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

Protocol for in silico characterization of natural-based molecules as quorum-sensing inhibitors

The search and development of new quorum-sensing (QS) inhibitors are ongoing processes for biofilm control. Here, we present a protocol for in silico characterization of natural-based molecules as QS inhibitors. We describe steps for preparing models of protein receptors for virtual screening. We then detail procedures for construction and virtual screening of phytochemical libraries and hit picking to be experimentally validated by in vitro assays. This protocol allows exploration of a broad range of potential inhibitors for a specific target. For complete details on the use and execution of this protocol, please refer to Fernandes et al.1.

Riddelline from Tamarix articulate as a potential anti-bacterial lead compound for novel antibiotics discovery: A comprehensive computational and toxicological studies

Tamarix articulate from the Tamaricaece family is a halophytic plant. This plant is commonly called Athal or Tamarix in different Arabic and Asian countries. Due to the high load of polyphenolic phytochemicals, the plant has been used as a therapeutic option against several diseases for decades. The plant is an anti-inflammatory, anti-bacterial, anti-viral, anti-cancer, anti-oxidant, and anti-inflammatory. In this work, the 222 phytochemical compounds of T. articulate from our previous study are used in different bioinformatic and biophysics techniques to explore their biological potency against different anti-bacterial, anti-cancer and anti-viral targets. By doing so, it was found that Riddelline ranked as the best binding molecule of biological macromolecules selected herein in particular the bacterial targets. The binding energy value of the compound for the KdsA enzyme was -14.64 kcal/mol, KdsB (-13.09 kcal/mol), MurC (-13.67 kcal/mol), MurD (-13.54 kcal/mol), MurF (-14.20 kcal/mol), Polo-like kinase 1 (Plk1) (-12.34 kcal/mol), Bcl-2 protein (-13.39 kcal/mol), SARS-CoV-2 main protease enzyme (-12.67 kcal/mol), and Human T cell leukemia virus protease (-13.67 kcal/mol). The mean Rg value of KdsA-Riddelline complex and KdsA-FPE complex is 32.67 Å, and average RMSD of KdsA-Riddelline complex and KdsA-FPE complex is 2.31 Å, respectively. The binding energy complexes was found to be dominated by van der Waals (-71.98 kcal/mol for KdsA-Riddelline complex and -65.09 kcal/mol for KdsA-FPE complex). The lead compound was also unveiled to show favorable druglike properties and pharmacokinetics. Together, the data suggest the good anti-bacterial activities of the T. articulate phytochemicals and thus can be subjected to experimental in vitro and in vivo investigations.

An in silico molecular docking and simulation study to identify potential anticancer phytochemicals targeting the RAS signaling pathway

The dysregulation of the rat sarcoma (RAS) signaling pathway, particularly the MAPK/ERK cascade, is a hallmark of many cancers, leading to uncontrolled cellular proliferation and resistance to apoptosis-inducing treatments. Dysregulation of the MAPK/ERK pathway is common in various cancers including pancreatic, lung, and colon cancers, making it a critical target for therapeutic intervention. Natural compounds, especially phytochemicals, offer a promising avenue for developing new anticancer therapies due to their potential to interfere with these signaling pathways. This study investigates the potential of anticancer phytochemicals to inhibit the MAPK/ERK pathway through molecular docking and simulation techniques. A total of 26 phytochemicals were screened from an initial set of 340 phytochemicals which were retrieved from Dr. Duke's database using in silico methods for their binding affinity and stability. Molecular docking was performed to identify key interactions with ERK2, followed by molecular dynamics (MD) simulations to evaluate the stability of these interactions. The study identified several phytochemicals, including luteolin, hispidulin, and isorhamnetin with a binding score of -10.1±0 Kcal/mol, -9.86±0.15 Kcal/mol, -9.76±0.025 Kcal/mol, respectively as promising inhibitors of the ERK2 protein. These compounds demonstrated significant binding affinities and stable interactions with ERK2 in MD simulation studies up to 200ns, particularly at the active site. The radius of gyration analysis confirmed the stability of these phytochemical-protein complexes' compactness, indicating their potential to inhibit ERK activity. The stability and binding affinity of these compounds suggest that they can effectively inhibit ERK2 activity, potentially leading to more effective and less toxic cancer treatments. The findings underscore the therapeutic promise of these phytochemicals, which could serve as a basis for developing new cancer therapies.

Prediction of aptamer affinity using an artificial intelligence approach

Aptamers are oligonucleotide sequences that can connect to particular target molecules, similar to monoclonal antibodies. They can be chosen by systematic evolution of ligands by exponential enrichment (SELEX), and are modifiable and can be synthesized. Even if the SELEX approach has been improved a lot, it is frequently challenging and time-consuming to identify aptamers experimentally. In particular, structure-based methods are the most used in computer-aided design and development of aptamers. For this purpose, numerous web-based platforms have been suggested for the purpose of forecasting the secondary structure and 3D configurations of RNAs and DNAs. Also, molecular docking and molecular dynamics (MD), which are commonly utilized in protein compound selection by structural information, are suitable for aptamer selection. On the other hand, from a large number of sequences, artificial intelligence (AI) may be able to quickly discover the possible aptamer candidates. Conversely, sophisticated machine and deep-learning (DL) models have demonstrated efficacy in forecasting the binding properties between ligands and targets during drug discovery; as such, they may provide a reliable and precise method for forecasting the binding of aptamers to targets. This research looks at advancements in AI pipelines and strategies for aptamer binding ability prediction, such as machine and deep learning, as well as structure-based approaches, molecular dynamics and molecular docking simulation methods.

Insilico assessment of hesperidin on SARS-CoV-2 main protease and RNA polymerase: Molecular docking and dynamics simulation approach

The present study uses molecular docking and dynamic simulations to evaluate the inhibitory effect of flavonoid glycosides-based compounds on coronavirus Main protease (Mpro) and RNA polymerase. The Molegro Virtual Docker (MVD) software is utilized to simulate and calculate the binding parameters of compounds with coronavirus. The docking results show that the selected herbal compounds are more effective than those of chemical compounds. It is also revealed that five herbal ligands and two chemical ligands have the best docking scores. Furthermore, a Molecular Dynamics (MD) simulation was conducted for Hesperidin, confirming docking results. Analysis based on different parameters such as Root-mean-square deviation (RMSD), Root mean square fluctuation (RMSF), Radius of gyration (Rg), Solvent accessibility surface area (SASA), and the total number of hydrogen bonds suggests that Hesperidin formed a stable complex with Mpro. Absorption, Distribution, Metabolism, Excretion, And Toxicity (ADMET) analysis was performed to compare Hesperidin and Grazoprevir as potential antiviral medicines, evaluating both herbal and chemical ligand results. According to the study, herbal compounds could be effective on coronavirus and are admissible candidates for developing potential operative anti-viral medicines. Hesperidin was found to be the most acceptable interaction. Grazoprevir is an encouraging candidate for drug development and clinical trials, with the potential to become a highly effective Mpro inhibitor. Compared to RNA polymerase, Mpro showed a greater affinity for bonding with Hesperidin. van der Waals and electrostatic energies dominated, creating a stable Hesperidin-Mpro and Hesperidin-RNA polymerase complex.

Screening of Potential Inhibitors Targeting the Main Protease Structure of SARS-CoV-2 via Molecular Docking, and Approach with Molecular Dynamics, RMSD, RMSF, H-Bond, SASA and MMGBSA

Severe Acute Respiratory Syndrome caused by a coronavirus is a recent viral infection. There is no scientific evidence or clinical trials to indicate that possible therapies have demonstrated results in suspected or confirmed patients. This work aims to perform a virtual screening of 1430 ligands through molecular docking and to evaluate the possible inhibitory capacity of these drugs about the Mpro protease of Covid-19. The selected drugs were registered with the FDA and available in the virtual drug library, widely used by the population. The simulation was performed using the MolAiCalD algorithm, with a Lamarckian genetic model (GA) combined with energy estimation based on rigid and flexible conformation grids. In addition, molecular dynamics studies were also performed to verify the stability of the receptor-ligand complexes formed through analyses of RMSD, RMSF, H-Bond, SASA, and MMGBSA. Compared to the binding energy of the synthetic redocking coupling (-6.8 kcal/mol/RMSD of 1.34 Å), which was considerably higher, it was then decided to analyze the parameters of only three ligands: ergotamine (-9.9 kcal/mol/RMSD of 2.0 Å), dihydroergotamine (-9.8 kcal/mol/RMSD of 1.46 Å) and olysio (-9.5 kcal/mol/RMSD of 1.5 Å). It can be stated that ergotamine showed the best interactions with the Mpro protease of Covid-19 in the in silico study, showing itself as a promising candidate for treating Covid-19.

Naringenin-4'-glucuronide as a new drug candidate against the COVID-19 Omicron variant: a study based on molecular docking, molecular dynamics, MM/PBSA and MM/GBSA

This study aimed to identify natural bioactive compounds (NBCs) as potential inhibitors of the spike (S1) receptor binding domain (RBD) of the COVID-19 Omicron variant using computer simulations (in silico). NBCs with previously proven biological in vitro activity were obtained from the ZINC database and analyzed through virtual screening, molecular docking, molecular dynamics (MD), molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA), and molecular mechanics/generalized Born surface area (MM/GBSA). Remdesivir was used as a reference drug in docking and MD calculations. A total of 170,906 compounds were analyzed. Molecular docking screening revealed the top four NBCs with a high affinity with the spike (affinity energy <-7 kcal/mol) to be ZINC000045789238, ZINC000004098448, ZINC000008662732, and ZINC000003995616. In the MD analysis, the four ligands formed a complex with the highest dynamic equilibrium S1 (mean RMSD <0.3 nm), lowest fluctuation of the complex amino acid residues (RMSF <1.3), and solvent accessibility stability. However, the ZINC000045789238-spike complex (naringenin-4'-O glucuronide) was the only one that simultaneously had minus signal (-) MM/PBSA and MM/GBSA binding free energy values (-3.74 kcal/mol and -15.65 kcal/mol, respectively), indicating favorable binding. This ligand (naringenin-4'-O glucuronide) was also the one that produced the highest number of hydrogen bonds in the entire dynamic period (average = 4601 bonds per nanosecond). Six mutant amino acid residues formed these hydrogen bonds from the RBD region of S1 in the Omicron variant: Asn417, Ser494, Ser496, Arg403, Arg408, and His505. Naringenin-4'-O-glucuronide showed promising results as a potential drug candidate against COVID-19. In vitro and preclinical studies are needed to confirm these findings.Communicated by Ramaswamy H. Sarma.

Consensus holistic virtual screening for drug discovery: a novel machine learning model approach

In drug discovery, virtual screening is crucial for identifying potential hit compounds. This study aims to present a novel pipeline that employs machine learning models that amalgamates various conventional screening methods. A diverse array of protein targets was selected, and their corresponding datasets were subjected to active/decoy distribution analysis prior to scoring using four distinct methods: QSAR, Pharmacophore, docking, and 2D shape similarity, which were ultimately integrated into a single consensus score. The fine-tuned machine learning models were ranked using the novel formula "w_new", consensus scores were calculated, and an enrichment study was performed for each target. Distinctively, consensus scoring outperformed other methods in specific protein targets such as PPARG and DPP4, achieving AUC values of 0.90 and 0.84, respectively. Remarkably, this approach consistently prioritized compounds with higher experimental PIC50 values compared to all other screening methodologies. Moreover, the models demonstrated a range of moderate to high performance in terms of R2 values during external validation. In conclusion, this novel workflow consistently delivered superior results, emphasizing the significance of a holistic approach in drug discovery, where both quantitative metrics and active enrichment play pivotal roles in identifying the best virtual screening methodology.Scientific contributionWe presented a novel consensus scoring workflow in virtual screening, merging diverse methods for enhanced compound selection. We also introduced 'w_new', a groundbreaking metric that intricately refines machine learning model rankings by weighing various model-specific parameters, revolutionizing their efficacy in drug discovery in addition to other domains.

Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening

Recent advances in computational methods provide the promise of dramatically accelerating drug discovery. While mathematical modeling and machine learning have become vital in predicting drug-target interactions and properties, there is untapped potential in computational drug discovery due to the vast and complex chemical space. This paper builds on our recently published computational fragment-based drug discovery (FBDD) method called fragment databases from screened ligand drug discovery (FDSL-DD). FDSL-DD uses in silico screening to identify ligands from a vast library, fragmenting them while attaching specific attributes based on predicted binding affinity and interaction with the target subdomain. In this paper, we further propose a two-stage optimization method that utilizes the information from prescreening to optimize computational ligand synthesis. We hypothesize that using prescreening information for optimization shrinks the search space and focuses on promising regions, thereby improving the optimization for candidate ligands. The first optimization stage assembles these fragments into larger compounds using genetic algorithms, followed by a second stage of iterative refinement to produce compounds with enhanced bioactivity. To demonstrate broad applicability, the methodology is demonstrated on three diverse protein targets found in human solid cancers, bacterial antimicrobial resistance, and the SARS-CoV-2 virus. Combined, the proposed FDSL-DD and a two-stage optimization approach yield high-affinity ligand candidates more efficiently than other state-of-the-art computational FBDD methods. We further show that a multiobjective optimization method accounting for drug-likeness can still produce potential candidate ligands with a high binding affinity. Overall, the results demonstrate that integrating detailed chemical information with a constrained search framework can markedly optimize the initial drug discovery process, offering a more precise and efficient route to developing new therapeutics.

Synthesis, In Vivo Anticonvulsant Activity Evaluation and In Silico Studies of Some Quinazolin-4(3H)-One Derivatives

Two series, "a" and "b", each consisting of nine chemical compounds, with 2,3-disubstituted quinazolin-4(3H)-one scaffold, were synthesized and evaluated for their anticonvulsant activity. They were investigated as dual potential positive allosteric modulators of the GABAA receptor at the benzodiazepine binding site and inhibitors of carbonic anhydrase II. Quinazolin-4(3H)-one derivatives were evaluated in vivo (D1-3 = 50, 100, 150 mg/kg, administered intraperitoneally) using the pentylenetetrazole (PTZ)-induced seizure model in mice, with phenobarbital and diazepam, as reference anticonvulsant agents. The in silico studies suggested the compounds act as anticonvulsants by binding on the allosteric site of GABAA receptor and not by inhibiting the carbonic anhydrase II, because the ligands-carbonic anhydrase II predicted complexes were unstable in the molecular dynamics simulations. The mechanism targeting GABAA receptor was confirmed through the in vivo flumazenil antagonism assay. The pentylenetetrazole experimental anticonvulsant model indicated that the tested compounds, 1a-9a and 1b-9b, present a potential anticonvulsant activity. The evaluation, considering the percentage of protection against PTZ, latency until the onset of the first seizure, and reduction in the number of seizures, revealed more favorable results for the "b" series, particularly for compound 8b.

In silico identification and validation of phenolic lipids as potential inhibitor against bacterial and viral strains

The recurrence of coronavirus disease and bacterial resistant strains has drawn attention to naturally occurring bioactive molecules that can demonstrate broad-spectrum efficacy against bacteria as well as viral strains. The drug-like abilities of naturally available "anacardic acids" (AA) and their derivatives against different bacterial and viral protein targets through in-silico tools were explored. Three viral protein targets [P DB: 6Y2E (SARS-CoV-2), 1AT3 (Herpes) and 2VSM (Nipah)] and four bacterial protein targets [P DB: 2VF5 (Escherichia coli), 2VEG (Streptococcus pneumoniae), 1JIJ (Staphylococcus aureus) and 1KZN (E. coli)] were selected to evaluate the activity of bioactive AA molecules. The potential ability to inhibit the progression of microbes has been discussed based on the structure, functionality and interaction ability of these molecules on the selected protein targets for multi-disease remediation. The number of interactions, full-fitness value and energy of the ligand-target system were determined from the docked structure in SwissDock and Autodock Vina. In order to compare the efficacy of these active derivatives to that of commonly used drugs against bacteria and viruses, a few of the selected molecules were subjected to 100 ns long MD simulations. It was found that the phenolic groups and alkyl chains of AA derivatives are more likely to bind with microbial targets, that could be responsible for the improved activity against these targets. The results suggest that the proposed AA derivatives have demonstrated potential to become active drug ingredients against microbial protein targets. Further, experimental investigations are essential for clinical verification of the drug-like abilities of AA derivatives.Communicated by Ramaswamy H. Sarma.

Comparison of cytotoxicity of Miltefosine and its niosomal form on chick embryo model

Various drugs have been used for the treatment of leishmaniasis, but they often have adverse effects on the body's organs. In this study, we aimed to explore the effects of one type of drug, Miltefosine (MIL), and its analogue or modifier, liposomal Miltefosine (NMIL), on several fetal organs using both in silico analysis and practical tests on chicken embryos. Our in silico approach involved predicting the affinities of MIL and NMIL to critical proteins involved in leishmaniasis, including Vascular Endothelial Growth Factor A (VEGF-A), the Kinase insert domain receptor (KDR1), and apoptotic-regulator proteins (Bcl-2-associate). We then validated and supported these predictions through in vivo investigations, analyzing gene expression and pathological changes in angiogenesis and apoptotic mediators in MIL- and NMIL-treated chicken embryos. The results showed that NMIL had a more effective action towards VEGF-A and KDR1 in leishmaniasis, making it a better candidate for potential operative treatment during pregnancy than MIL alone. In vivo, studies also showed that chicken embryos under MIL treatment displayed less vascular mass and more degenerative and apoptotic changes than those treated with NMIL. These results suggest that NMIL could be a better treatment option for leishmaniasis during pregnancy.

Novel Flurbiprofen Derivatives as Antioxidant and Anti-Inflammatory Agents: Synthesis, In Silico, and In Vitro Biological Evaluation

In this study, we present the synthesis of five novel compounds by combining flurbiprofen with various substituted 2-phenethylamines. The synthesized derivatives underwent comprehensive characterization using techniques such as 1H- and 13C-NMR spectroscopy, UV-Vis spectroscopy, and high-resolution mass spectrometry (HRMS). Detailed HRMS analysis was performed for each of these newly created molecules. The biological activities of these compounds were assessed through in vitro experiments to evaluate their potential as anti-inflammatory and antioxidant agents. Furthermore, the lipophilicity of these derivatives was determined, both theoretically using the cLogP method and experimentally through partition coefficient (RM) measurements. To gain insights into their binding affinity, we conducted an in silico analysis of the compounds' interactions with human serum albumin (HSA) using molecular docking studies. Our findings reveal that all of the newly synthesized compounds exhibit significant anti-inflammatory and antioxidant activities, with results statistically comparable to the reference compounds. Molecular docking studies further explain the observed in vitro results, shedding light on the molecular mechanisms behind their biological activities. Using in silico method, toxicity was calculated, resulting in LD50 values. Depending on the administration route, the novel flurbiprofen derivatives show lower toxicity compared to the standard flurbiprofen.

Natural sapogenins as potential inhibitors of aquaporins for targeted cancer therapy: computational insights into binding and inhibition mechanism

Aquaporins (AQPs) are membrane proteins that facilitate the transport of water and other small molecules across biological membranes. AQPs are involved in various physiological processes and pathological conditions, including cancer, making them as potential targets for anticancer therapy. However, the development of selective and effective inhibitors of AQPs remains a challenge. In this study, we explored the possibility of using natural sapogenins, a class of plant-derived aglycones of saponins with diverse biological activities, as potential inhibitors of AQPs. We performed molecular docking, dynamics simulation and binding energy calculation to investigate the binding and inhibition mechanism of 19 sapogenins against 13 AQPs (AQP0-AQP13) that are overexpressed in various cancers. Our results showed that out of 19 sapogenins, 8 (Diosgenin, Gitogenin, Tigogenin, Ruscogenin, Yamogenin, Hecogenin, Sarsasapogenin and Smilagenin) exhibited acceptable drug-like characteristics. These sapogenin also exhibited favourable binding affinities in the range of -7.6 to -13.4 kcal/mol, and interactions within the AQP binding sites. Furthermore, MD simulations provided insights into stability and dynamics of the sapogenin-AQP complexes. Most of the fluctuations in binding pocket were observed for AQP0-Gitogenin and AQP4-Diosgenin. However, remaining protein-ligand complex showed stable root mean square deviation (RMSD) plots, strong hydrogen bonding interactions, stable solvent-accessible surface area (SASA) values and minimum distance to the receptor. These observations suggest that natural sapogenin hold promise as novel inhibitors of AQPs, offering a basis for the development of innovative therapeutic agents for cancer treatment. However, further validation of the identified compounds through experiments is essential for translating these findings into therapeutic applications.Communicated by Ramaswamy H. Sarma.

In silico Screening of Food and Drug Administration-approved Compounds against Trehalose 2-sulfotransferase (Rv0295c) in Mycobacterium tuberculosis: Insights from Molecular Docking and Dynamics Simulations

BACKGROUND

Tuberculosis (TB) remains a prominent global health challenge, distinguished by substantial occurrences of infection and death. The upsurge of drug-resistant TB strains underscores the urgency to identify novel therapeutic targets and repurpose existing compounds. Rv0295c is a potentially druggable enzyme involved in cell wall biosynthesis and virulence. We evaluated the inhibitory activity of Food and Drug Administration (FDA)-approved compounds against Rv0295c of Mycobacterium tuberculosis, employing molecular docking, ADME evaluation, and dynamics simulations.

METHODS

The study screened 1800 FDA-approved compounds and selected the top five compounds with the highest docking scores. Following this, we subjected the initially screened ligands to ADME analysis based on their dock scores. In addition, the compound exhibited the highest binding affinity chosen for molecular dynamics (MD) simulation to investigate the dynamic behavior of the ligand-receptor complex.

RESULTS

Dihydroergotamine (CHEMBL1732) exhibited the highest binding affinity (-12.8 kcal/mol) for Rv0295c within this set of compounds. We evaluated the stability and binding modes of the complex over extended simulation trajectories.

CONCLUSION

Our in silico analysis demonstrates that FDA-approved drugs can serve as potential Rv0295c inhibitors through repurposing. The combination of molecular docking and MD simulation offers a comprehensive understanding of the interactions between ligands and the protein target, providing valuable guidance for further experimental validation. Identifying Rv0295c inhibitors may contribute to new anti-TB drugs.

In silico prediction and in vitro assessment of novel heterocyclics with antimalarial activity

The development of new antimalarials is paramount to keep the goals on reduction of malaria cases in endemic regions. The search for quality hits has been challenging as many inhibitory molecules may not progress to the next development stage. The aim of this work was to screen an in-house library of heterocyclic compounds (HCUV) for antimalarial activity combining computational predictions and phenotypic techniques to find quality hits. The physicochemical determinants, pharmacokinetic properties (ADME), and drug-likeness of HCUV were evaluated in silico, and compounds were selected for structure-based virtual screening and in vitro analysis. Seven Plasmodium target proteins were selected from the DrugBank Database, and ligands and receptors were processed using UCSF Chimera and Open Babel before being subjected to docking using Autodock Vina and Autodock 4. Growth inhibition of P. falciparum (3D7) cultures was tested by SYBR Green assays, and toxicity was assessed using hemolytic activity tests and the Galleria mellonella in vivo model. From a total of 792 compounds, 341 with good ADME properties, drug-likeness, and no interference structures were subjected to in vitro analysis. Eight compounds showed IC50 ranging from 0.175 to 0.990 µM, and active compounds included pyridyl-diaminopyrimido-diazepines, pyridyl-N-acetyl- and pyridyl-N-phenyl-pyrazoline derivatives. The most potent compound (UV802, IC50 0.178 µM) showed no toxicophoric and was predicted to interact with P. falciparum 1-cysperoxidredoxin (PfPrx1). For the remaining 7 hits (IC50 < 1 μM), 3 showed in silico binding to PfPrx1, one was predicted to bind the haloacid dehalogenase-like hydrolase and plasmepsin II, and one interacted with the plasmodial heat shock protein 90.

Biochemical and molecular docking-based strategies of Acalypha indica and Boerhavia diffusa extract by targeting bacterial strains and cancer proteins

Antibiotic-resistant microbes have emerged around the world, presenting a risk to health. Plant-derived drugs have become a potential source for the production of antibiotic-resistant drugs and cancer therapies. In this study, we investigated the antibacterial, cytotoxic and antioxidant properties of Acalypha indica and Boerhavia diffusa, and conducted in silico molecular docking experiments against EGFR and VEGFR-2 proteins. The metabolic extract of A. indica inhibited Streptococcus iniae and Staphylococcus sciuri with inhibition zones of 21.66 ± 0.57 mm and 20.33 ± 0.57 mm, respectively. The B. diffusa leaf extract produced inhibition zones of 20.3333 ± 0.5773 mm and 20.33 ± 0.57 mm against Streptococcus iniae and Edwardsiella anguillarum, respectively. A. indica and B. diffusa extracts had toxicities of 162.01 μg/ml and 175.6 μg/ml, respectively. Moreover, B. diffusa (IC50 =154.42 µg/ml) leaf extract exhibited moderately higher antioxidant activity compared with the A. indica (IC50 = 218.97 µg/ml) leaf extract. Multiple interactions were observed at Leu694, Met769 and Leu820 sites for EGFR and at Asp1046 and Cys1045 sites for VEGFR during the molecular docking study. CID-235030, CID-70825 and CID-156619353 had binding energies of -7.6 kJ/mol, -7.5 kJ/mol and -7.6 kJ/mol, respectively, with EGFR protein. VEGFR-2 protein had docking energies of -7.5 kJ/mol, -7.6 kJ/mol and -7.3 kJ/mol, respectively, for CID-6420353, CID-156619353 and CID-70825 compounds. The MD simulation trajectories revealed the hit compound; CID-235030 and EGFR complex, CID-6420353 and VEGFR-2 exhibit stable profile in the root mean square deviation (RMSD), radius of gyration (Rg), solvent accessible surface area (SASA), hydrogen bond and root mean square fluctuation (RMSF) and the binding free energy by MM-PBSA method. This study indicates that methanol extracts of A. indica and B. diffusa may play a crucial role in developing antibiotic-resistant and cancer drugs.Communicated by Ramaswamy H. Sarma.

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.

Structural and binding studies of 2'- and 3-fucosyllactose and its complexes with norovirus capsid protein by molecular dynamics simulations

Human breast milk contains free oligosaccharides (Human Milk Oligosaccharides-HMOs) that help to protect breastfed infants against a variety of infectious diseases and act as decoy receptors. In breast milk, HMOs are the third most abundant compounds after lactose and lipids. Structural and conformational models of HMOs are quite crucial to studying the interaction with proteins and molecular recognition phenomenon. Molecular dynamics simulations for two trisaccharides HMOs (2'-FL and 3-FL) were carried out for 250 ns and the conformational models were subsequently substantiated by three replicate simulations. The conformer models of HMOs 2'-FL and 3-FL were deposited in the 3-Dimensional Structural Database for Sialic acid-containing CARbohydrates (3DSDSCAR) database website (www.3dsdscar.in). HMOs were then docked into the active site of norovirus capsid protein and are simulated for 100 ns duration. Each complex system was stabilized by direct and water-mediated hydrogen bonding interactions. Binding free energy calculations predict two possible binding modes for each complex system. The conformational flexibility and binding stability of the complex systems were calculated. The protein folding/unfolding and compactness seem to be better for the two HMOs. From a general perspective, we found that both 2'-FL and 3-FL exhibited higher binding efficacy towards norovirus capsid protein and according to the structural stability, 3-FL might be used as a preventive inhibitor for norovirus infection.Communicated by Ramaswamy H. Sarma.

Inhibitor screening for volume-sensitive LRRC8A chloride channel

Leucine-rich repeat-containing protein 8 A (LRRC8A) protein is a critical member of volume-regulated anion channels. It plays a critical roles in the regulation of cellular volume and involves in the development of diseases like osteoarthritis. Screening of lead compounds to modulate its function may provide potential therapeutics of related diseases. Here, we employ virtual screening techniques and molecular dynamics (MD) simulation to screen potential inhibitors against LRRC8A. LRRC8A was regarded as the drug target to investigate potential compounds from the ZINC15 database via molecular docking. The final compound was selected among the top 10 Autodock Vina score (-8.8 Kcal/mol) with the ZINC ID ZINC000018195627 after druggability prediction. The docked complex from the virtual screening was subjected to MD simulation to analyze the stability of the LRRC8A protein-ligand complex, with parameters including root mean square deviation, root mean square fluctuation and radius of gyration. Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) method was further employed to predict the binding free energies from MD simulation trajectory. Our study provides insightful analysis for the potential compound to modulate LRRC8A and lay the foundation of therapeutics development against osteoarthritis.Communicated by Ramaswamy H. Sarma.

Identification of novel curcumin derivatives against pancreatic cancer: a comprehensive approach integrating 3D-QSAR pharmacophore modeling, virtual screening, and molecular dynamics simulations

Pancreatic cancer, known as the "silent killer," poses a daunting challenge in cancer therapy. The dysregulation of the PI3Kα signaling pathway in pancreatic cancer has attracted considerable interest as a promising target for therapeutic intervention. In this regard, the use of curcumin derivatives as inhibitors of PI3Kα has emerged, providing a novel and promising avenue for developing effective treatments for this devastating disease. Computational approaches were employed to explore this potential and investigate 58 curcumin derivatives with cytotoxic activity against the Panc-1 cell line. Our approach involved ligand-based pharmacophore modeling and atom-based 3D-QSAR analysis. The resulting QSAR model derived from the best-fitted pharmacophore hypothesis (AAHRR_1) demonstrated remarkable performance with high correlation coefficients (R2) of 0.990 for the training set and 0.977 for the test set. The cross-validation coefficient (Q2) of 0.971 also validated the model's predictive power. Tropsha's recommended criteria, including the Y-randomization test, were employed to ensure its reliability. Furthermore, an enrichment study was conducted to evaluate the model's performance in identifying active compounds. AAHRR_1 was used to screen a curated PubChem database of curcumin-related compounds. Two molecules (CID156189304 and CID154728220) exhibited promising pharmacokinetic properties and higher docking scores than Alpelisib, warranting further investigation. Extensive molecular dynamics simulations provided crucial insights into the conformational dynamics within the binding site, validating their stability and behavior. These findings contribute to our understanding of the potential therapeutic effectiveness of these compounds as PI3Kα inhibitors in pancreatic cancer.Communicated by Ramaswamy H. Sarma.

The inhibitory mechanism of echinacoside against Staphylococcus aureus Ser/Thr phosphatase Stp1 by virtual screening and molecular modeling

CONTEXT

Stp1 is a new potential target closely related to the pathogenicity of Staphylococcus aureus (S. aureus). In this study, effective Stp1 inhibitors were screened via virtual screening and enzyme activity experiments, and the inhibition mechanism was analyzed using molecular dynamics simulation.

METHODS

AutoDock Vina 4.0 software was used for virtual screening. The molecular structures of Stp1 and ligands were obtained from the RCSB Protein Data Bank and Zinc database, respectively. The molecular dynamics simulation used the Gromacs 4.5.5 software package with the Amberff99sb force field and TIP3P water model. AutoDock Tools was used to add polar hydrogen atoms to Stp1 and distribute part of the charge generated by Kollman's combined atoms. The binding free energies were calculated using the Amber 10 package.

RESULTS

The theoretical calculation results are consistent with the experimental results. We found that echinacoside (ECH) substantially inhibits the hydrolytic activity of Stp1. ECH competes with the substrate by binding to the active center of Stp1, resulting in a decrease in Stp1 activity. In addition, Met39, Gly41, Asp120, Asn162, and Ile163 were identified to play key roles in the binding of Stp1 to ECH. The benzene ring of ECH also plays an important role in complex binding. These findings provide a robust foundation for the development of innovative anti-infection drugs.

Deciphering Molecular Aspects of Potential α-Glucosidase Inhibitors within Aspergillus terreus: A Computational Odyssey of Molecular Docking-Coupled Dynamics Simulations and Pharmacokinetic Profiling

Hyperglycemia, as a hallmark of the metabolic malady diabetes mellitus, has been an overwhelming healthcare burden owing to its high rates of comorbidity and mortality, as well as prospective complications affecting different body organs. Available therapeutic agents, with α-glucosidase inhibitors as one of their cornerstone arsenal, control stages of broad glycemia while showing definitive characteristics related to their low clinical efficiency and off-target complications. This has propelled the academia and industrial section into discovering novel and safer candidates. Herein, we provided a thorough computational exploration of identifying candidates from the marine-derived Aspergillus terreus isolates. Combined structural- and ligand-based approaches using a chemical library of 275 metabolites were adopted for pinpointing promising α-glucosidase inhibitors, as well as providing guiding insights for further lead optimization and development. Structure-based virtual screening through escalating precision molecular docking protocol at the α-glucosidase canonical pocket identified 11 promising top-docked hits, with several being superior to the market drug reference, acarbose. Comprehensive ligand-based investigations of these hits' pharmacokinetics ADME profiles, physiochemical characterizations, and obedience to the gold standard Lipinski's rule of five, as well as toxicity and mutagenicity profiling, proceeded. Under explicit conditions, a molecular dynamics simulation identified the top-stable metabolites: butyrolactone VI (SK-44), aspulvinone E (SK-55), butyrolactone I 4''''-sulfate (SK-72), and terrelumamide B (SK-173). They depicted the highest free binding energies and steadiest thermodynamic behavior. Moreover, great structural insights have been revealed, including the advent of an aromatic scaffold-based interaction for ligand-target complex stability. The significance of introducing balanced hydrophobic/polar moieties, like triazole and other bioisosteres of carboxylic acid, has been highlighted across docking, ADME/Tox profiling, and molecular dynamics studies for maximizing binding interactions while assuring safety and optimal pharmacokinetics for targeting the intestinal-localized α-glucosidase enzyme. Overall, this study provided valuable starting points for developing new α-glucosidase inhibitors based on nature-derived unique scaffolds, as well as guidance for prospective lead optimization and development within future pre-clinical and clinical investigations.

Neuroprotective and nephroprotective effects of Ircinia sponge in polycyclic aromatic hydrocarbons (PAHs) induced toxicity in animal model: a pharmacological and computational approach

The present study investigated the neuroprotective and nephroprotective effects of the sponge Ircinia sp. ethyl acetate extract (ISPE) against persistent aromatic pollutants in vitro and in vivo. Different exponential experimental assays were applied to this study. An in vitro study to investigate the potential therapeutic effect of ISPE using antioxidants (for example, ABTS and DPPH) and anti-Alzheimer assays (inhibition of acetylcholinesterase); the in-vivo study was designed to evaluate the protective effect of ISPE as neuroprotective and nephroprotective against the destructive effect of PAH. Several assays included oxidative assays (LPO), antioxidant biomarkers (GSH, GST), and inflammatory and neurodegenerative biomarkers (PTK,SAA). Additionally, the results were confirmed using histopathological examination. The in silico screening study improved the in vitro and in vivo findings through interaction between the aryl hydrocarbon receptor (AHR) and the polyphenolic content of ISPE extract, which was determined using LCMSM. The results and discussion showed that ISPE exhibited a promising antioxidant and anti-acetylcholinesterase activity as evidenced by IC50 values of 49.74, 28.25, and 0.18 µg/mL in DPPH, ABTS, and acetylcholinesterase inhibition assays, respectively. In vivo, the study showed that animals receiving ISPE before poly aromatic hydrocarbons administration PAHs (Prot, ISPE) showed significant amelioration in kidney functions manifested by the reduction of serum urea, uric acid, and creatinine by 40.6%, 66.4%, and 134.8%, respectively, concerning PAH-injected mice (HAA). Prot, ISPE revealed a decline in malondialdehyde (MDA) and total proteins (TP) in kidney and brain tissues by 73.63% and 50.21%, respectively, for MDA and 59.82% and 80.41%, respectively, for TP with respect to HAA. Prot, ISPE showed significant elevation in reduced glutathione (GSH) and glutathione transferase (GST) in kidney and brain tissues and reduction in the inflammatory and pre-cancerous biomarkers, namely, serum protein tyrosine kinases (PTKs) and serum amyloid A (SAA). These findings were further supported by histopathological examination of kidney and brain tissues, which revealed normal structure approaching normal control. Metabolic profiling of ISPE using LC-MS-MS showed the presence of fourteen polyphenolic compounds belonging mainly to phenolic acids and flavonoids. In silico study revealed that all the tested compounds exerted certain binding with the aryl hydrocarbon receptor, where rutin showed the best fitting (ΔG =  - 7.6 kcal/mol-1) with considerable pharmacokinetic and pharmacodynamic properties revealed from in silico ADME (Absorption, Distribution, Metabolism, and Excretion) study. Hence, it can be concluded that the Ircinia sponge showed a promising protective effect versus kidney and brain toxicity triggered by PAHs.

Pharmacoinformatic Investigation of Silymarin as a Potential Inhibitor against Nemopilema nomurai Jellyfish Metalloproteinase Toxin-like Protein

Jellyfish stings pose a major threat to swimmers and fishermen worldwide. These creatures have explosive cells containing one large secretory organelle called a nematocyst in their tentacles, which contains venom used to immobilize prey. Nemopilema nomurai, a venomous jellyfish belonging to the phylum Cnidaria, produces venom (NnV) comprising various toxins known for their lethal effects on many organisms. Of these toxins, metalloproteinases (which belong to the toxic protease family) play a significant role in local symptoms such as dermatitis and anaphylaxis, as well as systemic reactions such as blood coagulation, disseminated intravascular coagulation, tissue injury, and hemorrhage. Hence, a potential metalloproteinase inhibitor (MPI) could be a promising candidate for reducing the effects of venom toxicity. For this study, we retrieved the Nemopilema nomurai venom metalloproteinase sequence (NnV-MPs) from transcriptome data and modeled its three-dimensional structure using AlphaFold2 in a Google Colab notebook. We employed a pharmacoinformatics approach to screen 39 flavonoids and identify the most potent inhibitor against NnV-MP. Previous studies have demonstrated the efficacy of flavonoids against other animal venoms. Based on our analysis, Silymarin emerged as the top inhibitor through ADMET, docking, and molecular dynamics analyses. In silico simulations provide detailed information on the toxin and ligand binding affinity. Our results demonstrate that Silymarin's strong inhibitory effect on NnV-MP is driven by hydrophobic affinity and optimal hydrogen bonding. These findings suggest that Silymarin could serve as an effective inhibitor of NnV-MP, potentially reducing the toxicity associated with jellyfish envenomation.

Uni-Dock: GPU-Accelerated Docking Enables Ultralarge Virtual Screening

Molecular docking, a structure-based virtual screening method, is a reliable tool to enrich potential bioactive molecules from molecular databases. With the rapid expansion of compound library sizes, the speed of existing molecular docking programs becomes less than adequate to meet the demand for screening ultralarge libraries containing tens of millions or billions of molecules. Here, we propose Uni-Dock, a GPU-accelerated molecular docking program that supports various scoring functions including vina, vinardo, and ad4. Uni-Dock achieves more than 1000-fold speedup with high accuracy compared with the AutoDock Vina running in single CPU core, outperforming reported GPU-accelerated docking programs including AutoDock-GPU and Vina-GPU based on head-to-head experiments. Uni-Dock docks molecules in batches simultaneously using concurrent threads of each molecule. The data flow between GPU and CPU is optimized to eliminate CPU hotspots and maximize GPU utility. Additionally, Uni-Dock also supports hydrogen bond biased docking for all scoring functions and can be migrated to multiple GPUs of different architectures and manufacturers. We analyzed the improved performance of Uni-Dock on the CASF-2016 and DUD-E datasets and recommend three combinations of hyperparameters corresponding to different docking scenarios. To demonstrate Uni-Dock's capability on routinely screening ultralarge libraries, we performed hierarchical virtual screening experiments with Uni-Dock on the Enamine Diverse REAL druglike set containing 38.2 million molecules to a popular target KRAS G12D in 12 h using 100 NVIDIA V100 GPUs. To the best of our knowledge, Uni-Dock should be the fastest GPU-accelerated docking program to date.

Docking experiments suggest that gloriosine has microtubule-targeting properties similar to colchicine

Gloriosine, the predominant metabolite of Gloriosa superba L., shares chemical properties with colchicine. We analyze the microtubule-binding affinity of gloriosine at the colchicine binding site (CBS) using an in silico-in vivo approach. The In silico docking of gloriosine showed a binding score of (-) 7.5 kcal/Mol towards β-tubulin at CBS and was validated by overlapping the coupling pose of the docked ligand with co-crystallized colchicine. 2D plots (Ligplot +) showed > 85% overlap between gloriosine and colchicine. The ADMET profile of gloriosine was in accordance with Lipinski's rule of five. Gloriosine belongs to class II toxicity with anLD₅₀ value of 6 mg/kg. In vivo and transmission electron microscopy studies revealed that gloriosine induces abnormalities in cell division such as condensed chromosomes in C-metaphase and enlarged nucleus with increased nuclear material. Gloriosine treated cells exhibited mitotic index of about 14% compared to control of 24% and high anti-proliferative activity i.e. 63.94% cell viability at a low concentration (0.0004 mg/ml). We conclude that gloriosine has a strong affinity for β-tubulin at CBS and thus can be used as a colchicine alternative in cytology and other clinical conditions.

Identification of a new class of proteasome inhibitors based on a naphthyl-azotricyclic-urea-phenyl scaffold

Proteasomes play an important role in protein degradation and regulation of many cellular pathways by maintaining protein balance. Inhibitors of proteasomes disrupt this balance affecting proteins that are key in malignancies and as such have found applications in the treatment of multiple myeloma and mantle cell lymphoma. However, resistance mechanisms have been reported for these proteasome inhibitors including mutations at the β5 site which necessitates the constant development of new inhibitors. In this work, we report the identification of a new class of proteasome inhibitors, polycyclic molecules bearing a naphthyl-azotricyclic-urea-phenyl scaffold, from screening of the ZINC library of natural products. The most potent of these compounds showed evidence of dose dependency through proteasome assays with IC50 values in the low micromolar range, and kinetic analysis revealed competitive binding at the β5c site with an estimated inhibition constant, K i, of 1.15 μM. Inhibition was also shown for the β5i site of the immunoproteasome at levels similar to those of the constitutive proteasome. Structure-activity relationship studies identified the naphthyl substituent to be crucial for activity and this was attributed to enhanced hydrophobic interactions within β5c. Further to this, halogen substitution within the naphthyl ring enhanced the activity and allowed for π-π interactions with Y169 in β5c and Y130 and F124 in β5i. The combined data highlight the importance of hydrophobic and halogen interactions in β5 binding and assist in the design of next generation inhibitors of proteasomes.

Design and Prediction of Aptamers Assisted by In Silico Methods

An aptamer is a single-stranded DNA or RNA that binds to a specific target with high binding affinity. Aptamers are developed through the process of systematic evolution of ligands by exponential enrichment (SELEX), which is repeated to increase the binding power and specificity. However, the SELEX process is time-consuming, and the characterization of aptamer candidates selected through it requires additional effort. Here, we describe in silico methods in order to suggest the most efficient way to develop aptamers and minimize the laborious effort required to screen and optimise aptamers. We investigated several methods for the estimation of aptamer-target molecule binding through conformational structure prediction, molecular docking, and molecular dynamic simulation. In addition, examples of machine learning and deep learning technologies used to predict the binding of targets and ligands in the development of new drugs are introduced. This review will be helpful in the development and application of in silico aptamer screening and characterization.

AlteQ: a new complementarity principle-centered method for the evaluation of docking poses

Molecular docking is the most popular and widely used method for identifying novel molecules against a target of interest. However, docking procedures and their validation are still under intense development. In the present investigation, we evaluate a quantum free-orbital AlteQ method for evaluating docking complexes generated by taking EGFR complexes as an example. The AlteQ method calculates the electron density using Slater's type atomic contributions in the interspace between the receptor and the ligand. Since the interactions are determined by the overlap of electron clouds, they follow the complementarity principle, and an equation can be obtained that describes these interactions. The AlteQ method evaluates the quality of the interaction between the receptor and the ligand, how complementary the interactions are, and due to this, it is used to reject less realistic structures obtained by docking methods. Here, three different equations were used to determine the quality of the interactions in experimental complexes and docked complexes obtained using AutoDock Vina and AutoDock 4.2.6.Communicated by Ramaswamy H. Sarma.

Investigation of the Effects of Glabridin on the Proliferation, Apoptosis, and Migration of the Human Colon Cancer Cell Lines SW480 and SW620 and Its Mechanism Based on Reverse Virtual Screening and Proteomics

Colon cancer is a relatively common malignant tumor of the digestive tract. Currently, most colon cancers originate from adenoma carcinogenesis. By screening various licorice flavonoids with anticancer effects, we found that glabridin (GBN) has a prominent anticolon cancer effect. First, we initially explored whether GBN can inhibit proliferation, migration, and invasion and induce apoptosis in SW480 and SW620 cells. Next, we exploited reverse virtual and proteomics technologies to screen out closely related target pathways on the basis of a drug and target database. At the same time, we constructed the structure of the GBN target pathway in colon cancer. We predicted that GBN can regulate the phosphatidylinositol 3-kinase (PI3K)-protein kinase B (AKT)-mammalian target of the rapamycin pathway (mTOR) pathway to fight colon cancer. Finally, through Western blot analysis and qRT-PCR, we verified that the expression levels of the PI3K, AKT, and mTOR proteins and genes in this pathway were significantly reduced after GBN administration. In short, the promising discovery of the anticolon cancer mechanism of GBN provides a reliable experimental basis for subsequent new drug development.

The inhibitory effect of 6-gingerol and cisplatin on ovarian cancer and antitumor activity: In silico, in vitro, and in vivo

Background

Epithelial ovarian cancer is very common in women and causes hundreds of deaths per year worldwide. Chemotherapy drugs including cisplatin have adverse effects on patients' health. Complementary treatments and the use of herbal medicines can help improve the performance of medicine. 6-Gingerol is the major pharmacologically active component of ginger. In this study, we compared the effects of 6-gingerol, cisplatin, and their combination in apoptotic and angiogenetic activities in silico, in test tubes, and in in vivo assays against two ovarian cancer cell lines: OVCAR-3 and human umbilical vein endothelial cells (HUVECs).

Methods

The drug-treated cell lines were evaluated for their cytotoxicity, cell cycle, and apoptotic and angiogenetic gene expression changes.

Results

The proportion of apoptosis treated by 6-gingerol coupled with cisplatin was significantly high. In the evaluation of the cell cycle, the combination therapy also showed a significant promotion of a higher extent of the S sequence. The expression of p53 level, Caspase-8, Bax, and Apaf1 genes was amplified again with combination therapy. Conversely, in both cell lines, the cumulative drug concentrations reduced the expression of VEGF, FLT1, KDR, and Bcl-2 genes. Similarly, in the control group, combination treatment significantly decreased the expression of VEGF, FLT1, KDR, and Bcl-2 genes in comparison to cisplatin alone.

Conclusions

The findings of the present study demonstrated that the cisplatin and 6-gingerol combination is more effective in inducing apoptosis and suppressing the angiogenesis of ovarian cancer cells than using each drug alone.

Pharmacophore model-aided virtual screening combined with comparative molecular docking and molecular dynamics for identification of marine natural products as SARS-CoV-2 papain-like protease inhibitors

Targeting SARS-CoV-2 papain-like protease using inhibitors is a suitable approach for inhibition of virus replication and dysregulation of host anti-viral immunity. Engaging all five binding sites far from the catalytic site of PLpro is essential for developing a potent inhibitor. We developed and validated a structure-based pharmacophore model with 9 features of a potent PLpro inhibitor. The pharmacophore model-aided virtual screening of the comprehensive marine natural product database predicted 66 initial hits. This hit library was downsized by filtration through a molecular weight filter of ≤ 500 g/mol. The 50 resultant hits were screened by comparative molecular docking using AutoDock and AutoDock Vina. Comparative molecular docking enables benchmarking docking and relieves the disparities in the search and scoring functions of docking engines. Both docking engines retrieved 3 same compounds at different positions in the top 1 % rank, hence consensus scoring was applied, through which CMNPD28766, aspergillipeptide F emerged as the best PLpro inhibitor. Aspergillipeptide F topped the 50-hit library with a pharmacophore-fit score of 75.916. Favorable binding interactions were predicted between aspergillipeptide F and PLpro similar to the native ligand XR8-24. Aspergillipeptide F was able to engage all the 5 binding sites including the newly discovered BL2 groove, site V. Molecular dynamics for quantification of Cα-atom movements of PLpro after ligand binding indicated that it exhibits highly correlated domain movements contributing to the low free energy of binding and a stable conformation. Thus, aspergillipeptide F is a promising candidate for pharmaceutical and clinical development as a potent SARS-CoV-2 PLpro inhibitor.

Systematic Investigation of Docking Failures in Large-Scale Structure-Based Virtual Screening

In recent years, large-scale structure-based virtual screening has attracted increasing levels of interest for identification of novel compounds corresponding to potential drug targets. It is critical to understand the strengths and weaknesses of docking algorithms to increase the success rate in practical applications. Here, we systematically investigated the docking successes and failures of two representative docking programs: UCSF DOCK 3.7 and AutoDock Vina. DOCK 3.7 performed better in early enrichment on the Directory of Useful Decoys: Enhanced (DUD-E) data set, although both docking methods were roughly comparable in overall enrichment performance. DOCK 3.7 also showed superior computational efficiency. Intriguingly, the Vina scoring function showed a bias toward compounds with higher molecular weights. Both the tested docking approaches yielded incorrectly predicted ligand binding poses caused by the limitations of torsion sampling. Based on a careful analysis of docking results from six representative cases, we propose the reasons underlying docking failures; furthermore, we provide a few solutions, representing practical guidance for large-scale virtual screening campaigns and future docking algorithm development.

Multi-Level High-Throughput Screening for Discovery of Ligands That Inhibit Insulin Aggregation

We have developed a multi-level virtual screening protocol to identify lead molecules from the FDA inactives database that can inhibit insulin aggregation. The method is based on the presence of structural and interaction specificity in non-native aggregation pathway protein-protein interactions. Some key challenges specific to the present problem, when compared with native protein association, include structural heterogeneity of the protein species involved, multiple association pathways, and relatively higher probability of conformational rearrangement of the association complex. In this multi-step method, the inactives database was first screened using the dominant pharmacophore features of previously identified molecules shown to significantly inhibit insulin aggregation nucleation by binding to its aggregation-prone conformers. We then performed ensemble docking of several low-energy ligand conformations on these aggregation-prone conformers followed by molecular dynamics simulations and binding affinity calculations on a subset of docked complexes to identify a final set of five potential lead molecules to inhibit insulin aggregation nucleation. Their effect on aggregation inhibition was extensively investigated by incubating insulin under aggregation-prone aqueous buffer conditions (low pH, high temperature). Aggregation kinetics were characterized using size exclusion chromatography and Thioflavin T fluorescence assay, and the secondary structure was determined using circular dichroism spectroscopy. Riboflavin provided the best aggregation inhibition, with 85% native monomer retention after 48 h incubation under aggregation-prone conditions, whereas the no-ligand formulation showed complete monomer loss after 36 h. Further, insulin incubated with two of the screened inactives (aspartame, riboflavin) had the characteristic α-helical dip in CD spectra, while the no-ligand formulation showed a change to β-sheet rich conformations.

Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery

Efficient identification of drug mechanisms of action remains a challenge. Computational docking approaches have been widely used to predict drug binding targets; yet, such approaches depend on existing protein structures, and accurate structural predictions have only recently become available from AlphaFold2. Here, we combine AlphaFold2 with molecular docking simulations to predict protein-ligand interactions between 296 proteins spanning Escherichia coli's essential proteome, and 218 active antibacterial compounds and 100 inactive compounds, respectively, pointing to widespread compound and protein promiscuity. We benchmark model performance by measuring enzymatic activity for 12 essential proteins treated with each antibacterial compound. We confirm extensive promiscuity, but find that the average area under the receiver operating characteristic curve (auROC) is 0.48, indicating weak model performance. We demonstrate that rescoring of docking poses using machine learning-based approaches improves model performance, resulting in average auROCs as large as 0.63, and that ensembles of rescoring functions improve prediction accuracy and the ratio of true-positive rate to false-positive rate. This work indicates that advances in modeling protein-ligand interactions, particularly using machine learning-based approaches, are needed to better harness AlphaFold2 for drug discovery.

Ellagic Acid as a Potential Inhibitor against the Nonstructural Protein NS3 Helicase of Zika Virus: A Molecular Modelling Study

Zika virus is a member of the Flaviviridae family and genus Flavivirus, which has a phylogenetic relationship with spondweni virus. It spreads to humans through a mosquito bite. To identify potential inhibitors for the Zika virus with biosafety, we selected natural antiviral compounds isolated from plant sources and screened against NS3 helicase of the Zika virus. The enzymatic activity of the NS3 helicase is associated with the C-terminal region and is concerned with RNA synthesis and genome replication. It serves as a crucial target for the Zika virus. We carried out molecular docking for the target NS3 helicase against the selected 25 phytochemicals using AutoDock Vina software. Among the 25 plant compounds, we identified NS3 helicase-ellagic acid (-9.9 kcal/mol), NS3 helicase-hypericin (-9.8 kcal/mol), and NS3 helicase-pentagalloylglucose (-9.5 kcal/mol) as the best binding affinity compounds based on their binding energies. To understand the stability of these complexes, molecular dynamic simulations were executed and the trajectory analysis exposed that the NS3 helicase-ellagic acid complex possesses greater stability than the other two complexes such as NS3 helicase-hypericin and NS3 helicase-pentagalloylglucose. The ADMET property prediction of these compounds resulted in nontoxicity and noncarcinogenicity.

Cryo-EM Structure and Activator Screening of Human Tryptophan Hydroxylase 2

Human tryptophan hydroxylase 2 (TPH2) is the rate-limiting enzyme in the synthesis of serotonin. Its dysfunction has been implicated in various psychiatric disorders such as depression, autism, and bipolar disorder. TPH2 is typically decreased in stability and catalytic activity in patients; thus, screening of molecules capable of binding and stabilizing the structure of TPH2 in activated conformation is desired for drug development in mental disorder treatment. Here, we solved the 3.0 Å cryo-EM structure of the TPH2 tetramer. Then, based on the structure, we conducted allosteric site prediction and small-molecule activator screening to the obtained cavity. ZINC000068568685 was successfully selected as the best candidate with highest binding affinity. To better understand the driving forces and binding stability of the complex, we performed molecular dynamics simulation, which indicates that ZINC000068568685 has great potential to stabilize the folding of the TPH2 tetramer to facilitate its activity. The research might shed light on the development of novel drugs targeting TPH2 for the treatment of psychological disorders.

Network pharmacological evaluation of strigolactones efficacy as potential inhibitors against therapeutic targets of hepatocellular carcinoma

PURPOSE

Hepatocellular carcinoma (HCC) is the uncontrolled growth of hepatocytes which results in nearly 5 million deaths worldwide. Specific strategies have been developed to treat HCC, including surgery, chemotherapy and radiotherapy. But, the effective disease dealing requires synergistic collaboration with other approaches, which often results in moderate to severe side effects during and after the treatment period. Therefore, the focus is now shifting to explore and retrieve those plant-based products that could be utilized to treat HCC with maximum efficacy without causing any side effects. Strigolactones (SL) are compounds of plant origin derived from Striga lutea responsible for controlling the branching pattern of stem and have reported anti-cancerous activity by promoting apoptosis at micromolar concentrations. However, little work has been done concerning determining the pharmacogenomic effect of strigolactones on HCC.

METHODS

Current work focuses on comparing therapeutic efficiencies of SL analogs against core targets of HCC using network pharmacology approach, pharmacokinetics analysis, gene ontogeny, functional enrichment analysis, molecular docking and Molecular Dynamics simulation.

RESULTS

Drug-target prediction and functional enrichment analysis showed that HDAC1 and HDAC2 are the core proteins involved in hepatocellular carcinoma that strigolactone analogs can target. Consequently, results from molecular docking and MD simulation analyses report that among all the SL analogs strigol, epistrigol and nijmegen1 can turn out to be most effective in downregulating the expression of HDAC1, HDAC2 and CYP19A.

CONCLUSION

Strigol, epistrigol and nijmegen1 could be used as potential inhibitors against HCC and can be further validated through in vitro/in vivo studies.

New N-Alkylated Heterocyclic Compounds as Prospective NDM1 Inhibitors: Investigation of In Vitro and In Silico Properties

A new family of pyrazole-based compounds (1–15) was synthesized and characterized using different physicochemical analyses, such as FTIR, UV-Visible, ¹H, ¹³C NMR, and ESI/LC-MS. The compounds were evaluated for their in vitro antifungal and antibacterial activities against several fungal and bacterial strains. The results indicate that some compounds showed excellent antibacterial activity against E. coli, S. aureus, C. freundii, and L. monocytogenes strains. In contrast, none of the compounds had antifungal activity. Molecular electrostatic potential (MEP) map analyses and inductive and mesomeric effect studies were performed to study the relationship between the chemical structure of our compounds and the biological activity. In addition, molecular docking and virtual screening studies were carried out to rationalize the antibacterial findings to characterize the modes of binding of the most active compounds to the active pockets of NDM1 proteins.

Integrated computer-aided drug design and biophysical simulation approaches to determine natural anti-bacterial compounds for Acinetobacter baumannii

Acinetobacter baumannii is a nosocomial bacterial pathogen and is responsible for a wide range of diseases including pneumonia, necrotizing fasciitis, meningitis, and sepsis. The enzyme 5-enolpyruvylshikimate-3-phosphate (EPSP) synthase (encoded by aroA gene) in ESKAPE pathogens catalyzes the sixth step of shikimate pathway. The shikimate pathway is an attractive drug targets pathway as it is present in bacteria but absent in humans. As EPSP is essential for the A. baumannii growth and needed during the infection process, therefore it was used as a drug target herein for high-throughput screening of a comprehensive marine natural products database (CMNPD). The objective was to identify natural molecules that fit best at the substrate binding pocket of the enzyme and interact with functionally critical residues. Comparative assessment of the docking scores allowed selection of three compounds namely CMNPD31561, CMNPD28986, and CMNPD28985 as best binding molecules. The molecules established a balanced network of hydrophobic and hydrophilic interactions, and the binding pose remained in equilibrium throughout the length of molecular simulation time. Radial distribution function (RDF) analysis projected key residues from enzyme active pocket which actively engaged the inhibitors. Further validation is performed through binding free energies estimation that affirms very low delta energy of <-22 kcal/mol in MM-GBSA method and <-12 kcal/mol in MM-PBSA method. Lastly, the most important active site residues were mutated and their ligand binding potential was re-investigated. The molecules also possess good druglike properties and better pharmacokinetics. Together, these findings suggest the potential biological potency of the leads and thus can be used by experimentalists in vivo and in vitro studies.

In Silico Drug Discovery Strategies Identified ADMET Properties of Decoquinate RMB041 and Its Potential Drug Targets against Mycobacterium tuberculosis

The highly adaptive cellular response of Mycobacterium tuberculosis to various antibiotics and the high costs for clinical trials, hampers the development of novel antimicrobial agents with improved efficacy and safety. Subsequently, in silico drug screening methods are more commonly being used for the discovery and development of drugs, and have been proven useful for predicting the pharmacokinetics, toxicities, and targets, of prospective new antimicrobial agents. In this investigation we used a reversed target fishing approach to determine potential hit targets and their possible interactions between M. tuberculosis and decoquinate RMB041, a propitious new antituberculosis compound. Two of the 13 identified targets, Cyp130 and BlaI, were strongly proposed as optimal drug-targets for dormant M. tuberculosis, of which the first showed the highest comparative binding affinity to decoquinate RMB041. The metabolic pathways associated with the selected target proteins were compared to previously published molecular mechanisms of decoquinate RMB041 against M. tuberculosis, whereby we confirmed disrupted metabolism of proteins, cell wall components, and DNA. We also described the steps within these pathways that are inhibited and elaborated on decoquinate RMB041’s activity against dormant M. tuberculosis. This compound has previously showed promising in vitro safety and good oral bioavailability, which were both supported by this in silico study. The pharmacokinetic properties and toxicity of this compound were predicted and investigated using the online tools pkCSM and SwissADME, and Discovery Studio software, which furthermore supports previous safety and bioavailability characteristics of decoquinate RMB041 for use as an antimycobacterial medication.

IMPORTANCE This article elaborates on the mechanism of action of a novel antibiotic compound against both, active and dormant Mycobacterium tuberculosis and describes its pharmacokinetics (including oral bioavailability and toxicity). Information provided in this article serves useful during the search for drugs that shorten the treatment regimen for Tuberculosis and cause minimal adverse effects.

Drug Repurposing Targeting Pseudomonas aeruginosa MvfR Using Docking, Virtual Screening, Molecular Dynamics, and Free-Energy Calculations

Pseudomonas aeruginosa is an opportunistic Gram-negative bacterium responsible for acute and chronic infections in planktonic state or in biofilms. The sessile structures are known to confer physical stability, increase virulence, and work as a protective armor against antimicrobial compounds. P. aeruginosa can control the expression of genes, population density, and biofilm formation through a process called quorum sensing (QS), a rather complex and hierarchical system of communication. A recent strategy to try and overcome bacterial resistance is to target QS proteins. In this study, a combined multi-level computational approach was applied to find possible inhibitors against P. aeruginosa QS regulator protein MvfR, also known as PqsR, using a database of approved FDA drugs, as a repurposing strategy. Fifteen compounds were identified as highly promising putative MvfR inhibitors. On those 15 MvfR ligand complexes, molecular dynamic simulations and MM/GBSA free-energy calculations were performed to confirm the docking predictions and elucidate on the mode of interaction. Ultimately, the five compounds that presented better binding free energies of association than the reference molecules (a known antagonist, M64 and a natural inducer, 2-nonyl-4-hydroxyquinoline) were highlighted as very promising MvfR inhibitors.

A Review on Parallel Virtual Screening Softwares for High-Performance Computers

Drug discovery is the most expensive, time-demanding, and challenging project in biopharmaceutical companies which aims at the identification and optimization of lead compounds from large-sized chemical libraries. The lead compounds should have high-affinity binding and specificity for a target associated with a disease, and, in addition, they should have favorable pharmacodynamic and pharmacokinetic properties (grouped as ADMET properties). Overall, drug discovery is a multivariable optimization and can be carried out in supercomputers using a reliable scoring function which is a measure of binding affinity or inhibition potential of the drug-like compound. The major problem is that the number of compounds in the chemical spaces is huge, making the computational drug discovery very demanding. However, it is cheaper and less time-consuming when compared to experimental high-throughput screening. As the problem is to find the most stable (global) minima for numerous protein-ligand complexes (on the order of 106 to 1012), the parallel implementation of in silico virtual screening can be exploited to ensure drug discovery in affordable time. In this review, we discuss such implementations of parallelization algorithms in virtual screening programs. The nature of different scoring functions and search algorithms are discussed, together with a performance analysis of several docking softwares ported on high-performance computing architectures.

New carvacrol and thymol derivatives as potential insecticides: synthesis, biological activity, computational studies and nanoencapsulation

New compounds with potential insecticide activity were synthesized by structural modifications performed in the monoterpenoid phenolic moieties of carvacrol and thymol, resulting in a set of derivatives with the ether function containing the propyl, chloropropyl or hydroxypropyl chains, as well as a bicyclic ether with an unsaturated chain containing a carboxylic acid terminal. In addition, an analogue of carvacrol and thymol isomers bearing methoxyl, 1-hydroxyethyl and (3-chlorobenzoyl)oxy, instead of the three original methyl groups, was also synthesized. Several structural changes that resulted in diminished insecticide activity have been identified, but two significantly active molecules have been synthesized, one of them being less toxic to human cells than the naturally-derived starting materials. Structure-based inverted virtual screening and molecular dynamics simulations demonstrate that these active molecules likely target the insect odorant binding proteins and/or acetylcholinesterase and are able to form stable complexes. For the most promising compounds, nanoencapsulation assays were carried out in liposomes of egg phosphatidylcholine/cholesterol (7 : 3) prepared by both thin film hydration and ethanolic injection methods. The compound-loaded liposomes were generally monodisperse and with sizes smaller than or around 200 nm. The thin film hydration method allowed high encapsulation efficiencies (above 85%) for both compounds and a delayed release, while for the systems prepared by ethanolic injection the encapsulation efficiency is lower than 50%, but the release is almost complete in two days.

In Silico Identification of Possible Inhibitors for Protein Kinase B (PknB) of Mycobacterium tuberculosis

With tuberculosis still being one of leading causes of death in the world and the emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb), researchers have been seeking to find further therapeutic strategies or more specific molecular targets. PknB is one of the 11 Ser/Thr protein kinases of Mtb and is responsible for phosphorylation-mediated signaling, mainly involved in cell wall synthesis, cell division and metabolism. With the amount of structural information available and the great interest in protein kinases, PknB has become an attractive target for drug development. This work describes the optimization and application of an in silico computational protocol to find new PknB inhibitors. This multi-level computational approach combines protein-ligand docking, structure-based virtual screening, molecular dynamics simulations and free energy calculations. The optimized protocol was applied to screen a large dataset containing 129,650 molecules, obtained from the ZINC/FDA-Approved database, Mu.Ta.Lig Virtual Chemotheca and Chimiothèque Nationale. It was observed that the most promising compounds selected occupy the adenine-binding pocket in PknB, and the main interacting residues are Leu17, Val26, Tyr94 and Met155. Only one of the compounds was able to move the active site residues into an open conformation. It was also observed that the P-loop and magnesium position loops change according to the characteristics of the ligand. This protocol led to the identification of six compounds for further experimental testing while also providing additional structural information for the design of more specific and more effective derivatives.

GC-MS- and NMR-Based Metabolomics and Molecular Docking Reveal the Potential Alpha-Glucosidase Inhibitors from Psychotria malayana Jack Leaves

Psychotria malayana Jack leaf, known in Indonesia as "daun salung", is traditionally used for the treatment of diabetes and other diseases. Despite its potential, the phytochemical study related to its anti-diabetic activity is still lacking. Thus, this study aimed to identify putative inhibitors of α-glucosidase, a prominent enzyme contributing to diabetes type 2 in P. malayana leaf extract using gas chromatography-mass spectrometry (GC-MS)- and nuclear magnetic resonance (NMR)-based metabolomics, and to investigate the molecular interaction between those inhibitors and the enzyme through in silico approach. Twenty samples were extracted with different solvent ratios of methanol-water (0, 25, 50, 75, and 100% v/v). All extracts were tested on the alpha-glucosidase inhibition (AGI) assay and analyzed using GC-MS and NMR. Multivariate data analysis through a partial least square (PLS) and orthogonal partial square (OPLS) models were developed in order to correlate the metabolite profile and the bioactivity leading to the annotation of the putative bioactive compounds in the plant extracts. A total of ten putative bioactive compounds were identified and some of them reported in this plant for the first time, namely 1,3,5-benzenetriol (1); palmitic acid (2); cholesta-7,9(11)-diene-3-ol (3); 1-monopalmitin (4); β-tocopherol (5); α-tocopherol (6); 24-epicampesterol (7); stigmast-5-ene (8); 4-hydroxyphenylpyruvic acid (10); and glutamine (11). For the evaluation of the potential binding modes between the inhibitors and protein, the in silico study via molecular docking was performed where the crystal structure of Saccharomyces cerevisiae isomaltase (PDB code: 3A4A) was used. Ten amino acid residues, namely ASP352, HIE351, GLN182, ARG442, ASH215, SER311, ARG213, GLH277, GLN279, and PRO312 established hydrogen bond in the docked complex, as well as hydrophobic interaction of other amino acid residues with the putative compounds. The α-glucosidase inhibitors showed moderate to high binding affinities (-5.5 to -9.4 kcal/mol) towards the active site of the enzymatic protein, where compounds 3, 5, and 8 showed higher binding affinity compared to both quercetin and control ligand.