Insights to the Binding of a Selective Adenosine A3 Receptor Antagonist Using Molecular Dynamic Simulations, MM-PBSA and MM-GBSA Free Energy Calculations, and Mutagenesis

Molecular recognition between volatile molecules and odorant binding proteins 7 by homology modeling, molecular docking and molecular dynamics simulation

BACKGROUND

Odorant-binding proteins (OBPs) in insects are key to detection and recognition of external chemical signals associated with survival. OBP7 in Spodoptera frugiperda's larval stage (SfruOBP7) may search for host plants by sensing plant volatiles, which are important sources of pest attractants and repellents. However, the atomic-level basis of binding modes remains elusive.

RESULTS

SfruOBP7 structure was constructed through homology modeling, and complex models of six plant volatiles ((E)-2-hexenol, α-pinene, (Z)-3-hexenyl acetate, lauric acid, O-cymene and 1-octanol) and SfruOBP7 were obtained through molecular docking. To study the detailed interactions between the six plant volatile molecules and SfruOBP7, we conducted three 300 ns molecular dynamics simulations for each study object. The correlation coefficients between binding free energy obtained by molecular mechanics/generalized Born surface area together with solvated interaction energy methods and experimental values are 0.90 and 0.88, respectively, showing a good correlation. By comparing binding free energy along with interaction patterns between SfruOBP7 and the six volatile molecules, hotspot residues of SfruOBP7 when binding with different volatile molecules were determined. Hydrophobic interactions stemming from van der Waals interactions play a significant role in SfruOBP7 and these plant volatile systems.

CONCLUSION

The optimized three-dimensional structure of SfruOBP7 and its binding modes with six plant volatiles revealed their interactions, thus providing a means for estimating the binding energies of other plant volatiles. Our study will help to guide the rational design of effective and selective insect attractants. © 2024 Society of Chemical Industry.

Exploring biased activation characteristics by molecular dynamics simulation and machine learning for the μ-opioid receptor

Biased ligands selectively activating specific downstream signaling pathways (termed as biased activation) exhibit significant therapeutic potential. However, the conformational characteristics revealed are very limited for the biased activation, which is not conducive to biased drug development. Motivated by the issue, we combine extensive accelerated molecular dynamics simulations and an interpretable deep learning model to probe the biased activation features for two complex systems constructed by the inactive μOR and two different biased agonists (G-protein-biased agonist TRV130 and β-arrestin-biased agonist endomorphin2). The results indicate that TRV130 binds deeper into the receptor core compared to endomorphin2, located between W2936.48 and D1142.50, and forms hydrogen bonding with D1142.50, while endomorphin2 binds above W2936.48. The G protein-biased agonist induces greater outward movements of the TM6 intracellular end, forming a typical active conformation, while the β-arrestin-biased agonist leads to a smaller extent of outward movements of TM6. Compared with TRV130, endomorphin2 causes more pronounced inward movements of the TM7 intracellular end and more complex conformational changes of H8 and ICL1. In addition, important residues determining the two different biased activation states were further identified by using an interpretable deep learning classification model, including some common biased activation residues across Class A GPCRs like some key residues on the TM2 extracellular end, ECL2, TM5 intracellular end, TM6 intracellular end, and TM7 intracellular end, and some specific important residues of ICL3 for μOR. The observations will provide valuable information for understanding the biased activation mechanism for GPCRs.

Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A3 Receptor

A structure-based drug design pipeline that considers both thermodynamic and kinetic binding data of ligands against a receptor will enable the computational design of improved drug molecules. For unresolved GPCR-ligand complexes, a workflow that can apply both thermodynamic and kinetic binding data in combination with alpha-fold (AF)-derived or other homology models and experimentally resolved binding modes of relevant ligands in GPCR-homologs needs to be tested. Here, as test case, we studied a congeneric set of ligands that bind to a structurally unresolved G protein-coupled receptor (GPCR), the inactive human adenosine A3 receptor (hA3R). We tested three available homology models from which two have been generated from experimental structures of hA1R or hA2AR and one model was a multistate alphafold 2 (AF2)-derived model. We applied alchemical calculations with thermodynamic integration coupled with molecular dynamics (TI/MD) simulations to calculate the experimental relative binding free energies and residence time (τ)-random accelerated MD (τ-RAMD) simulations to calculate the relative residence times (RTs) for antagonists. While the TI/MD calculations produced, for the three homology models, good Pearson correlation coefficients, correspondingly, r = 0.74, 0.62, and 0.67 and mean unsigned error (mue) values of 0.94, 1.31, and 0.81 kcal mol-1, the τ-RAMD method showed r = 0.92 and 0.52 for the first two models but failed to produce accurate results for the multistate AF2-derived model. With subsequent optimization of the AF2-derived model by reorientation of the side chain of R1735.34 located in the extracellular loop 2 (EL2) that blocked ligand's unbinding, the computational model showed r = 0.84 for kinetic data and improved performance for thermodynamic data (r = 0.81, mue = 0.56 kcal mol-1). Overall, after refining the multistate AF2 model with physics-based tools, we were able to show a strong correlation between predicted and experimental ligand relative residence times and affinities, achieving a level of accuracy comparable to an experimental structure. The computational workflow used can be applied to other receptors, helping to rank candidate drugs in a congeneric series and enabling the prioritization of leads with stronger binding affinities and longer residence times.

Unveiling the ESR1 Conformational Stability and Screening Potent Inhibitors for Breast Cancer Treatment

BACKGROUND

The current study recognizes the significance of estrogen receptor alpha (ERα) as a member of the nuclear receptor protein family, which holds a central role in the pathophysiology of breast cancer. ERα serves as a valuable prognostic marker, with its established relevance in predicting disease outcomes and treatment responses.

METHODS

In this study, computational methods are utilized to search for suitable drug-like compounds that demonstrate analogous ligand binding kinetics to ERα.

RESULTS

Docking-based simulation screened out the top 5 compounds - ZINC13377936, NCI35753, ZINC35465238, ZINC14726791, and NCI663569 against the targeted protein. Further, their dynamics studies reveal that the compounds ZINC13377936 and NCI35753 exhibit the highest binding stability and affinity.

CONCLUSION

Anticipating the competitive inhibition of ERα protein expression in breast cancer, we envision that both ZINC13377936 and NCI35753 compounds hold substantial promise as potential therapeutic agents. These candidates warrant thorough consideration for rigorous In vitro and In vivo evaluations within the context of clinical trials. The findings from this current investigation carry significant implications for the advancement of future diagnostic and therapeutic approaches for breast cancer.

Deciphering insights into the binding mechanism and plasticity of Telacebec with M. tuberculosis cytochrome bcc-aa3 supercomplex through an unbiased molecular dynamics simulation, free-energy analysis, and DFT study

The cytochrome bcc-aa3 supercomplex, a key component in the electron transport chain pathway involved in bacterial energy production and homeostasis, is a clinically validated target for tuberculosis (TB), leading to Telacebec (Q203). Telacebec is a potent candidate drug under Phase II clinical development for the treatment of drug-sensitive and drug-resistant TB. Recently, the cryo-electron microscopy structure of this supercomplex from Mycobacterium tuberculosis (Mtb) complexed with Q203 was resolved at 6.9 Å resolution (PDB ID: 7E1W). To understand the binding site (QP site) flexibility and Q203's stability at the QP site of the Mtb cytochrome bcc complex, we conducted molecular dynamics (MD) simulation and free energy analysis on this complex in an explicit hydrated lipid bilayer environment for 500 ns. Through this study, the persistence of a range of direct and indirect interactions was observed over the course of the simulation. The significance of the interactions with His375, Tyr161, Ala178, Ala179, Ile183, His355, Leu356, and Thr313 is underlined. Electrostatic energy was the primary source of the net binding free energy, regardless of the important interacting residues. The overall binding free energy for Q203 was -112.84 ± 7.73 kcal/mol, of which the electrostatic and lipophilic energy contributions were -116.31 ± 1.14 and -21.32 ± 2.35 kcal/mol, respectively. Meanwhile, DFT calculations were utilized to elucidate Q203's molecular properties. Overall, this study deciphers key insights into the cytochrome bcc-aa3 supercomplex with Q203 on the ground of molecular mechanics and quantum mechanics that may facilitate structure-based drug design and optimization for the discovery of the next-generation antitubercular drug(s).Communicated by Ramaswamy H. Sarma.

Dual A1/A3 Adenosine Receptor Antagonists: Binding Kinetics and Structure-Activity Relationship Studies Using Mutagenesis and Alchemical Binding Free Energy Calculations

Drugs targeting adenosine receptors (AR) can provide treatment for diseases. We report the identification of 7-(phenylamino)-pyrazolo[3,4-c]pyridines L2-L10, A15, and A17 as low-micromolar to low-nanomolar A₁R/A₃R dual antagonists, with 3-phenyl-5-cyano-7-(trimethoxyphenylamino)-pyrazolo[3,4-c]pyridine (A17) displaying the highest affinity at both receptors with a long residence time of binding, as determined using a NanoBRET-based assay. Two binding orientations of A17 produce stable complexes inside the orthosteric binding area of A₁R in molecular dynamics (MD) simulations, and we selected the most plausible orientation based on the agreement with alanine mutagenesis supported by affinity experiments. Interestingly, for drug design purposes, the mutation of L250^6.51 to alanine increased the binding affinity of A17 at A₁R. We explored the structure-activity relationships against A₁R using alchemical binding free energy calculations with the thermodynamic integration coupled with the MD simulation (TI/MD) method, applied on the whole G-protein-coupled receptor-membrane system, which showed a good agreement (r = 0.73) between calculated and experimental relative binding free energies.

Discovery of a High Affinity Adenosine A1/A3 Receptor Antagonist with a Novel 7-Amino-pyrazolo[3,4-d]pyridazine Scaffold

Here we describe the design and synthesis of pyrazolo[3,4-d]pyridazines as adenosine receptor (AR) ligands. We demonstrate that the introduction of a 3-phenyl group, together with a 7-benzylamino and 1-methyl group at the pyrazolopyridazine scaffold, generated the antagonist compound 10b, which displayed 21 nM affinity and a residence time of ∼60 min, for the human A₁R, 55 nM affinity and a residence time of ∼73 min, for the human A₃R and 1.7 μΜ affinity for the human A_2BR while not being toxic. Strikingly, the 2-methyl analog of 10b, 15b, had no significant affinity. Docking calculations and molecular dynamics simulations of the ligands inside the orthosteric binding area suggested that the 2-methyl group in 15b hinders the formation of hydrogen bonding interactions with N^6.55 which are considered critical for the stabilization inside the orthosteric binding cavity. We, therefore, demonstrate that 10a is a novel scaffold for the development of high affinity AR ligands. From the mutagenesis experiments the biggest effect was observed for the Y271^7.46A mutation which caused an ∼10-fold reduction in the binding affinity of 10b.

Molecular basis for redox control by the human cystine/glutamate antiporter system xc. Nat Commun 2021;12:7147. [PMID: 34880232 PMCID: PMC8654953 DOI: 10.1038/s41467-021-27414-1]

Cysteine plays an essential role in cellular redox homoeostasis as a key constituent of the tripeptide glutathione (GSH). A rate limiting step in cellular GSH synthesis is the availability of cysteine. However, circulating cysteine exists in the blood as the oxidised di-peptide cystine, requiring specialised transport systems for its import into the cell. System xc- is a dedicated cystine transporter, importing cystine in exchange for intracellular glutamate. To counteract elevated levels of reactive oxygen species in cancerous cells system xc- is frequently upregulated, making it an attractive target for anticancer therapies. However, the molecular basis for ligand recognition remains elusive, hampering efforts to specifically target this transport system. Here we present the cryo-EM structure of system xc- in both the apo and glutamate bound states. Structural comparisons reveal an allosteric mechanism for ligand discrimination, supported by molecular dynamics and cell-based assays, establishing a mechanism for cystine transport in human cells.

Acacetin Alleviates Listeria monocytogenes Virulence Both In Vitro and In Vivo via the Inhibition of Listeriolysin O

Listeria monocytogenes is a ubiquitous Gram-positive foodborne pathogen that is responsible for listeriosis in both humans and several animal species. The bacterium secretes a pore-forming cholesterol-dependent cytolysin, listeriolysin O (LLO), a major virulence factor involved in the activation of cellular processes. The ability of LLO to lyse erythrocytes is a measure of LLO activity. We used hemolytic activity assay to screen the LLO inhibitors. Acacetin was found to be an LLO inhibitor, which is a di-hydroxy and mono-methoxy flavone present in various plants, including Black locust, Damiana, and Silver birch. As the features of acacetin are of low toxicity and have less acquired resistance, it comes to a hotspot in drug development. In our study, we report that acacetin antagonized the hemolytic activity of L. monocytogenes culture supernatants and purified LLO by directly interfering with the formation of oligomers without inhibiting the bacterial growth and the expression of LLO. Acacetin also relieved the injury of alveolar epithelial cells by inhibiting LLO activity. Further, acacetin significantly promoted the clearance of L. monocytogenes and alleviated the histopathological damage, thereby raising survival rate, which conferred mice with effective protection against L. monocytogenes infection. Using molecular docking and dynamics simulation, we further proved the mechanism of acacetin antagonizing LLO pore-forming activity by direct binding to the second membrane-inserting helix bundle (HB2) of LLO domain 3. These data suggested that acacetin recedes the virulence of L. monocytogenes both in vivo and in vitro, and this study provided a promising candidate and potential alternative for the prevention and treatment of L. monocytogenes infections.

Probing Allosteric Regulation Mechanism of W7.35 on Agonist-Induced Activity for μOR by Mutation Simulation

The residue located at 15 positions before the most conserved residue in TM7 (7.35 of Ballesteros-Weinstein number) plays important roles in ligand binding and the receptor activity for class A GPCRs. Nevertheless, its regulation mechanism has not been clearly clarified in experiments, and some controversies also exist for its impact on μ-opioid receptors (μOR) bound by agonists. Thus, we chose the μ-opioid receptor (μOR) of class A GPCRs as a representative and utilized a microsecond accelerated molecular dynamics simulation (aMD) coupled with a protein structure network (PSN) to explore the effect of W318^7.35 on its functional activity induced by the agonist endomorphin2 mainly by a comparison of the wild system and its W7.35A mutant. When endomorphin2 binds to the wild-type μOR, TM6 in μOR moves outward to form an open intracellular conformation that is beneficial to accommodating the β-arrestin transducer, rather than the G-protein transducer due to the clash with the α5 helix of G-protein, thus acting as a β-arrestin biased agonist. However, the W318A mutation induces the intracellular part of μOR to form a closed state, which disfavors coupling with either G-protein or β-arrestin. The allosteric pathway analysis further reveals that the binding of endomorphin2 to the wild-type μOR transmits more activation signals to the β-arrestin binding site while the W318A mutation induces more structural signals to transmit to common binding residues of the G protein and β-arrestin. More interestingly, the residue at the 7.35 position regulates the shortest allosteric pathway in indirect ways by influencing the interactions between other ligand-binding residues and endomorphin2. W293^6.48 and F289^6.44 are important for regulating the different activities of μOR induced either by the agonist or by the mutation. Y336^7.53, F343^8.50, and D340^8.47 play crucial roles in activating the β-arrestin biased signal induced by the agonist endomorphin2, while L158^3.43 and V286^6.41 devote important contributions to the change in the activity of endomorphin2 from the β-arrestin biased agonist to the antagonist upon the W318A mutation.

Perspective of structural flexibility on selective inhibition towards CYP1B1 over CYP1A1 by α-naphthoflavone analogs

Research on action selectivity between CYP1A1 and CYP1B1 is particularly valuable for cancer chemoprevention and chemotherapy. However, they share a very close similarity in their ligand-binding pockets that α-naphthoflavone (ANF) is the co-crystal ligand for both isoforms, which poses a major challenge in revealing their selectivity mechanism. Therefore, three selective CYP1B1 inhibitors derived from ANF were selected to illustrate the structural basis for the selectivity between the two isoforms via a comprehensive computational strategy. It was found that the sustainability of the π-π stacking interactions with the phenylalanine residues of the two isoforms, namely, Phe123, Phe224, and Phe258 for CYP1A1, and Phe134, Phe231, and Phe268 for CYP1B1, played a crucial role in determining the selectivity of ligands with a classic aromatic conjugation system like ANF and its derivatives for CYP1B1 versus CYP1A1. Of note, the structural flexibility of the corresponding protein domains mainly orchestrated the sustainability of the corresponding π-π stacking interactions, thereby determining the binding selectivity. Therefore, the structure modification of naphthoflavone lead compounds into preferable binding configurations to satisfy the π-π stacking interactions of the key phenylalanine residues within CYP1B1 would be an inspiring strategy devised to improve the inhibitory selectivity towards CYP1B1. Collectively, this study revealed valuable insight into understanding the selective mechanism between CYP1A1 and CYP1B1 from the perspective of structural flexibility, which sheds light on the future rational design of CYP1B1 selective inhibitors.

Recent Developments in Free Energy Calculations for Drug Discovery

The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.

Molecular mechanisms of inhibitor bindings to A-FABP deciphered by using molecular dynamics simulations and calculations of MM-GBSA

Adipocyte fatty-acid binding protein (A-FABP) plays a central role in many aspects of metabolic diseases. It is an important target in drug design for treatment of FABP-related diseases. In this study, molecular dynamics (MD) simulations followed by calculations of molecular mechanics generalized Born surface area (MM-GBSA) and principal components analysis (PCA) were implemented to decipher molecular mechanism correlating with binding of inhibitors 57Q, 57P and L96 to A-FABP. The results show that van der Waals interactions are the leading factors to control associations of 57Q, 57P, and L96 with A-FABP, which reveals an energetic basis for designing of clinically available inhibitors towards A-FABP. The information from PCA and cross-correlation analysis rationally unveils that inhibitor bindings affect conformational changes of A-FABP and change relative movements between residues. Decomposition of binding affinity into contributions of individual residues not only detects hot spots of inhibitor/A-FABP binding but also shows that polar interactions of the positively charged residue Arg126 with three inhibitors provide a significant contribution for stabilization of the inhibitor/A-FABP bindings. Furthermore, the binding strength of L96 to residues Ser55, Phe57 and Lys58 are stronger than that of inhibitors 57Q and 57P to these residues.

Pharmacological characterisation of novel adenosine A3 receptor antagonists

The adenosine A₃ receptor (A₃R) belongs to a family of four adenosine receptor (AR) subtypes which all play distinct roles throughout the body. A₃R antagonists have been described as potential treatments for numerous diseases including asthma. Given the similarity between (adenosine receptors) orthosteric binding sites, obtaining highly selective antagonists is a challenging but critical task. Here we screen 39 potential A₃R, antagonists using agonist-induced inhibition of cAMP. Positive hits were assessed for AR subtype selectivity through cAMP accumulation assays. The antagonist affinity was determined using Schild analysis (pA₂ values) and fluorescent ligand binding. Structure–activity relationship investigations revealed that loss of the 3-(dichlorophenyl)-isoxazolyl moiety or the aromatic nitrogen heterocycle with nitrogen at α-position to the carbon of carboximidamide group significantly attenuated K18 antagonistic potency. Mutagenic studies supported by molecular dynamic simulations combined with Molecular Mechanics—Poisson Boltzmann Surface Area calculations identified the residues important for binding in the A₃R orthosteric site. We demonstrate that K18, which contains a 3-(dichlorophenyl)-isoxazole group connected through carbonyloxycarboximidamide fragment with a 1,3-thiazole ring, is a specific A₃R (< 1 µM) competitive antagonist. Finally, we introduce a model that enables estimates of the equilibrium binding affinity for rapidly disassociating compounds from real-time fluorescent ligand-binding studies. These results demonstrate the pharmacological characterisation of a selective competitive A₃R antagonist and the description of its orthosteric binding mode. Our findings may provide new insights for drug discovery.