion assembly. African horse sickness virus (AHSV) VP7 self-assembles into highly insoluble crystalline particles - an attribute that may be related to the role of AHSV VP7 in virus assembly but also prevents crystallization. Given that this inherent insolubility is unique to AHSV VP7, we use amino acid sequence conservation analysis between AHSV VP7 and other orbiviruses to identify putative key residues that drive AHSV VP7 self-assembly. A homology model of the AHSV VP7 trimer was generated to analyze surface properties of the trimer and to identify surface residues as candidates for the AHSV VP7 trimer-trimer interactions that drive AHSV VP7 self-assembly. Nine regions were identified as candidate residues for future site-directed mutagenesis experiments that will likely result in a soluble AHSV VP7 protein. Additionally, we identified putative residues that function in the intermolecular interactions within the AHSV VP7 trimer as well as several epitopes. Given the many previous efforts of solubilizing AHSV VP7, we propose a useful strategy that will yield a soluble AHSV VP7 that can be used to study AHSV assembly and increase yield of recombinant vaccine preparations.

Functional Evaluation of a De Novo GRIN2A Mutation Identified in a Patient with Profound Global Developmental Delay and Refractory Epilepsy.

The N-methyl-d-aspartate receptor (NMDAR), a ligand-gated ionotropic glutamate receptor, plays important roles in normal brain development and a wide range of neurologic disorders, including epilepsy. Here, we evaluate for the first time the functional properties of a de novo GRIN2A missense mutation (p.M817V) in the pre-M4 linker in a child with profound global developmental delay and refractory epilepsy. Electrophysiologic recordings revealed that the mutant GluN2A(M817V)-containing receptors showed enhanced agonist potency, reduced sensitivity to endogenous negative inhibitors (Mg2+, proton, and zinc), prolonged synaptic-like response time course, increased single-channel mean open time, and increased channel open probability. These results suggest that the gain-of-function M817V mutation causes overactivation of NMDAR and drives neuronal hyperexcitability, which may contribute to the patient's observed epileptic phenotype. Molecular modeling of the closed channel conformation reveals that this mutation weakens the interaction between GluN2 transmembrane helix M4 and two GluN1 transmembrane helices, and increases atomic fluctuation or movement of the pre-M1 region of GluN1 subunit, suggesting a mechanism by which channel function is enhanced. The functional changes of this mutation on agonist potency occur when the mutation is introduced into all other GluN2 subunits, suggesting a conserved role of this residue in control of NMDAR function through interactions of membrane spanning GluN2 and GluN1 helices. A number of NMDAR-targeted drugs including U.S. Food and Drug Association-approved NMDAR channel blockers were evaluated for their ability to inhibit receptors containing GluN2A(M817V) as a first step to exploring the potential for rescue pharmacology and personalized medicine.