1001 Ways to run AutoDock Vina for virtual screening

Ameliorative effects of Asiasarum root and rhizome extract on high fat diet‑induced obesity in mice through regulation of the SIRT1/PGC1α/AMPK pathways in muscle and liver tissues

Asiasarum root and rhizome (Asarum) is commonly used as a diaphoretic. Due to its warm and pungent characteristics in traditional Chinese and Korean medicine, it is considered as having the potential to prevent disease. The present study investigated the effects of Asarum extract on the symptoms of obesity in mice, and the regulation of energy metabolism in the liver and skeletal muscle tissues. In addition, to identify the potential molecular targets and signaling pathways involved in the mechanism of action of Asarum extract in obesity, network pharmacological and molecular docking analysis was performed. In vitro studies demonstrated that Asarum extract significantly increased the expression of regulators of energy metabolism [sirtuin 1 (SIRT1), peroxisome proliferator‑activated receptor γ coactivator 1‑α (PGC1α), nuclear respiratory factor 1, AMP‑activated protein kinase (AMPK) and glucose transporter type 4 (GLUT4)] and myogenic regulatory factors (MyoD, myogenin and myosin heavy chain) in C2C12 myotubes. Furthermore, the in vivo studies demonstrated that Asarum extract could reduce increases in body weight, and the levels of blood glucose, insulin, total cholesterol, triglycerides and low‑density lipoprotein cholesterol in the sera of obese mice. Asarum extract also improved pathological changes in the liver and pancreatic tissues of obese mice, and significantly increased the ratio of brown fat mass to body weight. In addition, Asarum extract reversed the expression of energy metabolism regulators and myogenic regulatory factors in the gastrocnemius tissues of obese mice. Asarum extract also activated the expression of SIRT1, PGC1α and AMPK in the liver tissues of obese mice. These findings indicated that Asarum extract may exert anti‑obesity effects, such as body weight loss, decreases in lipid metabolite levels, and inhibition of pancreatic and liver damage. Using network pharmacological analysis, the mechanisms underlying the effects of Asarum extract on the regulation of energy metabolism were explored, particularly in skeletal muscle and liver tissues.

Integrating machine learning and high throughput screening for the discovery of allosteric AKT1 inhibitors

Evidence from clinical and experimental investigations reveals the role of AKT in oral cancer, which has led to the development of therapeutic and pharmacological medications for inhibiting AKT protein. Despite prodigious effort, researchers are searching for new allosteric inhibitors as orthosteric inhibitors are non-selective and exert off-target effects. In the current study, we proposed an integrated computational workflow for identifying allosteric AKT1 inhibitors as this isoform is highly correlated with poor prognosis and survival. To achieve this objective, 84 classification QSAR models with six different machine learning algorithms were developed. The models created with RDKit_RF and RDKit_kstar outperformed internal and test set validation with an ROC of 0.98. The outperformed models were then used to screen Chembl, which contains over a million compounds, for AKT1 inhibitors. The Tanimoto similarity search approach identified the compounds structurally resembling AKT allosteric inhibitors. The filtered compounds were further subjected to docking phases, molecular dynamic simulation and mmpbsa to verify the binding mode of selected ones. All these analyses suggested hit 5 (CHEMBL3948083) as the potential allosteric inhibitor of AKT1 as the stability parameters, favourable binding affinity (-107.78 ± 11.56 KJ/mol) and ligand interaction were better in comparison to other compounds and reference compound. The residual analysis demonstrated that allosteric and isoform-specific residues such as Trp80 and Val270 contributed the larger energy for ligand binding. The proposed integrated approach in this study might achieve a futuristic outcome when employed in a pharmaceutical scheme different from the conventional method.Communicated by Ramaswamy H. Sarma.

Computational exploration in search for novel natural product-derived EZH2 inhibitors for advancing anti-cancer therapy

Epigenetic regulation intricately governs cellular mechanisms, including proliferation, death, differentiation, and cell cycle orchestration. One such target, Enhancer of zeste homolog 2 (EZH2), is essential for epigenetic regulation. EZH2 trimethylates histone H3 lys27 (H3K27me3), inhibiting target gene transcription and promoting chromatin condensation, thereby initiating tumorigenesis, thus a potentially plausible target to disrupt cancer progression. In this virtual screening study, we utilized two large, open-source natural product libraries, NPASS and LOTUS, to search for potential natural product scaffolds capable of EZH2 inhibition. The merged library was filtered through increasingly rigorous criteria at each stage, including Medchem-based rule filters, 2D Tanimoto similarity, sequential rounds of docking, rescoring via ML-based functions, and binding pose visualization, funneling down to the most promising candidates for further pharmacokinetics and toxicological profiles. The best hits were analyzed for their binding stability through molecular dynamics simulation and their binding free energy estimations. Exploratory chemical analysis was conducted to understand the similarity of hits with known EZH2 chemical space. This comprehensive workflow identified one potential inhibitor, LTS0131784, which exhibited favorable pharmacokinetic toxicity profiling with binding stability and free energy better than the FDA-approved EZH2 inhibitor, Tazemetostat. Furthermore, the plausible binding mechanism was also elucidated by analyzing the per residue-free decomposition of the simulated trajectories, which indicated the involvement of the LTS0131784 with the key residues TYR:111, TRP:521, CYS:560, ASN:585, and SER:561.

Multifaceted Analysis of Lactobacillus plantarum DMR14 Reveals Promising Antidiabetic Properties Through In Vivo Assays and Molecular Simulations

Due to the growing concern about diabetes worldwide, we investigated the antidiabetic potential of Lactobacillus plantarum DMR14, assessing its effects on the diabetic mice and identifying safe, bioactive compounds targeting DPP4 protein for drug development through various methods, including in vivo assays, GC-MS analysis and molecular docking simulations. The animal experiments showed that after 3 weeks of treatment, the blood sugar levels of mice given the bacteria were reduced by 35.03% compared to baseline. The treatment also significantly lowered blood lipids such as triglycerides, total cholesterol and LDL cholesterol, but did not affect HDL cholesterol levels. Additionally, we identified three compounds that effectively targeted a protein (DPP4) involved in diabetes (PDB ID: 4A5S). These compounds were predicted to be safe for absorption, processing and elimination by the body, and showed no signs of inducing cancer in computer simulations. Further simulations indicated that these compounds bind stably to the protein over time. Diabetic mice treated with Lactobacillus plantarum DMR14 exhibited improved organ health, reduced glucose levels and better metabolic markers. Computer analysis suggested compounds that could enhance enzyme inhibition, indicating potential antidiabetic properties in this strain. These suggested compounds could be considered potential candidates for developing antidiabetic drugs.

Vina-FPGA-Cluster: Multi-FPGA Based Molecular Docking Tool With High-Accuracy and Multi-Level Parallelism

AutoDock Vina (Vina) stands out among numerous molecular docking tools due to its precision and comparatively high speed, playing a key role in the drug discovery process. Hardware acceleration of Vina on FPGA platforms offers a high energy-efficiency approach to speed up the docking process. However, previous FPGA-based Vina accelerators exhibit several shortcomings: 1) Simple uniform quantization results in inevitable accuracy drop; 2) Due to Vina's complex computing process, the evaluation and optimization phase for hardware design becomes extended; 3) The iterative computations in Vina constrain the potential for further parallelization. 4) The system's scalability is limited by its unwieldy architecture. To address the above challenges, we propose Vina-FPGA-cluster, a multi-FPGA-based molecular docking tool enabling high-accuracy and multi-level parallel Vina acceleration. Standing upon the shoulders of Vina-FPGA, we first adapt hybrid fixed-point quantization to minimize accuracy loss. We then propose a SystemC-based model, accelerating the hardware accelerator architecture design evaluation. Next, we propose a novel bidirectional AG module for data-level parallelism. Finally, we optimize the system architecture for scalable deployment on multiple Xilinx ZCU104 boards, achieving task-level parallelism. Vina-FPGA-cluster is tested on three representative molecular docking datasets. The experiment results indicate that in the context of RMSD (for successful docking outcomes with metrics below 2Å), Vina-FPGA-cluster shows a mere 0.2% lose. Relative to CPU and Vina-FPGA, Vina-FPGA-cluster achieves 27.33 and 7.26 speedup, respectively. Notably, Vina-FPGA-cluster is able to deliver the 1.38 speedup as GPU implementation (Vina-GPU), with just the 28.99% power consumption.

Pharmacophore-guided in-silico discovery of SIRT1 inhibitors for targeted cancer therapy

Epigenetic modifier, Sirtuin (SIRTs) is a family of seven isoforms (SIRT1-7) and nicotinamide adenine dinucleotide (NAD+) dependent class III histone deacetylase (HDACs) protein. SIRT1 in association with the p53 protein can regulate crucial cell processes such as glucose metabolism, lipid metabolism, mitochondrial biogenesis, DNA repair, oxidative stress, apoptosis, and inflammation through the process of deacetylation. When SIRT1 deacetylates p53, it loses its tumor suppression property. To promote apoptosis and decrease cell proliferation by inhibiting SIRT1 protein and ultimately raising the acetylation of p53 to regain its tumor suppressor function. Though we have many SIRT1 protein inhibitors, they exhibited off-target effects and inefficiency at the clinical trial stage. This study has been executed to identify more potentially effective and reliable SIRT1 inhibitors that can perform better than the existing options. To do so, pharmacophore-based screening of compound libraries followed by virtual screening, pharmacokinetic, drug-likeness, and toxicity studies were conducted which gave 42 compounds to evaluate further. Subsequently, exhaustive molecular docking and molecular dynamics simulation predicted four potential hits to inhibit the SIRT1 protein better than the reference compound. Further studies such as principal components analysis, free energy landscape, and estimation of binding free energy were done which concluded Hit4 (PubChem ID: 55753455) to be a novel and potent SIRT1 small molecule inhibitor among the others. The total binding free energy for Hit4 was found to be -44.68 kcal/mol much better than the reference complex i.e., -29.38 kcal/mol.

Unlocking the potential of edible Ulva sp. seaweeds: Metabolomic profiling, neuroprotective mechanisms, and implications for Parkinson's disease management

Green seaweed (Ulva sp.) is frequently used as a food component and nutraceutical agent because of its high polysaccharide and natural fiber content in Asian countries. This study investigates both metabolomic profiling of Ulva sp. and the neuroprotective efficacy of its ethanol extract and its underlying mechanisms in a rotenone-induced rat model of neurodegeneration, mimicking Parkinson's disease (PD) in humans. Metabolomic profiling of Ulva sp. extract was done using liquid chromatography high resolution electrospray ionization mass spectrometry and led to the identification of 22 compounds belonging to different chemical classes.Catenin Beta Additionally, this study demonstrated the neuroprotective properties against rotenone-induced PD, which was achieved through the suppression of elevated levels of tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and IL-6 together with the inhibition of reactive oxygen species (ROS) generation, apoptosis, inflammatory mediators, and the phosphoinositide 3-kinases/serine/threonine protein kinase (PI3K/AKT) pathway. Using a protein-protein interaction network, AKT1, GAPDH, TNF-α, IL-6, caspase 3, signal transducer and activator of transcription 3, Catenin Beta 1, epidermal growth factor receptor, B-cell lymphoma -2, and HSP90AA1 were identified as the top 10 most significant genes. Finally, molecular docking results showed that compounds 1, 3, and 7 might possess a promising anti-parkinsonism effect by binding to active sites of selected hub genes. Therefore, it is hypothesized that the Ulva sp. extract has the potential to be further developed as a potential therapeutic agent for the treatment of PD.

Hyrtios sp.-associated Cladosporium sp. UR3 as a potential source of antiproliferative metabolites

BACKGROUND

Sponge-associated microorganisms are promising resources for the production of bioactive compounds with cytotoxic potential. The main goal of our study is to isolate the fungal endophytes from the Red Sea sponge Hyrtios sp. followed by investigating their cytotoxicity against number of cell lines.

RESULTS

The fungal strain UR3 was isolated from the Red Sea sponge using Sabouraud dextrose agar media. It was identified based on partial 18 S rRNA gene and ITS sequence analyses as Cladosporium sp. UR3. The in vitro cytotoxic potential of the ethyl acetate extract of the fungal isolate was evaluated using MTT assay against three cancer cell lines: CACO2, MCF7, and HEPG2. Metabolomics profiling of the obtained ethyl acetate extract using LC-HR-ESI-MS, along with molecular docking and pharmacological network studies for the dereplicated compounds were performed to explore its chemical profile and the possible cytotoxic mechanism of the sponge-associated fungi.

CONCLUSION

These results highlighted the role of sponge-associated fungi as a fruitful resource for the discovery of cytotoxic metabolites.

Extension of impurity profiling on eltrombopag olamine to in-silico predictions: An effort to exploit correlated forced degradation products and known drug-related substances in drug discovery

The recent pandemic has highlighted the impact of diseases on global health and the economy. The rapid discovery of new hit molecules remains a tough challenge. Pharmaceutical impurity profiling can be linked to drug discovery through the identification of new hits from compounds identified during the analytical profiling. The present study demonstrates this linkage through the extension of the impurity (forced degradation) profiling of eltrombopag (ELT) olamine, a thrombopoietin (TPO) receptor agonist. The drug was exposed to standard degradation and the degradation products were primarily resolved and identified by UPLC-ESI-MS. This led to the identification of five forced degradation products (FDP). Thirty-three other known related substances (RS) of ELT, identified in the literature, were also considered. Molecular similarity checks were performed using Tanimoto/Jaccard's similarity searches. A set of structurally and topologically similar molecules, including ELT and 15 RS, was established and subjected to in-silico toxicity-, absorption-, distribution-, metabolism-, and elimination (ADME) predictions. The RS, predicted with similar or lower toxicity than ELT and a comparable ADME profile, were subjected to molecular docking to trace changes in TPO receptor affinity. The results indicated that five RS had a high Jaccard's similarity with ELT and higher or comparable docking scores. These compounds, along with few other impurities were predicted to have lower toxicity, better or comparable absorption, distribution, metabolism, and also a better excretion profile than ELT. This justifies their entry as potential novel TPO receptor agonists in drug discovery.

Targeting STAT3, FOXO3a, and Pim-1 kinase by FDA-approved tyrosine kinase inhibitor-Radotinib: An in silico and in vitro approach

Cancer, a multifactorial pathological condition, is primarily caused due to mutations in multiple genes. Hepatocellular carcinoma (HCC) is a form of primary liver cancer that is often diagnosed at the advanced stage. Current treatment strategies for advanced HCC involve systemic therapies which are often hindered due to the emergence of resistance and toxicity. Therefore, a multitarget approach might prove more effective in HCC treatment. The present study focuses on targeting signal transducer and activator of transcription 3 (STAT3), forkhead box class O3a (FOXO3a), and proviral integration site for Moloney murine leukemia virus-1 (Pim-1) kinase, using a Food and Drug Administration (FDA)-approved anticancer drug library. Two compounds, namely, radotinib and capmatinib, were identified as top compounds using molecular docking. Among the two compounds, radotinib exhibited significant binding values towards the targeted proteins and their heterodimers. Furthermore, in vitro experiments involving 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT), live/dead, 4',6-diamidino-2-phenylindole, and clonogenic assays were performed to evaluate the effect of radotinib in human hepatoblastoma cell line/hepatocellular carcinoma cells. The gene expression data indicated reduced expression of FOXO3a and Pim-1, but no basal-level alteration of STAT3. The Western blot analysis assay showed that the phosphorylation level of STAT3 was significantly decreased upon radotinib treatment. Taken together, our findings suggest that radotinib, which is currently used in the treatment of chronic myeloid leukemia (CML), could be considered as a potential candidate for repurposing in the treatment of HCC.

Regioselective photocyclodimerization of 2-anthracenecarboxylic acid through ATP hydrolysis-driven conformational change using simulation prediction-designed GroEL mutant

GroEL, a chaperone protein responsible for peptide and denatured protein folding, undergoes substantial conformational changes driven by ATP binding and hydrolysis during folding. Utilizing these conformational changes, we demonstrated the GroEL-mediated regioselective photocyclodimerization of 2-anthracenecarboxylic acid (AC) using ATP hydrolysis as an external stimulus. We designed and prepared an optimal GroEL mutant to employ in a docking simulation that has been actively used in recent years. Based on the large difference in the motif of hydrogen bonds between AC and GroEL mutant compared with the wild-type, we predicted that GroELMEL, in which the 307‒309th amino acid residues were mutated to Ala, could alter the orientation of bound AC in GroEL. The GroELMEL-mediated photocyclodimerization of AC can be used for regioselective inversion upon ATP addition to a moderate extent.

Molecular docking and molecular dynamic simulation studies to identify potential terpenes against Internalin A protein of Listeria monocytogenes

Introduction

Ever since the outbreak of listeriosis and other related illnesses caused by the dreadful pathogen Listeria monocytogenes, the lives of immunocompromised individuals have been at risk.

Objectives and Methods

The main goal of this study is to comprehend the potential of terpenes, a major class of secondary metabolites in inhibiting one of the disease-causing protein Internalin A (InlA) of the pathogen via in silico approaches.

Results

The best binding affinity value of -9.5 kcal/mol was observed for Bipinnatin and Epispongiadiol according to the molecular docking studies. The compounds were further subjected to ADMET and biological activity estimation which confirmed their good pharmacokinetic properties and antibacterial activity.

Discussion

Molecular dynamic simulation for a timescale of 100 ns finally revealed Epispongiadiol to be a promising drug-like compound that could possibly pave the way to the treatment of this disease.

In silico identification of putative druggable pockets in PRL3, a significant oncology target

Protein tyrosine phosphatases (PTP) have emerged as targets in diseases characterized by aberrant phosphorylations such as cancers. The activity of the phosphatase of regenerating liver 3, PRL3, has been linked to several oncogenic and metastatic pathways, particularly in breast, ovarian, colorectal, and blood cancers. Development of small molecules that directly target PRL3, however, has been challenging. This is partly due to the lack of structural information on how PRL3 interacts with its inhibitors. Here, computational methods are used to bridge this gap by evaluating the druggability of PRL3. In particular, web-based pocket prediction tools, DoGSite3 and FTMap, were used to identify binding pockets using structures of PRL3 currently available in the Protein Data Bank. Druggability assessment by molecular dynamics simulations with probes was also performed to validate these results and to predict the strength of binding in the identified pockets. While several druggable pockets were identified, those in the closed conformation show more promise given their volume and depth. These two pockets flank the active site loops and roughly correspond to pockets predicted by molecular docking in previous papers. Notably, druggability simulations predict the possibility of low nanomolar affinity inhibitors in these sites implying the potential to identify highly potent small molecule inhibitors for PRL3. Putative pockets identified here can be leveraged for high-throughput virtual screening to further accelerate the drug discovery against PRL3 and development of PRL3-directed therapeutics.

Structure based virtual screening and discovery of novel inhibitors against FabD protein of Mycobacterium tuberculosis

The highly flexible nature of Mycobacterium tuberculosis (Mtb) can be owed to its tough cell wall and multiple gene interaction system which makes it resistant to frontline TB drugs. Mycolic acids are the key components of the unique cell wall that protects the organism from external threats. Proteins of the fatty acid synthesis pathway are evolutionarily conserved that enables cellular survival in harsh conditions and hence have become attractive targets. Malonyl Co-A Acyl carrier protein transacylase (FabD; MCAT, EC2.3.1.39) is an enzyme in the branching point of the unique and vast fatty acid synthase (FAS-I and FAS-II) systems of Mtb. In the present investigation, in-silico structure based drug discovery with the compounds from an open source library (NPASS) is used for target fishing and employed to understand the interaction with the target protein FabD. The potential hit compounds were filtered using exhaustive docking, considering the binding energy, key residue interaction and drug likeness property. Three compounds from the library namely NPC475074 (Hit 1), NPC260631 (Hit 2) and NPC313985 (Hit 3) with binding energies -14.45, -13.29 and -12.37 respectively were taken for molecular dynamic simulation. The results suggested that Hit 3 (NPC313985) has stable interaction with FabD protein. This article further elaborates the interaction of the identified novel compounds Hit 1 and Hit 3 along with the other known compound (Hit 2) against Mtb FabD protein. The hit compounds identified from this study could be further evaluated against mutated FabD protein and considered for in-vitro evaluation.Communicated by Ramaswamy H. Sarma.

Artificial intelligence assisted identification of potential tau aggregation inhibitors: ligand- and structure-based virtual screening, in silico ADME, and molecular dynamics study

Alzheimer's disease (AD) is a severe, growing, multifactorial disorder affecting millions of people worldwide characterized by cognitive decline and neurodegeneration. The accumulation of tau protein into paired helical filaments is one of the major pathological hallmarks of AD and has gained the interest of researchers as a potential drug target to treat AD. Lately, Artificial Intelligence (AI) has revolutionized the drug discovery process by speeding it up and reducing the overall cost. As a part of our continuous effort to identify potential tau aggregation inhibitors, and leveraging the power of AI, in this study, we used a fully automated AI-assisted ligand-based virtual screening tool, PyRMD to screen a library of 12 million compounds from the ZINC database to identify potential tau aggregation inhibitors. The preliminary hits from virtual screening were filtered for similar compounds and pan-assay interference compounds (the compounds containing reactive functional groups which can interfere with the assays) using RDKit. Further, the selected compounds were prioritized based on their molecular docking score with the binding pocket of tau where the binding pockets were identified using replica exchange molecular dynamics simulation. Thirty-three compounds showing good docking scores for all the tau clusters were selected and were further subjected to in silico pharmacokinetic prediction. Finally, top 10 compounds were selected for molecular dynamics simulation and MMPBSA binding free energy calculations resulting in the identification of UNK_175, UNK_1027, UNK_1172, UNK_1173, UNK_1237, UNK_1518, and UNK_2181 as potential tau aggregation inhibitors.

Computational identification of potential tau tubulin kinase 1 (TTBK1) inhibitors: a structural analog approach

Abnormal deposition or aggregation of protein alpha-synuclein and tau in the brain leads to neurodegenerative disorders. Excessive hyperphosphorylation of tau protein and aggregations destroys the microtubule structure resulting in neurofibrillary tangles in neurons and affecting cytoskeleton structure, mitochondrial axonal transport, and loss of synapses in neuronal cells. Tau tubulin kinase 1 (TTBK1), a specific neuronal kinase is a potential therapeutic target for neurodegenerative disorders as it is involved in hyperphosphorylation and aggregation of tau protein. TTBK inhibitors are now the subject of intense study, but limited numbers are found. Hence, this study involves structure-based virtual screening of TTBK1 inhibitor analogs to obtain efficient compounds targeting the TTBK1 using docking, molecular dynamics simulation and protein-ligand interaction profile. The initial analogs set containing 3884 compounds was subjected to Lipinski rule and the non-violated compounds were selected. Docking analysis was done on 2772 compounds through Autodock vina and Autodock 4.2. Data Warrior and SwissADME was utilized to filter the toxic compounds. The stability and protein-ligand interaction of the docked complex was analyzed through Gromacs and VMD. Molecular simulation results such as RMSD, Rg, and hydrogen bond interaction along with pharmacokinetic properties showed CID70794974 as the potential hit targeting TTBKl prompting the need for further experimental investigation to evaluate their potential therapeutic efficacy in Alzheimer's disease.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00242-z.

Exploring Cannabinoids as Potential Inhibitors of SARS-CoV-2 Papain-like Protease: Insights from Computational Analysis and Molecular Dynamics Simulations

The emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has triggered a global COVID-19 pandemic, challenging healthcare systems worldwide. Effective therapeutic strategies against this novel coronavirus remain limited, underscoring the urgent need for innovative approaches. The present research investigates the potential of cannabis compounds as therapeutic agents against SARS-CoV-2 through their interaction with the virus's papain-like protease (PLpro) protein, a crucial element in viral replication and immune evasion. Computational methods, including molecular docking and molecular dynamics (MD) simulations, were employed to screen cannabis compounds against PLpro and analyze their binding mechanisms and interaction patterns. The results showed cannabinoids with binding affinities ranging from -6.1 kcal/mol to -4.6 kcal/mol, forming interactions with PLpro. Notably, Cannabigerolic and Cannabidiolic acids exhibited strong binding contacts with critical residues in PLpro's active region, indicating their potential as viral replication inhibitors. MD simulations revealed the dynamic behavior of cannabinoid-PLpro complexes, highlighting stable binding conformations and conformational changes over time. These findings shed light on the mechanisms underlying cannabis interaction with SARS-CoV-2 PLpro, aiding in the rational design of antiviral therapies. Future research will focus on experimental validation, optimizing binding affinity and selectivity, and preclinical assessments to develop effective treatments against COVID-19.

Natural Products Inhibition of Cytochrome P450 2B6 Activity and Methadone Metabolism

Methadone is cleared predominately by hepatic cytochrome P450 (CYP) 2B6-catalyzed metabolism to inactive metabolites. CYP2B6 also catalyzes the metabolism of several other drugs. Methadone and CYP2B6 are susceptible to pharmacokinetic drug-drug interactions. Use of natural products such as herbals and other botanicals is substantial and growing, and concomitant use of prescription medicines and non-prescription herbals is common and may result in interactions, often precipitated by CYP inhibition. Little is known about herbal product effects on CYP2B6 activity, and CYP2B6-catalyzed methadone metabolism. We screened a family of natural product compounds used in traditional medicines, herbal teas, and synthetic analogs of compounds found in plants, including kavalactones, flavokavains, chalcones and gambogic acid, for inhibition of expressed CYP2B6 activity and specifically inhibition of CYP2B6-mediated methadone metabolism. An initial screen evaluated inhibition of CYP2B6-catalyzed 7-ethoxy-4-(trifluoromethyl) coumarin O-deethylation. Hits were further evaluated for inhibition of racemic methadone metabolism, including mechanism of inhibition and kinetic constants. In order of decreasing potency, the most effective inhibitors of methadone metabolism were dihydromethysticin (competitive, K i 0.074 µM), gambogic acid (noncompetitive, K i 6 µM), and 2,2'-dihydroxychalcone (noncompetitive, K i 16 µM). Molecular modeling of CYP2B6-methadone and inhibitor binding showed substrate and inhibitor binding position and orientation and their interactions with CYP2B6 residues. These results show that CYP2B6 and CYP2B6-catalyzed methadone metabolism are inhibited by certain natural products, at concentrations which may be clinically relevant. SIGNIFICANCE STATEMENT: This investigation identified several natural product constituents which inhibit in vitro human recombinant CYP2B6 and CYP2B6-catalyzed N-demethylation of the opioid methadone. The most potent inhibitors (K i) were dihydromethysticin (0.074 µM), gambogic acid (6 µM) and 2,2'-dihydroxychalcone (16 µM). Molecular modeling of ligand interactions with CYP2B6 found that dihydromethysticin and 2,2'-dihydroxychalcone bound at the active site, while gambogic acid interacted with an allosteric site on the CYP2B6 surface. Natural product constituents may inhibit CYP2B6 and methadone metabolism at clinically relevant concentrations.

Novel glycosyl prodrug of RXP03 as MMP-11 prodrug: design, synthesis and virtual screening

Like most phosphinic acids, the potent and selective RXP03 inhibitor of different MMPs exhibited moderate absorption and low bioavailability, which impaired its use. In an unprecedented attempt, we present an interesting synthetic approach to a new class of phosphinate prodrug, glycosyl ester of RXP03, to provide a potentially improved blood-brain barrier (BBB) behavior compared to the former lead compound RXP03. To validate this speculation, a predictive study for permeability enhancer of glycosyl ester of RXP03 showed encouraging insights to improve drug delivery across biological barriers.

Decoding the mechanism of Eleutheroside E in treating osteoporosis via network pharmacological analysis and molecular docking of osteoclast-related genes and gut microbiota

Objective

Eleutheroside E (EE) is an anti-inflammatory natural compound derived from the edible medicinal herb Acanthopanax senticosus. This study aims to investigate the underlying mechanism of the anti-osteoporosis action of EE through network pharmacology, molecular docking and gut microbiota.

Materials and methods

Network pharmacology was used to explore the potential core targets and main pathways mediated by EE in osteoporosis (OP) treatment. Molecular docking was exploited to investigate the interactions between the active anti-OP compounds in EE and the potential downstream targets. Following the multi-approach bioinformatics analysis, ovariectomy (OVX) model was also established to investigate the in vivo anti-OP effects of EE.

Results

The top 10 core targets in PPI network were TP53, AKT1, JUN, CTNNB1, STAT3, HIF1A, EP300, CREB1, IL1B and ESR1. Molecular docking results that the binding energy of target proteins and the active compounds was approximately between -5.0 and -7.0 kcal/mol, which EE has the lowest docking binding energy with HIF1A. Enrichment analysis of GO and KEGG pathways of target proteins indicated that EE treatment could potentially alter numerous biological processes and cellular pathways. In vivo experiments demonstrated the protective effect of EE treatment against accelerated bone loss, where reduced serum levels of TRAP, CTX, TNF-α, LPS, and IL-6 and increased bone volume and serum levels of P1NP were observed in EE-treated mice. In addition, changes in gut microbiota were spotted by 16S rRNA gene sequencing, showing that EE treatment increased the relative abundance of Lactobacillus and decreased the relative abundance of Clostridiaceae.

Conclusion

In summary, these findings suggested that the characteristics of multi-target and multi-pathway of EE against OP. In vivo, EE prevents the onset of OP by regulating gut microbiota and inflammatory response and is therefore a potential OP drug.

Identification and screening of potential inhibitors obtained from Plumeria rubra L. compounds against type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is the inability of the body's cells to retaliate to insulin, which can periodically culminate into absolute insulin deficiency. Hyperinsulinemia can be alleviated by administering oral medications or insulin. Prevailing medicaments engender repercussions with prolonged use, as they transmute to an inefficacious form. Hence, it will be advantageous to design plant-derived antihyperglycemic drugs with remarkable efficacy and safety quotients to address T2DM and associated comorbidities. Based on prior research, we have identified 7 novel phytocompounds from Plumeria rubra L. and 5 co-crystals that serve as an important residence for T2DM. The compounds are assessed for their inhibitory activity and dynamic stability against five major receptors which are responsible for T2DM. Additionally, in silico ADMET assessment followed by GPU-enabled GROMACS was performed on the selected compounds. The results demonstrated that β-d-Hexaglucoside had the highest binding affinity, hydrophobicity and bond length in contrast to all the targeted receptors. β-d-Hexaglucoside was subjected to dynamic simulation to analyze the root mean square deviation and root mean square fluctuation graph rates using the GROMOS force field in GROMACS software. Furthermore, β-d-Hexaglucoside exhibited inhibitory activity against diabetic receptors with a docking score of -9.5 kcal/mol. The current study proposes β-d-Hexaglucoside as a potential candidate for in-vitro or pre-clinical investigations to ameliorate T2DM management.Communicated by Ramaswamy H. Sarma.

Application of Molecular Dynamics Simulations to Determine Interactions between Canary Seed (Phalaris canariensis L.) Bioactive Peptides and Skin-Aging Enzymes

Food bioactive peptides are well recognized for their health benefits such as antimicrobial, antioxidant, and antihypertensive benefits, among others. Their drug-like behavior has led to their potential use in targeting skin-related aging factors like the inhibition of enzymes related with the skin-aging process. In this study, canary seed peptides (CSP) after simulated gastrointestinal digestion (<3 kDa) were fractioned by RP-HPLC and their enzyme-inhibition activity towards elastase and tyrosinase was evaluated in vitro. CSP inhibited elastase (IC50 = 6.2 mg/mL) and tyrosinase (IC50 = 6.1 mg/mL), while the hydrophobic fraction-VI (0.2 mg/mL) showed the highest inhibition towards elastase (93%) and tyrosinase (67%). The peptide fraction with the highest inhibition was further characterized by a multilevel in silico workflow, including physicochemical descriptor calculations, antioxidant activity predictions, and molecular dynamics-ensemble docking towards elastase and tyrosinase. To gain insights into the skin permeation process during molecular dynamics simulations, based on their docking scores, five peptides (GGWH, VPPH, EGLEPNHRVE, FLPH, and RPVNKYTPPQ) were identified to have favorable intermolecular interactions, such as hydrogen bonding of polar residues (W, H, and K) to lipid polar groups and 2-3 Å van der Waals close contact of hydrophobic aliphatic residues (P, V, and L). These interactions can play a critical role for the passive insertion of peptides into stratum corneum model skin-membranes, suggesting a promising application of CSP for skin-aging treatments.

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.

Structural and functional analysis of the cerato-platanin-like protein Cpl1 suggests diverging functions in smut fungi

Plant-pathogenic fungi are causative agents of the majority of plant diseases and can lead to severe crop loss in infected populations. Fungal colonization is achieved by combining different strategies, such as avoiding and counteracting the plant immune system and manipulating the host metabolome. Of major importance are virulence factors secreted by fungi, which fulfil diverse functions to support the infection process. Most of these proteins are highly specialized, with structural and biochemical information often absent. Here, we present the atomic structures of the cerato-platanin-like protein Cpl1 from Ustilago maydis and its homologue Uvi2 from Ustilago hordei. Both proteins adopt a double-Ψβ-barrel architecture reminiscent of cerato-platanin proteins, a class so far not described in smut fungi. Our structure-function analysis shows that Cpl1 binds to soluble chitin fragments via two extended grooves at the dimer interface of the two monomer molecules. This carbohydrate-binding mode has not been observed previously and expands the repertoire of chitin-binding proteins. Cpl1 localizes to the cell wall of U. maydis and might synergize with cell wall-degrading and decorating proteins during maize infection. The architecture of Cpl1 harbouring four surface-exposed loop regions supports the idea that it might play a role in the spatial coordination of these proteins. While deletion of cpl1 has only mild effects on the virulence of U. maydis, a recent study showed that deletion of uvi2 strongly impairs U. hordei virulence. Our structural comparison between Cpl1 and Uvi2 reveals sequence variations in the loop regions that might explain a diverging function.

Exploring the components and mechanisms of Shen-qi-wang-mo granule in the treatment of retinal vein occlusion by UPLC-Triple TOF MS/MS and network pharmacology

This study aimed to explore the substance basis and mechanisms of Shen-qi-wang-mo Granule (SQWMG), a traditional Chinese medicine prescription that had been clinically utilized to treat retinal vein occlusion (RVO) for 38 years. Components in SQWMG were analyzed by UPLC-Triple-TOF/MS and a total of 63 components were identified with ganoderic acids (GA) being the largest proportion. Potential targets of active components were retrieved from SwissTargetPrediction. RVO-related targets were acquired from related disease databases. Core targets of SQWMG against RVO were acquired by overlapping the above targets. The 66 components (including 5 isomers) and 169 targets were obtained and concluded into a component-target network. Together with biological enrichment analysis of targets, it revealed the crucial role of the "PI3K-Akt signaling pathway", "MAPK signaling pathway" and their downstream factor iNOS and TNF-α. The 20 key targets of SQWMG in treating RVO were acquired from the network and pathway analysis. The effects of SQWMG on targets and pathways were validated by molecular docking based on AutoDock Vina and qPCR experiment. The molecular docking showed great affinity for these components and targets, especially on ganoderic acids (GA) and alisols (AS), which were both triterpenoids and qPCR exhibited remarkably reduced inflammatory factor gene expression through regulation of these two pathways. Finally, the key components were also identified from rat serum after treatment of SQWMG.

Vina-GPU 2.0: Further Accelerating AutoDock Vina and Its Derivatives with Graphics Processing Units

Modern drug discovery typically faces large virtual screens from huge compound databases where multiple docking tools are involved for meeting various real scenes or improving the precision of virtual screens. Among these tools, AutoDock Vina and its numerous derivatives are the most popular and have become the standard pipeline for molecular docking in modern drug discovery. Our recent Vina-GPU method realized 14-fold acceleration against AutoDock Vina on a piece of NVIDIA RTX 3090 GPU in one virtual screening case. Further speedup of AutoDock Vina and its derivatives with graphics processing units (GPUs) is beneficial to systematically push their popularization in large-scale virtual screens due to their high benefit-cost ratio and easy operation for users. Thus, we proposed the Vina-GPU 2.0 method to further accelerate AutoDock Vina and the most common derivatives with new docking algorithms (QuickVina 2 and QuickVina-W) with GPUs. Caused by the discrepancy in their docking algorithms, our Vina-GPU 2.0 adopts different GPU acceleration strategies. In virtual screening for two hot protein kinase targets, RIPK1 and RIPK3, from the DrugBank database, our Vina-GPU 2.0 reaches an average of 65.6-fold, 1.4-fold, and 3.6-fold docking acceleration against the original AutoDock Vina, QuickVina 2, and QuickVina-W while ensuring their comparable docking accuracy. In addition, we develop a friendly and installation-free graphical user interface tool for their convenient usage. The codes and tools of Vina-GPU 2.0 are freely available at https://github.com/DeltaGroupNJUPT/Vina-GPU-2.0, coupled with explicit instructions and examples.

QPoweredCompound2DeNovoDrugPropMax - a novel programmatic tool incorporating deep learning and in silico methods for automated in silico bio-activity discovery for any compound of interest

Network data is composed of nodes and edges. Successful application of machine learning/deep learning algorithms on network data to make node classification and link prediction have been shown in the area of social networks through which highly customized suggestions are offered to social network users. Similarly one can attempt the use of machine learning/deep learning algorithms on biological network data to generate predictions of scientific usefulness. In the presented work, compound-drug target interaction network data set from bindingDB has been used to train deep learning neural network and a multi class classification has been implemented to classify PubChem compound queried by the user into class labels of PBD IDs. This way target interaction prediction for PubChem compounds is carried out using deep learning. The user is required to input the PubChem Compound ID (CID) of the compound the user wishes to gain information about its predicted biological activity and the tool outputs the RCSB PDB IDs of the predicted drug target interaction for the input CID. Further the tool also optimizes the compound of interest of the user toward drug likeness properties through a deep learning based structure optimization protocol. The tool also incorporates a feature to perform automated In Silico modelling to find the interaction between the compounds and the predicted drug targets to uncover their protein-ligand interaction profiles. The program is hosted, supported and maintained at the following GitHub repository. https://github.com/bengeof/Compound2DeNovoDrugPropMax. Anticipating the use of quantum computing and quantum machine learning in drug discovery we use the Penny-lane interface to quantum hardware to turn classical Keras layers used in our machine/deep learning models into a quantum layer and introduce quantum layers into classical models to produce a quantum-classical machine/deep learning hybrid model of our tool and the code corresponding to the same is provided below. https://github.com/bengeof/QPoweredCompound2DeNovoDrugPropMax.HIGHLIGHTSDeep learning based network pharmacology approach to predict the bio-activity of compounds.Further optimization of the compound toward drug like properties using deep learning techniques.Automated in silico modeling and interaction profiling of deep learning predicted target protein-ligand interaction.Communicated by Ramaswamy H. Sarma.

Study of potential inhibition of the estrogen receptor α by cannabinoids using an in silico approach: Agonist vs antagonist mechanism

Breast cancer is the main cancer type with more than 2.2 million cases in 2020, and is the principal cause of death in women; with 685000 deaths in 2020 worldwide. The estrogen receptor is involved at least in 70% of breast cancer diagnoses, and the agonist and antagonist properties of the drug in this receptor play a pivotal role in the control of this illness. This work evaluated the agonist and antagonist mechanisms of 30 cannabinoids by employing molecular docking and dynamic simulations. Compounds with docking scores < -8 kcal/mol were analyzed by molecular dynamic simulation at 300 ns, and relevant insights are given about the protein's structural changes, centered on the helicity in alpha-helices H3, H8, H11, and H12. Cannabicitran was the cannabinoid that presented the best relative binding-free energy (-34.96 kcal/mol), and based on rational modification, we found a new natural-based compound with relative binding-free energy (-44.83 kcal/mol) better than the controls hydroxytamoxifen and acolbifen. Structure modifications that could increase biological activity are suggested.

The Impact of Software Used and the Type of Target Protein on Molecular Docking Accuracy

The modern development of computer technology and different in silico methods have had an increasing impact on the discovery and development of new drugs. Different molecular docking techniques most widely used in silico methods in drug discovery. Currently, the time and financial costs for the initial hit identification can be significantly reduced due to the ability to perform high-throughput virtual screening of large compound libraries in a short time. However, the selection of potential hit compounds still remains more of a random process, because there is still no consensus on what the binding energy and ligand efficiency (LE) of a potentially active compound should be. In the best cases, only 20-30% of compounds identified by molecular docking are active in biological tests. In this work, we evaluated the impact of the docking software used as well as the type of the target protein on the molecular docking results and their accuracy using an example of the three most popular programs and five target proteins related to neurodegenerative diseases. In addition, we attempted to determine the "reliable range" of the binding energy and LE that would allow selecting compounds with biological activity in the desired concentration range.

Wound Healing and Antioxidant Properties of Launaea procumbens Supported by Metabolomic Profiling and Molecular Docking

Wounds adversely affect people’s quality of life and have psychological, social, and economic impacts. Herbal remedies of Launaea procumbens (LP) are used to treat wounds. In an excision wound model, topical application of LP significantly promoted wound closure (on day 14, LP-treated animals had the highest percentages of wound closure in comparison with the other groups, as the wound was entirely closed with a closure percentage of 100%, p < 0.05). Histological analysis revealed a considerable rise in the number of fibroblasts, the amount of collagen, and its cross-linking in LP-treated wounds. Gene expression patterns showed significant elevation of TGF-β levels (2.1-fold change after 7 days treatment and 2.7-fold change in 14 days treatment) and downregulation of the inflammatory TNF-α and IL-1β levels in LP-treated wounds. Regarding in vitro antioxidant activity, LP extract significantly diminished the formation of H2O2 radical (IC50 = 171.6 μg/mL) and scavenged the superoxide radical (IC50 of 286.7 µg/mL), indicating antioxidant potential in a dose-dependent manner. Dereplication of the secondary metabolites using LC-HRMS resulted in the annotation of 16 metabolites. The identified compounds were docked against important wound-healing targets, including vascular endothelial growth factor (VEGF), collagen α-1, tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and transforming growth factor-β (TGF-β). Among dereplicated compounds, luteolin 8-C-glucoside (orientin) demonstrated binding potential to four investigated targets (VEGF, interleukin 1β, TNF-α, and collagen α-1). To conclude, Launaea procumbens extract could be regarded as a promising topical therapy to promote wound healing in excisional wounds, and luteolin 8-C-glucoside (orientin), one of its constituents, is a potential wound-healing drug lead.

Volatile compounds of Bacillus pseudomycoides induce growth and drought tolerance in wheat (Triticum aestivum L.)

The plant growth-boosting biofilm-forming bacteria Bacillus pseudomycoides is able to promote growth and drought stress tolerance in wheat by suppressing the MYB gene, which synthesizes Myb protein (TaMpc1-D4) through secreted volatile compounds. In the present study, Triticum aestivum seeds were inoculated with five distinct bacterial strains. The growth, germination rate, root-shoot length, RWC, and chlorophyll content of seedlings were investigated. Furthermore, the levels of soluble sugars, proteins, H₂O₂, NO, cell death, and antioxidant enzymes (CAT, SOD, POD, and APX) were observed throughout the growth stage. All of the results showed that B. pseudomycoides had a substantially higher ability to form biofilm and promote these traits than the other strains. In terms of molecular gene expression, B. pseudomycoides inoculation strongly expressed the Dreb1 gene by silencing the expression of MYB gene through secreted volatile compounds. For identifying the specific volatile compound that silenced the MYB gene, molecular docking with Myb protein was performed. Out of 45 volatile compounds found, 2,6-ditert-butylcyclohexa-2,5-diene-1,4-dione and 3,5-ditert-butylphenol had a binding free energy of - 6.2 and - 6.5, Kcal/mol, respectively, which predicted that these compounds could suppress this protein's expression. In molecular dynamics simulations, the RMSD, SASA, Rg, RMSF, and hydrogen bonding values found assured the docked complexes' binding stability. These findings suggest that these targeted compounds may be suppressing Myb protein expression as well as the expression of Dreb1 and other drought response genes in wheat. More research (field trial) into plant growth and drought stress is needed to support the findings of this study.

In vitro and in silico assessment of new beta amino ketones with antiplasmodial activity

BACKGROUND

Based on the current need for new drugs against malaria, our study evaluated eight beta amino ketones in silico and in vitro for potential antimalarial activity.

METHODS

Using the Brazilian Malaria Molecular Targets (BraMMT) and OCTOPUS® software programs, the pattern of interactions of beta-amino ketones was described against different proteins of P. falciparum and screened to evaluate their physicochemical properties. The in vitro antiplasmodial activities of the compounds were evaluated using a SYBR Green-based assay. In parallel, in vitro cytotoxic data were obtained using the MTT assay.

RESULTS

Among the eight compounds, compound 1 was the most active and selective against P. falciparum (IC50 = 0.98 µM; SI > 60). Six targets were identified in BraMMT that interact with compounds exhibiting a stronger binding energy than the crystallographic ligand: P. falciparum triophosphate phosphoglycolate complex (1LYX), P. falciparum reductase (2OK8), PfPK7 (2PML), P. falciparum glutaredoxin (4N0Z), PfATP6, and PfHT.

CONCLUSIONS

The physicochemical properties of compound 1 were compatible with the set of criteria established by the Lipinski rule and demonstrated its potential as a drug prototype for antiplasmodial activity.

Identification of novel inhibitors of S-adenosyl-L-homocysteine hydrolase via structure-based virtual screening and molecular dynamics simulations

S-adenosyl-L-homocysteine hydrolase (SAHase) is an important regulator in the methylation reactions in many organisms and thus is crucial for numerous cellular functions. In recent years, SAHase has become one of the popular targets for drug design, and SAHase inhibitors have exhibited potent antiviral activity. In this study, we established the complex-based pharmacophore models based on the known crystal complex of SAHase (PDB ID: 1A7A) to screen the drug-likeness compounds of ChEMBL database. Then, three molecular docking programs were used to validate the reliability of compounds, involving Libdock, CDOCKER, and AutoDock Vina programs. The four promising hit compounds (CHEMBL420751, CHEMBL346387, CHEMBL1569958, and CHEMBL4206648) were performed molecular dynamics simulations and MM-PBSA calculations to evaluate their stability and binding-free energy in the binding site of SAHase. After screening and analyzing, the hit compounds CHEMBL420751 and CHEMBL346387 were suggested to further research to obtain novel potential SAHase inhibitors. A series of computer-aided drug design methods, including pharmacophore, molecular docking, molecular dynamics simulation and MM-PBSA calculations, were employed in this study to identity novel inhibitors of S-adenosyl-L-homocysteine hydrolase (SAHase). Some compounds from virtual screening could form various interactions with key residues of SAHase. Among them, compounds CHEMBL346387 and CHEMBL420751 exhibited potent binding affinity from molecular docking and MM-PBSA, and maintained good stability at the binding site during molecular dynamics simulations as well. All these results indicated that the selected compounds might have the potential to be novel SAHase inhibitors.

Gaining Insights on the Interactions of a Class of Decorated (2-([2,2'-Bipyridin]-6-yl)phenyl)platinum Compounds with c-Myc Oncogene Promoter G-Quadruplex and Other DNA Structures

Organometallic molecules offer some of the most promising scaffolds for interaction with G-quadruplex nucleic acids. We report the efficient synthesis of a family of organoplatinum(II) complexes, featuring a 2-([2,2'-bipyridin]-6-yl)phenyl tridentate (N∧ N∧ C) ligand, that incorporates peripheral side-chains aiming at enhancing and diversifying its interaction capabilities. These include a di-isopropyl carbamoyl amide, a morpholine ethylenamide, two enantiomeric proline imides and an oxazole. The binding affinities of the Pt-complexes were evaluated via UV-vis and fluorescence titrations, against 5 topologically-distinct DNA structures, including c-myc G-quadruplex, two telomeric (22AG) G-quadruplexes, a duplex (ds26) and a single-stranded (polyT) DNA. All compounds exhibited binding selectivity in favour of c-myc, with association constants (Ka ) in the range of 2-5×105  M-1 , lower affinity for both folds of 22AG and for ds26 and negligible affinity for polyT. Remarkable emission enhancements (up to 200-fold) upon addition of excess DNA were demonstrated by a subset of the compounds with c-myc, providing a basis for optical selectivity, since optical response to all other tested DNAs was low. A c-myc DNA-melting experiment showed significant stabilizing abilities for all compounds, with the most potent binder, the morpholine-Pt-complex, exhibiting a ΔTm >30 °C, at 1 : 5 DNA-to-ligand molar ratio. The same study implied contributions of the diverse side-chains to helix stabilization. To gain direct evidence of the nature of the interactions, mixtures of c-myc with the four most promising compounds were studied via UV Resonance Raman (UVRR) spectroscopy, which revealed end-stacking binding mode, combined with interactions of side-chains with loop nucleobase residues. Docking simulations were conducted to provide insights into the binding modes for the same four Pt-compounds, suggesting that the binding preference for two alternative orientations of the c-myc G-quadruplex thymine 'cap' ('open' vs. 'closed'), as well as the relative contributions to affinity from end-stacking and H-bonding, are highly dependent on the nature of the interacting Pt-complex side-chain.

Molecular Modeling Guided Drug Designing for the Therapeutic Treatment of Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a systemic inflammatory disorder that can cause destructive joint disease, significant disability, and increased mortality. RA is the most frequent of all chronic inflammatory joint diseases, and its prevalence frequency in Pakistan is 1.6 per thousand people. Different cytokines and receptors were involved in the triggering of RA, including interleukin-6 (ILR-6), major histocompatibility complex (MHC) antigen human leukocyte (HLA-DR) receptor, and CD20. Several studies illustrated RA as an inherent immune response and triggered due to the “shared epitope.” Therefore, the involvement of all these receptors (IL-6, HLA-DR, and CD20) leads to the neurological, ocular, respiratory, cardiac, skin, and hematological manifestations that have been considered a potential therapeutic target for drug design. Various herbal, natural, and synthetic source inhibitors of interleukin-6 (IL-6), human leukocyte (HLA-DR), and CD20 were studied and reported previously. Reported inhibitors are compared to elucidate the best inhibitor for clinical trials, leading to the orally active drug. In this study, a computer-aided drug designing approach disclosed the potential inhibitors for all receptors based on their distinct binding affinity. Moreover, drug suitability was carried out using Lipinski’s rule by considering the adsorption, distribution, metabolism, and excretion (ADME) of ligands. Results elucidated “calycosin 7-O-glucoside” and “angeliferulate” as putative ligands for IL-6 and HLA-DR, respectively. However, the pharmacokinetic properties (ADMET) revealed angeliferulate as an effete ligand for the biological system compared to calycosin 7-O-glucoside. Based on docking, drug toxicity profiling or pharmacokinetics, and MD simulation stability, this study highlights orally active therapeutic inhibitors to inhibit the activity of pivotal receptors (IL6, HLA-DR, and CD20) of RA in humans. After clinical trials, the resultant inhibitors could be potential therapeutic agents in the drug development against RA.

Virtual screening and molecular dynamics study of natural products against Rab10 for the treatment of Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disorder associated with aging. Various enzymatic targets have been and are still being studied in an attempt to discover new drugs for the treatment of AD; however, Rab GTPases are still relatively unexplored. These enzymes regulate cellular processes by alternating of GDP and GTP nucleotides. In vitro studies have shown that the knockdown of Rab10 reduces the production of Aβ40 and Aβ42 peptides, making it a promising target for the treatment of AD. In order to identify potential Rab10 inhibitors, the structure-based virtual screening (SBVS) was used considering a subset of 80763 natural products obtained from ZINC15 database. Tertiary structure of Rab10 was obtained from the Protein Data Bank and the Autodock Vina program was used in the SBVS to filter potential bioactive substances against this enzyme. The SBVS protocol was validated by redocking the co-crystallized GNP and the binding energies of the GDP and GTP were used as controls in the pharmacodynamic analysis. Thus, it was possible to select 45 compounds with binding energy less or equal -11 kcal.mol^-1. ADME/T properties of these compounds were evaluated by the SwissADME program, where it was possible to identify 6 promising molecules. The resulting complexes were subjected to molecular dynamics simulations to analyze the pharmacodynamics over time. The results suggest that the compound ZINC4090657 (derived from quinolizidine) and the compounds ZINC4000106 and ZINC0630250 (derived from coumarin) have favorable pharmacological characteristics for the inhibition of Rab10, with ZINC4090657 being the most promising one. Communicated by Ramaswamy H. Sarma.

Effective components and mechanism analysis of anti-platelet aggregation effect of Justicia procumbens L

ETHNOPHARMACOLOGICAL RELEVANCE

Justicia procumbens L. is a traditional Chinese medicine, first recorded in "Shen Nong's Herbal Classic", for the treatment of lumbar pain and fever. As a widely distributed herb, it has also been documented in India, Nepal, and Malaysia. In "Tang Materia Medica", a famous medicinal book of Tang Dynasty in ancient China, it was first used to treat diseases associated with blood stasis. Blood stasis syndrome is closely related to thrombus formation and platelet aggregation. Although some compounds isolated from this plant have anti-platelet aggregation effects, the main chemical components and mechanism of J. procumbens in terms of these effects are little known.

AIMS OF THE STUDY

Through in vivo and in vitro experiments, this studsy revealed the characteristic components and action mechanism of anti-platelet aggregation by J. procumbens from an overall perspective.

MATERIALS AND METHODS

The effective crude extracts of the whole plant were screened via an in vitro anti-platelet aggregation test. After incubating these extracts with apheresis platelets, high affinity compounds were detected by HPLC-MS and regulatory genes were detected using gene chips. The effective components and potential target proteins were analyzed using computational docking technology. Furthermore, the compound with the strongest predicted activity was evaluated in vivo via an anti-thrombotic test.

RESULTS

Integrin a_Ⅱbβ₃, PKCα, PI3Kγ, and mitogen-activated protein kinase 14 were found to be potential targets. Justicidin B, tuberculatin, chinensinaphthol methyl ether, and neojusticin B were effective compounds that inhibited human platelet aggregation by suppressing Gq-PLC-PKC and Gi-PI3K-MAPK signaling pathways. Among the compounds that bind to platelets, justicidin B showed the strongest virtual binding force. The test of carotid artery thrombosis induced by ferric chloride in SD rats confirmed that justicidin B inhibited thrombus formation.

CONCLUSION

Experimental investigation showed that arylnaphthalene lignan aglycones with one methylenedioxy group and two methoxy groups are effective components for anti-platelet aggregation by J. procumbens. These compounds inhibit Gq-PLC-PKC and Gi-PI3K-MAPK signaling pathways by suppressing the expression of genes such as ITGB3, PRKCA, PIK3CG, and MAPK14. These results reflected the characteristics of multi-component and multi-target synergistic treatment of Chinese medicine.

Combined ligand-based and structure-based design of PDE 9A inhibitors against Alzheimer's disease

PDE9 enzyme hydrolyzes cGMP, which is involved in the regulation of synaptic plasticity through the NMDA pathway (a well-known excitotoxic target for AD) via activation of calcium/calmodulin-dependent neuronal NO synthases in the postsynaptic neurons. The inhibition of PDE9 leads to elevated cGMP levels, causing enhanced NMDA signaling and thus contributing to an increase in synaptic plasticity and stabilization. Therefore, it could be considered a pertinent target for AD drug discovery. PF-04447943 and BI-409306 targeting PDE9 are undergoing clinical trials (Phase II). The present study encompasses a pharmacophoric approach to identify potent PDE9 inhibitors using various computational methods. Pharmacophores generated from the PDB 6A3N yielded 37,554 virtual hits, which underwent drug likeliness and PAINS filtering to arrive at a few virtual leads. The leads were further subjected to extra precision docking, ADMET predictions, and molecular dynamics. The final hits, ZINC000001305675 and ZINC000000377099, showed superior docking scores of - 10.90 and - 10.30 kcal/mol and satisfactory predicted ADMET scores. The hits were subjected to molecular dynamics (MD) studies, wherein they formed stable complexes with PDE9 protein and had ligand RMSDs within acceptable limits. The processes involved in the combined ligand and structure-based strategies.

Chiral Fibers Formation Upon Assembly of Tetraphenylalanine Peptide Conjugated to a PNA Dimer

Self‐assembly of biomolecules such as peptides, nucleic acids or their analogues affords supramolecular objects, exhibiting structures and physical properties dependent on the amino‐acid or nucleobase composition. Conjugation of the peptide diphenylalanine (FF) to peptide nucleic acids triggers formation of self‐assembled structures, mainly stabilized by interactions between FF. In this work we report formation of homogeneous chiral fibers upon self‐assembly of the hybrid composed of the tetraphenylalanine peptide (4F) conjugated to the PNA dimer adenine‐thymine (at). In this case nucleobases seem to play a key role in determining the morphology and chirality of the fibers. When the PNA “at” is replaced by guanine‐cytosine dimer “gc”, disordered structures are observed. Spectroscopic characterization of the self‐assembled hybrids, along with AFM and SEM studies is reported. Finally, a structural model consistent with the experimental evidence has also been obtained, showing how the building blocks of 4Fat arrange to give helical fibers.

Predicting Protein-Ligand Docking Structure with Graph Neural Network

Modern day drug discovery is extremely expensive and time consuming. Although computational approaches help accelerate and decrease the cost of drug discovery, existing computational software packages for docking-based drug discovery suffer from both low accuracy and high latency. A few recent machine learning-based approaches have been proposed for virtual screening by improving the ability to evaluate protein-ligand binding affinity, but such methods rely heavily on conventional docking software to sample docking poses, which results in excessive execution latencies. Here, we propose and evaluate a novel graph neural network (GNN)-based framework, MedusaGraph, which includes both pose-prediction (sampling) and pose-selection (scoring) models. Unlike the previous machine learning-centric studies, MedusaGraph generates the docking poses directly and achieves from 10 to 100 times speedup compared to state-of-the-art approaches, while having a slightly better docking accuracy.

Identification and validation of potent inhibitor of Escherichia coli DHFR from MMV pathogen box

The present study is conducted to find the solution of rising antimicrobial resistance (AMR) in Escherichia coli which is a pathogen responsible for fatal systemic infections in human and animals. The enzyme dihydrofolate reductase (DHFR) is found in all organisms. In this study DHFR of E. coli (ec-DHFR) and human DHFR (h-DHFR) is targeted by novel chemical entities (NCE) from the Pathogen box of Medicines for Malaria Venture, Switzerland (MMV) using molecular modelling. The in-silico studies were further validated by in-vitro assays. The virtual screening of 400 MMV compounds was conducted using PyRx standard tool followed by manual docking of selected compounds by Autodock vina and Ligplot program. The in-silico studies showed good binding energy and strong hydrogen bond in docking of MMV675968 with ec-DHFR and no hydrogen bond with h-DHFR. This was further validated by the Molecular dynamic studies that revealed high binding free energy in ec-DHFR and in-vitro assays that produced good synergy in combination study of MMV675968 with last line (meropenem) and last resort (colistin) antibiotics. The extensive MD simulation and energetic analysis thus concludes that MMV675968 targets ec-DHFR. The combination studies were conducted with MMV675968 and FDA approved drugs against a panel of multidrug resistant Escherichia coli isolates. The synergistic results obtained in combination studies concluded that in-vitro data is consistent with in-silico data and that MMV675968 is a potential lead for future process of antimicrobial drug development against the multidrug resistance E. coli.Communicated by Ramaswamy H. Sarma.

Accelerating AutoDock Vina with GPUs

AutoDock Vina is one of the most popular molecular docking tools. In the latest benchmark CASF-2016 for comparative assessment of scoring functions, AutoDock Vina won the best docking power among all the docking tools. Modern drug discovery is facing a common scenario of large virtual screening of drug hits from huge compound databases. Due to the seriality characteristic of the AutoDock Vina algorithm, there is no successful report on its parallel acceleration with GPUs. Current acceleration of AutoDock Vina typically relies on the stack of computing power as well as the allocation of resource and tasks, such as the VirtualFlow platform. The vast resource expenditure and the high access threshold of users will greatly limit the popularity of AutoDock Vina and the flexibility of its usage in modern drug discovery. In this work, we proposed a new method, Vina-GPU, for accelerating AutoDock Vina with GPUs, which is greatly needed for reducing the investment for large virtual screens and also for wider application in large-scale virtual screening on personal computers, station servers or cloud computing, etc. Our proposed method is based on a modified Monte Carlo using simulating annealing AI algorithm. It greatly raises the number of initial random conformations and reduces the search depth of each thread. Moreover, a classic optimizer named BFGS is adopted to optimize the ligand conformations during the docking progress, before a heterogeneous OpenCL implementation was developed to realize its parallel acceleration leveraging thousands of GPU cores. Large benchmark tests show that Vina-GPU reaches an average of 21-fold and a maximum of 50-fold docking acceleration against the original AutoDock Vina while ensuring their comparable docking accuracy, indicating its potential for pushing the popularization of AutoDock Vina in large virtual screens.

PSnpBind: a database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow

A key concept in drug design is how natural variants, especially the ones occurring in the binding site of drug targets, affect the inter-individual drug response and efficacy by altering binding affinity. These effects have been studied on very limited and small datasets while, ideally, a large dataset of binding affinity changes due to binding site single-nucleotide polymorphisms (SNPs) is needed for evaluation. However, to the best of our knowledge, such a dataset does not exist. Thus, a reference dataset of ligands binding affinities to proteins with all their reported binding sites' variants was constructed using a molecular docking approach. Having a large database of protein-ligand complexes covering a wide range of binding pocket mutations and a large small molecules' landscape is of great importance for several types of studies. For example, developing machine learning algorithms to predict protein-ligand affinity or a SNP effect on it requires an extensive amount of data. In this work, we present PSnpBind: A large database of 0.6 million mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow. It provides a web interface to explore and visualize the protein-ligand complexes and a REST API to programmatically access the different aspects of the database contents. PSnpBind is open source and freely available at https://psnpbind.org .

Computational screening and biochemical analysis of Pistacia integerrima and Pandanus odorifer plants to find effective inhibitors against Receptor-Binding domain (RBD) of the spike protein of SARS-Cov-2

Although World Health Organization-approved emergency vaccines are available in many countries, the mortality rate from COVID-19 remains high due to the fourth or fifth wave and the delta variant of the coronavirus. Thus, an effective mechanistic investigation in treating this disease is urgently needed. In this work, we extracted phytochemicals from two mangrove plants, Pistacia integerrima and Pandanus odorifer, assessing their potential actions against the receptor-binding domain (RBD) of the spike protein of SARS-CoV-2. The antioxidant activities of Pistacia integerrima leaves and fruits were 142.10 and 97.13 µg/mL, respectively, whereas Pandanus odorifer leaves and fruits were 112.50 and 292.71 µg/mL, respectively. Furthermore, leaf extracts from both plants had lower cytotoxicity against Artemia salina than fruit extracts. Gas chromatography-mass spectrometry analysis revealed a total of 145 potential phytochemicals from these extracts. Three phytochemicals, 28-demethyl-beta-amyrone, 24-Noroleana-3,12-diene, and stigmasterol, displayed binding free energy values of - 8.3, -7.5, and - 8.1 Kcal/mol, respectively, in complexes with the spike protein of SARS-CoV-2. The root-mean-square deviation, solvent-accessible surface area, radius of gyration, root-mean-square fluctuations, and hydrogen bonds were used to ensure the binding stability of the docked complexes in the atomistic simulation. Thus, wet-lab validations are necessary to support these findings.

A novel inhibitor of the JA signaling pathway represses herbivore resistance in tea plants

The jasmonic acid (JA) signaling pathway plays a vital role in mediating plant resistance to herbivores. Tea plant (Camellia sinensis) is one of the most important woody cash crops in the world. Due to the lack of genetic transformation systems for tea plants, how the JA signaling pathway works in tea plants has not yet been determined. Now, with the development of cross-disciplines, chemical biology provides new means for analysing the JA signaling pathway. In the present study, the small molecule isoquinoline compound ZINC71820901 (lyn3) was obtained from the ZINC molecular library through virtual screening based on the structure of the crystal COI1-JAZ1 co-receptor and was found to act as an inhibitor of the JA signaling pathway both in Arabidopsis and tea plants. Our results revealed that lyn3 repressed tea plant resistance to Ectropis grisescens mainly by decreasing the accumulation of (-)-epicatechin (EC) and (-)-epigallocatechin (EGC) via repression of the JA signaling pathway, which functioned in the different modulation manner to the already known inhibitor SHAM. As a novel inhibitor of JA signaling pathway, lyn3 provides a specific option for further research on the JA pathway.