Navigating from cellular phenotypic screen to clinical candidate: selective targeting of the NLRP3 inflammasome.

The NLRP3 inflammasome plays a pivotal role in host defense and drives inflammation against microbial threats, crystals, and danger-associated molecular patterns (DAMPs). Dysregulation of NLRP3 activity is associated with various human diseases, making it an attractive therapeutic target. Patients with NLRP3 mutations suffer from Cryopyrin-Associated Periodic Syndrome (CAPS) emphasizing the clinical significance of modulating NLRP3.

Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules.

We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM) dataset that contains the structural and electronic information of 59,783 low-and high-energy conformers of 1,653 molecules with a total number of atoms ranging from 2 to 92 (mean: 50.9), and containing up to 54 (mean: 28.2) non-hydrogen atoms.

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.

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.

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.

Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks [Article v0.1].

Free energy calculations are rapidly becoming indispensable in structure-enabled drug discovery programs. As new methods, force fields, and implementations are developed, assessing their expected accuracy on real-world systems () becomes critical to provide users with an assessment of the accuracy expected when these methods are applied within their domain of applicability, and developers with a way to assess the expected impact of new methodologies. These assessments require construction of a benchmark-a set of well-prepared, high quality systems with corresponding experimental measurements designed to ensure the resulting calculations provide a realistic assessment of expected performance when these methods are deployed within their domains of applicability.

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.

Modeling the β-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 β-secretase cleavage of APP and Aβ 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).

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.

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.

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.

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.

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.

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

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 , , 2019, ). 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.

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.

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.

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.

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.

Mechanisms Underlying Allosteric Molecular Switches of Metabotropic Glutamate Receptor 5.

The metabotropic glutamate 5 (mGlu) 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 mGlu 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.

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.

Design of a True Bivalent Ligand with Picomolar Binding Affinity for a G Protein-Coupled Receptor Homodimer.

Bivalent ligands have emerged as chemical tools to study G protein-coupled receptor dimers. Using a combination of computational, chemical, and biochemical tools, here we describe the design of bivalent ligand 13 with high affinity ( K = 21 pM) for the dopamine D receptor (DR) homodimer. Bivalent ligand 13 enhances the binding affinity relative to monovalent compound 15 by 37-fold, indicating simultaneous binding at both protomers.

Computationally Guided Identification of Allosteric Agonists of the Metabotropic Glutamate 7 Receptor.

The metabotropic glutamate 7 (mGlu) receptor belongs to the group III of mGlu receptors. Since the mGlu receptor can control excitatory neurotransmission in the hippocampus and cortex, modulation of the receptor may have therapeutic benefit in several CNS diseases. However, mGlu remains relatively unexplored among the eight known mGlu receptors partly because of the limited availability of tool compounds to interrogate its potential therapeutic utility.

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.