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.

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.

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.

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 size matters? A computational tool to design bivalent ligands.

Motivation: Bivalent ligands are increasingly important such as for targeting G protein-coupled receptor (GPCR) dimers or proteolysis targeting chimeras (PROTACs). They contain two pharmacophoric units that simultaneously bind in their corresponding binding sites, connected with a spacer chain. Here, we report a molecular modelling tool that links the pharmacophore units via the shortest pathway along the receptors van der Waals surface and then scores the solutions providing prioritization for the design of new bivalent ligands. Results: Bivalent ligands of known dimers of GPCRs, PROTACs and a model bivalent antibody/antigen system were analysed. The tool could rapidly assess the preferred linker length for the different systems and recapitulated the best reported results. In the case of GPCR dimers the results suggest that in some cases these ligands might bind to a secondary binding site at the extracellular entrance (vestibule or allosteric site) instead of the orthosteric binding site. Availability and implementation: Freely accessible from the Molecular Operating Environment svl exchange server (https://svl.chemcomp.com/). Supplementary information: Supplementary data are available at Bioinformatics online.

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.

Cross-communication between Gi and Gs in a G-protein-coupled receptor heterotetramer guided by a receptor C-terminal domain.

BACKGROUND: G-protein-coupled receptor (GPCR) heteromeric complexes have distinct properties from homomeric GPCRs, giving rise to new receptor functionalities. Adenosine receptors (A1R or A2AR) can form A1R-A2AR heteromers (A1-A2AHet), and their activation leads to canonical G-protein-dependent (adenylate cyclase mediated) and -independent (ß-arrestin mediated) signaling. Adenosine has different affinities for A1R and A2AR, allowing the heteromeric receptor to detect its concentration by integrating the downstream Gi- and Gs-dependent signals. cAMP accumulation and ß-arrestin recruitment assays have shown that, within the complex, activation of A2AR impedes signaling via A1R. RESULTS: We examined the mechanism by which A1-A2AHet integrates Gi- and Gs-dependent signals. A1R blockade by A2AR in the A1-A2AHet is not observed in the absence of A2AR activation by agonists, in the absence of the C-terminal domain of A2AR, or in the presence of synthetic peptides that disrupt the heteromer interface of A1-A2AHet, indicating that signaling mediated by A1R and A2AR is controlled by both Gi and Gs proteins. CONCLUSIONS: We identified a new mechanism of signal transduction that implies a cross-communication between Gi and Gs proteins guided by the C-terminal tail of the A2AR. This mechanism provides the molecular basis for the operation of the A1-A2AHet as an adenosine concentration-sensing device that modulates the signals originating at both A1R and A2AR.

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.

Identification of Allosteric Modulators of Metabotropic Glutamate 7 Receptor Using Proteochemometric Modeling.

Proteochemometric modeling (PCM) is a computational approach that can be considered an extension of quantitative structure-activity relationship (QSAR) modeling, where a single model incorporates information for a family of targets and all the associated ligands instead of modeling activity versus one target. This is especially useful for situations where bioactivity data exists for similar proteins but is scarce for the protein of interest. Here we demonstrate the application of PCM to identify allosteric modulators of metabotropic glutamate (mGlu) receptors. Given our long-running interest in modulating mGlu receptor function we compiled a matrix of compound-target bioactivity data. Some members of the mGlu family are well explored both internally and in the public domain, while there are much fewer examples of ligands for other targets such as the mGlu7 receptor. Using a PCM approach mGlu7 receptor hits were found. In comparison to conventional single target modeling the identified hits were more diverse, had a better confirmation rate, and provide starting points for further exploration. We conclude that the robust structure-activity relationship from well explored target family members translated to better quality hits for PCM compared to virtual screening (VS) based on a single target.

Quaternary structure of a G-protein-coupled receptor heterotetramer in complex with Gi and Gs.

BACKGROUND: G-protein-coupled receptors (GPCRs), in the form of monomers or homodimers that bind heterotrimeric G proteins, are fundamental in the transfer of extracellular stimuli to intracellular signaling pathways. Different GPCRs may also interact to form heteromers that are novel signaling units. Despite the exponential growth in the number of solved GPCR crystal structures, the structural properties of heteromers remain unknown. RESULTS: We used single-particle tracking experiments in cells expressing functional adenosine A1-A2A receptors fused to fluorescent proteins to show the loss of Brownian movement of the A1 receptor in the presence of the A2A receptor, and a preponderance of cell surface 2:2 receptor heteromers (dimer of dimers). Using computer modeling, aided by bioluminescence resonance energy transfer assays to monitor receptor homomerization and heteromerization and G-protein coupling, we predict the interacting interfaces and propose a quaternary structure of the GPCR tetramer in complex with two G proteins. CONCLUSIONS: The combination of results points to a molecular architecture formed by a rhombus-shaped heterotetramer, which is bound to two different interacting heterotrimeric G proteins (Gi and Gs). These novel results constitute an important advance in understanding the molecular intricacies involved in GPCR function.

Gastrin-stimulated Gα13 Activation of Rgnef Protein (ArhGEF28) in DLD-1 Colon Carcinoma Cells.

The guanine nucleotide exchange factor Rgnef (also known as ArhGEF28 or p190RhoGEF) promotes colon carcinoma cell motility and tumor progression via interaction with focal adhesion kinase (FAK). Mechanisms of Rgnef activation downstream of integrin or G protein-coupled receptors remain undefined. In the absence of a recognized G protein signaling homology domain in Rgnef, no proximal linkage to G proteins was known. Utilizing multiple methods, we have identified Rgnef as a new effector for Gα13 downstream of gastrin and the type 2 cholecystokinin receptor. In DLD-1 colon carcinoma cells depleted of Gα13, gastrin-induced FAK Tyr(P)-397 and paxillin Tyr(P)-31 phosphorylation were reduced. RhoA GTP binding and promoter activity were increased by Rgnef in combination with active Gα13. Rgnef co-immunoprecipitated with activated Gα13Q226L but not Gα12Q229L. The Rgnef C-terminal (CT, 1279-1582) region was sufficient for co-immunoprecipitation, and Rgnef-CT exogenous expression prevented Gα13-stimulated SRE activity. A domain at the C terminus of the protein close to the FAK binding domain is necessary to bind to Gα13. Point mutations of Rgnef-CT residues disrupt association with active Gα13 but not Gαq. These results show that Rgnef functions as an effector of Gα13 signaling and that this linkage may mediate FAK activation in DLD-1 colon carcinoma cells.

Application of Free Energy Perturbation for the Design of BACE1 Inhibitors.

Novel spiroaminodihydropyrroles probing for optimized interactions at the P3 pocket of ß-secretase 1 (BACE1) were designed with the use of free energy perturbation (FEP) calculations. The resulting molecules showed pIC50 potencies in enzymatic BACE1 inhibition assays ranging from approximately 5 to 7. Good correlation was observed between the predicted activity from the FEP calculations and experimental activity. Simulations run with a default 5 ns approach delivered a mean unsigned error (MUE) between prediction and experiment of 0.58 and 0.91 kcal/mol for retrospective and prospective applications, respectively. With longer simulations of 10 and 20 ns, the MUE was in both cases 0.57 kcal/mol for the retrospective application, and 0.69 and 0.59 kcal/mol for the prospective application. Other considerations that impact the quality of the calculations are discussed. This work provides an example of the value of FEP as a computational tool for drug discovery.

A Versatile Approach to CF3 -Containing 2-Pyrrolidones by Tandem Michael Addition-Cyclization: Exemplification in the Synthesis of Amidine Class BACE1 Inhibitors.

The synthesis of new fluorinated pyrrolidones starting from unprotected amino esters and amino nitriles through a Michael addition-lactamization sequence is described. The resulting CF3 -containing building blocks, bearing a quaternary stereogenic center adjacent to the fluorinated group, have been converted into amino pyrrolidines that display potent ß-secretase 1 (BACE1) inhibitory activity. This work constitutes an example of selective fluorination as a valid strategy for the modulation of physicochemical and biological properties of lead compounds in drug discovery.

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.

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.

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.

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.

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.

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.