Ligand Docking Research Articles

Structure-based virtual screening of unbiased and RNA-focused libraries to identify new ligands for the HCV IRES model system

Using structure-based virtual screening, FRET and MST assays, novel ligands of the hepatitis C virus internal ribosome entry site were identified. This proof-of-concept study demonstrated the feasibility of RNA–ligand docking for hit identification.

CarsiDock: a deep learning paradigm for accurate protein-ligand docking and screening based on large-scale pre-training.

The expertise accumulated in deep neural network-based structure prediction has been widely transferred to the field of protein-ligand binding pose prediction, thus leading to the emergence of a variety of deep learning-guided docking models for predicting protein-ligand binding poses without relying on heavy sampling. However, their prediction accuracy and applicability are still far from satisfactory, partially due to the lack of protein-ligand binding complex data. To this end, we create a large-scale complex dataset containing ∼9 M protein-ligand docking complexes for pre-training, and propose CarsiDock, the first deep learning-guided docking approach that leverages pre-training of millions of predicted protein-ligand complexes. CarsiDock contains two main stages, i.e., a deep learning model for the prediction of protein-ligand atomic distance matrices, and a translation, rotation and torsion-guided geometry optimization procedure to reconstruct the matrices into a credible binding pose. The pre-training and multiple innovative architectural designs facilitate the dramatically improved docking accuracy of our approach over the baselines in terms of multiple docking scenarios, thereby contributing to its outstanding early recognition performance in several retrospective virtual screening campaigns. Further explorations demonstrate that CarsiDock can not only guarantee the topological reliability of the binding poses but also successfully reproduce the crucial interactions in crystalized structures, highlighting its superior applicability.

Harnessing deep learning for enhanced ligand docking

Ligand docking (LD), a technology for predicting protein–ligand (PL)-binding conformations and strengths, plays key roles in virtual screening (VS). However, the accuracy and speed of current LD methodologies remain suboptimal. Here, we discuss how deep learning (DL) could help to bridge this gap by examining recent advancements and projecting future trends.

Computational-guided approach for identification of PI3K alpha inhibitor in the treatment of hepatocellular carcinoma by virtual screening and water map analysis

Hepatocellular carcinoma (HCC) is one of the most deadly disorders, with a relative survival rate of 36% in the last 5 years. After an extensive literature survey and pathophysiology analysis, PI3Kα was found to be a promising biological target as PIK3CA gene upregulation was observed in HCC, resulting in the loss of apoptosis of cells, which leads to uncontrollable growth and proliferation. Due to superior selectivity and promising therapeutic activity, the PI3K-targeted molecule library was selected, and the ligand preparation was executed. The study mainly focused on e-pharmacophore development, virtual screening and receptor–ligand docking analysis. Then, MMGBSA and ADME prediction analysis was performed with the top 10 molecules; for further analysis of ligand–receptor binding affinity at the catalytic binding site, induced fit docking was performed with the top two molecules. The analysis of quantum chemical stability descriptors, i.e., frontier molecular orbital analysis, was performed followed by molecular dynamics simulation of 100 ns to better understand the ligand-receptor binding. In this study, water map analysis played a significant role in the hit optimization and analysis of the thermodynamic properties of the receptor–ligand complex. The two hit molecules K894-1435 and K894-1045 represented superior docking scores, enhanced stability, and inhibitory action targeting Valine 851 amino acid residue at the catalytic binding site. Hence, the study has significance for the quest for selective PI3Kα inhibitors through the process of hit-to-lead optimization. Communicated by Ramaswamy H. Sarma

Regression-Based Active Learning for Accessible Acceleration of Ultra-Large Library Docking.

Structure-based drug discovery is a process for both hit finding and optimization that relies on a validated three-dimensional model of a target biomolecule, used to rationalize the structure-function relationship for this particular target. An ultralarge virtual screening approach has emerged recently for rapid discovery of high-affinity hit compounds, but it requires substantial computational resources. This study shows that active learning with simple linear regression models can accelerate virtual screening, retrieving up to 90% of the top-1% of the docking hit list after docking just 10% of the ligands. The results demonstrate that it is unnecessary to use complex models, such as deep learning approaches, to predict the imprecise results of ligand docking with a low sampling depth. Furthermore, we explore active learning meta-parameters and find that constant batch size models with a simple ensembling method provide the best ligand retrieval rate. Finally, our approach is validated on the ultralarge size virtual screening data set, retrieving 70% of the top-0.05% of ligands after screening only 2% of the library. Altogether, this work provides a computationally accessible approach for accelerated virtual screening that can serve as a blueprint for the future design of low-compute agents for exploration of the chemical space via large-scale accelerated docking. With recent breakthroughs in protein structure prediction, this method can significantly increase accessibility for the academic community and aid in the rapid discovery of high-affinity hit compounds for various targets.

Unearthing the inhibitory potential of phytochemicals from Lawsonia inermis L. and some drugs against dengue virus protein NS1: an in silico approach

Dengue has received the status of an epidemic and endemic disease, with countless number of infections every year. Due to the unreliability of vaccines and non-specificity of drugs, it becomes necessary to find plant-based alternatives, with less harmful side effects. Lawsonia inermis L., is the sole source of dye, Mehendi. The rich repertoire of phytochemicals makes it useful, medicinally. The main objectives of the study are to explore the anti-dengue properties of the phytochemicals from Lawsonia inermis, and to shortlist potential candidates in curing the disease. Phytochemicals from the plant, and a set of drugs were screened and docked against NS1 protein, a less explored drug target, needed for maintenance of virus life cycle. Ligand screening and docking analysis concluded gallic acid, and chlorogenic acid to be good candidates, exhibiting high binding affinity and extensive interactions with the protein. From among the shortlisted drugs, only Vibegron showed effective binding affinity with NS1 protein with zero violations to the Lipinski’s Rule of 5. Molecular dynamic simulations, executed for a time period of 100 nanoseconds, reveal the performance of a ligand within a solvated system. Chlorogenic and gallic acid, formed more stable and compact complexes with protein, with stable energy parameters and strong binding affinity. This was further validated with snapshots taken every 50 nanoseconds, showing no change in binding site between the ligand and protein, within the stipulated time frame. It was interesting to see that, a phenol (chlorogenic acid), served as a better drug candidate, against the NS1 protein. Communicated by Ramaswamy H. Sarma

Monoterpenes as potential antifungal molecules against Candida cell membranes: in-vitro and in-silico studies

Azoles are the frequently used antifungal drugs that target the enzyme lanosterol 14 α-demethylase (erg11p). This enzyme plays a vital role in ergosterol biosynthesis and hence maintainenance of cell membrane fluidity and integrity. The emergence of resistance to azoles and their fungistatic nature against several fungal pathogens is the major challenge to combat invasive candidiasis. Therefore, there is an urgent need to discover new antifungals with better efficacy. This study targets erg11 protein using in silico approach and identifies the monoterpene compounds (α-terpineol, carveol, and terpinene-4-ol) based on docking score and ligand interaction analysis. Further dynamic behavior of best-docked compounds with erg11p was analyzed by various parameters of MD simulation. The binding free energy of selected compounds towards the definitive pocket was also calculated. To further investigate the antifungal activity of selected compounds, in vitro studies were conducted on C. albicans. Studies thus suggest that the proposed the mechanism of antifungal action of test compounds involves targeting the ergosterol biosynthetic pathway. The compounds were explored for their effect on the disruption of membrane integrity by studying ERG11gene expression analysis, scanning electron microscopy, PI uptake (fluorescence microscopy,) and H+-extrusion. The results suggest that the selected monoterpenes are safer natural antifungals that disrupt membrane integrity by inhibiting ergosterol biosynthesis and other membrane associated structures. Communicated by Ramaswamy H. Sarma

An Antennae-Enriched Odorant-Binding Protein EonuOBP43 Mediate the Behavioral Response of the Tea Green Leafhopper, Empoasca onukii Matsuda to the Host and Nonhost Volatiles.

Olfaction is crucial for Empoasca onukii Matsuda to recognize odors from the host and nonhost plants, and it has been proposed that odorant binding proteins are directly required for odorant discrimination and represent potential targets of interest for pest control. Here, we cloned EonuOBP43 and expressed the recombinant EonuOBP43 protein. Furthermore, competitive fluorescence binding assays with 19 ligands indicated that terpenoids and alkanes showed a relatively higher than for other classes of chemicals. Additionally, ligand docking and site-directed mutagenesis results revealed that seven hydrophobic residues, including Val-86, Met-89, Phe-90, Ile-104, Ile-105, Leu-130, and Val-134, played a key role in the binding of EonuOBP43 to plant volatiles. In olfactometer tests, E. onukii were significantly attracted to α-farnesene and repelled to β-caryophyllene, and dsOBP43 treated adult lost response to α-farnesene and β-caryophyllene. In summary, our results demonstrated that EonuOBP43 may function as a carrier in the process of sensing plant compounds of E. onukii.

Two-Step Covalent Docking with Attracting Cavities.

Due to their various advantages, interest in the development of covalent drugs has been renewed in the past few years. It is therefore important to accurately describe and predict their interactions with biological targets by computer-aided drug design tools such as docking algorithms. Here, we report a covalent docking procedure for our in-house docking code Attracting Cavities (AC), which mimics the two-step mechanism of covalent ligand binding. Ligand binding to the protein cavity is driven by nonbonded interactions, followed by the formation of a covalent bond between the ligand and the protein through a chemical reaction. To test the performance of this method, we developed a diverse, high-quality, openly accessible re-docking benchmark set of 95 covalent complexes bound by 8 chemical reactions to 5 different reactive amino acids. Combination with structures from previous studies resulted in a set of 304 complexes, on which AC obtained a success rate (rmsd ≤ 2 Å) of 78%, outperforming two state-of-the-art covalent docking codes, genetic optimization for ligand docking (GOLD (66%)) and AutoDock (AD (35%)). Using a more stringent success criterion (rmsd ≤ 1.5 Å), AC reached a success rate of 71 vs 55% for GOLD and 26% for AD. We additionally assessed the cross-docking performance of AC on a set of 76 covalent complexes of the SARS-CoV-2 main protease. On this challenging test set of mainly small and highly solvent-exposed ligands, AC yielded success rates of 58 and 28% for re-docking and cross-docking, respectively, compared to 45 and 17% for GOLD.

Docking and MM study of non-structural protein (NS5) of Japanese Encephalitis Virus (JEV) with some derivatives of adenosyl

Introduction: The flavivirus NS5, a non-structural protein of Japanese Encephalitis Virus (JEV), a serious deadly human pathogen responsible for epidemics in South East Asia, consists of N-terminal methyl transferase (MTase) domain and RNA-dependent RNA polymerase (RdRp) is known for unique viral genome replication and cap formation activity. S-adenosyl executes a crucial function in these viral activities. S-adenosyl derivatives are chosen as potential binders with the MTase domain of NS5 based on MM and docking studies.Methods: MM GBSA (Generalized Born Surface Area) simulation were performed to evaluate the binding energy, following the 100 nanosecond (ns) production MD simulation in the periodic boundary condition (PBC) for the selected docked ligands with NS5. Quasi-harmonic entropy of the ligands was also calculated with semi-empirical calculations at the PM3/PM6 level supporting docking and MM-GBSA results.Results and discussion: The residue-wise decomposition energy reveals that the key hydrophobic residues Gly 81, Phe 133, and Ile 147 in the RdRp-MTase interface, indicate the biological relevance. These residues act as the key residue stabilizer, binding vigorously with S-Adenosyl derivatives in the vicinity of the interface between the MTase domain and RdRp. This paves the way for the other potential drug as an inhibitor for the enzymatic activity of the NS5.

Comparative Assessment of Pose Prediction Accuracy in RNA-Ligand Docking.

Structure-based virtual high-throughput screening is used in early-stage drug discovery. Over the years, docking protocols and scoring functions for protein-ligand complexes have evolved to improve the accuracy in the computation of binding strengths and poses. In the past decade, RNA has also emerged as a target class for new small-molecule drugs. However, most ligand docking programs have been validated and tested for proteins and not RNA. Here, we test the docking power (pose prediction accuracy) of three state-of-the-art docking protocols on 173 RNA-small molecule crystal structures. The programs are AutoDock4 (AD4) and AutoDock Vina (Vina), which were designed for protein targets, and rDock, which was designed for both protein and nucleic acid targets. AD4 performed relatively poorly. For RNA targets for which a crystal structure of a bound ligand used to limit the docking search space is available and for which the goal is to identify new molecules for the same pocket, rDock performs slightly better than Vina, with success rates of 48% and 63%, respectively. However, in the more common type of early-stage drug discovery setting, in which no structure of a ligand-target complex is known and for which a larger search space is defined, rDock performed similarly to Vina, with a low success rate of ∼27%. Vina was found to have bias for ligands with certain physicochemical properties, whereas rDock performs similarly for all ligand properties. Thus, for projects where no ligand-protein structure already exists, Vina and rDock are both applicable. However, the relatively poor performance of all methods relative to protein-target docking illustrates a need for further methods refinement.

EXTH-14. MRNA-LOADED EXOSOMES FOR TARGETED GLIOBLASTOMA IMMUNOTHERAPY

Abstract Recent successes of mRNA therapeutics against pathogenic infections have increased interest in their investigations for other human diseases including cancer. However, precisely delivering the genetic cargo to cells and tissues of interest remains challenging. Extracellular vesicles such as exosomes have gained increased interest recently as potential nucleic acid and drug carriers given their favorable immunogenic profile, long circulatory half-life, and ability to cross biological barriers such as blood-brain barrier. Here, we show an adaptive strategy that enables the docking of different targeting ligands onto the surface of mRNA-loaded exosomes. This was achieved using a microfluidic electroporation approach where a combination of nano- and milli-second pulse electroporations produced a large quantity of IFN-γ mRNA-loaded exosomes with CD64 overexpressed on their surface. The CD64 molecule serves as an adaptor to dock targeting ligands, such as anti-CD71 and anti-programmed cell death-ligand 1 (PD-L1) antibodies. The resulting immunogenic exosomes (imExo) selectively targeted glioblastoma cells and generated potent antitumor activities in vivo, including against tumors intrinsically resistant to immunotherapy. Together, these results provide a new adaptive approach to engineering mRNA-loaded exosomes with targeting functionality and pave the way for their adoption in cancer immunotherapy applications.

Solution Structure of Poly(UG) RNA

Poly(UG) or “pUG” RNAs are UG or GU dinucleotide repeat sequences which are highly abundant in eukaryotes. Post-transcriptional addition of pUGs to RNA 3′ ends marks mRNAs as vectors for gene silencing in C. elegans. We previously determined the crystal structure of pUG RNA bound to the ligand N-methyl mesoporphyrin IX (NMM), but the structure of free pUG RNA is unknown. Here we report the solution structure of the free pUG RNA (GU)12, as determined by nuclear magnetic resonance spectroscopy and small and wide-angle x-ray scattering (NMR-SAXS-WAXS). The low complexity sequence and 4-fold symmetry of the structure result in overlapped NMR signals that complicate chemical shift assignment. We therefore utilized single site-specific deoxyribose modifications which did not perturb the structure and introduced well-resolved methylene signals that are easily identified in NMR spectra. The solution structure ensemble has a root mean squared deviation (RMSD) of 0.62 Å and is a compact, left-handed quadruplex with a Z-form backbone, or “pUG fold.” Overall, the structure agrees with the crystal structure of (GU)12 bound to NMM, indicating the pUG fold is unaltered by docking of the NMM ligand. The solution structure reveals conformational details that could not be resolved by x-ray crystallography, which explain how the pUG fold can form within longer RNAs.

Binding of perilipin 3 to membranes containing diacylglycerol is mediated by conserved residues within its PAT domain

Perilipins (PLINs) constitute an evolutionarily conserved family of proteins that specifically associate with the surface of lipid droplets (LDs). These proteins function in LD biogenesis and lipolysis and help to stabilize the surface of LDs. PLINs are typically composed of three different protein domains. They share an N-terminal PAT domain of unknown structure and function, a central region containing 11-mer repeats that form amphipathic helices, and a C-terminal domain that adopts a 4-helix bundle structure. How exactly these three distinct domains contribute to PLIN function remains to be determined. Here, we show that the N-terminal PAT domain of PLIN3 binds diacylglycerol (DAG), the precursor to triacylglycerol, a major storage lipid of LDs. PLIN3 and its PAT domain alone bind liposomes with micromolar affinity and PLIN3 binds artificial LDs containing low concentrations of DAG with nanomolar affinity. The PAT domain of PLIN3 is predicted to adopt an amphipathic triangular shaped structure. In silico ligand docking indicates that DAG binds to one of the highly curved regions within this domain. A conserved aspartic acid residue in the PAT domain, E86, is predicted to interact with DAG, and we found that its substitution abrogates high affinity binding of DAG as well as DAG-stimulated association with liposome and artificial LDs. These results indicate that the PAT domain of PLINs harbor specific lipid-binding properties that are important for targeting these proteins to the surface of LDs and to ER membrane domains enriched in DAG to promote LD formation.

Drug repurposing by in silico prediction of cyclizine derivatives as antihyperlipemic agents.

Cardiovascular diseases are the primary factor for increased mortality rates around the world. Atherosclerosis brought on by high serum cholesterol can result in coronary heart disease (CHD). The risk of CHD is markedly reduced by lowering serum cholesterol levels. Scientists across the world are inventing new treatment regimens for lowering blood lipid levels. In this work, we repurposed the already established drugs, i.e., cyclizine derivatives as antihyperlipidemic agents. The repurposing was done based on the similarity of the selected cyclizine derivatives with the already established antihyperlipidemic drug, fenofibrate. Computational studies were performed and the 16 cyclizine derivatives docked against PPAR. alpha scored higher than fenofibrate. Lifarizine and medibazine outperform fenofibrate inmmgbsa. Fenofibrate, etodroxizine, meclizine, and cinnarizine had similar mmgbsa scores. The ADME properties of these compounds were performed and from that etodroxizine and levocetirizine were found to have better properties. The computational studies were performed using the Schrodinger software, maestro 12.8. The "Protein Preparation Wizard" module in the Maestro panel was used to create the protein structure and OPLS4 force field was used for energy minimization. The maestro builder panel's "Ligprep", "Receptor Grid Generation" and "Ligand Docking" modules were then used to prepare ligands, receptor grids and to perform docking respectively. MMGBSA was performed on the "prime MMGBSA" segment. Using the "Qikprop" setting in the maestro panel, a number of ADMET properties were predicted, and the program was run in default mode using vsgb as the solvation model.

Adaptive design of mRNA-loaded extracellular vesicles for targeted immunotherapy of cancer

The recent success of mRNA therapeutics against pathogenic infections has increased interest in their use for other human diseases including cancer. However, the precise delivery of the genetic cargo to cells and tissues of interest remains challenging. Here, we show an adaptive strategy that enables the docking of different targeting ligands onto the surface of mRNA-loaded small extracellular vesicles (sEVs). This is achieved by using a microfluidic electroporation approach in which a combination of nano- and milli-second pulses produces large amounts of IFN-γ mRNA-loaded sEVs with CD64 overexpressed on their surface. The CD64 molecule serves as an adaptor to dock targeting ligands, such as anti-CD71 and anti-programmed cell death-ligand 1 (PD-L1) antibodies. The resulting immunogenic sEVs (imsEV) preferentially target glioblastoma cells and generate potent antitumour activities in vivo, including against tumours intrinsically resistant to immunotherapy. Together, these results provide an adaptive approach to engineering mRNA-loaded sEVs with targeting functionality and pave the way for their adoption in cancer immunotherapy applications.

The IbeA protein from adherent invasive Escherichia coli isa flavoprotein sharing structural homology with FAD-dependent oxidoreductases.

Invasion of brain endothelium protein A (IbeA) is a virulence factor specific to pathogenic Escherichia coli. Originally identified in the K1 strain causing neonatal meningitis, it was more recently found in avian pathogenic Escherichia coli (APEC) and adherent invasive Escherichia coli (AIEC). In these bacteria, IbeA facilitates host cell invasion and intracellular survival, in particular, under harsh conditions like oxidative stress. Furthermore, IbeA from AIEC contributes to intramacrophage survival and replication, thus enhancing the inflammatory response within the intestine. Therefore, this factor is a promising drug target for anti-AIEC strategies in the context of Crohn's disease. Despite such an important role, the biological function of IbeA remains largely unknown. In particular, its exact nature and cellular localization, i.e., membrane-bound invasin versus cytosolic factor, are still of debate. Here, we developed an efficient protocol for recombinant expression of IbeA under native conditions and demonstrated that IbeA from AIEC is a soluble, homodimeric flavoprotein. Using mass spectrometry and tryptophan fluorescence measurements, we further showed that IbeA preferentially binds flavin adenine dinucleotide (FAD), with an affinity in the one-hundred nanomolar range and optimal binding under reducing conditions. 3D-modeling with AlphaFold revealed that IbeA shares strong structural homology with FAD-dependent oxidoreductases. Finally, we used ligand docking, mutational analyses, and molecular dynamics simulations to identify the FAD binding pocket within IbeA and characterize possible conformational changes occurring upon ligand binding. Overall, we suggest that the role of IbeA in the survival of AIEC within host cells, notably macrophages, is linked to modulation of redox processes.

ReverseDock: a web server for blind docking of a single ligand to multiple protein targets using AutoDock Vina.

Several platforms exist to perform molecular docking to computationally predict binders to a specific protein target from a library of ligands. The reverse, that is, docking a single ligand to various protein targets, can currently be done by very few web servers, which limits the search to a small set of pre-selected human proteins. However, the possibility to in silico predict which targets a compound identified in a high-throughput drug screen bind would help optimize and reduce the costs of the experimental workflow needed to reveal the molecular mechanism of action of a ligand. Here, we present ReverseDock, a blind docking web server based on AutoDock Vina specifically designed to allow users with no computational expertise to dock a ligand to 100 protein structures of their choice. ReverseDock increases the number and type of proteins a ligand can be docked to, making the task of in silico docking of a ligand to entire families of proteins straightforward. We envision ReverseDock will support researchers by providing the possibility to apply inverse docking computations using web browser. ReverseDock is available at: https://reversedock.biologie.uni-freiburg.de/.

Reanalysis of a μ opioid receptor crystal structure reveals a covalent adduct with BU72

BackgroundThe first crystal structure of the active μ opioid receptor (μOR) exhibited several unexplained features. The ligand BU72 exhibited many extreme deviations from ideal geometry, along with unexplained electron density. I previously showed that inverting the benzylic configuration resolved these problems, establishing revised stereochemistry of BU72 and its analog BU74. However, another problem remains unresolved: additional unexplained electron density contacts both BU72 and a histidine residue in the N-terminus, revealing the presence of an as-yet unidentified atom.ResultsThese short contacts and uninterrupted density are inconsistent with non-covalent interactions. Therefore, BU72 and μOR form a covalent adduct, rather than representing two separate entities as in the original model. A subsequently proposed magnesium complex is inconsistent with multiple lines of evidence. However, oxygen fits the unexplained density well. While the structure I propose is tentative, similar adducts have been reported previously in the presence of reactive oxygen species. Moreover, known sources of reactive oxygen species were present: HEPES buffer, nickel ions, and a sequence motif that forms redox-active nickel complexes. This motif contacts the unexplained density. The adduct exhibits severe strain, and the tethered N-terminus forms contacts with adjacent residues. These forces, along with the nanobody used as a G protein substitute, would be expected to influence the receptor conformation. Consistent with this, the intracellular end of the structure differs markedly from subsequent structures of active μOR bound to Gi protein.ConclusionsLater Gi-bound structures are likely to be more accurate templates for ligand docking and modelling of active G protein-bound μOR. The possibility of reactions like this should be considered in the choice of protein truncation sites and purification conditions, and in the interpretation of excess or unexplained density.

In Silico Study of Potential Small Molecule TIPE2 Inhibitors for the Treatment of Cancer

Context: Chronic inflammation has been linked to cancer since the 19th century. Tumor growth is supported by the proangiogenic factors that chronic inflammation requires. Polarized leukocytes initiate these angiogenic and tumorigenic factors. TIPE2, a transport protein, manages the cytoskeletal rearrangement that gives a polarized leukocyte its motility. Inhibition of this protein could lead to a therapeutic option for solid tumor cancers; however, no such inhibitors have been developed so far due to the large cavity size of the TIPE2 protein. Here we have examined possible small molecule inhibitors by combining structure-based and fragment-based drug design approaches. The highest binding ligands were complexed with the protein, and fragment libraries were docked with the complex with the intention of linking the hit compounds and fragments to design a more potent ligand. Three hit compounds were identified by in silico structure-based screening and a linked compound, C2–F14, of excellent binding affinity, was identified by linking fragments to the hit compounds. C2–F14 demonstrates good binding stability in molecular dynamic simulations and great predicted ADME properties. Methods: High throughput molecular docking calculations of mass libraries were performed using AutoDock Vina 1.1.2. Molecular docking of individual ligands was performed using AutoDock Vina with PyRx. Ligand libraries were prepared using OpenBabel, linked ligands were prepared using Avogadro. The protein was prepared using AutoDockTools-1.5.6. Protein-ligand complexes were visualized with PyMOL. Two- and three-dimensional representations of protein–ligand interactions were plotted with BIOVIA Discovery Studio Visualizer. In silico absorption, distribution, metabolism, and excretion (ADME) properties were calculated using SwissADME. Molecular dynamics simulations were conducted with GROMACS.