Crystal Structure of the Human Cannabinoid Receptor CB2.

The cannabinoid receptor CB2 is predominately expressed in the immune system, and selective modulation of CB2 without the psychoactivity of CB1 has therapeutic potential in inflammatory, fibrotic, and neurodegenerative diseases. Here, we report the crystal structure of human CB2 in complex with a rationally designed antagonist, AM10257, at 2.8 Å resolution. The CB2-AM10257 structure reveals a distinctly different binding pose compared with CB1. However, the extracellular portion of the antagonist-bound CB2 shares a high degree of conformational similarity with the agonist-bound CB1, which led to the discovery of AM10257's unexpected opposing functional profile of CB2 antagonism versus CB1 agonism. Further structural analysis using mutagenesis studies and molecular docking revealed the molecular basis of their function and selectivity for CB2 and CB1. Additional analyses of our designed antagonist and agonist pairs provide important insight into the activation mechanism of CB2. The present findings should facilitate rational drug design toward precise modulation of the endocannabinoid system.

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.

Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites.

The coronavirus disease 2019 (COVID-19) pandemic has spurred a wide range of approaches to control and combat the disease. However, selecting an effective antiviral drug target remains a time-consuming challenge. Computational methods offer a promising solution by efficiently reducing the number of candidates. In this study, we propose a structure- and deep learning-based approach that identifies vulnerable regions in viral proteins corresponding to drug binding sites. Our approach takes into account the protein dynamics, accessibility and mutability of the binding site and the putative mechanism of action of the drug. We applied this technique to validate drug targeting toward severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein S. Our findings reveal a conformation- and oligomer-specific glycan-free binding site proximal to the receptor binding domain. This site comprises topologically important amino acid residues. Molecular dynamics simulations of Spike in complex with candidate drug molecules bound to the potential binding sites indicate an equilibrium shifted toward the inactive conformation compared with drug-free simulations. Small molecules targeting this binding site have the potential to prevent the closed-to-open conformational transition of Spike, thereby allosterically inhibiting its interaction with human angiotensin-converting enzyme 2 receptor. Using a pseudotyped virus-based assay with a SARS-CoV-2 neutralizing antibody, we identified a set of hit compounds that exhibited inhibition at micromolar concentrations.

graphLambda: Fusion Graph Neural Networks for Binding Affinity Prediction.

Predicting the binding affinity of protein-ligand complexes is crucial for computer-aided drug discovery (CADD) and the identification of potential drug candidates. The deep learning-based scoring functions have emerged as promising predictors of binding constants. Building on recent advancements in graph neural networks, we present graphLambda for protein-ligand binding affinity prediction, which utilizes graph convolutional, attention, and isomorphism blocks to enhance the predictive capabilities. The graphLambda model exhibits superior performance across CASF16 and CSAR HiQ NRC benchmarks and demonstrates robustness with respect to different types of train-validation set partitions. The development of graphLambda underscores the potential of graph neural networks in advancing binding affinity prediction models, contributing to more effective CADD methodologies.

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.

Mutations in transmembrane proteins: diseases, evolutionary insights, prediction and comparison with globular proteins.

Membrane proteins are unique in that they interact with lipid bilayers, making them indispensable for transporting molecules and relaying signals between and across cells. Due to the significance of the protein's functions, mutations often have profound effects on the fitness of the host. This is apparent both from experimental studies, which implicated numerous missense variants in diseases, as well as from evolutionary signals that allow elucidating the physicochemical constraints that intermembrane and aqueous environments bring. In this review, we report on the current state of knowledge acquired on missense variants (referred to as to single amino acid variants) affecting membrane proteins as well as the insights that can be extrapolated from data already available. This includes an overview of the annotations for membrane protein variants that have been collated within databases dedicated to the topic, bioinformatics approaches that leverage evolutionary information in order to shed light on previously uncharacterized membrane protein structures or interaction interfaces, tools for predicting the effects of mutations tailored specifically towards the characteristics of membrane proteins as well as two clinically relevant case studies explaining the implications of mutated membrane proteins in cancer and cardiomyopathy.

Evaluating and comparing success rates for inpatient treatment of alcohol addiction in the Czech Republic.

OBJECTIVES: This systematic review seeks to present and compare data from studies evaluating the success of medium-term inpatient treatment of alcohol-dependent patients in the Czech Republic. Another aim was to identify the problems that make such comparisons difficult. No previous review comparing the efficiency of various therapeutic programmes has been published in the Czech Republic. METHODS: Bibliographia medica Cechoslovaca and PubMed were used to find studies published in professional medical journals since 1970 evaluating the abstinence of patients who voluntarily completed medium-term inpatient treatment of alcohol dependence. RESULTS: Medium-term inpatient treatment of alcohol addiction leads to one year of abstinence in 34% to 76% of patients. Such variance in value is largely caused by selection bias, differences in the definition of abstinence, and differences in data collection methods. CONCLUSION: The comparison of studies presented many challenges. Further steps should be taken to help compare treatment programmes in the future, as the programmes provide different therapeutic interventions of different intensities and lengths to different patients. Adequate demographic and other pretreatment characteristics data collection, detailed descriptions of therapeutic interventions, and identification of effective components of the therapeutic programme could support further research in this area, optimize existing programmes, and increase the overall treatment efficiency.

Crystal structure of misoprostol bound to the labor inducer prostaglandin E2 receptor.

Misoprostol is a life-saving drug in many developing countries for women at risk of post-partum hemorrhaging owing to its affordability, stability, ease of administration and clinical efficacy. However, misoprostol lacks receptor and tissue selectivities, and thus its use is accompanied by a number of serious side effects. The development of pharmacological agents combining the advantages of misoprostol with improved selectivity is hindered by the absence of atomic details of misoprostol action in labor induction. Here, we present the 2.5 Å resolution crystal structure of misoprostol free-acid form bound to the myometrium labor-inducing prostaglandin E2 receptor 3 (EP3). The active state structure reveals a completely enclosed binding pocket containing a structured water molecule that coordinates misoprostol's ring structure. Modeling of selective agonists in the EP3 structure reveals rationales for selectivity. These findings will provide the basis for the next generation of uterotonic drugs that will be suitable for administration in low resource settings.

Publisher Correction: Crystal structure of misoprostol bound to the labor inducer prostaglandin E2 receptor.

In the version of this article originally published, the present address for Petr Popov was incorrectly listed as 'Koltech Institute of Science & Technology, Moscow, Russia'. The correct present address is 'Skolkovo Institute of Science and Technology, Moscow, Russia'. The error has been corrected in the HTML and PDF versions of the paper.

Protein-Peptide Binding Site Detection Using 3D Convolutional Neural Networks.

Peptides and peptide-based molecules represent a promising therapeutic modality targeting intracellular protein-protein interactions, potentially combining the beneficial properties of biologics and small-molecule drugs. Protein-peptide complexes occupy a unique niche of interaction interfaces with respect to protein-protein and protein-small molecule complexes. Protein-peptide binding site identification resembles image object detection, a field that had been revolutionalized with computer vision techniques. We present a new protein-peptide binding site detection method called BiteNetPp by harnessing the power of 3D convolutional neural network. Our method employs a tensor-based representation of spatial protein structures, which is fed to 3D convolutional neural network, resulting in probability scores and coordinates of the binding "hot spots" in the input structures. We used the domain adaptation technique to fine-tune model trained on protein-small molecule complexes using a manually curated set of protein-peptide structures. BiteNetPp consistently outperforms existing state-of-the-art methods in the independent test benchmark. It takes less than a second to analyze a single-protein structure, making BiteNetPp suitable for the large-scale analysis of protein-peptide binding sites.

Controlled-advancement rigid-body optimization of nanosystems.

In this study, we propose a novel optimization algorithm, with application to the refinement of molecular complexes. Particularly, we consider optimization problem as the calculation of quasi-static trajectories of rigid bodies influenced by the inverse-inertia-weighted energy gradient and introduce the concept of advancement region that guarantees displacement of a molecule strictly within a relevant region of conformational space. The advancement region helps to avoid typical energy minimization pitfalls, thus, the algorithm is suitable to work with arbitrary energy functions and arbitrary types of molecular complexes without necessary tuning of its hyper-parameters. Our method, called controlled-advancement rigid-body optimization of nanosystems (Carbon), is particularly useful for the large-scale molecular refinement, as for example, the putative binding candidates obtained with protein-protein docking pipelines. Implementation of Carbon with user-friendly interface is available in the SAMSON platform for molecular modeling at https://www.samson-connect.net. © 2019 Wiley Periodicals, Inc.

RapidRMSD: rapid determination of RMSDs corresponding to motions of flexible molecules.

Motivation: The root mean square deviation (RMSD) is one of the most used similarity criteria in structural biology and bioinformatics. Standard computation of the RMSD has a linear complexity with respect to the number of atoms in a molecule, making RMSD calculations time-consuming for the large-scale modeling applications, such as assessment of molecular docking predictions or clustering of spatially proximate molecular conformations. Previously, we introduced the RigidRMSD algorithm to compute the RMSD corresponding to the rigid-body motion of a molecule. In this study, we go beyond the limits of the rigid-body approximation by taking into account conformational flexibility of the molecule. We model the flexibility with a reduced set of collective motions computed with e.g. normal modes or principal component analysis. Results: The initialization of our algorithm is linear in the number of atoms and all the subsequent evaluations of RMSD values between flexible molecular conformations depend only on the number of collective motions that are selected to model the flexibility. Therefore, our algorithm is much faster compared to the standard RMSD computation for large-scale modeling applications. We demonstrate the efficiency of our method on several clustering examples, including clustering of flexible docking results and molecular dynamics (MD) trajectories. We also demonstrate how to use the presented formalism to generate pseudo-random constant-RMSD structural molecular ensembles and how to use these in cross-docking. Availability and implementation: We provide the algorithm written in C++ as the open-source RapidRMSD library governed by the BSD-compatible license, which is available at http://team.inria.fr/nano-d/software/RapidRMSD/. The constant-RMSD structural ensemble application and clustering of MD trajectories is available at http://team.inria.fr/nano-d/software/nolb-normal-modes/. Supplementary information: Supplementary data are available at Bioinformatics online.

The complex relation between access to opioid agonist therapy and diversion of opioid medications: a case example of large-scale misuse of buprenorphine in the Czech Republic.

Opioid agonist therapy (OAT) has been available in a standard regime in the Czech Republic since 2000. Buprenorphine is the leading medication, while methadone is available only in a few specialised centres. There is an important leakage of buprenorphine onto the illicit market, and the majority of Czech opioid users are characterised by the misuse (and injecting) of diverted buprenorphine medications. Most prescribed buprenorphine for OAT is not covered by current national health insurance schemes, and patients have to pay considerable prices to afford their treatment. This affordability barrier together with limited accessibility is likely the leading factor of limited coverage of OAT and of recent stagnation in the number of patients in the official treatment programmes in the Czech Republic. It also encourages doctor shopping and the re-selling of parts of their medication at a higher price, which represents the main factor that drives the Czech illicit market for buprenorphine, but at the same time co-finances the medication of clients in official OAT programmes. Improving access to OAT by making it financially affordable is essential to further increase OAT coverage and is one of the factors that can reduce the illicit market with OAT medications.

Problematic and risky sexual behaviour under the influence of alcohol among university students.

OBJECTIVE: The objective of this research was to determine the prevalence of problematic and risky sexual behaviour after alcohol consumption and the correlation between this prevalence and sex, behavioural factors, problematic drinking, and alcohol consumption characteristics. METHODS: A survey of students was carried out at four faculties. Data were gathered via internet and self-administered paper-pencil questionnaires. The analysis employed Pearson's chi-squared test, gross odds ratios and logistic regression to calculate the adjusted odds ratios (OR) and their confidence interval (CI). RESULTS: Problematic drinking was detected by the CAGE test. Sixteen percent of students reached the CAGE score of 2, which indicates a potential threat of addiction, while 6% of students reached even higher problematic scores (3 or 4). Among those respondents who did drink alcohol, 23% had unprotected sex and 21% had sex which they later regretted. There were some differences between male and female respondents with men reporting more instances of risky behaviour. Among university students, problematic and risky sexual behaviour after alcohol use is associated with sex, the intensity of problematic drinking, first drunkenness, the place of alcohol use, and attitude to alcohol use. CONCLUSIONS: Problematic drinking and risky sexual behaviour after alcohol consumption exist among students and deserve special attention and response in the form of suitable measures. Problematic and risky sexual behaviour after alcohol consumption among university students is associated with behavioural factors and characteristics of alcohol use that allow a targeted approach to preventive efforts.

PEPSI-Dock: a detailed data-driven protein-protein interaction potential accelerated by polar Fourier correlation.

MOTIVATION: Docking prediction algorithms aim to find the native conformation of a complex of proteins from knowledge of their unbound structures. They rely on a combination of sampling and scoring methods, adapted to different scales. Polynomial Expansion of Protein Structures and Interactions for Docking (PEPSI-Dock) improves the accuracy of the first stage of the docking pipeline, which will sharpen up the final predictions. Indeed, PEPSI-Dock benefits from the precision of a very detailed data-driven model of the binding free energy used with a global and exhaustive rigid-body search space. As well as being accurate, our computations are among the fastest by virtue of the sparse representation of the pre-computed potentials and FFT-accelerated sampling techniques. Overall, this is the first demonstration of a FFT-accelerated docking method coupled with an arbitrary-shaped distance-dependent interaction potential. RESULTS: First, we present a novel learning process to compute data-driven distant-dependent pairwise potentials, adapted from our previous method used for rescoring of putative protein-protein binding poses. The potential coefficients are learned by combining machine-learning techniques with physically interpretable descriptors. Then, we describe the integration of the deduced potentials into a FFT-accelerated spherical sampling provided by the Hex library. Overall, on a training set of 163 heterodimers, PEPSI-Dock achieves a success rate of 91% mid-quality predictions in the top-10 solutions. On a subset of the protein docking benchmark v5, it achieves 44.4% mid-quality predictions in the top-10 solutions when starting from bound structures and 20.5% when starting from unbound structures. The method runs in 5-15 min on a modern laptop and can easily be extended to other types of interactions. AVAILABILITY AND IMPLEMENTATION: https://team.inria.fr/nano-d/software/PEPSI-Dock CONTACT: sergei.grudinin@inria.fr.

Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment.

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323-348. © 2016 Wiley Periodicals, Inc.

Predicting Binding Poses and Affinities in the CSAR 2013-2014 Docking Exercises Using the Knowledge-Based Convex-PL Potential.

The 2013-2014 CSAR docking exercise was the opportunity to assess the performance of the novel knowledge-based potential we are developing, named Convex-PL. The data used to derive the potential consists only of structural information from protein-ligand interfaces found in the PDBBind database. As expected, our potential proved to be very efficient in the near-native pose detection exercises, where we correctly predicted two near-native poses in the 2013 exercise and also ranked 22 near-native poses first and 2 second in the 2014 exercise. Somewhat more surprisingly, we obtained a fair performance in some of the CSAR affinity ranking exercises, where the Spearman correlation coefficients between our predictions and the experiments are greater than 0.5 for several protein-ligand sets. Nonetheless, affinity prediction exercises turned out to be a challenge, and significant progress in the development of our method is needed before we can successfully predict binding constants.

Knowledge of Native Protein-Protein Interfaces Is Sufficient To Construct Predictive Models for the Selection of Binding Candidates.

Selection of putative binding poses is a challenging part of virtual screening for protein-protein interactions. Predictive models to filter out binding candidates with the highest binding affinities comprise scoring functions that assign a score to each binding pose. Existing scoring functions are typically deduced by collecting statistical information about interfaces of native conformations of protein complexes along with interfaces of a large generated set of non-native conformations. However, the obtained scoring functions become biased toward the method used to generate the non-native conformations, i.e., they may not recognize near-native interfaces generated with a different method. The present study demonstrates that knowledge of only native protein-protein interfaces is sufficient to construct well-discriminative predictive models for the selection of binding candidates. Here we introduce a new scoring method that comprises a knowledge-based potential called KSENIA deduced from structural information about the native interfaces of 844 crystallographic protein-protein complexes. We derive KSENIA using convex optimization with a training set composed of native protein complexes and their near-native conformations obtained using deformations along the low-frequency normal modes. As a result, our knowledge-based potential has only marginal bias toward a method used to generate putative binding poses. Furthermore, KSENIA is smooth by construction, which allows it to be used along with rigid-body optimization to refine the binding poses. Using several test benchmarks, we demonstrate that our method discriminates well native and near-native conformations of protein complexes from non-native ones. Our methodology can be easily adapted to the recognition of other types of molecular interactions, such as protein-ligand, protein-RNA, etc. KSENIA will be made publicly available as a part of the SAMSON software platform at https://team.inria.fr/nano-d/software .

DockTrina: docking triangular protein trimers.

In spite of the abundance of oligomeric proteins within a cell, the structural characterization of protein-protein interactions is still a challenging task. In particular, many of these interactions involve heteromeric complexes, which are relatively difficult to determine experimentally. Hence there is growing interest in using computational techniques to model such complexes. However, assembling large heteromeric complexes computationally is a highly combinatorial problem. Nonetheless the problem can be simplified greatly by considering interactions between protein trimers. After dimers and monomers, triangular trimers (i.e. trimers with pair-wise contacts between all three pairs of proteins) are the most frequently observed quaternary structural motifs according to the three-dimensional (3D) complex database. This article presents DockTrina, a novel protein docking method for modeling the 3D structures of nonsymmetrical triangular trimers. The method takes as input pair-wise contact predictions from a rigid body docking program. It then scans and scores all possible combinations of pairs of monomers using a very fast root mean square deviation test. Finally, it ranks the predictions using a scoring function which combines triples of pair-wise contact terms and a geometric clash penalty term. The overall approach takes less than 2 min per complex on a modern desktop computer. The method is tested and validated using a benchmark set of 220 bound and seven unbound protein trimer structures. DockTrina will be made available at http://nano-d.inrialpes.fr/software/docktrina.

Rapid determination of RMSDs corresponding to macromolecular rigid body motions.

Finding the root mean sum of squared deviations (RMSDs) between two coordinate vectors that correspond to the rigid body motion of a macromolecule is an important problem in structural bioinformatics, computational chemistry, and molecular modeling. Standard algorithms compute the RMSD with time proportional to the number of atoms in the molecule. Here, we present RigidRMSD, a new algorithm that determines a set of RMSDs corresponding to a set of rigid body motions of a macromolecule in constant time with respect to the number of atoms in the molecule. Our algorithm is particularly useful for rigid body modeling applications, such as rigid body docking, and also for high-throughput analysis of rigid body modeling and simulation results. We also introduce a constant-time rotation RMSD as a similarity measure for rigid molecules. A C++ implementation of our algorithm is available at http://nano-d.inrialpes.fr/software/RigidRMSD.