5-HT2C Receptor Structures Reveal the Structural Basis of GPCR Polypharmacology.

Drugs frequently require interactions with multiple targets-via a process known as polypharmacology-to achieve their therapeutic actions. Currently, drugs targeting several serotonin receptors, including the 5-HT2C receptor, are useful for treating obesity, drug abuse, and schizophrenia. The competing challenges of developing selective 5-HT2C receptor ligands or creating drugs with a defined polypharmacological profile, especially aimed at G protein-coupled receptors (GPCRs), remain extremely difficult. Here, we solved two structures of the 5-HT2C receptor in complex with the highly promiscuous agonist ergotamine and the 5-HT2A-C receptor-selective inverse agonist ritanserin at resolutions of 3.0 Å and 2.7 Å, respectively. We analyzed their respective binding poses to provide mechanistic insights into their receptor recognition and opposing pharmacological actions. This study investigates the structural basis of polypharmacology at canonical GPCRs and illustrates how understanding characteristic patterns of ligand-receptor interaction and activation may ultimately facilitate drug design at multiple GPCRs.

Crystal Structure of the Human Cannabinoid Receptor CB1.

Cannabinoid receptor 1 (CB1) is the principal target of Δ9-tetrahydrocannabinol (THC), a psychoactive chemical from Cannabis sativa with a wide range of therapeutic applications and a long history of recreational use. CB1 is activated by endocannabinoids and is a promising therapeutic target for pain management, inflammation, obesity, and substance abuse disorders. Here, we present the 2.8 Å crystal structure of human CB1 in complex with AM6538, a stabilizing antagonist, synthesized and characterized for this structural study. The structure of the CB1-AM6538 complex reveals key features of the receptor and critical interactions for antagonist binding. In combination with functional studies and molecular modeling, the structure provides insight into the binding mode of naturally occurring CB1 ligands, such as THC, and synthetic cannabinoids. This enhances our understanding of the molecular basis for the physiological functions of CB1 and provides new opportunities for the design of next-generation CB1-targeting pharmaceuticals.

Crystal structure of the Frizzled 4 receptor in a ligand-free state.

Frizzled receptors (FZDs) are class-F G-protein-coupled receptors (GPCRs) that function in Wnt signalling and are essential for developing and adult organisms1,2. As central mediators in this complex signalling pathway, FZDs serve as gatekeeping proteins both for drug intervention and for the development of probes in basic and in therapeutic research. Here we present an atomic-resolution structure of the human Frizzled 4 receptor (FZD4) transmembrane domain in the absence of a bound ligand. The structure reveals an unusual transmembrane architecture in which helix VI is short and tightly packed, and is distinct from all other GPCR structures reported so far. Within this unique transmembrane fold is an extremely narrow and highly hydrophilic pocket that is not amenable to the binding of traditional GPCR ligands. We show that such a pocket is conserved across all FZDs, which may explain the long-standing difficulties in the development of ligands for these receptors. Molecular dynamics simulations on the microsecond timescale and mutational analysis uncovered two coupled, dynamic kinks located at helix VII that are involved in FZD4 activation. The stability of the structure in its ligand-free form, an unfavourable pocket for ligand binding and the two unusual kinks on helix VII suggest that FZDs may have evolved a novel ligand-recognition and activation mechanism that is distinct from that of other GPCRs.

Crystal structures of agonist-bound human cannabinoid receptor CB1.

The cannabinoid receptor 1 (CB1) is the principal target of the psychoactive constituent of marijuana, the partial agonist Δ9-tetrahydrocannabinol (Δ9-THC). Here we report two agonist-bound crystal structures of human CB1 in complex with a tetrahydrocannabinol (AM11542) and a hexahydrocannabinol (AM841) at 2.80 Å and 2.95 Å resolution, respectively. The two CB1-agonist complexes reveal important conformational changes in the overall structure, relative to the antagonist-bound state, including a 53% reduction in the volume of the ligand-binding pocket and an increase in the surface area of the G-protein-binding region. In addition, a 'twin toggle switch' of Phe2003.36 and Trp3566.48 (superscripts denote Ballesteros-Weinstein numbering) is experimentally observed and appears to be essential for receptor activation. The structures reveal important insights into the activation mechanism of CB1 and provide a molecular basis for predicting the binding modes of Δ9-THC, and endogenous and synthetic cannabinoids. The plasticity of the binding pocket of CB1 seems to be a common feature among certain class A G-protein-coupled receptors. These findings should inspire the design of chemically diverse ligands with distinct pharmacological properties.

Modified Electrostatic Complementary Score Function and Its Application Boundary Exploration in Drug Design.

In recent years, machine learning (ML) models have been found to quickly predict various molecular properties with accuracy comparable to high-level quantum chemistry methods. One such example is the calculation of electrostatic potential (ESP). Different ESP prediction ML models were proposed to generate surface molecular charge distribution. Electrostatic complementarity (EC) can apply ESP data to quantify the complementarity between a ligand and its binding pocket, leading to the potential to increase the efficiency of drug design. However, there is not much research discussing EC score functions and their applicability domain. We propose a new EC score function modified from the one originally developed by Bauer and Mackey, and confirm its effectiveness against the available Pearson's R correlation coefficient. Additionally, the applicability domain of the EC score and two indices used to define the EC score application scope will be discussed.

Structural basis for DNA recognition by STAT6.

STAT6 participates in classical IL-4/IL-13 signaling and stimulator of interferon genes-mediated antiviral innate immune responses. Aberrations in STAT6-mediated signaling are linked to development of asthma and diseases of the immune system. In addition, STAT6 remains constitutively active in multiple types of cancer. Therefore, targeting STAT6 is an attractive proposition for treating related diseases. Although a lot is known about the role of STAT6 in transcriptional regulation, molecular details on how STAT6 recognizes and binds specific segments of DNA to exert its function are not clearly understood. Here, we report the crystal structures of a homodimer of phosphorylated STAT6 core fragment (STAT6CF) alone and bound with the N3 and N4 DNA binding site. Analysis of the structures reveals that STAT6 undergoes a dramatic conformational change on DNA binding, which was further validated by performing molecular dynamics simulation studies and small angle X-ray scattering analysis. Our data show that a larger angle at the intersection where the two protomers of STAT meet and the presence of a unique residue, H415, in the DNA-binding domain play important roles in discrimination of the N4 site DNA from the N3 site by STAT6. H415N mutation of STAT6CF decreased affinity of the protein for the N4 site DNA, but increased its affinity for N3 site DNA, both in vitro and in vivo. Results of our structure-function studies on STAT6 shed light on mechanism of DNA recognition by STATs in general and explain the reasons underlying STAT6's preference for N4 site DNA over N3.

Patient-specific analysis of co-expression to measure biological network rewiring in individuals.

To effectively understand the underlying mechanisms of disease and inform the development of personalized therapies, it is critical to harness the power of differential co-expression (DCE) network analysis. Despite the promise of DCE network analysis in precision medicine, current approaches have a major limitation: they measure an average differential network across multiple samples, which means the specific etiology of individual patients is often overlooked. To address this, we present Cosinet, a DCE-based single-sample network rewiring degree quantification tool. By analyzing two breast cancer datasets, we demonstrate that Cosinet can identify important differences in gene co-expression patterns between individual patients and generate scores for each individual that are significantly associated with overall survival, recurrence-free interval, and other clinical outcomes, even after adjusting for risk factors such as age, tumor size, HER2 status, and PAM50 subtypes. Cosinet represents a remarkable development toward unlocking the potential of DCE analysis in the context of precision medicine.

Deep learning model accurately classifies metastatic tumors from primary tumors based on mutational signatures.

Metastatic propagation is the leading cause of death for most cancers. Prediction and elucidation of metastatic process is crucial for the treatment of cancer. Even though somatic mutations have been linked to tumorigenesis and metastasis, it is less explored whether metastatic events can be identified through genomic mutational signatures, which are concise descriptions of the mutational processes. Here, we developed MetaWise, a Deep Neural Network (DNN) model, by applying mutational signatures as input features calculated from Whole-Exome Sequencing (WES) data of TCGA and other metastatic cohorts. This model can accurately classify metastatic tumors from primary tumors and outperform traditional machine learning (ML) models and a deep learning (DL) model, DiaDeL. Signatures of non-coding mutations also have a major impact on the model's performance. SHapley Additive exPlanations (SHAP) and Local Surrogate (LIME) analyses identify several mutational signatures which are directly correlated to metastatic spread in cancers, including APOBEC-mutagenesis, UV-induced signatures, and DNA damage response deficiency signatures.

Using graph-based model to identify cell specific synthetic lethal effects.

Synthetic lethal (SL) pairs are pairs of genes whose simultaneous loss-of-function results in cell death, while a damaging mutation of either gene alone does not affect the cell's survival. This makes SL pairs attractive targets for precision cancer therapies, as targeting the unimpaired gene of the SL pair can selectively kill cancer cells that already harbor the impaired gene. Limited by the difficulty of finding true SL pairs, especially on specific cell types, current computational approaches provide only limited insights because of overlooking the crucial aspects of cellular context dependency and mechanistic understanding of SL pairs. As a result, the identification of SL targets still relies on expensive, time-consuming experimental approaches. In this work, we applied cell-line specific multi-omics data to a specially designed deep learning model to predict cell-line specific SL pairs. Through incorporating multiple types of cell-specific omics data with a self-attention module, we represent gene relationships as graphs. Our approach achieves the prediction of SL pairs in a cell-specific manner and demonstrates the potential to facilitate the discovery of cell-specific SL targets for cancer therapeutics, providing a tool to unearth mechanisms underlying the origin of SL in cancer biology. The code and data of our approach can be found at https://github.com/promethiume/SLwise.

Identifying New Ligands for JNK3 by Fluorescence Thermal Shift Assays and Native Mass Spectrometry.

The c-Jun N-terminal kinases (JNKs) are evolutionary highly conserved serine/threonine kinases. Numerous findings suggest that JNK3 is involved in the pathogenesis of neurodegenerative diseases, so the inhibition of JNK3 may be a potential therapeutic intervention. The identification of novel compounds with promising pharmacological properties still represents a challenge. Fluorescence thermal shift screening of a chemically diversified lead-like scaffold library of 2024 pure compounds led to the initial identification of seven JNK3 binding hits, which were classified into four scaffold groups according to their chemical structures. Native mass spectrometry validated the interaction of 4 out of the 7 hits with JNK3. Binding geometries and interactions of the top 2 hits were evaluated by docking into a JNK3 crystal structure. Hit 5 had a K d of 21 µM with JNK3 suggested scaffold 5-(phenylamino)-1H-1,2,3-triazole-4-carboxamide as a novel and selective JNK3 binder.

All Ca(2+)-binding loops of light-sensitive ctenophore photoprotein berovin bind magnesium ions: The spatial structure of Mg(2+)-loaded apo-berovin.

Light-sensitive photoprotein berovin accounts for a bright bioluminescence of ctenophore Beroe abyssicola. Berovin is functionally identical to the well-studied Ca(2+)-regulated photoproteins of jellyfish, however in contrast to those it is extremely sensitive to the visible light. Berovin contains three EF-hand Ca(2+)-binding sites and consequently belongs to a large family of the EF-hand Ca(2+)-binding proteins. Here we report the spatial structure of apo-berovin with bound Mg(2+) determined at 1.75Å. The magnesium ion is found in each functional EF-hand loop of a photoprotein and coordinated by oxygen atoms donated by the side-chain groups of aspartate, carbonyl groups of the peptide backbone, or hydroxyl group of serine with characteristic oxygen-Mg(2+) distances. As oxygen supplied by the side-chain of the twelfth residue of all Ca(2+)-binding loops participates in the magnesium ion coordination, it was suggested that Ca(2+)-binding loops of berovin belong to the mixed Ca(2+)/Mg(2+) rather than Ca(2+)-specific type. In addition, we report an effect of physiological concentration of Mg(2+) on bioluminescence of berovin (sensitivity to Ca(2+), rapid-mixed kinetics, light-sensitivity, thermostability, and apo-berovin conversion into active protein). The different impact of physiological concentration of Mg(2+) on berovin bioluminescence as compared to hydromedusan photoproteins was attributed to different affinities of the Ca(2+)-binding sites of these photoproteins to Mg(2+).

Native phasing of x-ray free-electron laser data for a G protein-coupled receptor.

Serial femtosecond crystallography (SFX) takes advantage of extremely bright and ultrashort pulses produced by x-ray free-electron lasers (XFELs), allowing for the collection of high-resolution diffraction intensities from micrometer-sized crystals at room temperature with minimal radiation damage, using the principle of "diffraction-before-destruction." However, de novo structure factor phase determination using XFELs has been difficult so far. We demonstrate the ability to solve the crystallographic phase problem for SFX data collected with an XFEL using the anomalous signal from native sulfur atoms, leading to a bias-free room temperature structure of the human A2A adenosine receptor at 1.9 Å resolution. The advancement was made possible by recent improvements in SFX data analysis and the design of injectors and delivery media for streaming hydrated microcrystals. This general method should accelerate structural studies of novel difficult-to-crystallize macromolecules and their complexes.