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Elucidation of Agonist and Antagonist Dynamic Binding Patterns in ER-α by Integration of Molecular Docking, Molecular Dynamics Simulations and Quantum Mechanical Calculations
Estrogen receptor alpha (ERα) is a ligand-dependent transcriptional factor in the nuclear receptor superfamily. Many structures of ERα bound with agonists and antagonists have been determined. However, the dynamic binding patterns of agonists and antagonists in the binding site of ERα remains unclear. Therefore, we performed molecular docking, molecular dynamics (MD) simulations, and quantum mechanical calculations to elucidate agonist and antagonist dynamic binding patterns in ERα. 17β-estradiol (E2) and 4-hydroxytamoxifen (OHT) were docked in the ligand binding pockets of the agonist and antagonist bound ERα. The best complex conformations from molecular docking were subjected to 100 nanosecond MD simulations. Hierarchical clustering was conducted to group the structures in the trajectory from MD simulations. The representative structure from each cluster was selected to calculate the binding interaction energy value for elucidation of the dynamic binding patterns of agonists and antagonists in the binding site of ERα. The binding interaction energy analysis revealed that OHT binds ERα more tightly in the antagonist conformer, while E2 prefers the agonist conformer. The results may help identify ERα antagonists as drug candidates and facilitate risk assessment of chemicals through ER-mediated responses.
Effect of calmodulin antagonists on the growth and graviresponsiveness of primary roots of maize
We examined the effect of calmodulin (CaM) antagonists applied at the root tip on root growth, gravity-induced root curvature, and the movement of calcium across the root tip and auxin (IAA) across the elongation zone of gravistimulated roots. All of the CaM antagonists used in these studies delayed gravity-induced curvature at a concentration (1 micromole) that did not affect root growth. Calmodulin antagonists (> or = 1 micromole) inhibited downward transport of label from 45Ca2+ across the caps of gravistimulated roots relative to the downward transport of 45Ca2+ in gravistimulated roots which were not treated with CaM antagonists. Application of CaM antagonists at the root tip (> or = 1 micromole) also decreased the relative downward movement of label from 3H-IAA applied to the upper side of the elongation zone of gravistimulated roots. In general, tip application of antagonists inhibited neither the upward transport of 45Ca2+ in the root tip nor the upward movement of label from 3H-IAA in the elongation zone of gravistimulated roots. Thus, roots treated with CaM antagonists > or = 1 micromole become less graviresponsive and exhibit reduced or even a reversal of downward polarity of calcium transport across the root tip and IAA transport across the elongation zone. The results indicate that calmodulin-regulated events play a role in root gravitropism.
La 4 Co 4 X ( X = Pb , Bi , Sb ) : A demonstration of antagonistic pairs as a route to quasi-low-dimensional ternary compounds
We outline how pairs of strongly immiscible elements, referred to here as antagonistic pairs, can be used to synthesize ternary compounds with low or quasi-reduced-dimensional motifs intrinsically built into their crystal structures. By identifying third elements that are mutually compatible with a given antagonistic pair, ternary compounds can be formed in which the third element segregates the immiscible atoms into spatially separated substructures. Quasi-low-dimensional structural units, such as sheets, chains, or clusters are a natural consequence of the immiscible atoms seeking to avoid close contact in the solid state. Further, as proof of principle, we present the discovery, crystal growth, and basic physical properties of La 4 Co 4 $\mathrm{X}$ (X = Pb, Bi, Sb), a family of intermetallic compounds based on the antagonistic pairs Co-Pb and Co-Bi. La 4 Co 4 $\mathrm{X}$ adopts an orthorhombic crystal structure (space group Pbam) containing quasi-two-dimensional Co slabs and La-X polyhedra that stack in an alternating manner along the α axis. Consistent with our proposal, the La atoms separate the Co and X substructures, ensuring there are no direct contacts between the members of the immiscible (antagonistic) pair. Within the Co slabs, the atoms occupy the vertices of corner sharing tetrahedra and triangles, and this bonding motif produces narrow electronic bands near the Fermi level that favor magnetism. The Co is moment bearing in each La 4 Co 4 $\mathrm{X}$ compound studied, and we show that whereas La 4 Co 4 Pb behaves as a three-dimensional antiferromagnet with T N =220K, La 4 Co 4 Bi and La 4 Co 4 Sb have behavior consistent with low-dimensional magnetic coupling and ordering, with T N =153K and 143 K, respectively. In addition to the Pb-, Bi-, and Sb-based La 4 Co 4 $\mathrm{X}$ compounds, we also were likely able to produce an analogous La 4 Co 4 Sn in polycrystalline form, although we were unable to isolate single crystals. We anticipate that identifying and using mutually compatible third elements together with an antagonistic pair represents a generalizable design principle for discovering new materials and new structure types containing low-dimensional substructures.
The Effect of Antagonistic Behavior in Reinforcement Learning
The significant achievements of deep reinforcement learning (RL) have motivated researchers to also investigate its shortcomings. Such work has shown that typical methods in deep RL tend to produce brittle policies that overfit to the training environment. In this paper, we introduce the notion of purely antagonistic behavior in value-based agents, where the objective is not to maximize reward but to minimize the victim’s value over time. This notion is motivated by the scenario in which an antagonistic human architect, without access to the environment’s reward function, wants to build an RL agent that can impede another well-trained RL victim agent. First, we formalize a notion of antagonistic behavior in RL. Then, we provide experiments that show how a purely antagonistic agent performs compared to a well-trained victim that learns directly from the game’s rewards. Our results suggest that if one’s goal is to find vulnerabilities in well-trained agents, direct access to the environment’s rewards is not necessary, and antagonistic behavior can be measured independently from environment wins and losses.
De novo design of miniprotein antagonists of cytokine storm inducers
Cytokine release syndrome (CRS), commonly known as cytokine storm, is an acute systemic inflammatory response that is a significant global health threat. Interleukin-6 (IL-6) and interleukin-1 (IL-1) are key pro-inflammatory cytokines involved in CRS and are hence critical therapeutic targets. Current antagonists, such as tocilizumab and anakinra, target IL-6R/IL-1R but have limitations due to their long half-life and systemic anti-inflammatory effects, making them less suitable for acute or localized treatments. Here we present the de novo design of small protein antagonists that prevent IL-1 and IL-6 from interacting with their receptors to activate signaling. The designed proteins bind to the IL-6R, GP130 (an IL-6 co-receptor), and IL-1R1 receptor subunits with binding affinities in the picomolar to low-nanomolar range. X-ray crystallography studies reveal that the structures of these antagonists closely match their computational design models. In a human cardiac organoid disease model, the IL-1R antagonists demonstrated protective effects against inflammation and cardiac damage induced by IL-1β. These minibinders show promise for administration via subcutaneous injection or intranasal/inhaled routes to mitigate acute cytokine storm effects.
Development of an ultrahigh affinity, trimeric ACE2 biologic as a universal SARS-CoV-2 antagonist
Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for the COVID-19 pandemic, utilizes membrane-bound, angiotensin-converting enzyme II (ACE2) for internalization and infection. We describe the development of a biologic that takes advantage of the proximity of the N-terminus of bound ACE2 to the three-fold symmetry axis of the spike protein to create an ultrapotent, trivalent ACE2 entry antagonist. Distinct disulfide bonds were added to enhance serum stability and a single point mutation was introduced to eliminate enzymatic activity. Through surface plasmon resonance, pseudovirus neutralization assays, and single-particle cryo-electron microscopy, we show this antagonist binds to and inhibits SARS-CoV-2 variants. We further show the antagonist binds to and inhibits a 2003 SARS-CoV-1 strain. Collectively, structural insight has allowed us to design a universal trivalent antagonist against all variants of SARS-CoV-2 tested, suggesting it will be active against the emergence of future mutants.
Application and assessment of deep learning for the generation of potential NMDA receptor antagonists
Uncompetitive antagonists of the N-methyl D-aspartate receptor (NMDAR) have demonstrated therapeutic benefit in the treatment of neurological diseases such as Parkinson's and Alzheimer's, but some also cause dissociative effects that have led to the synthesis of illicit drugs. The ability to generate NMDAR antagonists in silico is therefore desirable for both new medication development and preempting and identifying new designer drugs. Recently, generative deep learning models have been applied to de novo drug design as a means to expand the amount of chemical space that can be explored for potential drug-like compounds. In this study, we assess the application of a generative model to the NMDAR to achieve two primary objectives: (i) the creation and release of a comprehensive library of experimentally validated NMDAR phencyclidine (PCP) site antagonists to assist the drug discovery community and (ii) an analysis of both the advantages conferred by applying such generative artificial intelligence models to drug design and the current limitations of the approach. We apply, and provide source code for, a variety of ligand- and structure-based assessment techniques used in standard drug discovery analyses to the deep learning-generated compounds. Overall, we present twelve candidate antagonists that are not available in existing chemical databases to provide an example of what this type of workflow can achieve, though synthesis and experimental validation of these compounds are still required.
Non-NMDA receptor antagonist-induced drinking in rat
Glutamate has been implicated in the central control of mechanisms that maintain body fluid homeostasis. The present studies demonstrate that intracerebroventricular (i.c.v.) injections of the non-N-methyl-d-aspartate (NMDA) receptor antagonists 6, 7-dinitroquinoxaline-2,3-dione (DNQX) and 6-cyano-7-nitroquinoxaline-2,3 dione (CNQX) induce drinking in rats. The dipsogenic effect of i.c.v. DNQX was antagonized by the non-NMDA receptor agonist alpha-amino-3-hydroxy-5-methylisoxazole-4-propionic acid (AMPA). The water intake induced by DNQX was also blocked by pretreatment with a NMDA receptor antagonist, MK-801, but not by angiotensin type 1 (AT1) or acetylcholine muscarinic receptor antagonists (losartan and atropine). The results indicate that non-NMDA receptors may exert a tonic inhibitory effect within brain circuits that control dipsogenic activity and that functional integrity of NMDA receptors may be required for the non-NMDA receptor antagonists to induce water intake. Copyright 1998 Published by Elsevier Science B.V.
Synthesis of actively adjustable springs by antagonistic redundant actuation
A methodology for active spring generation is presented based on antagonistic redundant actuation. Antagonistic properties are characterized using an effective system stiffness. 'Antagonistic stiffness' is generated by preloading a closed-chain (parallel) linkage system. Internal load distribution is investigated along with the necessary conditions for spring synthesis. The performance and stability of a proposed active spring are shown by simulation, and applications are discussed.
A bifunctional zeolitic porous liquid with incompatible Lewis pairs for antagonistic cascade catalysis
The emergence of porous liquids (PLs) opened opportunities to form unique antagonistic systems capable of fulfilling cascade reactions promoted by incompatible active sites in one pot, which is a long-term challenging subject in catalysis. Herein, unique bifunctional type III PL-based systems were facilely fabricated via assembly of zeolite nanosheets with ionic liquids. Rational structural design afforded PLs that feature high zeolite concentration, stable dispersion after 2 years, abundant cavity distribution, and involvement of antagonistic groups (acid and base sites) in separated and active form via steric hindrance control and electronic repulsion regulation. These unique properties worked cooperatively to fulfill the cascade deacetalization-Knoevenagel/Aldol condensation in one pot with superior catalytic efficiency outperforming the traditional systems. The key to success lies in the formation of bifunctional composites, transformation of zeolite from heterogeneous to homogeneous via surface modification, as well as rapid mass transfer ensured by the rigid porous architecture.
Three-Dimensional Structural Insights Have Revealed the Distinct Binding Interactions of Agonists, Partial Agonists, and Antagonists with the µ Opioid Receptor
The United States is experiencing the most profound and devastating opioid crisis in history, with the number of deaths involving opioids, including prescription and illegal opioids, continuing to climb over the past two decades. This severe public health issue is difficult to combat as opioids remain a crucial treatment for pain, and at the same time, they are also highly addictive. Opioids act on the opioid receptor, which in turn activates its downstream signaling pathway that eventually leads to an analgesic effect. Among the four types of opioid receptors, the µ subtype is primarily responsible for the analgesic cascade. This review describes available 3D structures of the µ opioid receptor in the protein data bank and provides structural insights for the binding of agonists and antagonists to the receptor. Comparative analysis on the atomic details of the binding site in these structures was conducted and distinct binding interactions for agonists, partial agonists, and antagonists were observed. The findings in this article deepen our understanding of the ligand binding activity and shed some light on the development of novel opioid analgesics which may improve the risk benefit balance of existing opioids.
An autoinhibited state of 53BP1 revealed by small molecule antagonists and protein engineering
The recruitment of 53BP1 to chromatin, mediated by its recognition of histone H4 dimethylated at lysine 20 (H4K20me2), is important for DNA double-strand break repair. Using a series of small molecule antagonists, we demonstrate a conformational equilibrium between an open and a pre-existing lowly populated closed state of 53BP1 in which the H4K20me2 binding surface is buried at the interface between two interacting 53BP1 molecules. In cells, these antagonists inhibit the chromatin recruitment of wild type 53BP1, but do not affect 53BP1 variants unable to access the closed conformation despite preservation of the H4K20me2 binding site. Thus, this inhibition operates by shifting the conformational equilibrium toward the closed state. Our work therefore identifies an auto-associated form of 53BP1—autoinhibited for chromatin binding—that can be stabilized by small molecule ligands encapsulated between two 53BP1 protomers. Such ligands are valuable research tools to study the function of 53BP1 and have the potential to facilitate the development of new drugs for cancer therapy.
Synergistic and Antagonistic Effects of Aromatics on the Agglomeration of Gas Hydrates
Abstract Surfactants are often used to stabilize aqueous dispersions. For example, surfactants can be used to prevent hydrate particles from forming large plugs that can clog, and sometimes rupture pipelines. Changes in oil composition, however dramatically affect the performance of said surfactants. In this work we demonstrate that aromatic compounds, dissolved in the hydrocarbon phase, can have both synergistic and antagonistic effects, depending on their molecular structure, with respect to surfactants developed to prevent hydrate agglomerations. While monocyclic aromatics such as benzene were found to disrupt the structure of surfactant films at low surfactant density, they are expelled from the interfacial film at high surfactant density. On the other hand, polycyclic aromatics, in particular pyrene, are found to induce order and stabilize the surfactant films both at low and high surfactant density. Based on our simulation results, polycyclic aromatics could behave as natural anti-agglomerants and enhance the performance of the specific surfactants considered here, while monocyclic aromatics could, in some cases, negatively affect performance. Although limited to the conditions chosen for the present simulations, the results, explained in terms of molecular features, could be valuable for better understanding synergistic and antagonistic effects relevant for stabilizing aqueous dispersions used in diverse applications, ranging from foodstuff to processing of nanomaterials and advanced manufacturing.
Genome, transcriptome and secretome analyses of the antagonistic, yeast-like fungus Aureobasidium pullulans to identify potential biocontrol genes
Aureobasidium pullulans is an extremotolerant, cosmopolitan yeast-like fungus that successfully colonises vastly different ecological niches. The species is widely used in biotechnology and successfully applied as a commercial biocontrol agent against postharvest diseases and fireblight. However, the exact mechanisms that are responsible for its antagonistic activity against diverse plant pathogens are not known at the molecular level. Thus, it is difficult to optimise and improve the biocontrol applications of this species. As a foundation for elucidating biocontrol mechanisms, we have de novo assembled a high-quality reference genome of a strongly antagonistic A. pullulans strain, performed dual RNA-seq experiments, and analysed proteins secreted during the interaction with the plant pathogen Fusarium oxysporum. Based on the genome annotation, potential biocontrol genes were predicted to encode secreted hydrolases or to be part of secondary metabolite clusters (e.g., NRPS-like, NRPS, T1PKS, terpene, and β-lactone clusters). Transcriptome and secretome analyses defined a subset of 79 A. pullulans genes (among the 10,925 annotated genes) that were transcriptionally upregulated or exclusively detected at the protein level during the competition with F. oxysporum. These potential biocontrol genes comprised predicted secreted hydrolases such as glycosylases, esterases, and proteases, as well as genes encoding enzymes, which are predicted to be involved in the synthesis of secondary metabolites. This study highlights the value of a sequential approach starting with genome mining and consecutive transcriptome and secretome analyses in order to identify a limited number of potential target genes for detailed, functional analyses.
Antagonistic otolith-visual units in cat vestibular nuclei
The nature of neural coding of visual (Vis) and vestibular (Vst) information on translational motion in the region of the vestibular nuclei was investigated using extracellular single-unit recordings in alert adult cats. Responses were recorded and averaged over 60 cycles of stimulation in the vertical and horizontal planes, which included the Vst (movement of the animal in the dark), Vis (movement within lighted visual surround), and combined Vis and Vst (movement of the animal within the lighted stationary visual surround). Data are reported on responses to stimulations along the axis showing maximal sensitivity. A small number of units were identified that showed an antagonistic relationship between their Vis and Vst responses (since they were maximally excited by Vis and by Vst stimulations in the same direction). Results suggest that antagonistic units may belong to an infrequently encountered, but functionally distinct, class of neurons.
Material-Dependent Antagonistic Effects between Soot and ZDDP
While soot in engine oil is known to accelerate the wear of diesel and gasoline direct-injection compression-ignition engines, there is a lack of consensus on the wear mechanism though various hypotheses have been proposed in the literature. Particularly, some recently observed antagonistic effects between soot and a common lubricant antiwear additive, zinc dialkyldithiophosphate (ZDDP), while others did not. The discrepancy is, in part, explained by the strong alloy dependence of such antagonism discovered in this study. Specifically, four alloys, 52100 steel and M2, M50, and A2 tool steels, are tested in lubricants containing carbon black (CB, a soot surrogate) with and without ZDDP present. Adding the CB alone to the oil increases the wear rate for all steel alloys as expected. However, distinct wear performance is observed for the four steel alloys when ZDDP is introduced to the CB-containing oil: while the 52100 steel has notable wear reduction, the three tool steels suffer significant wear increase. Comprehensive tribofilm characterization suggests that the Mo content in the steel alloy and the sulfur from ZDDP strongly influence wear behavior. The combination of CB/Mo-catalyzed sulfidation and CB-accelerated abrasion is hypothesized to be responsible for the high wear of the Mo-alloyed tool steels.
Effect of Antagonistic Binder–Catalyst Interactions on Catalytic Activity in Lithium–Sulfur Batteries
Lithium-sulfur (Li-S) batteries are a promising next-generation energy storage solution, as they can reduce reliance on critical transition metals while offering high energy densities. However, their deployment is hindered by low sulfur utilization and the formation/diffusion of lithium polysulfides (LiPSs). While transition-metal catalysts and polymeric binders have been independently developed to enhance redox kinetics and LiPS adsorption, their mutual compatibility has remained largely unexplored. We show here that binder-catalyst interactions can significantly impact catalytic performance. Employing TiO 2 as a generic catalyst, the electrochemical performance is shown to depend strongly on the binder environment. TiO 2 paired with lithiated polyacrylic acid (LiPAA) shows benign interactions, resulting in enhanced cycle life. In contrast, pairing TiO 2 with protonated PAA produces antagonistic interactions that hinder Li 2 S growth. A mechanistic analysis unveils that the carboxylic H atom in PAA promotes COO − coordination to Ti sites, occupying catalytic centers and suppressing LiPS adsorption, increasing charge transfer and diffusion resistances. This phenomenon is observed across multiple catalysts, indicating that COOH-functionalized binders may broadly hinder catalytic activity. Overall, this study underscores the need for holistic cathode design and identifies binder-catalyst compatibility as an important parameter for high-performance Li-S batteries.