Computationally driven discovery of SARS-CoV-2 Mpro inhibitors: from design to experimental validation.

We report a fast-track computationally driven discovery of new SARS-CoV-2 main protease (Mpro) inhibitors whose potency ranges from mM for the initial non-covalent ligands to sub-µM for the final covalent compound (IC50 = 830 ± 50 nM). The project extensively relied on high-resolution all-atom molecular dynamics simulations and absolute binding free energy calculations performed using the polarizable AMOEBA force field. The study is complemented by extensive adaptive sampling simulations that are used to rationalize the different ligand binding poses through the explicit reconstruction of the ligand-protein conformation space. Machine learning predictions are also performed to predict selected compound properties. While simulations extensively use high performance computing to strongly reduce the time-to-solution, they were systematically coupled to nuclear magnetic resonance experiments to drive synthesis and for in vitro characterization of compounds. Such a study highlights the power of in silico strategies that rely on structure-based approaches for drug design and allows the protein conformational multiplicity problem to be addressed. The proposed fluorinated tetrahydroquinolines open routes for further optimization of Mpro inhibitors towards low nM affinities.

From Target to Drug: Generative Modeling for the Multimodal Structure-Based Ligand Design.

Chemical space is impractically large, and conventional structure-based virtual screening techniques cannot be used to simply search through the entire space to discover effective bioactive molecules. To address this shortcoming, we propose a generative adversarial network to generate, rather than search, diverse three-dimensional ligand shapes complementary to the pocket. Furthermore, we show that the generated molecule shapes can be decoded using a shape-captioning network into a sequence of SMILES enabling directly the structure-based de novo drug design. We evaluate the quality of the method by both structure- (docking) and ligand-based [quantitative structure-activity relationship (QSAR)] virtual screening methods. For both evaluation approaches, we observed enrichment compared to random sampling from initial chemical space of ZINC drug-like compounds.

Insecticidal spider toxins are high affinity positive allosteric modulators of the nicotinic acetylcholine receptor.

The insecticidal effects of ω-hexatoxin-Hv1a, κ-hexatoxin-Hv1c and ω/κ-hexatoxin-Hv1h are currently attributed to action at calcium and potassium channels. By characterizing the binding of these toxins to neuronal membranes, we show that they have more potent effects as positive allosteric modulators (PAMs) of insect nicotinic acetylcholine receptors (nAChRs), consistent with their neuroexcitatory toxicology. Alanine scanning analysis of ω-hexatoxin-Hv1a reveals a structure-activity relationship for binding that mirrors that for insecticidal activity. Spinosyn A does not compete with ω-hexatoxin-Hv-1a for binding, and we show that these two PAMs have distinct pharmacology of binding indicating that they act at different receptor populations. These toxins represent valuable tools for the characterization of insect nAChRs and for the development of more selective agrochemicals.

Shape-Based Generative Modeling for de Novo Drug Design.

In this work, we propose a machine learning approach to generate novel molecules starting from a seed compound, its three-dimensional (3D) shape, and its pharmacophoric features. The pipeline draws inspiration from generative models used in image analysis and represents a first example of the de novo design of lead-like molecules guided by shape-based features. A variational autoencoder is used to perturb the 3D representation of a compound, followed by a system of convolutional and recurrent neural networks that generate a sequence of SMILES tokens. The generative design of novel scaffolds and functional groups can cover unexplored regions of chemical space that still possess lead-like properties.

Defining and Exploiting Hypersensitivity Hotspots to Facilitate Abscisic Acid Agonist Optimization.

Pyrabactin resistance 1 (PYR1) and related abscisic acid (ABA) receptors are new targets for manipulating plant drought tolerance. Here, we identify and use PYR1 hypersensitive mutants to define ligand binding hotspots and show that these can guide improvements in agonist potency. One hotspot residue defined, A160, is part of a pocket that is occupied by ABA's C6 methyl or by the toluyl methyl of the synthetic agonist quinabactin (QB). A series of QB analogues substituted at the toluyl position were synthesized and provide up to 10-fold gain in activity in vitro. Furthermore, we demonstrate that hypersensitive receptors can be used to improve the sensitivity of a previously described mammalian cell ABA-regulated transcriptional circuit by three orders of magnitude. Collectively, our data show that the systematic mapping of hypersensitivity sites in a ligand-binding pocket can help guide ligand optimization and tune the sensitivity of engineered receptors.

PathwayMap: Molecular Pathway Association with Self-Normalizing Neural Networks.

Drug discovery suffers from high attrition because compounds initially deemed as promising can later show ineffectiveness or toxicity resulting from a poor understanding of their activity profile. In this work, we describe a deep self-normalizing neural network model for the prediction of molecular pathway association and evaluate its performance, showing an AUC ranging from 0.69 to 0.91 on a set of compounds extracted from ChEMBL and from 0.81 to 0.83 on an external data set provided by Novartis. We finally discuss the applicability of the proposed model in the domain of lead discovery. A usable application is available via PlayMolecule.org .

Supervised Molecular Dynamics (SuMD) Approaches in Drug Design.

Supervised MD (SuMD) is a computational method that enables the exploration of ligand-receptor recognition pathway in a reduced timescale. The performance speedup is due to the incorporation of a tabu-like supervision algorithm on the ligand-receptor approaching distance into a classic molecular dynamics (MD) simulation. SuMD enables the investigation of ligand-receptor binding events independently from the starting position, chemical structure of the ligand (small molecules or peptides), and also from its receptor-binding affinity. The application of SuMD highlights an appreciable capability of the technique to reproduce the crystallographic structures of several ligand-protein complexes and can provide high-quality protein-ligand models of for which yet experimental confirmation of binding mode is not available.

New Trends in Inspecting GPCR-ligand Recognition Process: the Contribution of the Molecular Modeling Section (MMS) at the University of Padova.

In this review, we present a survey of the recent advances carried out by our research groups in the field of ligand-GPCRs recognition process simulations recently implemented at the Molecular Modeling Section (MMS) of the University of Padova. We briefly describe a platform of tools we have tuned to aid the identification of novel GPCRs binders and the better understanding of their binding mechanisms, based on two extensively used computational techniques such as molecular docking and MD simulations. The developed methodologies encompass: (i) the selection of suitable protocols for docking studies, (ii) the exploration of the dynamical evolution of ligand-protein interaction networks, (iii) the detailed investigation of the role of water molecules upon ligand binding, and (iv) a glance at the way the ligand might go through prior reaching the binding site.

Deciphering the Complexity of Ligand-Protein Recognition Pathways Using Supervised Molecular Dynamics (SuMD) Simulations.

Molecular recognition is a crucial issue when aiming to interpret the mechanism of known active substances as well as to develop novel active candidates. Unfortunately, simulating the binding process is still a challenging task because it requires classical MD experiments in a long microsecond time scale that are affordable only with a high-level computational capacity. In order to overcome this limiting factor, we have recently implemented an alternative MD approach, named supervised molecular dynamics (SuMD), and successfully applied it to G protein-coupled receptors (GPCRs). SuMD enables the investigation of ligand-receptor binding events independently from the starting position, chemical structure of the ligand, and also from its receptor binding affinity. In this article, we present an extension of the SuMD application domain including different types of proteins in comparison with GPCRs. In particular, we have deeply analyzed the ligand-protein recognition pathways of six different case studies that we grouped into two different classes: globular and membrane proteins. Moreover, we introduce the SuMD-Analyzer tool that we have specifically implemented to help the user in the analysis of the SuMD trajectories. Finally, we emphasize the limit of the SuMD applicability domain as well as its strengths in analyzing the complexity of ligand-protein recognition pathways.

Advances in Computational Techniques to Study GPCR-Ligand Recognition.

G-protein-coupled receptors (GPCRs) are among the most intensely investigated drug targets. The recent revolutions in protein engineering and molecular modeling algorithms have overturned the research paradigm in the GPCR field. While the numerous ligand-bound X-ray structures determined have provided invaluable insights into GPCR structure and function, the development of algorithms exploiting graphics processing units (GPUs) has made the simulation of GPCRs in explicit lipid-water environments feasible within reasonable computation times. In this review we present a survey of the recent advances in structure-based drug design approaches with a particular emphasis on the elucidation of the ligand recognition process in class A GPCRs by means of membrane molecular dynamics (MD) simulations.

Modeling ligand recognition at the P2Y12 receptor in light of X-ray structural information.

The G protein-coupled P2Y12 receptor (P2Y12R) is an important antithrombotic target and of great interest for pharmaceutical discovery. Its recently solved, highly divergent crystallographic structures in complex either with nucleotides (full or partial agonist) or with a nonnucleotide antagonist raise the question of which structure is more useful to understand ligand recognition. Therefore, we performed extensive molecular modeling studies based on these structures and mutagenesis, to predict the binding modes of major classes of P2Y12R ligands previously reported. Various nucleotide derivatives docked readily to the agonist-bound P2Y12R, but uncharged nucleotide-like antagonist ticagrelor required a hybrid receptor resembling the agonist-bound P2Y12R except for the top portion of TM6. Supervised molecular dynamics (SuMD) of ticagrelor binding indicated interactions with the extracellular regions of P2Y12R, defining possible meta-binding sites. Ureas, sulfonylureas, sulfonamides, anthraquinones and glutamic acid piperazines docked readily to the antagonist-bound P2Y12R. Docking dinucleotides at both agonist- and antagonist-bound structures suggested interactions with two P2Y12R pockets. Thus, our structure-based approach consistently rationalized the main structure-activity relationships within each ligand class, giving useful information for designing improved ligands.

The xylanase inhibitor TAXI-III counteracts the necrotic activity of a Fusarium graminearum xylanase in vitro and in durum wheat transgenic plants.

The xylanase inhibitor TAXI-III has been proven to delay Fusarium head blight (FHB) symptoms caused by Fusarium graminearum in transgenic durum wheat plants. To elucidate the molecular mechanism underlying the capacity of the TAXI-III transgenic plants to limit FHB symptoms, we treated wheat tissues with the xylanase FGSG_03624, hitherto shown to induce cell death and hydrogen peroxide accumulation. Experiments performed on lemmas of flowering wheat spikes and wheat cell suspension cultures demonstrated that pre-incubation of xylanase FGSG_03624 with TAXI-III significantly decreased cell death. Most interestingly, a reduced cell death relative to control non-transgenic plants was also obtained by treating, with the same xylanase, lemmas of TAXI-III transgenic plants. Molecular modelling studies predicted an interaction between the TAXI-III residue H395 and residues E122 and E214 belonging to the active site of xylanase FGSG_03624. These results provide, for the first time, clear indications in vitro and in planta that a xylanase inhibitor can prevent the necrotic activity of a xylanase, and suggest that the reduced FHB symptoms on transgenic TAXI-III plants may be a result not only of the direct inhibition of xylanase activity secreted by the pathogen, but also of the capacity of TAXI-III to avoid host cell death.

Perturbation of fluid dynamics properties of water molecules during G protein-coupled receptor-ligand recognition: the human A2A adenosine receptor as a key study.

Recent advances in structural biology revealed that water molecules play a crucial structural role in the protein architecture and ligand binding of G protein-coupled receptors. In this work, we present an alternative approach to monitor the time-dependent organization of water molecules during the final stage of the ligand-receptor recognition process by means of membrane molecular dynamics simulations. We inspect the variation of fluid dynamics properties of water molecules upon ligand binding with the aim to correlate the results with the binding affinities. The outcomes of this analysis are transferred into a bidimensional graph called water fluid dynamics maps, that allow a fast graphical identification of protein "hot-spots" characterized by peculiar shape and electrostatic properties that can play a critical role in ligand binding. We hopefully believe that the proposed approach might represent a valuable tool for structure-based drug discovery that can be extended to cases where crystal structures are not yet available, or have not been solved at high resolution.

Carboxylation-dependent conformational changes of human osteocalcin.

Osteocalcin (OCN) is a small noncollagenous protein mainly produced by osteoblasts and is highly represented in bones of most vertebrates. Human OCN contains up to three gamma-carboxyglutamic acid (Gla-OCN) residues at positions 17, 21 and 24 which are thought to increase calcium binding strength, improving mechanical properties of the bone matrix. Recent studies revealed that OCN exerts also important endocrine functions, affecting energy metabolism and male fertility. The latter effect seems to be mediated by the uncarboxylated form of OCN (Glu-OCN). We employed human and mouse OCN as models of fully carboxylated and uncarboxylated OCN forms to investigate, by the use of circular dichroism and molecular dynamics simulations, the respective conformational properties and Ca2+ affinity. Ca2+ binding was found to trigger a similar conformational transition in both Glu-OCN and Gla-OCN, from a disordered structure to a more compact/stable form. Notably, gamma-carboxylation increases the affinity of OCN for Ca2+ by > 30 fold suggesting that, in physiological conditions, Gla-OCN is essentially Ca2+-bound, whereas Glu-OCN circulates mainly in the Ca2+-free form.

Supervised molecular dynamics (SuMD) as a helpful tool to depict GPCR-ligand recognition pathway in a nanosecond time scale.

Supervised MD (SuMD) is a computational method that allows the exploration of ligand-receptor recognition pathway investigations in a nanosecond (ns) time scale. It consists of the incorporation of a tabu-like supervision algorithm on the ligand-receptor approaching distance into a classic molecular dynamics (MD) simulation technique. In addition to speeding up the acquisition of the ligand-receptor trajectory, this implementation facilitates the characterization of multiple binding events (such as meta-binding, allosteric, and orthosteric sites) by taking advantage of the all-atom MD simulations accuracy of a GPCR-ligand complex embedded into explicit lipid-water environment.

Bridging molecular docking to membrane molecular dynamics to investigate GPCR-ligand recognition: the human A2A adenosine receptor as a key study.

G protein-coupled receptors (GPCRs) represent the largest family of cell-surface receptors and about one-third of the actual targets of clinically used drugs. Following the progress made in the field of GPCRs structural determination, docking-based screening for novel potent and selective ligands is becoming an increasingly adopted strategy in the drug discovery process. However, this methodology is not yet able to anticipate the "bioactive" binding mode and discern it among other conformations. In the present work, we present a novel approach consisting in the integration of molecular docking and membrane MD simulations with the aim to merge the rapid sampling of ligand poses into in the binding site, typical of docking algorithms, with the thermodynamic accuracy of MD simulations in describing, at the molecular level, the stability a GPCR-ligand complex embedded into explicit lipid-water environment. To validate our approach, we have chosen as a key study the human A(2A) adenosine receptor (hA(2A) AR) and selected four receptor-antagonist complexes and one receptor-agonist complex that have been recently crystallized. In light of the obtained results, we believe that our novel strategy can be extended to other GPCRs and might represent a valuable tool to anticipate the "bioactive" conformation of high-affinity ligands.

The mitochondrial calcium uniporter is a multimer that can include a dominant-negative pore-forming subunit.

Mitochondrial calcium uniporter (MCU) channel is responsible for Ruthenium Red-sensitive mitochondrial calcium uptake. Here, we demonstrate MCU oligomerization by immunoprecipitation and Förster resonance energy transfer (FRET) and characterize a novel protein (MCUb) with two predicted transmembrane domains, 50% sequence similarity and a different expression profile from MCU. Based on computational modelling, MCUb includes critical amino-acid substitutions in the pore region and indeed MCUb does not form a calcium-permeable channel in planar lipid bilayers. In HeLa cells, MCUb is inserted into the oligomer and exerts a dominant-negative effect, reducing the [Ca(2+)]mt increases evoked by agonist stimulation. Accordingly, in vitro co-expression of MCUb with MCU drastically reduces the probability of observing channel activity in planar lipid bilayer experiments. These data unveil the structural complexity of MCU and demonstrate a novel regulatory mechanism, based on the inclusion of dominant-negative subunits in a multimeric channel, that underlies the fine control of the physiologically and pathologically relevant process of mitochondrial calcium homeostasis.

New insight into adenosine receptors selectivity derived from a novel series of [5-substituted-4-phenyl-1,3-thiazol-2-yl] benzamides and furamides.

A series of [5-substituted-4-phenyl-1,3-thiazol-2-yl] benzamide and furamide analogues were investigated in radioligand binding studies at adenosine receptor subtypes with an aim to obtain potent and selective adenosine receptor ligands. Benzamide and furamide linked to thiazole was found to be crucial for high adenosine receptor affinity. The most potent compound indentified in this study was 5d with low nanomolar affinity for all four adenosine receptor subtypes. Compounds 5a and 5g showed moderate selectivity for A2A adenosine receptors. Molecular docking versus all four human adenosine receptors combined with membrane molecular dynamics studies were performed to rationalise the peculiar selectivity profile of 5d antagonist.

Implementing the "Best Template Searching" tool into Adenosiland platform.

BACKGROUND: Adenosine receptors (ARs) belong to the G protein-coupled receptors (GCPRs) family. The recent release of X-ray structures of the human A2A AR (h A2A AR ) in complex with agonists and antagonists has increased the application of structure-based drug design approaches to this class of receptors. Among them, homology modeling represents the method of choice to gather structural information on the other receptor subtypes, namely A1, A2B, and A3 ARs. With the aim of helping users in the selection of either a template to build its own models or ARs homology models publicly available on our platform, we implemented our web-resource dedicated to ARs, Adenosiland, with the "Best Template Searching" facility. This tool is freely accessible at the following web address: http://mms.dsfarm.unipd.it/Adenosiland/ligand.php. FINDINGS: The template suggestions and homology models provided by the "Best Template Searching" tool are guided by the similarity of a query structure (putative or known ARs ligand) with all ligands co-crystallized with hA2A AR subtype. The tool computes several similarity indexes and sort the outcoming results according to the index selected by the user. CONCLUSIONS: We have implemented our web-resource dedicated to ARs Adenosiland with the "Best Template Searching" facility, a tool to guide template and models selection for hARs modelling. The underlying idea of our new facility, that is the selection of a template (or models built upon a template) whose co-crystallized ligand shares the highest similarity with the query structure, can be easily extended to other GPCRs.