Author Correction: Structural insights into µ-opioid receptor activation.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Structural insights into µ-opioid receptor activation.

Activation of the µ-opioid receptor (µOR) is responsible for the efficacy of the most effective analgesics. To shed light on the structural basis for µOR activation, here we report a 2.1 Å X-ray crystal structure of the murine µOR bound to the morphinan agonist BU72 and a G protein mimetic camelid antibody fragment. The BU72-stabilized changes in the µOR binding pocket are subtle and differ from those observed for agonist-bound structures of the ß2-adrenergic receptor (ß2AR) and the M2 muscarinic receptor. Comparison with active ß2AR reveals a common rearrangement in the packing of three conserved amino acids in the core of the µOR, and molecular dynamics simulations illustrate how the ligand-binding pocket is conformationally linked to this conserved triad. Additionally, an extensive polar network between the ligand-binding pocket and the cytoplasmic domains appears to play a similar role in signal propagation for all three G-protein-coupled receptors.

Improvement in ADMET Prediction with Multitask Deep Featurization.

The absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties of drug candidates are important for their efficacy and safety as therapeutics. Predicting ADMET properties has therefore been of great interest to the computational chemistry and medicinal chemistry communities in recent decades. Traditional cheminformatics approaches, using learners such as random forests and deep neural networks, leverage fingerprint feature representations of molecules. Here, we learn the features most relevant to each chemical task at hand by representing each molecule explicitly as a graph. By applying graph convolutions to this explicit molecular representation, we achieve, to our knowledge, unprecedented accuracy in prediction of ADMET properties. By challenging our methodology with rigorous cross-validation procedures and prognostic analyses, we show that deep featurization better enables molecular predictors to not only interpolate but also extrapolate to new regions of chemical space.

Synthesis of spiro-2,6-dioxopiperazine and spiro-2,6-dioxopyrazine scaffolds using amino acids in a three-component reaction to generate potential Sigma-1 (σ1) receptor selective ligands.

A library-friendly approach to generate new scaffolds is decisive for the development of molecular probes, drug like molecules and preclinical entities. Here, we present the design and synthesis of novel heterocycles with spiro-2,6-dioxopiperazine and spiro-2,6-pyrazine scaffolds through a three-component reaction using various amino acids, ketones, and isocyanides. Screening of select compounds over fifty CNS receptors including G-protein coupled receptors (GPCRs), ion channels, transporters, and enzymes through the NIMH psychoactive drug screening program indicated that a novel spiro-2,6-dioxopyrazine scaffold, UVM147, displays high binding affinity at sigma-1 (σ1) receptor in the nanomolar range. In addition, molecular docking of UVM147 at the human σ1 receptor have shown that it resides in the same binding site that was occupied by the ligand 4-IBP used to obtain a crystal structure of the human sigma-1 (σ1) receptor.

MoleculeNet: a benchmark for molecular machine learning.

Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods. This work introduces MoleculeNet, a large scale benchmark for molecular machine learning. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high quality open-source implementations of multiple previously proposed molecular featurization and learning algorithms (released as part of the DeepChem open source library). MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance. However, this result comes with caveats. Learnable representations still struggle to deal with complex tasks under data scarcity and highly imbalanced classification. For quantum mechanical and biophysical datasets, the use of physics-aware featurizations can be more important than choice of particular learning algorithm.

PotentialNet for Molecular Property Prediction.

The arc of drug discovery entails a multiparameter optimization problem spanning vast length scales. The key parameters range from solubility (angstroms) to protein-ligand binding (nanometers) to in vivo toxicity (meters). Through feature learning-instead of feature engineering-deep neural networks promise to outperform both traditional physics-based and knowledge-based machine learning models for predicting molecular properties pertinent to drug discovery. To this end, we present the PotentialNet family of graph convolutions. These models are specifically designed for and achieve state-of-the-art performance for protein-ligand binding affinity. We further validate these deep neural networks by setting new standards of performance in several ligand-based tasks. In parallel, we introduce a new metric, the Regression Enrichment Factor EFχ (R), to measure the early enrichment of computational models for chemical data. Finally, we introduce a cross-validation strategy based on structural homology clustering that can more accurately measure model generalizability, which crucially distinguishes the aims of machine learning for drug discovery from standard machine learning tasks.

Discovery of novel brain permeable and G protein-biased beta-1 adrenergic receptor partial agonists for the treatment of neurocognitive disorders.

The beta-1 adrenergic receptor (ADRB1) is a promising therapeutic target intrinsically involved in the cognitive deficits and pathological features associated with Alzheimer's disease (AD). Evidence indicates that ADRB1 plays an important role in regulating neuroinflammatory processes, and activation of ADRB1 may produce neuroprotective effects in neuroinflammatory diseases. Novel small molecule modulators of ADRB1, engineered to be highly brain permeable and functionally selective for the G protein with partial agonistic activity, could have tremendous value both as pharmacological tools and potential lead molecules for further preclinical development. The present study describes our ongoing efforts toward the discovery of functionally selective partial agonists of ADRB1 that have potential therapeutic value for AD and neuroinflammatory disorders, which has led to the identification of the molecule STD-101-D1. As a functionally selective agonist of ADRB1, STD-101-D1 produces partial agonistic activity on G protein signaling with an EC50 value in the low nanomolar range, but engages very little beta-arrestin recruitment compared to the unbiased agonist isoproterenol. STD-101-D1 also inhibits the tumor necrosis factor α (TNFα) response induced by lipopolysaccharide (LPS) both in vitro and in vivo, and shows high brain penetration. Other than the therapeutic role, this newly identified, functionally selective, partial agonist of ADRB1 is an invaluable research tool to study mechanisms of G protein-coupled receptor signal transduction.

Structural biology. Structural basis for chemokine recognition and activation of a viral G protein-coupled receptor.

Chemokines are small proteins that function as immune modulators through activation of chemokine G protein-coupled receptors (GPCRs). Several viruses also encode chemokines and chemokine receptors to subvert the host immune response. How protein ligands activate GPCRs remains unknown. We report the crystal structure at 2.9 angstrom resolution of the human cytomegalovirus GPCR US28 in complex with the chemokine domain of human CX3CL1 (fractalkine). The globular body of CX3CL1 is perched on top of the US28 extracellular vestibule, whereas its amino terminus projects into the central core of US28. The transmembrane helices of US28 adopt an active-state-like conformation. Atomic-level simulations suggest that the agonist-independent activity of US28 may be due to an amino acid network evolved in the viral GPCR to destabilize the receptor's inactive state.

Dialytic Separation of Bundled, Functionalized Carbon Nanotubes from Carbonaceous Impurities.

Separating functionalized single-wall carbon nanotubes (SWCNTs) from functionalized amorphous carbon is challenging, due to their polydispersity and similar physicochemical properties. We describe a single-step, dialytic separation method that takes advantage of the ability of heavily functionalized SWCNTs to bundle in a polar environment while maintaining their solubility. Experiments on functionalized SWCNTs were compared with functionalized, C60 fullerenes (buckyballs) to probe the general applicability of the method and further characterize the bundling process. This approach may simultaneously be used to purify a functionalization reaction mixture of unreacted small molecules and of residual solvents, such as dimethylformamide.

Synthesis, pharmacokinetics, and biological use of lysine-modified single-walled carbon nanotubes.

We aimed to create a more robust and more accessible standard for amine-modifying single-walled carbon nanotubes (SWCNTs). A 1,3-cycloaddition was developed using an azomethine ylide, generated by reacting paraformaldehyde and a side-chain-Boc (tert-Butyloxycarbonyl)-protected, lysine-derived alpha-amino acid, H-Lys(Boc)-OH, with purified SWCNT or C60. This cycloaddition and its lysine adduct provides the benefits of dense, covalent modification, ease of purification, commercial availability of reagents, and pH-dependent solubility of the product. Subsequently, SWCNTs functionalized with lysine amine handles were covalently conjugated to a radiometalated chelator, 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA). The (111)In-labeled construct showed rapid renal clearance in a murine model and a favorable biodistribution, permitting utility in biomedical applications. Functionalized SWCNTs strongly wrapped small interfering RNA (siRNA). In the first disclosed deployment of thermophoresis with carbon nanotubes, the lysine-modified tubes showed a desirable, weak SWCNT-albumin binding constant. Thus, lysine-modified nanotubes are a favorable candidate for medicinal work.