Structural basis for the oligomerization-facilitated NLRP3 activation.

The NACHT-, leucine-rich-repeat-, and pyrin domain-containing protein 3 (NLRP3) is a critical intracellular inflammasome sensor and an important clinical target against inflammation-driven human diseases. Recent studies have elucidated its transition from a closed cage to an activated disk-like inflammasome, but the intermediate activation mechanism remains elusive. Here we report the cryo-electron microscopy structure of NLRP3, which forms an open octamer and undergoes a ~ 90° hinge rotation at the NACHT domain. Mutations on open octamer's interfaces reduce IL-1ß signaling, highlighting its essential role in NLRP3 activation/inflammasome assembly. The centrosomal NIMA-related kinase 7 (NEK7) disrupts large NLRP3 oligomers and forms NEK7/NLRP3 monomers/dimers which is a critical step preceding the assembly of the disk-like inflammasome. These data demonstrate an oligomeric cooperative activation of NLRP3 and provide insight into its inflammasome assembly mechanism.

Alchemical Free Energy Calculations on Membrane-Associated Proteins.

Membrane proteins have diverse functions within cells and are well-established drug targets. The advances in membrane protein structural biology have revealed drug and lipid binding sites on membrane proteins, while computational methods such as molecular simulations can resolve the thermodynamic basis of these interactions. Particularly, alchemical free energy calculations have shown promise in the calculation of reliable and reproducible binding free energies of protein-ligand and protein-lipid complexes in membrane-associated systems. In this review, we present an overview of representative alchemical free energy studies on G-protein-coupled receptors, ion channels, transporters as well as protein-lipid interactions, with emphasis on best practices and critical aspects of running these simulations. Additionally, we analyze challenges and successes when running alchemical free energy calculations on membrane-associated proteins. Finally, we highlight the value of alchemical free energy calculations calculations in drug discovery and their applicability in the pharmaceutical industry.

Broadening the Scope of Binding Free Energy Calculations Using a Separated Topologies Approach.

Binding free energy calculations predict the potency of compounds to protein binding sites in a physically rigorous manner and see broad application in prioritizing the synthesis of novel drug candidates. Relative binding free energy (RBFE) calculations have emerged as an industry-standard approach to achieve highly accurate rank-order predictions of the potency of related compounds; however, this approach requires that the ligands share a common scaffold and a common binding mode, restricting the methods' domain of applicability. This is a critical limitation since complex modifications to the ligands, especially core hopping, are very common in drug design. Absolute binding free energy (ABFE) calculations are an alternate method that can be used for ligands that are not congeneric. However, ABFE suffers from a known problem of long convergence times due to the need to sample additional degrees of freedom within each system, such as sampling rearrangements necessary to open and close the binding site. Here, we report on an alternative method for RBFE, called Separated Topologies (SepTop), which overcomes the issues in both of the aforementioned methods by enabling large scaffold changes between ligands with a convergence time comparable to traditional RBFE. Instead of only mutating atoms that vary between two ligands, this approach performs two absolute free energy calculations at the same time in opposite directions, one for each ligand. Defining the two ligands independently allows the comparison of the binding of diverse ligands without the artificial constraints of identical poses or a suitable atom-atom mapping. This approach also avoids the need to sample the unbound state of the protein, making it more efficient than absolute binding free energy calculations. Here, we introduce an implementation of SepTop. We developed a general and efficient protocol for running SepTop, and we demonstrated the method on four diverse, pharmaceutically relevant systems. We report the performance of the method, as well as our practical insights into the strengths, weaknesses, and challenges of applying this method in an industrial drug design setting. We find that the accuracy of the approach is sufficiently high to rank order ligands with an accuracy comparable to traditional RBFE calculations while maintaining the additional flexibility of SepTop.

Selective inhibitors of the PSEN1-gamma-secretase complex.

Clinical development of γ-secretases, a family of intramembrane cleaving proteases, as therapeutic targets for a variety of disorders including cancer and Alzheimer's disease was aborted because of serious mechanism-based side effects in the phase III trials of unselective inhibitors. Selective inhibition of specific γ-secretase complexes, containing either PSEN1 or PSEN2 as the catalytic subunit and APH1A or APH1B as supporting subunits, does provide a feasible therapeutic window in preclinical models of these disorders. We explore here the pharmacophoric features required for PSEN1 versus PSEN2 selective inhibition. We synthesized a series of brain penetrant 2-azabicyclo[2,2,2]octane sulfonamides and identified a compound with low nanomolar potency and high selectivity (>250-fold) toward the PSEN1-APH1B subcomplex versus PSEN2 subcomplexes. We used modeling and site-directed mutagenesis to identify critical amino acids along the entry part of this inhibitor into the catalytic site of PSEN1. Specific targeting one of the different γ-secretase complexes might provide safer drugs in the future.

Reduction of stress responses in honey bees by synthetic ligands targeting an allatostatin receptor.

Honey bees are of great economic and ecological importance, but are facing multiple stressors that can jeopardize their pollination efficiency and survival. Therefore, understanding the physiological bases of their stress response may help defining treatments to improve their resilience. We took an original approach to design molecules with this objective. We took advantage of the previous identified neuropeptide allatostatin A (ASTA) and its receptor (ASTA-R) as likely mediators of the honey bee response to a biologically relevant stressor, exposure to an alarm pheromone compound. A first series of ASTA-R ligands were identified through in silico screening using a homology 3D model of the receptor and in vitro binding experiments. One of these (A8) proved also efficient in vivo, as it could counteract two behavioral effects of pheromone exposure, albeit only in the millimolar range. This putative antagonist was used as a template for the chemical synthesis of a second generation of potential ligands. Among these, two compounds showed improved efficiency in vivo (in the micromolar range) as compared to A8 despite no major improvement in their affinity for the receptor in vitro. These new ligands are thus promising candidates for alleviating stress in honey bees.

Modeling the ß-secretase cleavage site and humanizing amyloid-beta precursor protein in rat and mouse to study Alzheimer's disease.

BACKGROUND: Three amino acid differences between rodent and human APP affect medically important features, including ß-secretase cleavage of APP and Aß peptide aggregation (De Strooper et al., EMBO J 14:4932-38, 1995; Ueno et al., Biochemistry 53:7523-30, 2014; Bush, 2003, Trends Neurosci 26:207-14). Most rodent models for Alzheimer's disease (AD) are, therefore, based on the human APP sequence, expressed from artificial mini-genes randomly inserted in the rodent genome. While these models mimic rather well various biochemical aspects of the disease, such as Aß-aggregation, they are also prone to overexpression artifacts and to complex phenotypical alterations, due to genes affected in or close to the insertion site(s) of the mini-genes (Sasaguri et al., EMBO J 36:2473-87, 2017; Goodwin et al., Genome Res 29:494-505, 2019). Knock-in strategies which introduce clinical mutants in a humanized endogenous rodent APP sequence (Saito et al., Nat Neurosci 17:661-3, 2014) represent useful improvements, but need to be compared with appropriate humanized wildtype (WT) mice. METHODS: Computational modelling of the human ß-CTF bound to BACE1 was used to study the differential processing of rodent and human APP. We humanized the three pivotal residues we identified G676R, F681Y and R684H (labeled according to the human APP770 isoform) in the mouse and rat genomes using a CRISPR-Cas9 approach. These new models, termed mouse and rat Apphu/hu, express APP from the endogenous promotor. We also introduced the early-onset familial Alzheimer's disease (FAD) mutation M139T into the endogenous Rat Psen1 gene. RESULTS: We show that introducing these three amino acid substitutions into the rodent sequence lowers the affinity of the APP substrate for BACE1 cleavage. The effect on ß-secretase processing was confirmed as both humanized rodent models produce three times more (human) Aß compared to the original WT strain. These models represent suitable controls, or starting points, for studying the effect of transgenes or knock-in mutations on APP processing (Saito et al., Nat Neurosci 17:661-3, 2014). We introduced the early-onset familial Alzheimer's disease (FAD) mutation M139T into the endogenous Rat Psen1 gene and provide an initial characterization of Aß processing in this novel rat AD model. CONCLUSION: The different humanized APP models (rat and mouse) expressing human Aß and PSEN1 M139T are valuable controls to study APP processing in vivo allowing the use of a human Aß ELISA which is more sensitive than the equivalent system for rodents. These animals will be made available to the research community.

Discovery of Homobivalent Bitopic Ligands of the Cannabinoid CB2 Receptor*.

Single chemical entities with potential to simultaneously interact with two binding sites are emerging strategies in medicinal chemistry. We have designed, synthesized and functionally characterized the first bitopic ligands for the CB2 receptor. These compounds selectively target CB2 versus CB1 receptors. Their binding mode was studied by molecular dynamic simulations and site-directed mutagenesis.

GPCRmd uncovers the dynamics of the 3D-GPCRome.

G-protein-coupled receptors (GPCRs) are involved in numerous physiological processes and are the most frequent targets of approved drugs. The explosion in the number of new three-dimensional (3D) molecular structures of GPCRs (3D-GPCRome) over the last decade has greatly advanced the mechanistic understanding and drug design opportunities for this protein family. Molecular dynamics (MD) simulations have become a widely established technique for exploring the conformational landscape of proteins at an atomic level. However, the analysis and visualization of MD simulations require efficient storage resources and specialized software. Here we present GPCRmd (http://gpcrmd.org/), an online platform that incorporates web-based visualization capabilities as well as a comprehensive and user-friendly analysis toolbox that allows scientists from different disciplines to visualize, analyze and share GPCR MD data. GPCRmd originates from a community-driven effort to create an open, interactive and standardized database of GPCR MD simulations.

Publisher Correction: GPCRmd uncovers the dynamics of the 3D-GPCRome.

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

Accurate Prediction of GPCR Ligand Binding Affinity with Free Energy Perturbation.

The computational prediction of relative binding free energies is a crucial goal for drug discovery, and G protein-coupled receptors (GPCRs) are arguably the most important drug target class. However, they present increased complexity to model compared to soluble globular proteins. Despite breakthroughs, experimental X-ray crystal and cryo-EM structures are challenging to attain, meaning computational models of the receptor and ligand binding mode are sometimes necessary. This leads to uncertainty in understanding ligand-protein binding induced changes such as, water positioning and displacement, side chain positioning, hydrogen bond networks, and the overall structure of the hydration shell around the ligand and protein. In other words, the very elements that define structure activity relationships (SARs) and are crucial for accurate binding free energy calculations are typically more uncertain for GPCRs. In this work we use free energy perturbation (FEP) to predict the relative binding free energies for ligands of two different GPCRs. We pinpoint the key aspects for success such as the important role of key water molecules, amino acid ionization states, and the benefit of equilibration with specific ligands. Initial calculations following typical FEP setup and execution protocols delivered no correlation with experiment, but we show how results are improved in a logical and systematic way. This approach gave, in the best cases, a coefficient of determination (R2) compared with experiment in the range of 0.6-0.9 and mean unsigned errors compared to experiment of 0.6-0.7 kcal/mol. We anticipate that our findings will be applicable to other difficult-to-model protein ligand data sets and be of wide interest for the community to continue improving FE binding energy predictions.

The computational modeling of allosteric modulation of metabotropic glutamate receptors.

Allosteric modulation of GPCRs, especially metabotropic glutamate (mGlu) receptors, has become an important strategy for drug discovery. Positive and negative allosteric modulators (PAM, NAM) are widely reported for the mGlu receptor family with leads mostly originating by high-throughput screening followed by iterative medicinal chemistry. The progression of the field from mutagenesis and homology modeling to elaborate structure-enabled drug discovery is described. We detail how computational methods have delivered new chemical matter and revealed the functional details of PAM and NAM activity. The breakthrough in mGlu receptor 7-transmembrane (7TM) crystal structures enabled recent combined modeling and experimental studies to confirm common binding sites, interactions and the origins of ligand effect on functional activity. Focusing on allosteric modulation of the mGlu2 and mGlu5 receptors, similarities are seen that still accommodate the known differences in binding sites and SAR. This work reveals the promise of a methodical computational approach built upon deep analysis of 7TM receptor simulations and interpretation of results in the context of our current understanding of receptor function. A crucial aspect was the close collaboration between modeling and experiment necessary to build and interrogate the hypotheses.

Breaking the Glass Ceiling in Simulation and Modeling: Women in Pharmaceutical Discovery.

The topic of gender equality within the United States workforce is receiving a great deal of attention. The field of chemistry is no exception and is increasingly focused on taking steps to achieve gender diversity within the chemistry workforce. Over the past several years, many computational chemistry groups within large pharmaceutical companies have realized growth in the number of women, and here we discuss the key factors that we believe have played a role in attracting and retaining the authors of this review as computational chemists in pharma. Furthermore, we combine our professional experiences in the context of how computational methodology and technology have evolved over the past decades and how that evolution has facilitated the inclusion of more women into the field. Our hope is to be a part of a solution and provide insight that will allow the chemistry workforce to continue to make steps forward in attaining gender diversity in the workplace.

Monte Carlo simulations using PELE to identify a protein-protein inhibitor binding site and pose.

In silico binding site location and pose prediction for a molecule targeted at a large protein surface is a challenging task. We report a blind test with two peptidomimetic molecules that bind the flu virus hemagglutinin (HA) surface antigen, JNJ7918 and JNJ4796 (recently disclosed in van Dongen et al., Science, 2019, 363). Tests with a series of conventional approaches such as rigid (receptor) docking against available X-ray crystal structures or against an ensemble of structures generated by quick methodologies (NMA, homology modeling) gave mixed results, due to the shallowness and flexibility of the binding site and the sheer size of the target. However, tests with our Monte Carlo platform PELE in two protocols involving either exploration of the whole protein surface (global exploration), or the latter followed by refinement of best solutions (local exploration) yielded remarkably good results by locating the actual binding site and generating binding modes that recovered all native contacts found in the X-ray structures. Thus, the Monte Carlo scheme of PELE seems promising as a quick methodology to overcome the challenge of identifying entirely unknown binding sites and modes for protein-protein disruptors.

Accuracy and Precision of Alchemical Relative Free Energy Predictions with and without Replica-Exchange.

A systematic and statistically robust protocol is applied for the evaluation of free energy calculations with and without replica-exchange. The protocol is based on ensemble averaging to generate accurate assessments of the uncertainties in the predictions. Comparison is made between FEP+ and TIES-free energy perturbation and thermodynamic integration with enhanced sampling-the latter with and without the so-called "enhanced sampling" based on replica-exchange protocols. Standard TIES performs best for a reference set of targets and compounds; no benefits accrue from replica-exchange methods. Evaluation of FEP+ and TIES with REST-replica-exchange with solute tempering-reveals a systematic and significant underestimation of free energy differences in FEP+, which becomes increasingly large for long duration simulations, is confirmed by extensive analysis of previous publications, and raises a number of questions pertaining to the accuracy of the predictions with the REST technique not hitherto discussed.

Computational Drug Design Applied to the Study of Metabotropic Glutamate Receptors.

Metabotropic glutamate (mGlu) receptors are a family of eight GPCRs that are attractive drug discovery targets to modulate glutamate action and response. Here we review the application of computational methods to the study of this family of receptors. X-ray structures of the extracellular and 7-transmembrane domains have played an important role to enable structure-based modeling approaches, whilst we also discuss the successful application of ligand-based methods. We summarize the literature and highlight the areas where modeling and experiment have delivered important understanding for mGlu receptor drug discovery. Finally, we offer suggestions of future areas of opportunity for computational work.

Mechanisms Underlying Allosteric Molecular Switches of Metabotropic Glutamate Receptor 5.

The metabotropic glutamate 5 (mGlu5) receptor is a class C G protein-coupled receptor (GPCR) that is implicated in several CNS disorders making it a popular drug discovery target. Years of research have revealed allosteric mGlu5 ligands showing an unexpected complete switch in functional activity despite only small changes in their chemical structure, resulting in positive allosteric modulators (PAM) or negative allosteric modulators (NAM) for the same scaffold. Up to now, the origins of this effect are not understood, causing difficulties in a drug discovery context. In this work, experimental data was gathered and analyzed alongside docking and Molecular Dynamics (MD) calculations for three sets of PAM and NAM pairs. The results consistently show the role of specific interactions formed between ligand substituents and amino acid side chains that block or promote local movements associated with receptor activation. The work provides an explanation for how such small structural changes lead to remarkable differences in functional activity. While this work can greatly help drug discovery programs avoid these switches, it also provides valuable insight into the mechanisms of class C GPCR allosteric activation. Furthermore, the approach shows the value of applying MD to understand functional activity in drug design programs, even for such close structural analogues.

Predicting Activity Cliffs with Free-Energy Perturbation.

Activity cliffs (ACs) are an important type of structure-activity relationship in medicinal chemistry where small structural changes result in unexpectedly large differences in biological activity. Being able to predict these changes would have a profound impact on lead optimization of drug candidates. Free-energy perturbation is an ideal tool for predicting relative binding energy differences for small structural modifications, but its performance for ACs is unknown. Here, we show that FEP can on average predict ACs to within 1.39 kcal/mol of experiment (∼1 log unit of activity). We performed FEP calculations with two different software methods: Schrödinger-Desmond FEP+ and GROMACS implementations. There was qualitative agreement in the results from the two methods, and quantitatively the error for one data set was identical, 1.43 kcal/mol, but FEP+ performed better in the second, with errors of 1.17 versus 1.90 kcal/mol. The results have far-reaching implications, suggesting well-implemented FEP calculations can have a major impact on computational drug design.

Large scale relative protein ligand binding affinities using non-equilibrium alchemy.

Ligand binding affinity calculations based on molecular dynamics (MD) simulations and non-physical (alchemical) thermodynamic cycles have shown great promise for structure-based drug design. However, their broad uptake and impact is held back by the notoriously complex setup of the calculations. Only a few tools other than the free energy perturbation approach by Schrödinger Inc. (referred to as FEP+) currently enable end-to-end application. Here, we present for the first time an approach based on the open-source software pmx that allows to easily set up and run alchemical calculations for diverse sets of small molecules using the GROMACS MD engine. The method relies on theoretically rigorous non-equilibrium thermodynamic integration (TI) foundations, and its flexibility allows calculations with multiple force fields. In this study, results from the Amber and Charmm force fields were combined to yield a consensus outcome performing on par with the commercial FEP+ approach. A large dataset of 482 perturbations from 13 different protein-ligand datasets led to an average unsigned error (AUE) of 3.64 ± 0.14 kJ mol-1, equivalent to Schrödinger's FEP+ AUE of 3.66 ± 0.14 kJ mol-1. For the first time, a setup is presented for overall high precision and high accuracy relative protein-ligand alchemical free energy calculations based on open-source software.

Covalent Allosteric Probe for the Metabotropic Glutamate Receptor 2: Design, Synthesis, and Pharmacological Characterization.

Covalent labeling of G protein-coupled receptors (GPCRs) by small molecules is a powerful approach to understand binding modes, mechanism of action, pharmacology, and even facilitate structure elucidation. We report the first covalent positive allosteric modulator (PAM) for a class C GPCR, the mGlu2 receptor. Three putatively covalent mGlu2 PAMs were designed and synthesized. Pharmacological characterization identified 2 to bind the receptor covalently. Computational modeling combined with receptor mutagenesis revealed T7917.29×30 as the likely position of covalent interaction. We show how this covalent ligand can be used to characterize the PAM binding mode and that it is a valuable tool compound in studying receptor function and binding kinetics. Our findings advance the understanding of the mGlu2 PAM interaction and suggest that 2 is a valuable probe for further structural and chemical biology approaches.

DeltaDelta neural networks for lead optimization of small molecule potency.

The capability to rank different potential drug molecules against a protein target for potency has always been a fundamental challenge in computational chemistry due to its importance in drug design. While several simulation-based methodologies exist, they are hard to use prospectively and thus predicting potency in lead optimization campaigns remains an open challenge. Here we present the first machine learning approach specifically tailored for ranking congeneric series based on deep 3D-convolutional neural networks. Furthermore we prove its effectiveness by blindly testing it on datasets provided by Janssen, Pfizer and Biogen totalling over 3246 ligands and 13 targets as well as several well-known openly available sets, representing one the largest evaluations ever performed. We also performed online learning simulations of lead optimization using the approach in a predictive manner obtaining significant advantage over experimental choice. We believe that the evaluation performed in this study is strong evidence of the usefulness of a modern deep learning model in lead optimization pipelines against more expensive simulation-based alternatives.