Blind Docking Research Articles

Molecular insights into the inhibitory potential of anthocyanidins on glucokinase regulatory protein.

Computational methods were used to investigate six anthocyanidins exhibiting antidiabetic activity by inhibiting glucokinase regulatory protein (GKRP) activity. Density functional theory was used to optimise the geometry of anthocyanidins and calculate their quantum chemical properties. A blind docking method was employed to conduct a molecular docking study, which revealed that delphinidin (Del), cyanidin (Cya), and pelargonidin (Pel) as potential GKRP inhibitors with the lowest binding free energy of -8.7, -8.6, and -8.6 kcal/mol, corresponding to high binding affinity. The molecular dynamics study further verified the blind docking results by showing high GKRP-F1P complex stability and high binding affinity calculated through the MM/GBSA method, upon the binding of pelargonidin. The lower RMSF values of pivotal GK-interacting residues for GKRP-F1P-Pel compared to GKRP-F1P, as a positive control, indicating pelargonidin ability to maintain the inactive conformation of GKRP through the inhibition of GK binding. The key residues that control the binding of the F1P to GKRP and anthocyanidin to GKRP-F1P were also identified in this study. Altogether, pelargonidin is anthocyanidins-derived natural products that have the most potential to act as inhibitors of GKRP and as antidiabetic nutraceuticals.

Hop bitterness in beer evaluated by computational analysis

Beer flavour and aroma are greatly influenced by the hop(s) employed in the brewing process. The iso-α-acids post wort boiling are the major compounds responsible for bitterness, which are detected by the bitter taste receptors (TAS2Rs) in oral taste buds. This family of receptors is activated in the presence of bitter molecules, which send chemical signals to the brain, making it possible to differentiate whether the detected molecules have a pleasant taste (or not). It is of interest to predict the behaviour of hop compounds towards bitter receptors such that the bitterness of different hop varieties can be predicted based on quantitative analysis of composition. Computational simulation, based in high-performance computing (HPC), allow the simulation of interactions of molecules with the various TAS2Rs, enabling the prediction the bitterness of these hop compounds. These techniques, will soon enable the design of beverages with customised flavours, greatly reducing the need for experimental evaluation. In this work, α and β-acids, iso-α-acids, and prenylflavonoids are analysed against the bitter receptors TAS2R10, TAS2R14 and TAS2R46. Using computational blind docking and molecular dynamics, xanthohumol was identified to have the highest bitter profile.

DSDP: A Blind Docking Strategy Accelerated by GPUs.

Virtual screening, including molecular docking, plays an essential role in drug discovery. Many traditional and machine-learning-based methods are available to fulfill the docking task. However, the traditional docking methods are normally extensively time-consuming, and their performance in blind docking remains to be improved. Although the runtime of docking based on machine learning is significantly decreased, their accuracy is still limited. In this study, we take advantage of both traditional and machine-learning-based methods and present a method, deep site and docking pose (DSDP), to improve the performance of blind docking. For traditional blind docking, the entire protein is covered by a cube, and the initial positions of ligands are randomly generated in this cube. In contrast, DSDP can predict the binding site of proteins and provide an accurate searching shape and initial positions for further conformational sampling. The sampling task of DSDP makes use of the score function and a similar but modified searching strategy of AutoDock Vina, accelerated by implementation in GPUs. We systematically compare its performance in redocking, blind docking, and virtual screening tasks with state-of-the-art methods, including AutoDock Vina, GNINA, QuickVina, SMINA, and DiffDock. In the blind docking task, DSDP reaches a 29.8% top-1 success rate (root-mean-squared deviation < 2 Å) on an unbiased and challenging test dataset with 1.2 s wall-clock computational time per system. Its performances on the DUD-E dataset and the time-split PDBBind dataset used in EquiBind, TANKBind, and DiffDock are also evaluated, presenting a 57.2 and 41.8% top-1 success rate with 0.8 and 1.0 s per system, respectively.

Roles of Anti-Inflammatory Active Ingredients of Saussurea costus in Silico approach as Adjuvant Therapy in COVID-19 Cases

SARS-CoV-2 (COVID-19) remains a very high risk to this date. The COVID-19 mortality rate is relatively high since it potentially causes various complications and cytokine storms, thereby increasing the mortality rate of those infected. Consumption of healthy food/drink is one of the means to boost the immune system and prevent COVID-19 infection. One of the interesting plants to use in this case is Saussurea costus. This plant contains active ingredients that can serve as anti-inflammatory, antitumor, antibacterial, antiseptic, antifungal agents, etc. However, studies on the role of the active ingredients as an anti-inflammatory agent to treat COVID-19, prevent cytokine storms, and improve COVID-19 patient outcomes are rarely found. In this in silico study, a total of 75 compounds in Saussurea costus were analized and five of which showed the greatest potential as the drug candidates, namely isoalantolactone, isozaluzanin C, arbusculin a, β-costic acid, and picriside B. Three target proteins were utilized in this study, including IL-6R, NFKB1, and TNFR1. The ligand samples were minimized before the molecular simulation process, and then the target proteins were sterilized. Furthermore, biological activity tests were conducted on the (anti-inflammatory and immunosuppressant) drug candidate compounds, followed by a druglikeness analysis, and ended with blind dockings to screen the potential compounds of the natural ingredients. The analysis of the docking results was performed using LigPlot+. The analysis results signified that according to the predicted probability with medium confidence (Pa &gt; 0.3), all of the drug candidate compounds of Saussurea costus in silico indicated biological activities as anti-inflammatory and immunosuppressant agents, which could be categorized as drug-like molecules. In addition, the molecular docking analysis results in this study suggested that the five active compounds of Saussurea costus showed an affinity for the aforementioned target proteins. Among the five active compounds, picriside B had the lowest binding affinity for IL-6R, NFKB1, and TNFR1, with total energies of -6.3kcal/mol, - 6.5kcal/mol, and -9.0 kcal/mol, respectively. In addition, picriside B also demonstrated the most interactions with all of the target proteins. This compound was able to form hydrophobic and hydrogen bonds with the three target proteins. The other four active compounds could be potentially utilized as adjuvant therapy for COVID-19 because these compounds had an affinity for and many chemical bond interactions with the three target proteins.

Assessment of Morita-Baylis-Hillman adducts as novel modulators of quorum sensing phenotypes in Chromobacterium CV026 by targeting CviR

This work presents Morita-Baylis-Hillman adducts (MBHA) as new modulators of the violacein biosynthesis in the biosensor strain Chromobacterium CV026. Seven adducts displayed violacein reduction higher than 50±1% when tested at concentrations lower than 625 μM. The most active MBHA inhibited hydrolysis of chitin concomitantly with inhibition of violacein production in CV026, suggesting the disruption of its QS machinery. One MBHA induced the violacein biosynthesis in CV026 in the absence of the cognate AI, also supporting its role as a QS modulator of this QS system. Further, RT-qPCR and competition experiments characterize these two molecules as transcriptional modulators of the QS-regulated operon vioABCDE. Blind docking studies for MBHA showed a strong preference for adducts positioned within the CviR autoinducer-binding domain (AIBD). Our results show MBHA as a promising chemical scaffold for the development of novel QS inhibitors affecting LuxR/LuxI-systems.

Attracting Cavities 2.0: Improving the Flexibility and Robustness for Small-Molecule Docking.

Molecular docking is a computational approach for predicting the most probable position of a ligand in the binding site of a target macromolecule. Our docking algorithm Attracting Cavities (AC) has been shown to compare favorably to other widely used docking algorithms [Zoete, V.; et al. J. Comput. Chem. 2016, 37, 437]. Here we describe several improvements of AC, making the sampling more robust and providing more flexibility for either fast or high-accuracy docking. We benchmark the performance of AC 2.0 using the 285 complexes of the PDBbind Core set, version 2016. For redocking from randomized ligand conformations, AC 2.0 reaches a success rate of 73.3%, compared to 63.9% for GOLD and 58.0% for AutoDock Vina. Due to its force-field-based scoring function and its thorough sampling procedure, AC 2.0 also performs well for blind docking on the entire receptor surface. The accuracy of its scoring function allows for the detection of problematic experimental structures in the benchmark set. For cross-docking, the AC 2.0 success rate is about 30% lower than for redocking (42.5%), similar to GOLD (42.8%) and better than AutoDock Vina (33.1%), and it can be improved by an informed choice of flexible protein residues. For selected targets with a high success rate in cross-docking, AC 2.0 also achieves good enrichment factors in virtual screening.

Research on Repressing Allergen Cry j 1 Released from Japanese Cedar Pollen Using Todomatsu Oil

Japanese cedar (JC, Cryptomeria japonica) pollen allergens are the primary cause of JC pollinosis, a widespread seasonal allergic disorder and a significant public health issue in Japan. Nevertheless, rare information on repressing the pollen allergens released from JC pollen is available. This study aims to validate the repression of allergen Cry j 1 (the dominant JC pollen allergen that triggers JC pollinosis) using todomatsu oil produced from Abies sachalinensis waste, through surface plasmon resonance (SPR) experiments, sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), and blind docking simulation. The findings revealed that todomatsu oil reduces the antibody reactivity and content of allergen Cry j 1 by 85.96% and 42.12%, respectively. The docking modeling suggested that alkyl hydrophobic forces, pi–sigma bonding, and hydrogen bonding are the principal motivating forces for todomatsu oil components to dock with allergen Cry j 1. More than 50% of the amino-acid residues docked by each todomatsu oil component (except α-pinene) are hydrophobic. Furthermore, oil components, especially β-maaliene, exhibited potent repression of allergen Cry j 1. Consequently, todomatsu oil, due to its inexpensive, available, convenient, and ecologically beneficial nature, is a viable measure to repress allergen Cry j 1.

Computational study to find the goat serum albumin (GSA) binding site as A-esterase

A-esterases are a classical term applied to enzymatic activity of the proteins by a mechanism not involving intermediate covalent phosphorylation, but requiring a divalent cation cofactor. Recently, a copper-dependent A-esterase activity has been identified in goat serum albumin (GSA) on the organophosphorus insecticide trichloronate. This hydrolysis was identified ex vivo with spectrophotometry and chromatography techniques. Albumin mechanism of action and catalytic site as Cu2+-dependent A-esterase are still unknown. Therefore, to know the copper bind to albumin is relevant. N-terminal sequence has been reported as the high affinity site for this cation, due to the histidine in position 3. The aim of this work in silico is to explore how occurs this metallic binding and active the esterase catalytic function. The GSA crystallized structure (PDB: 5ORI) was chosen for molecular docking and dynamics. A site-directed docking, for N-terminal site and a blind docking was done with trichloronate as ligand. Root-mean-square deviation and frequency plot was calculated to find the most frequent predicted structure and visualize the amino acids involved in binding site. The affinity energy in the blind docking (−5.80 kcal/mol) is almost twice lower than site-directed docking (−3.81 kcal/mol) and N-terminal amino acids do not appear in the most repeated structure binding site, suggesting that the protein has a site with higher affinity to the trichloronate ligand. His145 could be involved in the binding site as has been reported in previous studies.

NARINGIN'S POTENTIAL AS A HEPATITIS B VIRUS REPLICATION INHIBITOR: AN IN-SILICO STUDY OF SECONDARY METABOLITE COMPOUND

Naringin is a secondary metabolite compound of the flavonoid group which is generally found in plants that are consumed and traditionally used as medicine. The aim of this study was to examine the potential of naringin as a candidate for hepatitis B virus replication inhibitor using an in-silico approach. This research uses exploratory descriptive method with molecular docking analysis was carried out using the blind docking technique. The 3D structures of naringin and reference ligands were collected from the PubChem database, and the 3D structures of target proteins were collected from the PDB database. The target protein used is the hepatitis B virus capsid protein with PDB ID: 5GMZ. Docking analysis was performed using AutoDock Vina which is integrated into PyRx. Docking results were visualized using the PyMol software and Biovia Discovery Studio 2019. The results of the analysis showed that the binding affinity of all simulation models between naringin and the HBV capsid protein ranged from -7.1 to -7.9 kcal/mol. The binding site formed between naringin and the receptor corresponds to the reference ligand, involving the same 12 amino acid residues, namely PHE 23, PRO 25, LEU 30, THR 33, TRP 102, ILE 105, SER 106, PHE 110, TYR 118, ILE 139, LEU 140, and SER 141. Based on these results, it can be concluded that the naringin compound has the same bioactivity as the reference ligand in inhibiting viral replication, so that naringin has the potential as a candidate for hepatitis B virus replication inhibitor

Predicting Protein-Peptide Interactions: Benchmarking Deep Learning Techniques and a Comparison with Focused Docking.

The accurate prediction of protein structures achieved by deep learning (DL) methods is a significant milestone and has deeply impacted structural biology. Shortly after its release, AlphaFold2 has been evaluated for predicting protein-peptide interactions and shown to significantly outperform RoseTTAfold as well as a conventional blind docking method: PIPER-FlexPepDock. Since then, new AlphaFold2 models, trained specifically to predict multimeric assemblies, have been released and a new ab initio folding model OmegaFold has become available. Here, we assess docking success rates for these new DL folding models and compare their performance with our state-of-the-art, focused peptide-docking software AutoDock CrankPep (ADCP). The evaluation is done using the same dataset and performance metric for all methods. We show that, for a set of 99 nonredundant protein-peptide complexes, the new AlphaFold2 model outperforms other Deep Learning approaches and achieves remarkable docking success rates for peptides. While the docking success rate of ADCP is more modest when considering the top-ranking solution only, it samples correct solutions for around 62% of the complexes. Interestingly, different methods succeed on different complexes, and we describe a consensus docking approach using ADCP and AlphaFold2, which achieves a remarkable 60% for the top-ranking results and 66% for the top 5 results for this set of 99 protein-peptide complexes.

Wnt/β-catenin signaling pathway inhibitors, glycyrrhizic acid, solanine, polyphyllin I, crocin, hypericin, tubeimoside-1, diosmin, and rutin in medicinal plants have better binding affinities and anticancer properties: Molecular docking and ADMET study.

Wnt/β-catenin signaling pathway plays a role in cancer development, organogenesis, and embryogenesis. The abnormal activation promotes cancer stem cell renewal, proliferation, and differentiation. In the present study, molecular docking simulation and ADMET studies were carried out on selected bioactive compounds in search of β-catenin protein inhibitors for drug discovery against cancer. Blind docking simulation was performed using PyRx software on Autodock Vina. β-catenin protein (PDB ID: 1jdh) and 313 bioactive compounds (from PubChem database) with selected standard anticancer drugs were used for molecular docking. The ADMET properties of the best-performing compounds were calculated using SwissADME and pkCMS web servers. The results obtained from the molecular docking study showed that glycyrrhizic acid, solanine, polyphyllin I, crocin, hypericin, tubeimoside-1, diosmin, and rutin had the best binding interactions with β-catenin protein based on their binding affinities. Glycyrrhizic acid and solanine had the same and lowest binding energy of -8.5 kcal/mol. This was followed by polyphyllin I with -8.4 kcal/mol, and crocin, hypericin, and tubeimoside-1 which all had a binding energy of 8.1 kcal/mol. Other top-performing compounds include diosmin and rutin with binding energy of -8.0 kcal/mol. The ADMET study revealed that the following compounds glycyrrhizic acid, solanine, polyphyllin I, crocin, hypericin, tubeimoside-1, diosmin, rutin, and baicalin all violated Lipinski's rule of 5 which implies poor oral bioavailability. However, based on the binding energy score, it was suggested that these pharmacologically active compounds are potential molecules to be tested against cancer.

The Specific Binding and Promotion Effect of Azoles on Human Aldo-Keto Reductase 7A2

Human AKR 7A2 broadly participates in the metabolism of a number of exogenous and endogenous compounds. Azoles are a class of clinically widely used antifungal drugs, which are usually metabolized by CYP 3A4, CYP2C19, and CYP1A1, etc. in vivo. The azole-protein interactions that human AKR7A2 participates in remain unreported. In this study, we investigated the effect of the representative azoles (miconazole, econazole, ketoconazole, fluconazole, itraconazole, voriconazole, and posaconazole) on the catalysis of human AKR7A2. The steady-state kinetics study showed that the catalytic efficiency of AKR7A2 enhanced in a dose-dependent manner in the presence of posaconazole, miconazole, fluconazole, and itraconazole, while it had no change in the presence of econazole, ketoconazole, and voriconazole. Biacore assays demonstrated that all seven azoles were able to specifically bind to AKR7A2, among which itraconazole, posaconazole, and voriconazole showed the strongest binding. Blind docking predicted that all azoles were apt to preferentially bind at the entrance of the substrate cavity of AKR7A2. Flexible docking showed that posaconazole, located at the region, can efficiently lower the binding energy of the substrate 2-CBA in the cavity compared to the case of no posaconazole. This study demonstrates that human AKR7A2 can interact with some azole drugs, and it also reveals that the enzyme activity can be regulated by some small molecules. These findings will enable a better understanding of azole-protein interactions.

Peptide Helix-Y12 as Potential Effector for Peroxisome Proliferator-Activated Receptors.

Peroxisome proliferator-activated receptors (PPARs) are nuclear receptors involved in the regulation of lipids and glucose metabolism, and immune response. Therefore, they have been considered pharmacological targets for treating metabolic diseases, such as dyslipidemia, atherosclerosis, and non-alcoholic fatty liver disease. However, the available synthetic ligands of PPARs have mild to significant side effects, generating the necessity to identify new molecules that are selective PPAR ligands with specific biological responses. This study aimed to evaluate some components of the atheroprotective and hepatoprotective HB-ATV-8 nanoparticles [the amphipathic peptide Helix-Y12, thermozeaxanthin, thermozeaxanthin-13, thermozeaxanthin-15, and a set of glycolipids], as possible ligands of PPARs through blind molecular docking. According to the change in free energy upon protein-ligand binding, ∆G b, thermozeaxanthins show a more favorable interaction with PPARs, followed by Helix-Y12. Moreover, Helix-Y12 interacts with most parts of the Y-shaped ligand-binding domain (LBD), surrounding helix 3 of PPARs, and reaching helix 12 of PPARα and PPARγ. As previously reported for other ligands, Tyr314 and Tyr464 of PPARα interact with Helix-Y12 through hydrogen bonds. Several PPARα's amino acids are involved in the ligand binding by hydrophobic interactions. Furthermore, we identified additional PPARs' amino acids interacting with Helix-Y12 through hydrogen bonds still not reported for known ligands. Our results show that, from the studied ligand set, the Helix-Y12 peptide and Tzeaxs have the most significant probability of binding to the PPARs' LBD, suggesting novel ligands for PPARs.

Exploring polyamine interactions and binding pockets in SARS-CoV-2 ORF3a

Ongoing global pandemic caused by coronavirus (COVID-19) requires urgent development of vaccines, treatments, and diagnostic tools. Open reading frame 3a (ORF3a) from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is considered to be a potential drug target for COVID-19 treatment. ORF3a is an accessory protein that plays a significant role in virus-host interactions and in facilitating host immune responses. Using putrescine, spermidine and spermine, an aliphatic polyamine for the activity suppression of ORF3a appears to be a promising approach in finding new targets for drug design. In this study, we explored the possible binding poses of polyamines to the ORF3a protein using a combination of various computational approaches i.e. pocket prediction, blind and site-specific molecular docking, molecular dynamics and ligand flooding simulations. The results showed that the tip of cytoplasmic domain and the upper tunnel of transmembrane domain of ORF3a provide a suitable binding site specific for the polyamines. MD simulations revealed the stability of spermidine binding in the upper tunnel pocket of ORF3a through salt bridge and hydrogen bond interactions between the amine groups of the ligand and negatively charged residues of ORF3a. These findings can be helpful in designing new therapeutic drugs.

Development of NS2B-NS3 protease inhibitor that impairs Zika virus replication

Zika virus (ZIKV) is a mosquito-borne flavivirus that causes severe neurological disorders, such as microcephaly in fetuses. Most recently, an outbreak of ZIKV started in Brazil in 2015. To date, no therapeutic agents have been approved to treat ZIKV infection in the clinic. Here, we screened a small molecule inhibitor that can inhibit the function of ZIKV non-structural protein 2B (NS2B)-NS3 protease (ZIKV NS2B-NS3 protease), thereby interfering with viral replication and spread. First, we identified the half maximal inhibitory concentration (IC50) of compound 3 (14.01 μM), 8 (6.85 μM), and 9 (14.2 μM) and confirmed that they are all non-competitive inhibitors. In addition, we have used the blind molecular docking method to simulate the inhibition area of three non-competitive inhibitors (compound 3, 8, and 9) with the ZIKV NS2B-NS3 protease. The results indicated that the four allosteric binding residues (Gln139, Trp148, Leu150, and Val220) could form hydrogen bonds or non-bonding interactions most frequently with the three compounds. The interaction might induce the reaction center conformation change of NS2B-NS3 protease to reduce catalyzed efficiency. The concentration of compounds required to reduce cell viability by 50% (CC50), and the concentration of compounds required to inhibit virus-induced cytopathic effect by 50% (EC50) of three potential compounds are >200 μM, 2.15 μM (compound 3), > 200 μM, 0.52 μM (compound 8) and 61.48 μM, 3.52 μM (compound 9), and Temoporfin are 61.05 μM, 2 μM, respectively. To select candidate compounds for further animal experiments, we analyzed the selectivity index (SI) of compound 3 (93.02), 8 (384.61), 9 (17.46), and Temoporfin (30.53, FDA-approved drug against cancer). Compound 8 has the highest SI value. Therefore, compound 8 was selected for verification in animal models. In vivo, compound 8 significantly delayed ZIKV-induced lethality and illness symptoms and decreased ZIKV-induced weight loss in a ZIKV-infected suckling mouse model. We conclude that compound 8 is worth further investigation for use as a potential future therapeutic agent against ZIKV infection.

In-silico and In-vitro Evaluation of the Anti-diabetic Potential of p-Propoxybenzoic Acid

Background: p-propoxybenzoic acid (p-PBA) is reported as an active chemical constituent of medicinal plants that possess anti-diabetic activity. It is termed a Multiple-Designed Ligand (MDL) having the ability to block more than one enzyme. A molecular docking study justifies the binding ability of p-PBA with acarbose and NaVO4 which were considered standard compounds having the ability to block target enzymes. α-amylase inhibition assay was used as an in-vitro screening model to evaluate the activity of p-PBA against diabetes on an initial basis. Methods: For the molecular docking study, a PDB file of p-PBA was prepared and PDB files of α-amylase (1C8Q), α-glucosidase (5KZW) and PTP1B (5K9W) were procured. p-PBA was docked against the enzymes using the blind docking method. The binding score of p-PBA and standard with enzymes was obtained and compared. The percentage inhibition of an α-amylase enzyme by p-PBA was measured by using a DNS-modified α-amylase inhibition assay and half-maximal inhibitory concentration (IC50) was calculated. Results: p-PBA has a significant inhibitory effect against α-amylase, α-glucosidase, and PTP1B with docking scores of 8.43 ± 0.44 kcal/mol, 9.19 ± 0.49 kcal/mol, and 9.40 ± 0.47 kcal/mol respectively. IC50calculated from the results of α-amylase inhibition assay p-PBA was 56.59 μg/mL. Conclusion: A combination of in-silico and in-vitro methods assessed p-PBA’s anti-diabetic potential on an initial basis. A molecular docking study involving p-PBA concluded the affinity of p-PBA to α-amylase, α-glucosidase, and PTP1B was significantly correlated with the affinity of acarbose and NaVO4. In-vitro α-amylase assay validated the compound’s inhibitory action against the enzyme.

Edible Bird’s Nest as Potential Food with Anti-Viral and Anti-Inflammatory Properties Against Covid-19: an in Silico Study

The Chinese believe consuming edible bird’s nests (EBN) can increase immunity to various diseases, including Covid-19. This study attempts to identify SARS COV-2-specific anti-viral and anti-inflammatory agents of EBN. We gathered samples from PubChem and Protein Data Bank (PDB). Afterwards, drug likeness was examined using the Lipinski model from the SCFBIO online service. The PASS web server analyzed the bioactive likelihood of chemicals found in EBN. Using PyRx 0.8 software with the blind docking technique. The PoseView web server and PyMol v2.4.1 software were utilized to ascertain molecular interactions. The in silico results show the potential of EBN as food therapy for Covid-19 sufferers, which is indicated by the presence of bioactive compounds from edible bird’s nest consisting of 9-O-acetylated GD3, glycopeptide, N-acetyl neuraminic acid, N-glycolyl-neuraminic acid, sialic acid, and tetra acetyl-thymol-beta-D-glucoside. These bio compounds are predicted to work as anti-viral and anti-inflammatory candidates against SARS-COV-2.

Identification of potential therapeutic intervening targets by in-silico analysis of nsSNPs in preterm birth-related genes.

Prematurity is the foremost cause of death in children under 5 years of age. Genetics contributes to 25-40% of all preterm births (PTB) yet we still need to identify specific targets for intervention based on genetic pathways. This study involved the effect of region-specific non-synonymous variations and their transcript level mutational impact on protein functioning and stability by various in-silico tools. This investigation identifies potential therapeutic targets to manage the challenge of PTB, corresponding protein cavities and explores their binding interactions with intervening compounds. We searched 20 genes coding 55 PTB proteins from NCBI. Single Nucleotide Polymorphisms (SNPs) of concerned genes were extracted from ENSEMBL, and filtration of exonic variants (non-synonymous) was performed. Several in-silico downstream protein functional effect prediction tools were used to identify damaging variants. Rare coding variants were selected with an allele frequency of ≤1% in 1KGD, further supported by South Asian ALFA frequencies and GTEx gene/tissue expression database. CNN1, COL24A1, IQGAP2 and SLIT2 were identified with 7 rare pathogenic variants found in 17 transcript sequences. The functional impact analyses of rs532147352 (R>H) of CNN1 computed through PhD-SNP, PROVEAN, SNP&GO, PMut and MutPred2 algorithms showed impending deleterious effects, and the presence of this pathogenic mutation in CNN1 resulted in large decrease in protein structural stability (ΔΔG (kcal/mol). After structural protein identification, homology modelling of CNN1, which has been previously reported as a biomarker for the prediction of PTB, was performed, followed by the stereochemical quality checks of the 3D model. Blind docking approach were used to search the binding cavities and molecular interactions with progesterone, ranked with energetic estimations. Molecular interactions of CNN1 with progesterone were investigated through LigPlot 2D. Further, molecular docking experimentation of CNN1 showed the significant interactions at S102, L105, A106, K123, Y124 with five selected PTB-drugs, Allylestrenol (-7.56 kcal/mol), Hydroxyprogesterone caproate (-8.19 kcal/mol), Retosiban (-9.43 kcal/mol), Ritodrine (-7.39 kcal/mol) and Terbutaline (-6.87 kcal/mol). Calponin-1 gene and its molecular interaction analysis could serve as an intervention target for the prevention of PTB.

Breynia cernua: Chemical Profiling of Volatile Compounds in the Stem Extract and Its Antioxidant, Antibacterial, Antiplasmodial and Anticancer Activity In Vitro and In Silico

Breynia cernua has been used as an alternative medicine for wounds, smallpox, cervical cancer, and breast cancer. This plant is a potential source of new plant-derived drugs to cure numerous diseases for its multiple therapeutic functions. An in vitro study revealed that the methanol extract of B. cernua (stem) exhibits antioxidant activity according to DPPH and SOD methods, with IC50 values of 33 and 8.13 ppm, respectively. The extract also exerts antibacterial activity against Staphylococcus aureus with minimum bactericidal concentration of 1875 ppm. Further analysis revealed that the extract with a concentration of 1-2 ppm protects erythrocytes from the ring formation stage of Plasmodium falciparum, while the extract with a concentration of 1600 ppm induced apoptosis in the MCF-7 breast cancer cell line. GC-MS analysis showed 45 bioactive compounds consisting of cyclic, alkyl halide, organosulfur, and organoarsenic compounds. Virtual screening via a blind docking approach was conducted to analyze the binding affinity of each metabolite against various target proteins. The results unveiled that two compounds, namely, N-[β-hydroxy-β-[4-[1-adamantyl-6,8-dichloro]quinolyl]ethyl]piperidine and 1,3-phenylene, bis(3-phenylpropenoate), demonstrated the best binding score toward four tested proteins with a binding affinity varying from -8.3 to -10.8 kcal/mol. Site-specific docking analysis showed that the two compounds showed similar binding energy with native ligands. This finding indicated that the two phenolic compounds could be novel antioxidant, antibacterial, antiplasmodial, and anticancer drugs. A thorough analysis by monitoring drug likeness and pharmacokinetics revealed that almost all the identified compounds can be considered as drugs, and they have good solubility, oral bioavailability, and synthetic accessibility. Altogether, the in vitro and in silico analysis suggested that the extract of B. cernua (stem) contains various compounds that might be correlated with its bioactivities.

Identification of the molecular determinants of antagonist potency in the allosteric binding pocket of human P2X4.

P2X receptors are a family of ATP-gated cation channels comprising seven subtypes in mammals, which play key roles in nerve transmission, pain sensation and inflammation. The P2X4 receptor in particular has attracted significant interest from pharmaceutical companies due to its physiological roles in neuropathic pain and modulation of vascular tone. A number of potent small-molecule P2X4 receptor antagonists have been developed, including the allosteric P2X4 receptor antagonist BX430, which is approximately 30-fold more potent at human P2X4 compared with the rat isoform. A single amino-acid difference between human and rat P2X4 (I312T), located in an allosteric pocket, has previously been identified as critical for BX430 sensitivity, implying that BX430 binds in this pocket. Using a combination of mutagenesis, functional assay in mammalian cells and in silico docking we confirmed these findings. Induced-fit docking, permitting the sidechains of the amino-acids of P2X4 to move, showed that BX430 could access a deeper portion of the allosteric pocket, and that the sidechain of Lys-298 was important for shaping the cavity. We then performed blind docking of 12 additional P2X4 antagonists into the receptor extracellular domain, finding that many of these compounds favored the same pocket as BX430 from their calculated binding energies. Induced-fit docking of these compounds in the allosteric pocket enabled us to show that antagonists with high potency (IC50 ≤ 100nM) bind deep in the allosteric pocket, disrupting a network of interacting amino acids including Asp-85, Ala-87, Asp-88, and Ala-297, which are vital for transmitting the conformational change following ATP binding to channel gating. Our work confirms the importance of Ile-312 for BX430 sensitivity, demonstrates that the allosteric pocket where BX430 binds is a plausible binding pocket for a series of P2X4 antagonists, and suggests a mode of action for these allosteric antagonists involving disruption of a key structural motif required for the conformational change induced in P2X4 when ATP binds.