Recent Advances and Applications of Molecular Docking to G Protein-Coupled Receptors

Phenolic compounds of Theobroma cacao L. show potential against dengue RdRp protease enzyme inhibition by In-silico docking, DFT study, MD simulation and MMGBSA calculation

BACKGROUND

Currently, there is no antiviral medication for dengue, a potentially fatal tropical infectious illness spread by two mosquito species, Aedes aegypti and Aedes albopictus. The RdRp protease of dengue virus is a potential therapeutic target. This study focused on the in silico drug discovery of RdRp protease inhibitors.

METHODS

To assess the potential inhibitory activity of 29 phenolic acids from Theobroma cacao L. against DENV3-NS5 RdRp, a range of computational methods were employed. These included docking, drug-likeness analysis, ADMET prediction, density functional theory (DFT) calculations, and molecular dynamics (MD) simulations. The aim of these studies was to confirm the stability of the ligand-protein complex and the binding pose identified during the docking experiment.

RESULTS

Twenty-one compounds were found to have possible inhibitory activities against DENV according to the docking data, and they had a binding affinity of ≥-37.417 kcal/mol for DENV3- enzyme as compared to the reference compound panduratin A. Additionally, the drug-likeness investigation produced four hit compounds that were subjected to ADMET screening to obtain the lead compound, catechin. Based on ELUMO, EHOMO, and band energy gap, the DFT calculations showed strong electronegetivity, favouravle global softness and chemical reactivity with considerable intra-molecular charge transfer between electron-donor to electron-acceptor groups for catechin. The MD simulation result also demonstrated favourable RMSD, RMSF, SASA and H-bonds in at the binding pocket of DENV3-NS5 RdRp for catechin as compared to panduratin A.

CONCLUSION

According to the present findings, catechin showed high binding affinity and sufficient drug-like properties with the appropriate ADMET profiles. Moreover, DFT and MD studies further supported the drug-like action of catechin as a potential therapeutic candidate. Therefore, further in vitro and in vivo research on cocoa and its phytochemical catechin should be taken into consideration to develop as a potential DENV inhibitor.

Network pharmacology combined with experimental verification to explore the potential mechanism of naringenin in the treatment of cervical cancer

Cervical cancer is the second leading cause of morbidity and mortality in women worldwide. Traditional treatment methods have become limited. Naringenin, a flavonoid abundant in various fruits and herbal medicines, has demonstrated anti-tumor properties among other effects. This research undertook to elucidate the mechanism of naringenin in the context of cervical cancer treatment by leveraging network pharmacology and performing experimental validation. Initial steps involved predicting potential naringenin targets and subsequently screening for overlaps between these targets and those related to cervical cancer, followed by analysis of their interrelationships. Molecular docking was subsequently utilized to verify the binding effect of the central target. Within the framework of network pharmacology, it was discovered that naringenin might possess anti-cancer properties specific to cervical cancer. Following this, the anti-tumor effects of naringenin on Hela cell viability, migration, and invasion were assessed employing CCK-8, transwell, wound healing assays, and western blotting. Experimental data indicated that naringenin attenuates the migration and invasion of Hela cells via downregulation EGFR/PI3K/AKT signaling pathway. Thus, our findings suggest that naringenin has therapeutic impacts on cervical cancer via multiple mechanisms, primarily by inhibiting the migration and invasion through the EGFR/PI3K/AKT/mTOR pathway. This study offers fresh insights for future clinical studies.

Drug Repositioning Using Computer-aided Drug Design (CADD)

Drug repositioning is a method of using authorized drugs for other unusually complex diseases. Compared to new drug development, this method is fast, low in cost, and effective. Through the use of outstanding bioinformatics tools, such as computer-aided drug design (CADD), computer strategies play a vital role in the re-transformation of drugs. The use of CADD's special strategy for target-based drug reuse is the most promising method, and its realization rate is high. In this review article, we have particularly focused on understanding the various technologies of CADD and the use of computer-aided drug design for target-based drug reuse, taking COVID-19 and cancer as examples. Finally, it is concluded that CADD technology is accelerating the development of repurposed drugs due to its many advantages, and there are many facts to prove that the new ligand-targeting strategy is a beneficial method and that it will gain momentum with the development of technology.

Protein-Protein Docking Approach to GPCR Oligomerization

G-protein-coupled receptors (GPCRs), the largest family of human membrane proteins, play a crucial role in cellular control and are the target of approximately one-third of all drugs on the market. Targeting these complexes with selectivity or formulating small molecules capable of modulating receptor-receptor interactions could potentially offer novel avenues for drug discovery, fostering the development of more refined and safer pharmacotherapies. Due to the lack of experimentally derived X-ray crystallography spectra of GPCR oligomers, there is growing evidence supporting the development of new in silico approaches for predicting GPCR self-assembling structures. The significance of GPCR oligomerization, the challenges in modeling these structures, and the potential of protein-protein docking algorithms to address these challenges are discussed. The study also underscores the use of various software solutions for modeling GPCR oligomeric structures and presents practical cases where these techniques have been successfully applied.

Revealing anti-fungal potential of plant-derived bioactive therapeutics in targeting secreted aspartyl proteinase (SAP) of Candida albicans: a molecular dynamics approach

Candida species have established themselves as a major source of nosocomial infections. Increased expression of secreted aspartyl proteinases (SAP5) plays a crucial role in the pathogenesis of Candida species. Phytotherapeutics continue to serve as a viable resource for discovering novel antifungal agents. Hence the main aim of the present investigation is to explore the possible inhibitory role of the selected bioactive molecules against the SAP5 enzyme of C. albicans using in silico approach. Molecular docking and dynamic simulations were utilized to predict the binding affinity of the lead molecules using the AutoDock and Gromacs in-silico screening tools. Results of preliminary docking simulations show that the compounds hesperidin, vitexin, berberine, adhatodine, piperine, and chlorogenic acid exhibit significant interactions with the core catalytic residues of the target protein. The best binding ligands (hesperidin, vitexin, fluconazole) were subjected to molecular dynamics (MD) and essential dynamics of the trajectories. Results of the MD simulation confirm that the ligand-protein complexes became more stable from 20 ns until 100 ns. The calculated residue-level contributions to the interaction energy along a steady simulation trajectory of all three hits (hesperidin (-132.720 kJ/mol), vitexin (-83.963 kJ/mol) and fluconazole (-98.864 kJ/mol)) ensure greater stability of the leads near the catalytic region. Essential dynamics of PCA and DCCM analysis signifies that the binding of hesperidin and vitexin created a more structurally stable environment in the protein target. The overall outcomes of this study clearly emphasize that the bioactive therapeutics found in medicinal herbs may have remarkable scope in managing Candida infection.

In Silico Analysis, Anticonvulsant Activity, and Toxicity Evaluation of Schisandrin B in Zebrafish Larvae and Mice

The aim of this study is to evaluate the anticonvulsant potential of schisandrin B, a main ingredient of Schisandra chinensis extracts. Schisandrin B showed anticonvulsant activity in the zebrafish larva pentylenetetrazole acute seizure assay but did not alter seizure thresholds in the intravenous pentylenetetrazole test in mice. Schisandrin B crosses the blood-brain barrier, which we confirmed in our in silico and in vivo analyses; however, the low level of its unbound fraction in the mouse brain tissue may explain the observed lack of anticonvulsant activity. Molecular docking revealed that the anticonvulsant activity of the compound in larval zebrafish might have been due to its binding to a benzodiazepine site within the GABAA receptor and/or the inhibition of the glutamate NMDA receptor. Although schisandrin B showed a beneficial anticonvulsant effect, toxicological studies revealed that it caused serious developmental impairment in zebrafish larvae, underscoring its teratogenic properties. Further detailed studies are needed to precisely identify the properties, pharmacological effects, and safety of schisandrin B.

Insight into the Potential Mechanisms of Endocrine Disruption by Dietary Phytoestrogens in the Context of the Etiopathogenesis of Endometriosis

Phytoestrogens (PEs) are estrogen-like nonsteroidal compounds derived from plants (e.g., nuts, seeds, fruits, and vegetables) and fungi that are structurally similar to 17β-estradiol. PEs bind to all types of estrogen receptors, including ERα and ERβ receptors, nuclear receptors, and a membrane-bound estrogen receptor known as the G protein-coupled estrogen receptor (GPER). As endocrine-disrupting chemicals (EDCs) with pro- or antiestrogenic properties, PEs can potentially disrupt the hormonal regulation of homeostasis, resulting in developmental and reproductive abnormalities. However, a lack of PEs in the diet does not result in the development of deficiency symptoms. To properly assess the benefits and risks associated with the use of a PE-rich diet, it is necessary to distinguish between endocrine disruption (endocrine-mediated adverse effects) and nonspecific effects on the endocrine system. Endometriosis is an estrogen-dependent disease of unknown etiopathogenesis, in which tissue similar to the lining of the uterus (the endometrium) grows outside of the uterus with subsequent complications being manifested as a result of local inflammatory reactions. Endometriosis affects 10-15% of women of reproductive age and is associated with chronic pelvic pain, dysmenorrhea, dyspareunia, and infertility. In this review, the endocrine-disruptive actions of PEs are reviewed in the context of endometriosis to determine whether a PE-rich diet has a positive or negative effect on the risk and course of endometriosis.

Synthesis, Structural and Behavioral Studies of Indole Derivatives D2AAK5, D2AAK6 and D2AAK7 as Serotonin 5-HT1A and 5-HT2A Receptor Ligands

Serotonin receptors are involved in a number of physiological functions and regulate aggression, anxiety, appetite, cognition, learning, memory, mood, nausea, sleep, and thermoregulation. Here we report synthesis and detailed structural and behavioral studies of three indole derivatives: D2AAK5, D2AAK6, and D2AAK7 as serotonin 5-HT_1A and 5-HT_2A receptor ligands. X-ray studies revealed that the D2AAK5 compound crystallizes in centrosymmetric triclinic space group with one molecule in the asymmetric unit. The main interaction between the ligands and the receptors is the salt bridge between the protonatable nitrogen atom of the ligands and the conserved Asp (3.32) of the receptors. The complexes were stable in the molecular dynamic simulations. MD revealed that the studied ligands are relatively stable in their binding sites, with the exception of D2AAK7 in the serotonin 5-HT_1A receptor. D2AAK7 exerts anxiolytic activity in the EPM test, while D2AAK5 has a beneficial effect on the memory processes in the PA test.

Allosteric Modulators of Dopamine D2 Receptors for Fine-Tuning of Dopaminergic Neurotransmission in CNS Diseases: Overview, Pharmacology, Structural Aspects and Synthesis

Allosteric modulation of G protein-coupled receptors (GPCRs) is nowadays a hot topic in medicinal chemistry. Allosteric modulators, i.e., compounds which bind in a receptor site topologically distinct from orthosteric sites, exhibit a number of advantages. They are more selective, safer and display a ceiling effect which prevents overdosing. Allosteric modulators of dopamine D₂ receptor are potential drugs against a number of psychiatric and neurological diseases, such as schizophrenia and Parkinson's disease. In this review, an insightful summary of current research on D₂ receptor modulators is presented, ranging from their pharmacology and structural aspects of ligand-receptor interactions to their synthesis.

A Comprehensive Survey of Prospective Structure-Based Virtual Screening for Early Drug Discovery in the Past Fifteen Years

Structure-based virtual screening (SBVS), also known as molecular docking, has been increasingly applied to discover small-molecule ligands based on the protein structures in the early stage of drug discovery. In this review, we comprehensively surveyed the prospective applications of molecular docking judged by solid experimental validations in the literature over the past fifteen years. Herein, we systematically analyzed the novelty of the targets and the docking hits, practical protocols of docking screening, and the following experimental validations. Among the 419 case studies we reviewed, most virtual screenings were carried out on widely studied targets, and only 22% were on less-explored new targets. Regarding docking software, GLIDE is the most popular one used in molecular docking, while the DOCK 3 series showed a strong capacity for large-scale virtual screening. Besides, the majority of identified hits are promising in structural novelty and one-quarter of the hits showed better potency than 1 μM, indicating that the primary advantage of SBVS is to discover new chemotypes rather than highly potent compounds. Furthermore, in most studies, only in vitro bioassays were carried out to validate the docking hits, which might limit the further characterization and development of the identified active compounds. Finally, several successful stories of SBVS with extensive experimental validations have been highlighted, which provide unique insights into future SBVS drug discovery campaigns.

Natural Phytocompounds from Common Indian Spices for Identification of Three Potential Inhibitors of Breast Cancer: A Molecular Modelling Approach

Breast cancer is the second most common cancer-related cause of death for women throughout the globe. In spite of some effective measures, the main concerns with traditional anti-cancer chemotherapy are its low bioavailability, physical side effects, acquired resistance of cancer cells and non-specific targeting. Now researchers have taken the initiative to establish natural product-based therapy methods and to identify viable hits for future lead optimization in the development of breast cancer medication. Our study aims to identify the potent phytocompounds from five very popular Indian spices (Zingiber officinale Roscoe, Cuminum cyminum L., Piper nigrum L., Curcuma longa L., and Allium sativum L.). From these spices, a total of 200 phytocompounds were identified and screened against three target genes, namely, cyclin-dependent kinase 8 (CDK 8), progesterone receptor (PR) and epidermal growth factor receptor (EGFR), through structure-based virtual screening using iGEMDOCK 2.1 software. Based on the binding affinity score, the top three phytocompounds against each target protein (cynaroside (-149.66 Kcal/mol), apigetrin (-139.527 Kcal/mol) and curcumin (-138.149 Kcal/mol) against CDK8; apigetrin (-123.298 Kcal/mol), cynaroside (-118.635 Kcal/mol) and xyloglucan (-113.788 Kcal/mol) against PR; cynaroside (-119.18 Kcal/mol), apigetrin (-105.185 Kcal/mol) and xyloglucan (-105.106 Kcal/mol) against EGFR) were selected. Apigetrin, cynaroside, curcumin, and xyloglucan were finally identified for further docking analysis with the respective three target proteins. Autodock 4.2 was applied to screen the optimal binding position and to assess the relative intensity of binding interactions. In addition, the ADME/T property checks and bioactivity scores analysis of were performed to understand the suitability of these four phytocompounds to be potential candidates for developing effective and non-toxic anticancer agents. Based on this in silico analysis, we believe this study could contribute to current efforts to develop new drugs for treating breast cancer.

Synthesis, Docking Studies and Pharmacological Evaluation of Serotoninergic Ligands Containing a 5-Norbornene-2-Carboxamide Nucleus

A new series of 5-norbornene-2-carboxamide derivatives was prepared and their affinities to the 5-HT_1A, 5-HT_2A, and 5-HT_2C receptors were evaluated and compared to a previously synthesized series of derivatives characterized by exo-N-hydroxy-5-norbornene-2,3-dicarboximidenucleus, in order to identify selective ligands for the above-mentioned subtype receptors. Arylpiperazines represents one of the most important classes of 5-HT_1AR ligands, and recent research concerning new derivatives has been focused on the modification of one or more portions of such pharmacophore. The combination of structural elements (heterocyclic nucleus, propyl chain and 4-substituted piperazine), known to be critical to the affinity to 5-HT_1A receptors, and the proper selection of substituents led to compounds with high specificity and affinity towards serotoninergic receptors. The most active compounds were selected for further in vivo assays to determine their functional activity. Finally, to rationalize the obtained results, molecular docking studies were performed. The results of the pharmacological studies showed that Norbo-4 and Norbo-18 were the most active and promising derivatives for the serotonin receptor considered in this study.

A narrative review: The pharmaceutical evolution of phenolic syringaldehyde

To better understand the pharmacological characters of syringaldehyde (SA), which is a key-odorant compound of whisky and brandy, this review article is the first to compile the published literature for molecular docking that were subsequently validated by in vitro and in vivo assays to predict and develop insights into the medicinal properties of SA in terms of anti-oxidation, anti-inflammation, and anti-diabetes. The molecular docking displayed significantly binding affinity for SA towards tumor necrosis factor-α, interleukin-6, and antioxidant enzymes when inflammation from myocardial infarction and spinal cord ischemia. Moreover, SA nicely docked with dipeptidyl peptidase-IV, glucagon-like peptide 1 receptor, peroxisome proliferator-activated receptor, acetylcholine M2 receptor, and acetylcholinesterase in anti-diabetes investigations. These are associated with (1) an increase glucose utilization and insulin sensitivity to an anti-hyperglycemic effect; and (2) to potentiate intestinal contractility to abolish the α-amylase reaction when concurrently reducing retention time and glucose absorption of the intestinal tract to achieve a glucose-lowering effect. In silico screening of multi-targets concomitantly with preclinical tests could provide a potential exploration for new indications for drug discovery and development.

Computational studies on the cholinesterase, beta-secretase 1 (BACE1) and monoamine oxidase (MAO) inhibitory activities of endophytes-derived compounds: towards discovery of novel neurotherapeutics

Cholinesterases, beta-secretase 1 (BACE1) and monoamine oxidase (MAO) are significant in the etiology of neurodegenerative diseases. Inhibition of these enzymes is therefore a major strategy for the development of neurotherapeutics. Even though, this strategy has birthed some approved synthetic drugs, they are characterized by adverse effects. It is therefore, imperative to explore promising alternatives. Consequently, we assessed the inhibitory activities of some endophytes-derived compounds against selected targets towards discovery of novel neurotherapeutics. Standard inhibitors and 83 endophytes-derived compounds were docked against acetylcholinesterase (AChE), butyrylcholinesterase (BChE), BACE 1 and MAO using AutodockVina while the molecular interactions between the selected targets and the compounds with notable binding affinity were viewed through Discovery Studio Visualizer. Druglikeness and Absorption-Distribution-Metabolism-Excretion-Toxicity (ADMET) and blood brain barrier (BBB) properties of the top 4 compounds were evaluated using the Swiss online ADME web tool and OSIRIS server; ligands-enzymes complex stability was assessed through molecular dynamics (MD) simulation. From the 83 compounds, asperflavin, ascomfurans C, camptothecine and corynesidone A exhibited remarkable inhibitory activity against all the four target enzymes compared to the respective standard inhibitors. However, only corynesidone A could transverse the BBB and predicted to be safe. MD simulation of the unbound and complexed enzymes with corynesidone A showed that the complexes were stable throughout the simulation time. Given the exceptional inhibitory activity of endophytes-derived corynesidone A against the four selected targets, its ability to permeate the BBB, excellent drugability properties as well as its stability when complexed with the enzymes, it is a good candidate for further studies towards development of new neurotherapeutics.Communicated by Ramaswamy H. Sarma.

Molecular Docking and Dynamics Studies to Explore Effective Inhibitory Peptides Against the Spike Receptor Binding Domain of SARS-CoV-2

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a pandemic due to the high transmission and mortality rate of this virus. The world health and economic sectors have been severely affected by this deadly virus, exacerbated by the lack of sufficient efficient vaccines. The design of effective drug candidates and their rapid development is necessary to combat this virus. In this study, we selected 23 antimicrobial peptides from the literature and predicted their structure using PEP-FOLD 3.5. In addition, we docked them to the SARS-CoV-2 spike protein receptor-binding domain (RBD) to study their capability to inhibit the RBD, which plays a significant role in virus binding, fusion and entry into the host cell. We used several docking programs including HDOCK, HPEPDOCK, ClusPro, and HawkDock to calculate the binding energy of the protein-peptide complexes. We identified four peptides with high binding free energy and docking scores. The docking results were further verified by molecular dynamics (MD) simulations to characterize the protein-peptide complexes in terms of their root-mean-square fluctuation (RMSF), root-mean-square deviation (RMSD), radius of gyration (Rg), solvent-accessible surface area (SASA), and hydrogen bond formation. Allergenicity and toxicity predictions suggested that the peptides we identified were non-allergenic and non-toxic. This study suggests that these four antimicrobial peptides could inhibit the RBD of SARS-CoV-2. Future in vitro and in vivo studies are necessary to confirm this.

Self-docking and cross-docking simulations of G protein-coupled receptor-ligand complexes: Impact of ligand type and receptor activation state

G protein-coupled receptors (GPCR) are the largest family of cell surface receptors in vertebrates. Their abundance and role in nearly all physiological systems make GPCR the largest protein family targeted for development of pharmaceuticals. Ligand discovery aimed at identification of chemical tools and drug leads is aided by molecular docking simulations that allow critical analysis of the potential interactions between small molecules and proteins in resulting complexes. However, blind assessments of ligand pose quality and affinity prediction have thus far not provided broadly generalizable performance expectations for docking into experimentally-characterized GPCR targets. Likewise, the relative importance of receptor activation state and ligand function differences have also not been systematically assessed. This study compares performance when docking ligands of varied function into varied GPCR activation states in the absence of extensive resampling of the input GPCR structure, and only limited sidechain flexibility after ligand placement. Simulations were performed using 37 experimental structures of 11 Class A GPCR crystallized in multiple activation states (giving rise to 37 self-docking and 68 cross docking simulations). Our results show that one specific subset of cross-docking simulations gave results of similar quality to self-docking. Median ligand RMSD values for top-scored poses were 1.2 Å and 2.0 Å for self-docking and State_Match/Function_Match cross-docking, respectively. The distributions of ligand RMSD values were not statistically different for these two conditions, according to a Kolmogorov-Smirnov test. Therefore, docking performance against GPCR targets can be estimated in advance based on docking target structure activation states, with higher accuracy expected when docking agonists into active state structures and inverse agonists or antagonists into inactive state structures. Receptor conformational sampling in advance of docking or receptor conformational adjustment after docking are more likely to produce substantial improvements for other pairings of receptor activation state and ligand function.

In Silico Approaches to Identify Polyphenol Compounds as α-Glucosidase and α-Amylase Inhibitors against Type-II Diabetes

Type-II diabetes mellitus (T2DM) results from a combination of genetic and lifestyle factors, and the prevalence of T2DM is increasing worldwide. Clinically, both α-glucosidase and α-amylase enzymes inhibitors can suppress peaks of postprandial glucose with surplus adverse effects, leading to efforts devoted to urgently seeking new anti-diabetes drugs from natural sources for delayed starch digestion. This review attempts to explore 10 families e.g., Bignoniaceae, Ericaceae, Dryopteridaceae, Campanulaceae, Geraniaceae, Euphorbiaceae, Rubiaceae, Acanthaceae, Rutaceae, and Moraceae as medicinal plants, and folk and herb medicines for lowering blood glucose level, or alternative anti-diabetic natural products. Many natural products have been studied in silico, in vitro, and in vivo assays to restrain hyperglycemia. In addition, natural products, and particularly polyphenols, possess diverse structures for exploring them as inhibitors of α-glucosidase and α-amylase. Interestingly, an in silico discovery approach using natural compounds via virtual screening could directly target α-glucosidase and α-amylase enzymes through Monte Carto molecular modeling. Autodock, MOE-Dock, Biovia Discovery Studio, PyMOL, and Accelrys have been used to discover new candidates as inhibitors or activators. While docking score, binding energy (Kcal/mol), the number of hydrogen bonds, or interactions with critical amino acid residues have been taken into concerning the reliability of software for validation of enzymatic analysis, in vitro cell assay and in vivo animal tests are required to obtain leads, hits, and candidates in drug discovery and development.

Advances in the molecular understanding of G protein-coupled receptors and their future therapeutic opportunities

Background

Understanding the mechanisms, activated and inhibited pathways as well as other molecular targets involved in existing and emerging disease conditions provides useful insights into their proper diagnosis and treatment and aids drug discovery, development and production. G protein-coupled receptors (GPCRs) are one of the most important classes of targets for small-molecule drug discovery. Of all drug targets, GPCRs are the most studied, undoubtedly because of their pharmacological tractability and role in the pathophysiology as well as the pathogenesis of human diseases.

Main body of the abstract

GPCRs are regarded as the largest target class of the “druggable genome” representing approximately 19% of the currently available drug targets. They have long played a prominent role in drug discovery, such that as of this writing, 481 drugs (about 34% of all FDA-approved drugs) act on GPCRs. More than 320 therapeutic agents are currently under clinical trials, of which a significant percentage targets novel GPCRs. GPCRs are implicated in a wide variety of diseases including CNS disorders, inflammatory diseases such as rheumatoid arthritis and Crohn’s disease, as well as metabolic disease and cancer. The non-olfactory human GPCRs yet to be clinically explored or tried are endowed with perhaps a huge untapped potential drug discovery especially in the field of immunology and genetics.

Short conclusion

This review discusses the recent advances in the molecular pharmacology and future opportunities for targeting GPCRs with a view to drug development.

Non-carboxylic acid inhibitors of aldose reductase based on N-substituted thiazolidinedione derivatives

In search of dually active PPAR-modulators/aldose reductase (ALR2) inhibitors, 16 benzylidene thiazolidinedione derivatives, previously reported as partial PPARγ agonists, together with additional 18 structural congeners, were studied for aldose reductase inhibitory activity. While no compounds had dual property, our efforts led to the identification of promising inhibitors of ALR2. Eight compounds (11, 15-16, 20-24, 30) from the library of 33 compounds were identified as potent and selective inhibitors of ALR2. Compound 21 was the most effective and selective inhibitor with an IC50 value of 0.95 ± 0.11 and 13.52 ± 0.81 μM against ALR2 and aldehyde reductase (ALR1) enzymes, respectively. Molecular docking and dynamics studies were performed to understand inhibitor-enzyme interactions at the molecular level that determine the potency and selectivity. Compound 21 was further subjected to in silico and in vitro studies to evaluate the pharmacokinetic profile. Being less acidic (pKa = 9.8), the compound might have a superior plasma membrane permeability and reach the cytosolic ALR2. This fact together with excellent drug-likeness criteria points to improved bioavailability compared to the clinically used compound Epalrestat. The designed compounds represent a novel group of non-carboxylate inhibitors of aldose reductase with an improved physicochemical profile.

Tomatidine and Patchouli Alcohol as Inhibitors of SARS-CoV-2 Enzymes (3CLpro, PLpro and NSP15) by Molecular Docking and Molecular Dynamics Simulations

Considering the current dramatic and fatal situation due to the high spreading of SARS-CoV-2 infection, there is an urgent unmet medical need to identify novel and effective approaches for prevention and treatment of Coronavirus disease (COVID 19) by re-evaluating and repurposing of known drugs. For this, tomatidine and patchouli alcohol have been selected as potential drugs for combating the virus. The hit compounds were subsequently docked into the active site and molecular docking analyses revealed that both drugs can bind the active site of SARS-CoV-2 3CLpro, PLpro, NSP15, COX-2 and PLA2 targets with a number of important binding interactions. To further validate the interactions of promising compound tomatidine, Molecular dynamics study of 100 ns was carried out towards 3CLpro, NSP15 and COX-2. This indicated that the protein-ligand complex was stable throughout the simulation period, and minimal backbone fluctuations have ensued in the system. Post dynamic MM-GBSA analysis of molecular dynamics data showed promising mean binding free energy 47.4633 ± 9.28, 51.8064 ± 8.91 and 54.8918 ± 7.55 kcal/mol, respectively. Likewise, in silico ADMET studies of the selected ligands showed excellent pharmacokinetic properties with good absorption, bioavailability and devoid of toxicity. Therefore, patchouli alcohol and especially, tomatidine may provide prospect treatment options against SARS-CoV-2 infection by potentially inhibiting virus duplication though more research is guaranteed and secured.

Antiviral peptides against the main protease of SARS-CoV-2: A molecular docking and dynamics study

The recent coronavirus outbreak has changed the world's economy and health sectors due to the high mortality and transmission rates. Because the development of new effective vaccines or treatments against the virus can take time, an urgent need exists for the rapid development and design of new drug candidates to combat this pathogen. Here, we obtained antiviral peptides obtained from the data repository of antimicrobial peptides (DRAMP) and screened their predicted tertiary structures for the ability to inhibit the main protease of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using multiple combinatorial docking programs, including PatchDock, FireDock, and ClusPro. The four best peptides, DRAMP00877, DRAMP02333, DRAMP02669, and DRAMP03804, had binding energies of -1125.3, -1084.5, -1005.2, and -924.2 Kcal/mol, respectively, as determined using ClusPro, and binding energies of -55.37, -50.96, -49.25, -54.81 Kcal/mol, respectively, as determined using FireDock, which were better binding energy values than observed for other peptide molecules. These peptides were found to bind with the active cavity of the SARS-CoV-2 main protease; at Glu166, Cys145, Asn142, Phe140, and Met165, in addition to the substrate-binding sites, Domain 2 and Domain 3, whereas fewer interactions were observed with Domain 1. The docking studies were further confirmed by a molecular dynamics simulation study, in which several descriptors, including the root-mean-square difference (RMSD), root-mean-square fluctuation (RMSF), solvent-accessible surface area (SASA), radius of gyration (Rg), and hydrogen bond formation, confirmed the stable nature of the peptide-main protease complexes. Toxicity and allergenicity studies confirmed the non-allergenic nature of the peptides. This present study suggests that these identified antiviral peptide molecules might inhibit the main protease of SARS-CoV-2, although further wet-lab experiments remain necessary to verify these findings.

Preliminary modelling as the first stage of targeted organic synthesis

The review aims to present a classification and applicability analysis of methods for preliminary molecular modelling for targeted organic, catalytic and biocatalytic synthesis. The following three main approaches are considered as a primary classification of the methods: modelling of the target – ligand coordination without structural information on both the target and the resulting complex; calculations based on experimentally obtained structural information about the target; and dynamic simulation of the target – ligand complex and the reaction mechanism with calculation of the free energy of the reaction. The review is meant for synthetic chemists to be used as a guide for building an algorithm for preliminary modelling and synthesis of structures with specified properties.

The bibliography includes 353 references.

Computational Study on Temperature Driven Structure-Function Relationship of Polysaccharide Producing Bacterial Glycosyl Transferase Enzyme

Glycosyltransferase (GTs) is a wide class of enzymes that transfer sugar moiety, playing a key role in the synthesis of bacterial exopolysaccharide (EPS) biopolymer. In recent years, increased demand for bacterial EPSs has been observed in pharmaceutical, food, and other industries. The application of the EPSs largely depends upon their thermal stability, as any industrial application is mainly reliant on slow thermal degradation. Keeping this in context, EPS producing GT enzymes from three different bacterial sources based on growth temperature (mesophile, thermophile, and hyperthermophile) are considered for in silico analysis of the structural–functional relationship. From the present study, it was observed that the structural integrity of GT increases significantly from mesophile to thermophile to hyperthermophile. In contrast, the structural plasticity runs in an opposite direction towards mesophile. This interesting temperature-dependent structural property has directed the GT–UDP-glucose interactions in a way that thermophile has finally demonstrated better binding affinity (−5.57 to −10.70) with an increased number of hydrogen bonds (355) and stabilizing amino acids (Phe, Ala, Glu, Tyr, and Ser). The results from this study may direct utilization of thermophile-origin GT as best for industrial-level bacterial polysaccharide production.

Synthesis, docking studies, and pharmacological evaluation of 2-hydroxypropyl-4-arylpiperazine derivatives as serotoninergic ligands

A new series of norbornene and exo-N-hydroxy-7-oxabicyclo[2.2.1]hept-5-ene-2,3-dicarboximide derivatives was prepared, and their affinities to the 5-HT_1A , 5-HT_2A , and 5-HT_2C receptors were evaluated and compared with a previously synthesized series of derivatives characterized by the same nuclei, to identify selective ligands for the subtype receptors. Arylpiperazines represent one of the most important classes of 5-HT_1A R ligands, and the research of new derivatives has been focused on the modification of one or more portions of this pharmacophore. The combination of structural elements (heterocyclic nucleus, hydroxyalkyl chain, and 4-substituted piperazine), known to be critical for the affinity to 5-HT_1A receptors, and the proper selection of substituents resulted in compounds with high specificity and affinity toward serotoninergic receptors. The most active compounds were selected for further in vivo assays to determine their functional activity. Finally, to rationalize the obtained results, molecular docking studies were performed. The results of the pharmacological studies showed that 3e, 4j, and 4n were the most active and promising derivatives for the serotonin receptor considered in this study.

G protein-coupled receptors: structure- and function-based drug discovery

As one of the most successful therapeutic target families, G protein-coupled receptors (GPCRs) have experienced a transformation from random ligand screening to knowledge-driven drug design. We are eye-witnessing tremendous progresses made recently in the understanding of their structure-function relationships that facilitated drug development at an unprecedented pace. This article intends to provide a comprehensive overview of this important field to a broader readership that shares some common interests in drug discovery.

Recent Advances in Molecular Docking for the Research and Discovery of Potential Marine Drugs

Marine drugs have long been used and exhibit unique advantages in clinical practices. Among the marine drugs that have been approved by the Food and Drug Administration (FDA), the protein–ligand interactions, such as cytarabine–DNA polymerase, vidarabine–adenylyl cyclase, and eribulin–tubulin complexes, are the important mechanisms of action for their efficacy. However, the complex and multi-targeted components in marine medicinal resources, their bio-active chemical basis, and mechanisms of action have posed huge challenges in the discovery and development of marine drugs so far, which need to be systematically investigated in-depth. Molecular docking could effectively predict the binding mode and binding energy of the protein–ligand complexes and has become a major method of computer-aided drug design (CADD), hence this powerful tool has been widely used in many aspects of the research on marine drugs. This review introduces the basic principles and software of the molecular docking and further summarizes the applications of this method in marine drug discovery and design, including the early virtual screening in the drug discovery stage, drug target discovery, potential mechanisms of action, and the prediction of drug metabolism. In addition, this review would also discuss and prospect the problems of molecular docking, in order to provide more theoretical basis for clinical practices and new marine drug research and development.

Docking of peptides to GPCRs using a combination of CABS-dock with FlexPepDock refinement

The structural description of peptide ligands bound to G protein-coupled receptors (GPCRs) is important for the discovery of new drugs and deeper understanding of the molecular mechanisms of life. Here we describe a three-stage protocol for the molecular docking of peptides to GPCRs using a set of different programs: (1) CABS-dock for docking fully flexible peptides; (2) PD2 method for the reconstruction of atomistic structures from C-alpha traces provided by CABS-dock and (3) Rosetta FlexPepDock for the refinement of protein–peptide complex structures and model scoring. We evaluated the proposed protocol on the set of seven different GPCR–peptide complexes (including one containing a cyclic peptide), for which crystallographic structures are available. We show that CABS-dock produces high resolution models in the sets of top-scored models. These sets of models, after reconstruction to all-atom representation, can be further improved by Rosetta high-resolution refinement and/or minimization, leading in most of the cases to sub-Angstrom accuracy in terms of interface root-mean-square-deviation measure.

N-(2-Hydroxyphenyl)-1-[3-(2-oxo-2,3-dihydro-1H- benzimidazol-1-yl)propyl]piperidine-4-Carboxamide (D2AAK4), a Multi-Target Ligand of Aminergic GPCRs, as a Potential Antipsychotic

N-(2-hydroxyphenyl)-1-[3-(2-oxo-2,3-dihydro-1H-benzimidazol -1-yl)propyl]piperidine-4-carboxamide (D2AAK4) is a multitarget ligand of aminergic G protein-coupled receptors (GPCRs) identified in structure-based virtual screening. Here we present detailed in vitro, in silico and in vivo investigations of this virtual hit. D2AAK4 has an atypical antipsychotic profile and low affinity to off-targets. It interacts with aminergic GPCRs, forming an electrostatic interaction between its protonatable nitrogen atom and the conserved Asp 3.32 of the receptors. At the dose of 100 mg/kg D2AAK4 decreases amphetamine-induced hyperactivity predictive of antipsychotic activity, improves memory consolidation in passive avoidance test and has anxiogenic properties in elevated plus maze test (EPM). Further optimization of the virtual hit D2AAK4 will be aimed to balance its multitarget profile and to obtain analogs with anxiolytic activity.

Prediction and targeting of GPCR oligomer interfaces

GPCR oligomerization has emerged as a hot topic in the GPCR field in the last years. Receptors that are part of these oligomers can influence each other's function, although it is not yet entirely understood how these interactions work. The existence of such a highly complex network of interactions between GPCRs generates the possibility of alternative targets for new therapeutic approaches. However, challenges still exist in the characterization of these complexes, especially at the interface level. Different experimental approaches, such as FRET or BRET, are usually combined to study GPCR oligomer interactions. Computational methods have been applied as a useful tool for retrieving information from GPCR sequences and the few X-ray-resolved oligomeric structures that are accessible, as well as for predicting new and trustworthy GPCR oligomeric interfaces. Machine-learning (ML) approaches have recently helped with some hindrances of other methods. By joining and evaluating multiple structure-, sequence- and co-evolution-based features on the same algorithm, it is possible to dilute the issues of particular structures and residues that arise from the experimental methodology into all-encompassing algorithms capable of accurately predict GPCR-GPCR interfaces. All these methods used as a single or a combined approach provide useful information about GPCR oligomerization and its role in GPCR function and dynamics. Altogether, we present experimental, computational and machine-learning methods used to study oligomers interfaces, as well as strategies that have been used to target these dynamic complexes.

Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening

AutoDock and Vina are two of the most widely used protein–ligand docking programs. The fact that these programs are free and available under an open source license, also makes them a very popular first choice for many users and a common starting point for many virtual screening campaigns, particularly in academia. Here, we evaluated the performance of AutoDock and Vina against an unbiased dataset containing 102 protein targets, 22,432 active compounds and 1,380,513 decoy molecules. In general, the results showed that the overall performance of Vina and AutoDock was comparable in discriminating between actives and decoys. However, the results varied significantly with the type of target. AutoDock was better in discriminating ligands and decoys in more hydrophobic, poorly polar and poorly charged pockets, while Vina tended to give better results for polar and charged binding pockets. For the type of ligand, the tendency was the same for both Vina and AutoDock. Bigger and more flexible ligands still presented a bigger challenge for these docking programs. A set of guidelines was formulated, based on the strengths and weaknesses of both docking program and their limits of validation.

Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking

Docking calculations can accelerate drug discovery by predicting the bound poses of ligands for a targeted protein. However, it is not clear which docking methods work best. Furthermore, predicting poses requires steps outside the docking algorithm itself, such as preparation of the protein and ligand, and it is not known which components are most in need of improvement. The Continuous Evaluation of Ligand Protein Predictions (CELPP) is a blinded prediction challenge designed to address these issues. Participants create a workflow to predict protein-ligand binding poses, which is then tasked with predicting 10-100 new protein-ligand crystal structures each week. CELPP evaluates the accuracy of each workflow's predictions and posts the scores online. The results can be used to identify the strengths and weaknesses of current approaches, help map docking problems to the algorithms most likely to overcome them, and illuminate areas of unmet need in structure-guided drug design.

Novel drug candidates for treating esophageal carcinoma: A study on differentially expressed genes, using connectivity mapping and molecular docking

Patients with esophageal carcinoma (ESCA) have a poor prognosis and high mortality rate. Although standard therapies have had effect, there is an urgent requirement to develop novel options, as increasing drug tolerance has been identified in clinical practice. In the present study, differentially expressed genes (DEGs) of ESCA were identified in The Cancer Genome Atlas and Genotype-Tissue Expression databases. Functional and protein-protein interaction (PPI) analyses were performed. The Connectivity Map (CMAP) was selected to predict drugs for the treatment of ESCA, and their target genes were acquired from the Search Tool for Interactions of Chemicals (STITCH) by uploading the Simplified Molecular-Input Line-Entry System structure. Additionally, significant target genes and ESCA-associated hub genes were extracted using another PPI analysis, and the corresponding drugs were added to construct a network. Furthermore, the binding affinity between predicted drug candidates and ESCA-associated hub genes was calculated using molecular docking. Finally, 827 DEGs (|log₂ fold-change|≥2; q-value <0.05), which are principally involved in protein digestion and absorption (P<0.005), the plasminogen-activating cascade (P<0.01), as well as the ‘biological regulation’ of the Biological Process, ‘membrane’ of the Cellular Component and ‘protein binding’ of the Molecular Function categories, were obtained. Additionally, 11 hub genes were obtained from the PPI network (all degrees ≥30). Furthermore, the 15 first screen drugs were extracted from CMAP (score <−0.85) and the 9 second screen drugs with 70 target genes were extracted from STITCH. Furthermore, another PPI analysis extracted 51 genes, and apigenin, baclofen, Prestwick-685, menadione, butyl hydroxybenzoate, gliclazide and valproate were selected as drug candidates for ESCA. Those molecular docking results with a docking score of >5.52 indicated the significance of apigenin, Prestwick-685 and menadione. The results of the present study may lead to novel drug candidates for ESCA, among which Prestwick-685 and menadione were identified to be significant new drug candidates.

Binding Affinity via Docking: Fact and Fiction

In 1982, Kuntz et al. published an article with the title “A Geometric Approach to Macromolecule-Ligand Interactions”, where they described a method “to explore geometrically feasible alignment of ligands and receptors of known structure”. Since then, small molecule docking has been employed as a fast way to estimate the binding pose of a given compound within a specific target protein and also to predict binding affinity. Remarkably, the first docking method suggested by Kuntz and colleagues aimed to predict binding poses but very little was specified about binding affinity. This raises the question as to whether docking is the right tool to estimate binding affinity. The short answer is no, and this has been concluded in several comprehensive analyses. However, in this opinion paper we discuss several critical aspects that need to be reconsidered before a reliable binding affinity prediction through docking is realistic. These are not the only issues that need to be considered, but they are perhaps the most critical ones. We also consider that in spite of the huge efforts to enhance scoring functions, the accuracy of binding affinity predictions is perhaps only as good as it was 10–20 years ago. There are several underlying reasons for this poor performance and these are analyzed. In particular, we focus on the role of the solvent (water), the poor description of H-bonding and the lack of the systems’ true dynamics. We hope to provide readers with potential insights and tools to overcome the challenging issues related to binding affinity prediction via docking.

Dual-therapy strategy for modification of adiponectin receptor signaling in aging-associated chronic diseases

Given the paradigm of anti-insulin resistance in therapies for metabolic syndrome, there has been considerable interest in adiponectin (APN), an adipocyte-derived sensitizer of insulin receptor signaling. In contrast to hypoadiponectinemia in metabolic syndrome, evidence suggests that Alzheimer's disease (AD) and other diseases, including chronic heart failure (CHF) and chronic kidney disease (CKD), are characterized by hyperadiponectinemia as well as the APN/obesity paradoxes, indicating that a decrease in APN might also be beneficial for these diseases. Thus, distinct from metabolic syndrome, it is anticipated that APN receptor antagonists rather than agonists might be effective in therapy for some chronic diseases.

To probe interaction of morphine and IBNtxA with 7TM and 6TM variants of the human μ-opioid receptor using all-atom molecular dynamics simulations with an explicit membrane

IBNtxA, a morphine derivative, is 10-fold more potent and has a better safety profile than morphine. Animal studies indicate that the analgesic effect of IBNtxA appears to be mediated by the activation of truncated splice variants (6TM) of the Mu opioid receptor (MOR-1) where transmembrane helix 1 (TM1) is removed. Interestingly, morphine is unable to activate 6TM variants. To date, a high resolution structure of 6TM variants is missing, and the interaction of 6TM variants with IBNtxA and morphine remains elusive. In this study we used homology modeling, docking and molecular dynamics (MD) simulations to study a representative 6TM variant (G1) and a full-length 7TM variant of human MOR-1 in complex with IBNtxA and morphine respectively. The structural models of human G1 and 7TM were obtained by homology modeling using the X-ray solved crystal structure of the active mouse 7TM bound to an agonist BU72 (PDB id: ) as the template. Our 6000 ns MD data show that either TM1 truncation (i.e. from 7TM to 6TM) or ligand modification (i.e. from morphine to IBNtxA) alone causes the loss of key morphine-7TM interactions that are well-known to be required for MOR-1 activation. Receptor disruptions are mainly located at TMs 2, 3, 6 and 7 in comparison with the active crystal complex. However, when both perturbations occur in the 6TM-IBNtxA complex, the key ligand-receptor interactions and the receptor conformation are recovered to resemble those in the active 7TM-morphine complex. Our molecular switch analysis further explains well why morphine is not able to activate 6TM variants. The close resemblance between 6TM-IBTtxA and 7TM in complex with PZM21, a G-protein biased 7TM agonist, suggests the possible biased agonism of IBNtxA on G1, which is consistent with its reduced side effects.

Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery

GPCR modeling approaches are widely used in the hit-to-lead (H2L) and lead optimization (LO) stages of drug discovery. The aims of these modeling approaches are to predict the 3D structures of the receptor-ligand complexes, to explore the key interactions between the receptor and the ligand and to utilize these insights in the design of new molecules with improved binding, selectivity or other pharmacological properties. In this book chapter, we present a brief survey of key computational approaches integrated with hierarchical GPCR modeling protocol (HGMP) used in hit-to-lead (H2L) and in lead optimization (LO) stages of structure-based drug discovery (SBDD). We outline the differences in modeling strategies used in H2L and LO of SBDD and illustrate how these tools have been applied in three drug discovery projects.