Unraveling the Binding Mode of Cyclic Adenosine-Inosine Monophosphate (cAIMP) to STING through Molecular Dynamics Simulations.

The stimulator of interferon genes (STING) plays a significant role in immune defense and protection against tumor proliferation. Many cyclic dinucleotide (CDN) analogues have been reported to regulate its activity, but the dynamic process involved when the ligands activate STING remains unclear. In this work, all-atom molecular dynamics simulations were performed to explore the binding mode between human STING (hSTING) and four cyclic adenosine-inosine monophosphate analogs (cAIMPs), as well as 2',3'-cGMP-AMP (2',3'-cGAMP). The results indicate that these cAIMPs adopt a U-shaped configuration within the binding pocket, forming extensive non-covalent interaction networks with hSTING. These interactions play a significant role in augmenting the binding, particularly in interactions with Tyr167, Arg238, Thr263, and Thr267. Additionally, the presence of hydrophobic interactions between the ligand and the receptor further contributes to the overall stability of the binding. In this work, the conformational changes in hSTING upon binding these cAIMPs were also studied and a significant tendency for hSTING to shift from open to closed state was observed after binding some of the cAIMP ligands.

Effect of phenolic-hydroxy-group incorporation on the biological activity of a simplified aplysiatoxin analog with an (R)-(-)-carvone-based core.

We synthesized a phenolic hydroxy group-bearing version (1) of a simplified analog of aplysiatoxin comprising a carvone-based conformation-controlling unit. Thereafter, we evaluated its antiproliferative activity against human cancer cell lines and its binding affinity to protein kinase C (PKC) isozymes. The antiproliferative activity and PKC-binding ability increased with the introduction of the phenolic hydroxy group. The results of molecular dynamics simulations and subsequent relative binding free-energy calculations conducted using an alchemical transformation procedure showed that the phenolic hydroxy group in 1 could form a hydrogen bond with a phospholipid and the PKC. The former hydrogen bonding formation facilitated the partitioning of the compound from water to the phospholipid membrane and the latter compensated for the loss of hydrogen bond with the phospholipid upon binding to the PKC. This information may facilitate the development of rational design methods for PKC ligands with additional hydrogen bonding groups.

Discovery of an EP300 Inhibitor using Structure-based Virtual Screening and Bioactivity Evaluation.

BACKGROUND: EP300 (E1A binding protein p300) played a significant role in serial diseases such as cancer, neurodegenerative disease. Therefore, it became a significant target. METHODS: Targeting EP300 discovery of a novel drug to alleviate these diseases. In this paper, 17 candidate compounds were obtained using a structure-based virtual screening approach, 4449-0460, with an IC50 of 5.89 ± 2.08 uM, which was identified by the EP300 bioactivity test. 4449-0460 consisted of three rings. The middle benzene ring connected the 5-ethylideneimidazolidine-2,4-dione group and the 3-F-Phenylmethoxy group. RESULTS: Furthermore, the interaction mechanism between 4449-0460 and EP300 was explored by combining molecular dynamics (MD) simulations and binding free energy calculation methods. CONCLUSION: The binding free energy of EP300 with 4449-0460 was -10.93 kcal/mol, and mainly came from the nonpolar energy term (ΔGnonpolar). Pro1074, Phe1075, Val1079, Leu1084, and Val1138 were the key residues in EP300/4449-0460 binding with more -1 kcal/mol energy contribution. 4449-0460 was a promising inhibitor targeting EP300, which had implications for the development of drugs for EP300-related diseases.

Highlighting the Major Role of Cyclin C in Cyclin-Dependent Kinase 8 Activity through Molecular Dynamics Simulations.

Dysregulation of cyclin-dependent kinase 8 (CDK8) activity has been associated with many diseases, including colorectal and breast cancer. As usual in the CDK family, the activity of CDK8 is controlled by a regulatory protein called cyclin C (CycC). But, while human CDK family members are generally activated in two steps, that is, the binding of the cyclin to CDK and the phosphorylation of a residue in the CDK activation loop, CDK8 does not require the phosphorylation step to be active. Another peculiarity of CDK8 is its ability to be associated with CycC while adopting an inactive form. These specificities raise the question of the role of CycC in the complex CDK8-CycC, which appears to be more complex than the other members of the CDK family. Through molecular dynamics (MD) simulations and binding free energy calculations, we investigated the effect of CycC on the structure and dynamics of CDK8. In a second step, we particularly focused our investigation on the structural and molecular basis of the protein-protein interaction between the two partners by finely analyzing the energetic contribution of residues and simulating the transition between the active and the inactive form. We found that CycC has a stabilizing effect on CDK8, and we identified specific interaction hotspots within its interaction surface compared to other human CDK/Cyc pairs. Targeting these specific interaction hotspots could be a promising approach in terms of specificity to effectively disrupt the interaction between CDK8. The simulation of the conformational transition from the inactive to the active form of CDK8 suggests that the residue Glu99 of CycC is involved in the orientation of three conserved arginines of CDK8. Thus, this residue may assume the role of the missing phosphorylation step in the activation mechanism of CDK8. In a more general view, these results point to the importance of keeping the CycC in computational studies when studying the human CDK8 protein in both the active and the inactive form.

Synthesis and unexpected binding of monofluorinated N,N'-diacetylchitobiose and LacdiNAc to wheat germ agglutinin.

Fluorination of carbohydrate ligands of lectins is a useful approach to examine their binding profile, improve their metabolic stability and lipophilicity, and convert them into 19F NMR-active probes. However, monofluorination of monovalent carbohydrate ligands often leads to a decreased or completely lost affinity. By chemical glycosylation, we synthesized the full series of methyl ß-glycosides of N,N'-diacetylchitobiose (GlcNAcß(1-4)GlcNAcß1-OMe) and LacdiNAc (GalNAcß(1-4)GlcNAcß1-OMe) systematically monofluorinated at all hydroxyl positions. A competitive enzyme-linked lectin assay revealed that the fluorination at the 6'-position of chitobioside resulted in an unprecedented increase in affinity to wheat germ agglutinin (WGA) by one order of magnitude. For the first time, we have characterized the binding profile of a previously underexplored WGA ligand LacdiNAc. Surprisingly, 4'-fluoro-LacdiNAc bound WGA even stronger than unmodified LacdiNAc. These observations were interpreted using molecular dynamic calculations along with STD and transferred NOESY NMR techniques, which gave evidence for the strengthening of CH/π interactions after deoxyfluorination of the side chain of the non-reducing GlcNAc. These results highlight the potential of fluorinated glycomimetics as high-affinity ligands of lectins and 19F NMR-active probes.

Solvent Isotherms and Structural Transitions in Nanoparticle Superlattice Assembly.

We introduce a Molecular Theory for Compressible Fluids (MOLT-CF) that enables us to compute free energies and other thermodynamic functions for nanoparticle superlattices with any solvent content, including the dry limit. Quantitative agreement is observed between MOLT-CF and united-atom molecular dynamics simulations performed to assess the reliability and precision of the theory. Among other predictions, MOLT-CF shows that the amount of solvent within the superlattice decreases approximately linearly with its vapor pressure and that in the late stages of drying, solvent-filled voids form at lattice interstitials. Applied to single-component superlattices, MOLT-CF predicts fcc-to-bcc Bain transitions for decreasing vapor pressure and for increasing ligand length, both in agreement with experimental results. We explore the stability of other single-component phases and show that the C14 Frank-Kasper phase, which has been reported in experiments, is not a global free-energy minimum. Implications for precise assembly and prediction of multicomponent nanoparticle systems are discussed.

Structure-based virtual screening of novel USP5 inhibitors targeting the zinc finger ubiquitin-binding domain.

The equilibrium of cellular protein levels is pivotal for maintaining normal physiological functions. USP5 belongs to the deubiquitination enzyme (DUBs) family, controlling protein degradation and preserving cellular protein homeostasis. Aberrant expression of USP5 is implicated in a variety of diseases, including cancer, neurodegenerative diseases, and inflammatory diseases. In this paper, a multi-level virtual screening (VS) approach was employed to target the zinc finger ubiquitin-binding domain (ZnF-UBD) of USP5, leading to the identification of a highly promising candidate compound 0456-0049. Molecular dynamics (MD) simulations were then employed to assess the stability of complex binding and predict hotspot residues in interactions. The results indicated that the candidate stably binds to the ZnF-UBD of USP5 through crucial interactions with residues ARG221, TRP209, GLY220, ASN207, TYR261, TYR259, and MET266. Binding free energy calculations, along with umbrella sampling (US) simulations, underscored a superior binding affinity of the candidate relative to known inhibitors. Moreover, US simulations revealed conformational changes of USP5 during ligand dissociation. These insights provide a valuable foundation for the development of novel inhibitors targeting USP5.

Network pharmacology, molecular simulation, and binding free energy calculation-based investigation of Neosetophomone B revealed key targets for the treatment of cancer.

In the current study, Neosetophomone B (NSP-B) was investigated for its anti-cancerous potential using network pharmacology, quantum polarized ligand docking, molecular simulation, and binding free energy calculation. Using SwissTarget prediction, and Superpred, the molecular targets for NSP-B were predicted while cancer-associated genes were obtained from DisGeNet. Among the total predicted proteins, only 25 were reported to overlap with the disease-associated genes. A protein-protein interaction network was constructed by using Cytoscape and STRING databases. MCODE was used to detect the densely connected subnetworks which revealed three sub-clusters. Cytohubba predicted four targets, i.e., fibroblast growth factor , FGF20, FGF22, and FGF23 as hub genes. Molecular docking of NSP-B based on a quantum-polarized docking approach with FGF6, FGF20, FGF22, and FGF23 revealed stronger interactions with the key hotspot residues. Moreover, molecular simulation revealed a stable dynamic behavior, good structural packing, and residues' flexibility of each complex. Hydrogen bonding in each complex was also observed to be above the minimum. In addition, the binding free energy was calculated using the MM/GBSA (Molecular Mechanics/Generalized Born Surface Area) and MM/PBSA (Molecular Mechanics/Poisson-Boltzmann Surface Area) approaches. The total binding free energy calculated using the MM/GBSA approach revealed values of -36.85 kcal/mol for the FGF6-NSP-B complex, -43.87 kcal/mol for the FGF20-NSP-B complex, and -37.42 kcal/mol for the FGF22-NSP-B complex, and -41.91 kcal/mol for the FGF23-NSP-B complex. The total binding free energy calculated using the MM/PBSA approach showed values of -30.05 kcal/mol for the FGF6-NSP-B complex, -39.62 kcal/mol for the FGF20-NSP-B complex, -34.89 kcal/mol for the FGF22-NSP-B complex, and -37.18 kcal/mol for the FGF23-NSP-B complex. These findings underscore the promising potential of NSP-B against FGF6, FGF20, FGF22, and FGF23, which are reported to be essential for cancer signaling. These results significantly bolster the potential of NSP-B as a promising candidate for cancer therapy.

Structure-guided engineering and molecular simulations to design a potent monoclonal antibody to target aP2 antigen for adaptive immune response instigation against type 2 diabetes.

Introduction: Diabetes mellitus (DM) is recognized as one of the oldest chronic diseases and has become a significant public health issue, necessitating innovative therapeutic strategies to enhance patient outcomes. Traditional treatments have provided limited success, highlighting the need for novel approaches in managing this complex disease. Methods: In our study, we employed graph signature-based methodologies in conjunction with molecular simulation and free energy calculations. The objective was to engineer the CA33 monoclonal antibody for effective targeting of the aP2 antigen, aiming to elicit a potent immune response. This approach involved screening a mutational landscape comprising 57 mutants to identify modifications that yield significant enhancements in binding efficacy and stability. Results: Analysis of the mutational landscape revealed that only five substitutions resulted in noteworthy improvements. Among these, mutations T94M, A96E, A96Q, and T94W were identified through molecular docking experiments to exhibit higher docking scores compared to the wild-type. Further validation was provided by calculating the dissociation constant (KD), which showed a similar trend in favor of these mutations. Molecular simulation analyses highlighted T94M as the most stable complex, with reduced internal fluctuations upon binding. Principal components analysis (PCA) indicated that both the wild-type and T94M mutant displayed similar patterns of constrained and restricted motion across principal components. The free energy landscape analysis underscored a single metastable state for all complexes, indicating limited structural variability and potential for high therapeutic efficacy against aP2. Total binding free energy (TBE) calculations further supported the superior performance of the T94M mutation, with TBE values demonstrating the enhanced binding affinity of selected mutants over the wild-type. Discussion: Our findings suggest that the T94M substitution, along with other identified mutations, significantly enhances the therapeutic potential of the CA33 antibody against DM by improving its binding affinity and stability. These results not only contribute to a deeper understanding of antibody-antigen interactions in the context of DM but also provide a valuable framework for the rational design of antibodies aimed at targeting this disease more effectively.

Efficient and accurate binding free energy calculation of Aß9-40 protofilament propagation.

Self-assembled aggregation of peptides and proteins into regular amyloid fibrils is associated with several neurodegenerative diseases. In case of Alzheimer's disease proteolytic cleavage products of the amyloid precursor protein form pathological amyloid-beta fibrils in a nucleation and propagation phase. The molecular details and thermodynamic driving forces of amyloid formation are not well understood, but are of high relevance for potential pharmacological interference. We used atomistic binding free energy simulations to calculate the free energy of protofilament propagation by an additional Aß9-40 peptide binding to the protofilament tip. It requires sampling of relevant conformational transitions which is challenging since the monomeric Aß9-40 peptide is intrinsically disordered. However, the convergence of umbrella simulations can be enhanced by applying additional restraining potentials on the axial, orientational and conformational degrees of freedom. The improved convergence leads to a much closer agreement with experimental binding free energy data compared to unrestrained umbrella sampling. Moreover, the restraining approach results in a separation of contributions to the total binding free energy. The calculated contributions indicate that the free energy change associated with the restriction of conformational freedom upon propagation makes a large opposing contribution of higher magnitude than the total binding free energy. Finally, optimization of the approach leads to further significant reduction of the computational demand which is crucial for systematic studies on mutations, denaturants and inhibitors in the fibril propagation step.

Investigation of the Binding Interaction of Mfsd2a with NEDD4-2 via Molecular Dynamics Simulations.

Major facilitator superfamily domain-containing 2a (Mfsd2a) is a sodium-dependent lysophosphatidylcholine cotransporter that plays an important role in maintaining the integrity of the blood-brain barrier and neurological function. Abnormal degradation of Mfsd2a often leads to dysfunction of the blood-brain barrier, while upregulation of Mfsd2a can retrieve neurological damage. It has been reported that Mfsd2a can be specifically recognized and ubiquitinated by neural precursor cell-expressed developmentally downregulated gene 4 type 2 (NEDD4-2) ubiquitin ligase and finally degraded through the proteasome pathway. However, the structural basis for the specific binding of Mfsd2a to NEDD4-2 is unclear. In this work, we combined deep learning and molecular dynamics simulations to obtain a Mfsd2a structure with high quality and a stable Mfsd2a/NEDD4-2-WW3 interaction model. Moreover, molecular mechanics generalized Born surface area (MM-GBSA) methods coupled with per-residue energy decomposition studies were carried out to analyze the key residues that dominate the binding interaction. Based on these results, we designed three peptides containing the key residues by truncating the Mfsd2a sequences. One of them was found to significantly inhibit Mfsd2a ubiquitination, which was further validated in an oxygen-glucose deprivation (OGD) model in a human microvascular endothelial cell line. This work provides some new insights into the understanding of Mfsd2a and NEDD4-2 interaction and might promote further development of drugs targeting Mfsd2a ubiquitination.

Identification of novel small molecule inhibitors for solute carrier SGLT1; a computational exploration.

Diabetes results in substantial disabilities, diminished quality of life, and mortality that imposes a huge economic burden on societies and governments worldwide. Despite the absence of specific oral therapies at present, there exists an urgent requirement to develop a novel drug for the treatment of diabetes mellitus. The membrane protein sodium glucose co-transporters (SGLT1) present a captivating therapeutic target for diabetes, given its pivotal role in facilitating glucose absorption in the small intestine, offering immense promise for potential therapeutic intervention. In this connection, the present study is aimed at identifying potential inhibitors of SGLT1 from a small molecule database, including compounds from both natural as well as synthetic origins. A comprehensive approach was employed, by integrating homology modeling, ligand-based pharmacophore modeling, virtual screening, and molecular docking simulation. The process resulted in the identification of 16 new compounds, featuring similar attributes as observed for the documented actives. In a systematic screening procedure, five potential virtual hits were selected for simulation studies followed by subsequent binding free energy calculations, providing deeper insight into the time-dependent behavior of protein-ligand complexes in a dynamic state. In conclusion, our findings demonstrated that the identified compounds, particularly compounds 81 and 91, exhibit enhanced stability and favorable binding affinities with the target protein, marking them promising candidates for further investigations.Communicated by Ramaswamy H. Sarma.

Imidazole-4-N-acetamide Derivatives as a Novel Scaffold for Selective Targeting of Cyclin Dependent Kinases.

The rational design of cyclin-dependent protein kinase (CDK) inhibitors presumes the development of approaches for accurate prediction of selectivity and the activity of small molecular weight anticancer drug candidates. Aiming at attenuation of general toxicity of low selectivity compounds, we herein explored the new chemotype of imidazole-4-N-acetamide substituted derivatives of the pan-CDK inhibitor PHA-793887. Newly synthesized compounds 1-4 containing an aliphatic methyl group or aromatic radicals at the periphery of the scaffold were analyzed for the prediction of relative free energies of binding to CDK1, -2, -5, and -9 using a protocol based on non-equilibrium (NEQ) thermodynamics. This methodology allows for the demonstration of a good correlation between the calculated parameters of interaction of 1-4 with individual targets and the values of inhibitory potencies in in vitro kinase assays. We provide evidence in support of NEQ thermodynamics as a time sparing, precise, and productive approach for generating chemical inhibitors of clinically relevant anticancer targets.

Computational Exploration of the Effects of Mutations on GABA Aminotransferase in GABA Aminotransferase Deficiency.

Gamma-aminobutyric acid (GABA) transaminase-also called GABA aminotransferase (GABA-AT)-deficiency is a rare autosomal recessive disorder characterized by a severe neonatal-infantile epileptic encephalopathy with symptoms such as seizures, hypotonia, hyperreflexia, developmental delay, and growth acceleration. GABA transaminase deficiency is caused by mutations in GABA-AT, the enzyme responsible for the catabolism of GABA. Mutations in multiple locations on GABA-AT have been reported and their locations have been shown to influence the onset of the disease and the severity of symptoms. We examined how GABA-AT mutations influence the structural stability of the enzyme and GABA-binding affinity using computational methodologies such as molecular dynamics simulation and binding free energy calculation to understand the underlying mechanism through which GABA-AT mutations cause GABA-AT deficiency. GABA-AT 3D model depiction was carried out together with seven individual mutated models of GABA-AT. The structural stability of all the predicted models was analyzed using several tools and web servers. All models were evaluated based on their phytochemical values. Additionally, 100 ns MD simulation was carried out and the mutated models were evaluated using RMSD, RMSF, Rg, and SASA. gmxMMPBSA free energy calculation was carried out. Moreover, RMSD and free energy calculations were also compared with those obtained using online web servers. Our study demonstrates that P152S, Q296H, and R92Q play a more critical role in the structural instability of GABA-AT compared with the other mutated models: G465R, L211F, L478P, and R220K.

Targeting NMDA receptor in Alzheimer's disease: identifying novel inhibitors using computational approaches.

The glutamate-gated ion channels known as N-methyl-d-aspartate receptors (NMDARs) are important for both normal and pathological brain function. Subunit-selective antagonists have high therapeutic promise since many pathological conditions involve NMDAR over activation, although few clinical successes have been reported. Allosteric inhibitors of GluN2B-containing receptors are among the most potential NMDAR targeting drugs. Since the discovery of ifenprodil, a variety of GluN2B-selective compounds have been discovered, each with remarkably unique structural motifs. These results expand the allosteric and pharmacolog-ical spectrum of NMDARs and provide a new structural basis for the development of next-generation GluN2B antagonists that have therapeutic potential in brain diseases. Small molecule therapeutic inhibitors targeting NMDA have recently been developed to target CNS disorders such as Alzheimer's disease. In the current study, a cheminformatics method was used to discover potential antagonists and to identify the structural requirements for Gly/NMDA antagonism. In this case we have created a useful pharmacophore model with solid statistical values. Through pharmacophore mapping, the verified model was used to filter out virtual matches from the ZINC database. Assessing receptor-ligand binding mechanisms and affinities used molecular docking. To find the best hits, the GlideScore and the interaction of molecules with important amino acids were considered essential features. We found some molecular inhibitors, namely, ZINC13729211, ZINC07430424, ZINC08614951, ZINC60927204, ZINC12447511, and ZINC18889258 with high binding affinity using computational methods. The molecules in our studies showed characteristics such as good stability, hydrogen bonding and higher binding affinities in the solvation-based assessment method than ifenprodil with acceptable ADMET profile. Moreover, these six leads have been proposed as potential new perspectives for exploring potent Gly/NMDA receptor antagonists. In addition, it can be tested in the laboratory for potential therapeutic strategies for both in vitro and in vivo research.

Uni-GBSA: an open-source and web-based automatic workflow to perform MM/GB(PB)SA calculations for virtual screening.

Binding free energy calculation of a ligand to a protein receptor is a fundamental objective in drug discovery. Molecular mechanics/Generalized-Born (Poisson-Boltzmann) surface area (MM/GB(PB)SA) is one of the most popular methods for binding free energy calculations. It is more accurate than most scoring functions and more computationally efficient than alchemical free energy methods. Several open-source tools for performing MM/GB(PB)SA calculations have been developed, but they have limitations and high entry barriers to users. Here, we introduce Uni-GBSA, a user-friendly automatic workflow to perform MM/GB(PB)SA calculations, which can perform topology preparation, structure optimization, binding free energy calculation and parameter scanning for MM/GB(PB)SA calculations. It also offers a batch mode that evaluates thousands of molecules against one protein target in parallel for efficient application in virtual screening. The default parameters are selected after systematic testing on the PDBBind-2011 refined dataset. In our case studies, Uni-GBSA produced a satisfactory correlation with the experimental binding affinities and outperformed AutoDock Vina in molecular enrichment. Uni-GBSA is available as an open-source package at https://github.com/dptech-corp/Uni-GBSA. It can also be accessed for virtual screening from the Hermite web platform at https://hermite.dp.tech. A free Uni-GBSA web server of a lab version is available at https://labs.dp.tech/projects/uni-gbsa/. This increases user-friendliness because the web server frees users from package installations and provides users with validated workflows for input data and parameter settings, cloud computing resources for efficient job completions, a user-friendly interface and professional support and maintenance.

Acceleration of generalized replica exchange with solute tempering simulations of large biological systems on massively parallel supercomputer.

Generalized replica exchange with solute tempering (gREST) is one of the enhanced sampling algorithms for proteins or other systems with rugged energy landscapes. Unlike the replica-exchange molecular dynamics (REMD) method, solvent temperatures are the same in all replicas, while solute temperatures are different and are exchanged frequently between replicas for exploring various solute structures. Here, we apply the gREST scheme to large biological systems containing over one million atoms using a large number of processors in a supercomputer. First, communication time on a multi-dimensional torus network is reduced by matching each replica to MPI processors optimally. This is applicable not only to gREST but also to other multi-copy algorithms. Second, energy evaluations, which are necessary for the multistate bennet acceptance ratio (MBAR) method for free energy estimations, are performed on-the-fly during the gREST simulations. Using these two advanced schemes, we observed 57.72 ns/day performance in 128-replica gREST calculations with 1.5 million atoms system using 16,384 nodes in Fugaku. These schemes implemented in the latest version of GENESIS software could open new possibilities to answer unresolved questions on large biomolecular complex systems with slow conformational dynamics.

Receptor-Based Virtual Screening of Large Libraries in a Multi-Level In Silico Approach.

Structure-based drug design (SBDD) has become an alternative to high throughput screening (HTS) as it reduces experimental costs and time. It works like a funnel, filtering out compounds that do not show good affinity (or score) toward a particular target, with known 3D structure.Here, we describe a protocol for structure-based drug design using a multi-level in silico approach, combining Molecular Docking, Virtual Screening, Molecular Dynamics Simulations and Free energy calculations to find new lead molecules for experimental testing, predict binding affinities and characterize binding modes.

Identification and in silicon binding study of a novel NR2B selective NMDAR antagonist.

Alzheimer's disease (AD) is a major group of diseases that threaten human health, and the search for drugs and treatments for it has never stopped. Research and development of NMDA receptor antagonists as potential therapeutic targets have also been ongoing. Our group designed and synthesized 22 new tetrahydropyrrolo[2,1-b]quinazolines based on NR2B-NMDARs targets and evaluated them for their neuroprotective activity against NMDA-induced cytotoxicity in vitro, A21 exhibited excellent neuroprotective activity. Subsequently, the structure-activity relationships and inhibitor binding modes of the tetrahydropyrrolo[2,1-b]quinazolines were further analyzed by molecular docking, molecular dynamics (MD) simulations and binding free energy calculations. The results showed that A21 could match the two binding pockets of NR2B-NMDARs. The research results of this project will lay a certain foundation for the research of novel NR2B-NMDA receptor antagonists and also provide new ideas for the subsequent research and development of this target.

Structural requirement of RARγ agonism through computational aspects.

CONTEXT: RARγ is a therapeutic target for many skin diseases and has potential in cancer treatment. In the current study, we put forward a comprehensive structure-activity relationship study of third and fourth generations of RARγ agonists, addressing multiple crystal structures of RARγ complexes and approved drugs. Adapalene and Trifarotene, through hybrid strategies including protein contacts Atlas analysis, molecular docking, dynamics simulations, MM-GBSA, ASM, and pharmacophore modeling. Our result revealed crucial amino acids Arg267, Ser278, Phe288, Phe230, Met272, Leu271, and Leu268 within the RARγ pocket, as well as pharmacophore features such as two hydrophobic groups, two aromatic rings, and negative ionic features, which are essential for the binding of RARγ agonists. Based on this study, the binding mechanism of RARγ agonists was elucidated, which will be helpful for the rational design of new RARγ agonists for skin diseases and cancer treatment. METHODS: In this study, Schrödinger suite 2021-2 with OPLS_4 force field, Discovery Studio program 3.0, LigandScout 4.3, and PyMOL are utilized in the investigation.