SETD8 inhibition targets cancer cells with increased rates of ribosome biogenesis.

SETD8 is a methyltransferase that is overexpressed in several cancers, which monomethylates H4K20 as well as other non-histone targets such as PCNA or p53. We here report novel SETD8 inhibitors, which were discovered while trying to identify chemicals that prevent 53BP1 foci formation, an event mediated by H4K20 methylation. Consistent with previous reports, SETD8 inhibitors induce p53 expression, although they are equally toxic for p53 proficient or deficient cells. Thermal stability proteomics revealed that the compounds had a particular impact on nucleoli, which was confirmed by fluorescent and electron microscopy. Similarly, Setd8 deletion generated nucleolar stress and impaired ribosome biogenesis, supporting that this was an on-target effect of SETD8 inhibitors. Furthermore, a genome-wide CRISPR screen identified an enrichment of nucleolar factors among those modulating the toxicity of SETD8 inhibitors. Accordingly, the toxicity of SETD8 inhibition correlated with MYC or mTOR activity, key regulators of ribosome biogenesis. Together, our study provides a new class of SETD8 inhibitors and a novel biomarker to identify tumors most likely to respond to this therapy.

G protein-specific mechanisms in the serotonin 5-HT2A receptor regulate psychosis-related effects and memory deficits.

G protein-coupled receptors (GPCRs) are sophisticated signaling machines able to simultaneously elicit multiple intracellular signaling pathways upon activation. Complete (in)activation of all pathways can be counterproductive for specific therapeutic applications. This is the case for the serotonin 2 A receptor (5-HT2AR), a prominent target for the treatment of schizophrenia. In this study, we elucidate the complex 5-HT2AR coupling signature in response to different signaling probes, and its physiological consequences by combining computational modeling, in vitro and in vivo experiments with human postmortem brain studies. We show how chemical modification of the endogenous agonist serotonin dramatically impacts the G protein coupling profile of the 5-HT2AR and the associated behavioral responses. Importantly, among these responses, we demonstrate that memory deficits are regulated by Gαq protein activation, whereas psychosis-related behavior is modulated through Gαi1 stimulation. These findings emphasize the complexity of GPCR pharmacology and physiology and open the path to designing improved therapeutics for the treatment of stchizophrenia.

Characterization of p38α autophosphorylation inhibitors that target the non-canonical activation pathway.

p38α is a versatile protein kinase that can control numerous processes and plays important roles in the cellular responses to stress. Dysregulation of p38α signaling has been linked to several diseases including inflammation, immune disorders and cancer, suggesting that targeting p38α could be therapeutically beneficial. Over the last two decades, numerous p38α inhibitors have been developed, which showed promising effects in pre-clinical studies but results from clinical trials have been disappointing, fueling the interest in the generation of alternative mechanisms of p38α modulation. Here, we report the in silico identification of compounds that we refer to as non-canonical p38α inhibitors (NC-p38i). By combining biochemical and structural analyses, we show that NC-p38i efficiently inhibit p38α autophosphorylation but weakly affect the activity of the canonical pathway. Our results demonstrate how the structural plasticity of p38α can be leveraged to develop therapeutic opportunities targeting a subset of the functions regulated by this pathway.

High-Throughput Prediction of the Impact of Genetic Variability on Drug Sensitivity and Resistance Patterns for Clinically Relevant Epidermal Growth Factor Receptor Mutations from Atomistic Simulations.

Mutations in the kinase domain of the epidermal growth factor receptor (EGFR) can be drivers of cancer and also trigger drug resistance in patients receiving chemotherapy treatment based on kinase inhibitors. A priori knowledge of the impact of EGFR variants on drug sensitivity would help to optimize chemotherapy and design new drugs that are effective against resistant variants before they emerge in clinical trials. To this end, we explored a variety of in silico methods, from sequence-based to "state-of-the-art" atomistic simulations. We did not find any sequence signal that can provide clues on when a drug-related mutation appears or the impact of such mutations on drug activity. Low-level simulation methods provide limited qualitative information on regions where mutations are likely to cause alterations in drug activity, and they can predict around 70% of the impact of mutations on drug efficiency. High-level simulations based on nonequilibrium alchemical free energy calculations show predictive power. The integration of these "state-of-the-art" methods into a workflow implementing an interface for parallel distribution of the calculations allows its automatic and high-throughput use, even for researchers with moderate experience in molecular simulations.

Comprehensive in Vitro Characterization of the LSD1 Small Molecule Inhibitor Class in Oncology.

Lysine-specific demethylase 1 (LSD1 or KDM1A) is a chromatin modifying enzyme playing a key role in the cell cycle and cell differentiation and proliferation through the demethylation of histones and nonhistone substrates. In addition to its enzymatic activity, LSD1 plays a fundamental scaffolding role as part of transcription silencing complexes such as rest co-repressor (CoREST) and nucleosome remodeling and deacetylase (NuRD). A host of classical amine oxidase inhibitors such as tranylcypromine, pargyline, and phenelzine together with LSD1 tool compounds such as SP-2509 and GSK-LSD1 have been extensively utilized in LSD1 mechanistic cancer studies. Additionally, several optimized new chemical entities have reached clinical trials in oncology such as ORY-1001 (iadademstat), GSK2879552, SP-2577 (seclidemstat), IMG-7289 (bomedemstat), INCB059872, and CC-90011 (pulrodemstat). Despite this, no single study exists that characterizes them all under the same experimental conditions, preventing a clear interpretation of published results. Herein, we characterize the whole LSD1 small molecule compound class as inhibitors of LSD1 catalytic activity, disruptors of SNAIL/GFI1 (SNAG)-scaffolding protein-protein interactions, inducers of cell differentiation, and potential anticancer treatments for hematological and solid tumors to yield an updated, unified perspective of this field. Our results highlight significant differences in potency and selectivity among the clinical compounds with iadademstat being the most potent and reveal that most of the tool compounds have very low activity and selectivity, suggesting some conclusions derived from their use should be taken with caution.

Exploring the Conformational Landscape of Bioactive Small Molecules.

By using a combination of classical Hamiltonian replica exchange with high-level quantum mechanical calculations on more than one hundred drug-like molecules, we explored here the energy cost associated with binding of drug-like molecules to target macromolecules. We found that, in general, the drug-like molecules present bound to proteins in the Protein Data Bank (PDB) can access easily the bioactive conformation and in fact for 73% of the studied molecules the "bioactive" conformation is within 3kBT from the most-stable conformation in solution as determined by DFT/SCRF calculations. Cases with large differences between the most-stable and the bioactive conformations appear in ligands recognized by ionic contacts, or very large structures establishing many favorable interactions with the protein. There are also a few cases where we observed a non-negligible uncertainty related to the experimental structure deposited in PDB. Remarkably, the rough automatic force field used here provides reasonable estimates of the conformational ensemble of drugs in solution. The outlined protocol can be used to better estimate the cost of adopting the bioactive conformation.

Bioactive Conformational Ensemble Server and Database. A Public Framework to Speed Up In Silico Drug Discovery.

Modern high-throughput structure-based drug discovery algorithms consider ligand flexibility, but typically with low accuracy, which results in a loss of performance in the derived models. Here we present the bioactive conformational ensemble (BCE) server and its associated database. The server creates conformational ensembles of drug-like ligands and stores them in the BCE database, where a variety of analyses are offered to the user. The workflow implemented in the BCE server combines enhanced sampling molecular dynamics with self-consistent reaction field quantum mechanics (SCRF/QM) calculations. The server automatizes all of the steps to transform one-dimensional (1D) or 2D representation of drugs into 3D molecules, which are then titrated, parametrized, hydrated, and optimized before being subjected to Hamiltonian replica-exchange (HREX) molecular dynamics simulations. Ensembles are collected and subjected to a clustering procedure to derive representative conformers, which are then analyzed at the SCRF/QM level of theory. All structural data are organized in a noSQL database accessible through a graphical interface and in a programmatic manner through a REST API. The server allows the user to define a private workspace and offers a deposition protocol as well as input files for "in house" calculations in those cases where confidentiality is a must. The database and the associated server are available at https://mmb.irbbarcelona.org/BCE.

DFFR: A New Method for High-Throughput Recalibration of Automatic Force-Fields for Drugs.

We present drug force-field recalibration (DFFR), a new method for refining of automatic force-fields used to represent small drugs in docking and molecular dynamics simulations. The method is based on fine-tuning of torsional terms to obtain ensembles that reproduce observables derived from reference data. DFFR is fast and flexible and can be easily automatized for a high-throughput regime, making it useful in drug-design projects. We tested the performance of the method in a few model systems and also in a variety of druglike molecules using reference data derived from: (i) density functional theory coupled to a self-consistent reaction field (DFT/SCRF) calculations on highly populated conformers and (ii) enhanced sampling quantum mechanical/molecular mechanics (QM/MM) where the drug is reproduced at the QM level, while the solvent is represented by classical force-fields. Extension of the method to include other sources of reference data is discussed.

Δ9 -Tetrahydrocannabinolic acid alleviates collagen-induced arthritis: Role of PPARγ and CB1 receptors.

BACKGROUND AND PURPOSE: Δ9 -Tetrahydrocannabinolic acid (Δ9 -THCA-A), the precursor of Δ9 -THC, is a non-psychotropic phytocannabinoid that shows PPARγ agonist activity. Here, we investigated the ability of Δ9 -THCA-A to modulate the classic cannabinoid CB1 and CB2 receptors and evaluated its anti-arthritis activity in vitro and in vivo. EXPERIMENTAL APPROACH: Cannabinoid receptors binding and intrinsic activity, as well as their downstream signalling, were analysed in vitro and in silico. The anti-arthritis properties of Δ9 -THCA-A were studied in human chondrocytes and in the murine model of collagen-induced arthritis (CIA). Plasma disease biomarkers were identified by LC-MS/MS based on proteomic and elisa assays. KEY RESULTS: Functional and docking analyses showed that Δ9 -THCA-A can act as an orthosteric CB1 receptor agonist and also as a positive allosteric modulator in the presence of CP-55,940. Also, Δ9 -THCA-A seemed to be an inverse agonist for CB2 receptors. In vivo, Δ9 -THCA-A reduced arthritis in CIA mice, preventing the infiltration of inflammatory cells, synovium hyperplasia, and cartilage damage. Furthermore, Δ9 -THCA-A inhibited expression of inflammatory and catabolic genes on knee joints. The anti-arthritic effect of Δ9 -THCA-A was blocked by either SR141716 or T0070907. Analysis of plasma biomarkers, and determination of cytokines and anti-collagen antibodies confirmed that Δ9 -THCA-A mediated its activity mainly through PPARγ and CB1 receptor pathways. CONCLUSION AND IMPLICATIONS: Δ9 -THCA-A modulates CB1 receptors through the orthosteric and allosteric binding sites. In addition, Δ9 -THCA-A exerts anti-arthritis activity through CB1 receptors and PPARγ pathways, highlighting its potential for the treatment of chronic inflammatory diseases such as rheumatoid arthritis.

FragPELE: Dynamic Ligand Growing within a Binding Site. A Novel Tool for Hit-To-Lead Drug Design.

The early stages of drug discovery rely on hit-to-lead programs, where initial hits undergo partial optimization to improve binding affinities for their biological target. This is an expensive and time-consuming process, requiring multiple iterations of trial and error designs, an ideal scenario for applying computer simulation. However, most state-of-the-art modeling techniques fail to provide a fast and reliable answer to the Induced-Fit protein-ligand problem. To aid in this matter, we present FragPELE, a new tool for in silico hit-to-lead drug design, capable of growing a fragment from a bound core while exploring the protein-ligand conformational space. We tested the ability of FragPELE to predict crystallographic data, even in cases where cryptic sub-pockets open because of the presence of particular R-groups. Additionally, we evaluated the potential of the software on growing and scoring five congeneric series from the 2015 FEP+ dataset, comparing them to FEP+, SP and Induced-Fit Glide, and MMGBSA simulations. Results show that FragPELE could be useful not only for finding new cavities and novel binding modes in cases where standard docking tools cannot but also to rank ligand activities in a reasonable amount of time and with acceptable precision.

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.

Atomistic simulations shed new light on the activation mechanisms of RORγ and classify it as Type III nuclear hormone receptor regarding ligand-binding paths.

The molecular recognition of the RORγ nuclear hormone receptor (NHR) ligand-binding domain (LBD) has been extensively studied with numerous X-ray crystal structures. However, the picture afforded by these complexes is static and does not fully explain the functional behavior of the LBD. In particular, the apo structure of the LBD seems to be in a fully active state, with no obvious differences to the agonist-bound structure. Further, several atypical in vivo inverse agonists have surprisingly been found to co-crystallize with the LBD in agonist mode (with co-activator), leading to a disconnection between molecular recognition and functional activity. Moreover, the experimental structures give no clues on how RORγ LBD binders access the interior of the LBD. To address all these points, we probe here, with a variety of simulation techniques, the fine structural balance of the RORγ LBD in its apo vs. holo form, the differences in flexibility and stability of the LBD in complex with agonists vs. inverse agonists and how binders diffuse in and out of the LBD in unbiased simulations. Our data conclusively point to the stability afforded by the so-called "agonist lock" between H479 and Y502 and the precise location of Helix 12 (H12) for the competence of the LBD to bind co-activator proteins. We observe the "water trapping" mechanism suggested previously for the atypical inverse agonists and discover a different behavior for the latter when co-activator is present or absent, which might help explain their conflicting data. Additionally, we unveil the same entry/exit path for agonists and inverse agonist into and out of the LBD for RORγ, suggesting it belongs to the type III NHR sub-family.

PELE-MSM: A Monte Carlo Based Protocol for the Estimation of Absolute Binding Free Energies.

In this study, we present a fully automatic platform based on our Monte Carlo algorithm, the Protein Energy Landscape Exploration method (PELE), for the estimation of absolute protein-ligand binding free energies, one of the most significant challenges in computer aided drug design. Based on a ligand pathway approach, an initial short enhanced sampling simulation is performed to identify reasonable starting positions for more extended sampling. This stepwise approach allows for a significant faster convergence of the free energy estimation using the Markov State Model (MSM) technique. PELE-MSM was applied on four diverse protein and ligand systems, successfully ranking compounds for two systems. Based on the results, current limitations and challenges with physics-based methods in computational structural biology are discussed. Overall, PELE-MSM constitutes a promising step toward computing absolute binding free energies and in their application into drug discovery projects.

Extensive benchmark of rDock as a peptide-protein docking tool.

Peptide-protein interactions are ubiquitous in living cells and essential to a wide range of biological processes, as well as pathologies such as cancer or cardiovascular disease. Yet, obtaining reliable binding mode predictions in peptide-protein docking remains a great challenge for most computational docking programs. The main goal of this study was to assess the performance of the small molecule docking program rDock in comparison to other widely used small molecule docking programs, using 100 peptide-protein systems with peptides ranging from 2 to 12 residues. As we used two large independent benchmark sets previously published for other small-molecule docking programs (AutoDockVina benchmark and LEADSPEP), the performance of rDock could directly be compared to the performances of AutoDockVina, Surflex, GOLD, and Glide, as well as to the peptide docking protocol PIPER-FlexPepDock and the webserver HPepDock. Our benchmark reveals that rDock can dock the 100 peptides with an overall backbone RMSD below 2.5 Å in 58.5% of the cases (76% for the 47 systems of the AutoDockVina benchmark set and 43% for the 53 systems of the LEADSPEP benchmark set). More specifically, rDock docks up to 11-residue peptides with a backbone RMSD below 2.5 Å in 60.75% of the cases. rDock displays higher accuracy than most available small molecule docking programs for 6-10-residue peptides and can sometimes perform similarly to the peptide docking tool, especially at a high level of exhaustiveness (100 or 150 runs). Its performance, as is the case for many other unguided small molecule docking tools, is compromised when the peptides adopt secondary structures upon binding. However, our analyses suggest that rDock could be used for predicting how medium-sized biologically relevant peptides bind to their respective protein targets when the latter bind in an extended mode.

Pushing the Limits of Computational Structure-Based Drug Design with a Cryo-EM Structure: The Ca2+ Channel α2δ-1 Subunit as a Test Case.

Cryo-electron microscopy (cryo-EM) is emerging as a real alternative for structural elucidation. In spite of this, very few cryo-EM structures have been described so far as successful platforms for in silico drug design. Gabapentin and pregabalin are some of the most successful drugs in the treatment of epilepsy and neuropathic pain. Although both are in clinical use and are known to exert their effects by binding to the regulatory α2δ subunit of voltage gated calcium channels, their binding modes have never been characterized. We describe here the successful use of an exhaustive protein-ligand sampling algorithm on the α2δ-1 subunit of the recently published cryo-EM structure, with the goal of characterizing the ligand entry path and binding mode for gabapentin, pregabalin, and several other amino acidic α2δ-1 ligands. Our studies indicate that (i) all simulated drugs explore the same path for accessing the occluded binding site on the interior of the α2δ-1 subunit; (ii) they all roughly occupy the same pocket; (iii) the plasticity of the binding site allows the accommodation of a variety of amino acidic modulators, driven by the flexible "capping loop" delineated by residues Tyr426-Val435 and the floppy nature of Arg217; (iv) the predicted binding modes are in line with previously available mutagenesis data, confirming Arg217 as key for binding, with Asp428 and Asp467 highlighted as additional anchoring points for all amino acidic drugs. The study is one of the first proofs that latest-generation cryo-EM structures combined with exhaustive computational methods can be exploited in early drug discovery.

Challenges of docking in large, flexible and promiscuous binding sites.

After decades of work, the correct determination of the binding mode of a small molecule into a target protein is still a challenging problem, whose difficulty depends on: (i) the sizes of the binding site and the ligand; (ii) the flexibility of both interacting partners, and (iii) the differential solvation of bound and unbound partners. We have evaluated the performance of standard rigid(receptor)/flexible(ligand) docking approaches with respect to last-generation fully flexible docking methods to obtain reasonable poses in a very challenging case: soluble Epoxide Hydrolase (sEH), a flexible protein showing different binding sites. We found that full description of the flexibility of both protein and ligand and accurate description of solvation leads to significant improvement in the ability of docking to reproduce well known binding modes, and at the same time capture the intrinsic binding promiscuity of the protein.

Computational Prediction of HIV-1 Resistance to Protease Inhibitors.

The development of mutations in HIV-1 protease (PR) hinders the activity of antiretroviral drugs, forcing changes in drug prescription. Most resistance assessments used to date rely on expert-based rules on predefined sets of stereotypical mutations; such an information-driven approach cannot capture new polymorphisms or be applied for new drugs. Computational modeling could provide a more general assessment of drug resistance and could be made available to clinicians through the Internet. We have created a protocol involving sequence comparison and all-atom protein-ligand induced fit simulations to predict resistance at the molecular level. We first compared our predictions with the experimentally determined IC50 values of darunavir, amprenavir, ritonavir, and indinavir from reference PR mutants displaying different resistance levels. We then performed analyses on a large set of variants harboring more than 10 mutations. Finally, several sequences from real patients were analyzed for amprenavir and darunavir. Our computational approach detected all of the genotype changes triggering high-level resistance, even those involving a large number of mutations.

Allosteric regulation of PKCθ: understanding multistep phosphorylation and priming by ligands in AGC kinases.

Protein kinases play critical roles in cellular activation and differentiation, and are involved in numerous pathophysiological processes. As a critical component of the regulatory circuitry of the cell, the kinase domain has the ability to integrate multiple signals, yielding a predetermined output. In PKC and other protein kinases of the AGC family, several phosphorylation sites control the activity, but these are in turn influenced by the presence of ligands in the binding pocket, which promotes phosphorylation. Here, we take PKC-theta as a prototypical member of the family and use molecular dynamics simulations to investigate the cross-talk that exists between regulatory and functional sites. We first show how the apo-unphosphorylated form of the kinase is populating a conformational space in which access to the ATP binding site and to the activation loop (AL) are simultaneously hindered. This could explain why the inactive state is not only catalytically incompetent but also resistant to activation. AL phosphorylation induces ATP binding site opening, which can then readily accept the cofactor. But the signal transmission mechanism works both ways, and if ligand binding to the unphosphorylated form occurs first, the AL is de-protected and becomes exposed to phosphorylation, thus providing an explanation for the paradoxical activation of PKCs by their inhibitors.

Dissection of the recognition properties of p38 MAP kinase. Determination of the binding mode of a new pyridinyl-heterocycle inhibitor family.

The main recognition characteristics of the ATP binding site of p38 mitogen activated protein kinase alpha (p38alpha MAPK) have been explored by a combination of modeling and bioinformatics techniques, making special emphasis in the characteristics of the site that justifies binding specificity with respect to other MAP kinases. Particularly, we have analyzed the binding mode of a new family of p38 MAPK inhibitors based on the pyridinyl-heterocycle core. This family of compounds has a marked pseudosymmetry and can exist in different tautomeric forms, which makes the determination of the binding mode especially challenging. A combination of homology modeling, quantum mechanics, classical docking, and molecular dynamics calculations allowed us to determine the main characteristics defining the binding mode of this new scaffold in the ATP binding site of p38alpha. A set of free energy calculations allowed us to verify the binding mode proposed, giving an overall excellent agreement with the experimental values. Finally, the binding mode of this new family of compounds was compared to that of other members of the pyridinyl and pyrimidinyl heterocycle class.

A fast method for the determination of fractional contributions to solvation in proteins.

A fast method for the calculation of residue contributions to protein solvation is presented. The approach uses the exposed polar and apolar surface of protein residues and has been parametrized from the fractional contributions to solvation determined from linear response theory coupled to molecular dynamics simulations. Application of the method to a large subset of proteins taken from the Protein Data Bank allowed us to compute the expected fractional solvation of residues. This information is used to discuss when a residue or a group of residues presents an uncommon solvation profile.