Protein Binding Pocket Research Articles

Helicobacter pylori biofilm interference by N-acyl homoserine lactonases: in vitro and in silico approaches.

Qurom quenching enzyme have an impact on treatment efficacy and prevent the recurrence of Helicobacter pylori biofilm-related infections, although it has not been thoroughly investigated in vitro and in silico. The current study aims to characterize the N-acyl homoserine lactonase, the quorum quenching AiiA protein of Bacillus licheniformis against H. pylori biofilm. In this study, AiiA protein were screened for their anti-biofilm activity, was found to effectively control biofilm formation of H. pylori with concentrations ranging from 2 to 10µg/mL. According to CLSM and COMSTAT analysis, the untreated substratum had the robust biofilm biomass of 25-18 µM and biovolume of 3-4 mm3 /mm2. The total biofilm biovolume and average biofilm thickness were considerably reduced by 40% with a single application of 10µg/mL of AiiA protein. The biofilm treated with AiiA exhibited a lower urease and polysaccharides than to the untreated biofilm. Further, in silico analysis, exhibited a greater interaction of AiiA against the outer membrane proteins of H. pylori compared to virulence factors. The conserved domains in the binding pockets of AiiA proteins showed a highest binding affinity proving the catalytic activity of the protein. In this study, the H. pylori biofilm architecture, exopolysaccharide and urease were significantly controlled by our purified N-acyl homoserine lactonase from B. licheniformis. Furthermore, the molecular docking showed the significant interaction between AiiA and key biofilm forming and virulence proteins proved an excellent antibiofilm activity controlling the infections of H. pylori human pathogen.

A novel (-)-(2S)-7,4'-dihydroxyflavanone compound for treating age-related diabetes mellitus through immunoinformatics-guided activation of CISD3.

The iron-sulfur domain (CISD) proteins of CDGSH are classified into three classes: CISD1, CISD2, and CISD3. During premature ageing, mutations that affect these proteins, namely their binding sites, could result in reduced protein production and an inability to preserve cellular integrity. Consequently, this leads to the development of conditions such as diabetes. Notably, CISD3 plays a crucial role in the management of age-related disorders such as Wolfram syndrome, which is often referred to as DIDMOAD (diabetes insipidus, diabetes mellitus, optic atrophy, and deafness). Computational analyses have predicted that CISD3 regulates the redox state, safeguards the endoplasmic reticulum and mitochondria, and maintains intracellular calcium levels. CISD3, a member of a recently discovered gene family associated with the CDGSH iron protein apoptotic compensatory response, fulfils a crucial function in mitigating the effects of accelerated ageing. The compound "(-)-(2S)-7,4'-Dihydroxyflavanone" has been discovered by computational drug design as a possible activator of CISD3. It shows potential therapeutic benefits in ameliorating metabolic dysfunction and enhancing glucose regulation. The ligand binds to the binding pocket of the CISD3 protein, increasing the stability of the protein and enhancing its functionality. The current research investigates the binding processes of the molecule in various structures and its anticipated effects on these tissues, therefore providing valuable insights into the mitigation of age-related diabetes and metabolic dysfunction. The projected tripling of the worldwide population of individuals aged 50 and above by 2050 necessitates the urgent development of immunoinformatics-based approaches, including pharmaceutical therapies that target CISD3, to prevent age-related pathologies. The stimulation of CISD3, namely by compounds such as "(-)-(2S)-7,4'-Dihydroxyflavanone", has the potential to counteract telomere shortening and improve metabolic pathways.

An in silico design method of a peptide bioreceptor for cortisol using molecular modelling techniques

Cortisol is established as a reliable biomarker for stress prompting intensified research in developing wearable sensors to detect it via eccrine sweat. Since cortisol is present in sweat in trace quantities, typically 8–140 ng/mL, developing such biosensors necessitates the design of bioreceptors with appropriate sensitivity and selectivity. In this work, we present a systematic biomimetic methodology and a semi-automated high-throughput screening tool which enables rapid selection of bioreceptors as compared to ab initio design of peptides via computational peptidology. Candidate proteins from databases are selected via molecular docking and ranked according to their binding affinities by conducting automated AutoDock Vina scoring simulations. These candidate proteins are then validated via full atomistic steered molecular dynamics computations including umbrella sampling to estimate the potential of mean force using GROMACS version 2022.6. These explicit molecular dynamic calculations are carried out in an eccrine sweat environment taking into consideration the protein dynamics and solvent effects. Subsequently, we present a candidate baseline peptide bioreceptor selected as a contiguous sequence of amino acids from the selected protein binding pocket favourably interacting with the target ligand (i.e., cortisol) from the active binding site of the proteins and maintaining its tertiary structure. A unique cysteine residue introduced at the N-terminus allows orientation-specific surface immobilization of the peptide onto the gold electrodes and to ensure exposure of the binding site. Comparative binding affinity simulations of this peptide with the target ligand along with commonly interfering species e.g., progesterone, testosterone and glucose are also presented to demonstrate the validity of this proposed peptide as a candidate baseline bioreceptor for future cortisol biosensor development.

In-vitro enzyme inhibition, kinetics, molecular docking and dynamics simulation approaches to decoding the mechanism of Ficus virens in cholinesterase inhibition

In the current study, we investigate the bioactive potentials and mode of action of Ficus virens bark (FVB) extract against cholinesterase enzymes, via in-vitro cell-free cholinesterase inhibition kinetics, molecular docking, ADMET and dynamic simulation. Our results illustrated that FVBM extract showed better DPPH-free radical scavenging activity (IC50 = 17.8 ± 0.46 µg/ml) and AChE inhibitory ability (IC50 = 37.2 ± 0.43 µg/ml). Kinetics study explored the mixed inhibition of AChE enzyme by FVBM extract. Furthermore, molecular docking demonstrates that compounds Diethyl Phthalate and Dinopol NOP present in FVBM extract have good ΔG: −8.6 kcal/mol and Ki; 2.01 × 106 M−1 for AChE enzyme than BuChE enzyme (ΔG: −7.6 and Ki: 3.72 × 105 M−1). A simulation study of 200 ns of best two hits and Tacrine confirms that these compounds remained inside the binding pocket of proteins and formed a stable protein-ligand complex via interacting with key residue. Further studies could provide a better understanding of the therapeutic potential of these promising compounds.

Identification of potential inhibitors against Corynebacterium diphtheriae MtrA response regulator protein; an in-silico drug discovery approach

Corynebacterium diphtheriae is a multi-drug resistant bacteria responsible for the life-threatening respiratory illness, diphtheria which can lead to severe Nervous system disorders, mainly infecting the lungs, heart, and kidneys if left untreated. In the current study, Corynebacterium diphtheriae MtrA response regulator protein was targeted, which regulates a two-component system of bacterial pathogenesis, and initiates DNA replication and cell division. In the current study a computational approach have been described for drug development against C. diphtheriae infections by inhibiting MtrA protein by small molecules acting as potential inhibitors against it. Molecular docking analysis of the equilibrated MtrA protein revealed compound-0.2970, compound-0.3029, and compound-0.3016 from Asinex Library as the promising inhibitors based on their lowest binding energies (−9.8 kJ/mol, −9.2 kJ/mol, and −8.9 kJ/mol), highest gold scores (40.53, 47.41, and 48.41), drug-likeness and pharmacokinetic properties. The MD simulation studies of the identified top-ranked inhibitors at 100 ns elucidated the system stability and fluctuations in the binding pocket of MtrA protein. Molecular Dynamics Simulations of the top three docked complexes further revealed that the standard binding pocket was retained ensuring the system stability. The rearrangements of H-bonds, van der Waals, pi-pi, and solid hydrophobic interactions were also observed. The binding free energy calculations (MM/PBSA and MM/GBSA) suggested a fundamental binding capability of the ligand to the target receptor MtrA. Therefore, the current study has provided excellent candidates acting as potent inhibitors for developing therapeutic drugs against C. diphtheriae infections. However, in vivo and in vitro animal experiments and accurate clinical trials are needed to validate the potential inhibitory effect of these compounds.

Natural Products from Brazilian Biodiversity Explored as Anti-EBV Drug Candidates: In-Silico Database Mining, Docking Computations, Molecular Dynamics and DFT Calculations

A DNA tumor virus called Epstein–Barr virus (EBV) is the cause of 1–2% of human cancers. EBV-associated carcinogenesis is caused by a persistent latent infection. The shortage of antiviral medications or vaccinations to control the virus led to a significant percentage of critically sickness individuals needing to be hospitalized. In the absence of an effective vaccine and approved drug, there is an immediate need for treatments that can combat EBV infection. Epstein–Barr nuclear antigen 1 (EBNA1) is continually expressed in all malignancies linked to EBV; it is a desirable target for curative interference. Herein, natural product compounds from the Nuclei of Bioassays, Ecophysiology and Biosynthesis of Natural Products (NuBBE) database were mined to identify potent EBNA1 inhibitors. The performance of the molecular docking technique was initially assessed in expecting the ligand-target binding pose according to the available experimental data. On the basis of the estimated docking protocol, the NuBBE database was virtually screened toward the EBNA1 protein, and the natural compounds with binding scores less than KWG (calc. -7.8 kcal/mol) were subjected to molecular dynamics (MD) simulations followed by binding energy ([Formula: see text] calculation using MM/GBSA approach. Based on binding energy computations, NuBBE1460 revealed superior [Formula: see text] with a value of −36.2 kcal/mol, compared to KWG (calc. -32.4 kcal/mol). Post-MD analyses displayed a high steadiness of NuBBE1460 within the EBNA1 protein binding pocket. Furthermore, the physicochemical, pharmacokinetic and toxicity features of the identified compound were predicted, demonstrating high degrees of its oral bioavailability. The energetical and geometrical properties of NuBBE1460 were calculated using the DFT method. Conclusively, this study offers NuBBE1460 obtained from natural sources as a lead EBNA1 inhibitor for future investigation and development as a therapeutic for EBV.

Exploring the cytoprotective potential of flavonoid fraction from Carica papaya L. Cultivar ‘Red Lady' Leaf: Insights into Nrf2 activation via in-vitro and in-silico approaches

Flavonoids play a significant role in mitigating the production of reactive oxygen species (ROS) and subsequent oxidative stress. In the present study we aimed to evaluate the cytoprotective and antioxidant defense mechanism of flavonoid fraction from Carica papaya L. (FFCP) leaf in human PBMC. For this investigation, the human PBMCs were lotted into three groups viz., negative control, positive control (treated with H2O2), and experiment group (pretreated with FFCP and challenged with H2O2). The cytoprotective mechanism was evaluated by assessing the intracellular ROS and Ca2+ mobilization, membrane and DNA damages, antioxidant enzymes, and Nrf2 activation. In-silico analysis was employed for assessing the interactions of flavonoids in FFCP with the Nrf2 binding pocket of Keap 1 protein. The results of the experiment unveiled that FFCP protected the human PBMCs from H2O2 induced oxidative damages such as lipid peroxidation, LDH leakage, DNA breakage, reduced the intracellular ROS and Ca2+ mobilization as compared to positive control. FFCP pretreatment effectively improved the activity of key antioxidant enzyme markers, such as SOD (1.66 ± 0.032-fold increase), CAT (2.24 ± 0.09-fold increase), GPx (1.94 ± 0.05-fold increase), and GR (1.81 ± 0.14-fold increase). Furthermore, the FFCP modulated the Nrf2 gene expression (a 2.38 ± 0.25 -fold) and its translocation into the nucleus as compared to the positive control. In-silico analysis enunciated that all flavonoids exhibited strong affinity with the Nrf2 binding pocket of Keap1 protein via stable hydrogen bonding and hydrophobic interactions. These observations suggest that FFCP is a natural source of bioactive compounds with nutraceutical benefits for combating oxidative stress-associated diseases by protecting the cells from oxidative stress induced damages as well as by instigating antioxidant defense mechanisms.

Multitargeted molecular docking and dynamics simulation studies of 1,3,4-thiadiazoles synthesised from (R)-carvone against specific tumour protein markers: An In-silico study of two diastereoisomers

In the present work, we describe the synthesis of new 1,3,4-thiadiazole derivatives from natural (R)-carvone in three steps including, dichloro-cyclopropanation, a condensation with thiosemicarbazide and then a 1,3-dipolar cycloaddition reaction with various nitrilimines. the targeted compounds were structurally identified by 1H &13C NMR and HRMS analyses. The cytotoxic assay demonstrated that some synthesized novel compounds were potent on certain cancer cell lines. Molecular modeling studies were undertaken to rationalize the wet lab study results. Furthermore, molecular docking was performed to unveil the binding potential of the most active derivatives, 3a and 6c, to caspase-3 and COX-2. The stabilities of the protein-compound complexes obtained from the docking were evaluated using MD simulation. Furthermore, FMO and related parameters of the active compounds and their stereoisomers were examined through DFT studies. The docking study showed compound 6c had a higher binding potential than caspase-3. However, the binding strength of 6c was found to be less than that of the standard drug, doxorubicin, as it formed lower conventional hydrogen bonds. On the other hand, compound 3a had a higher binding potential to COX-2. However, the binding potential 3a was much lower than that of the standard COX-2 inhibitor, celecoxib. The MD simulation demonstrated that the caspase-3-6c complex was less stable than the caspase-3-doxorubicin complex. In contrast, the COX-2-3a complex was stable, and 3a was anticipated to remain inside the protein's binding pocket. The DFT study showed that 3a had higher chemical stability than 6c. The electron exchange capacity, chemical stability, and molecular orbital distributions of the stereoisomers of the active compounds were also found to be alike.

MDFit: automated molecular simulations workflow enables high throughput assessment of ligands-protein dynamics.

Molecular dynamics (MD) simulation is a powerful tool for characterizing ligand-protein conformational dynamics and offers significant advantages over docking and other rigid structure-based computational methods. However, setting up, running, and analyzing MD simulations continues to be a multi-step process making it cumbersome to assess a library of ligands in a protein binding pocket using MD. We present an automated workflow that streamlines setting up, running, and analyzing Desmond MD simulations for protein-ligand complexes using machine learning (ML) models. The workflow takes a library of pre-docked ligands and a prepared protein structure as input, sets up and runs MD with each protein-ligand complex, and generates simulation fingerprints for each ligand. Simulation fingerprints (SimFP) capture protein-ligand compatibility, including stability of different ligand-pocket interactions and other useful metrics that enable easy rank-ordering of the ligand library for pocket optimization. SimFPs from a ligand library are used to build & deploy ML models that predict binding assay outcomes and automatically infer important interactions. Unlike relative free-energy methods that are constrained to assess ligands with high chemical similarity, ML models based on SimFPs can accommodate diverse ligand sets. We present two case studies on how SimFP helps delineate structure-activity relationship (SAR) trends and explain potency differences across matched-molecular pairs of (1) cyclic peptides targeting PD-L1 and (2) small molecule inhibitors targeting CDK9.

Repurposing of FDA-approved drugs against oligomerization domain of dengue virus NS1 protein: a computational approach.

Dengue fever is a serious health hazard on a global scale and its primary causative agent is the dengue virus (DENV). The non-structural protein 1 (NS1) of DENV plays a pivotal role in pathogenesis. It is associated with several autoimmune events, endothelial cell apoptosis, and vascular leakage, which increase mainly during the critical phase of infection. In this study, important residues of the oligomerization domain of NS1 protein were identified by literature searches. Virtual screening has been conducted using the entire dataset of the DrugBank database and the potential small-molecule inhibitors against the NS1 protein have been chosen on the basis of binding energy values. This is succeeded by molecular dynamics (MD) simulations of the shortlisted compounds, ultimately giving rise to five compounds. These five compounds were further subjected to RAMD simulations by applying a random direction force of specific magnitude on the ligand center of mass in order to push the ligand out of the protein-binding pocket, for the quantitative estimation of their binding energy values to determine the interaction strength between protein and ligand which prevents ligand unbinding from its binding site, ultimately leading to the selection of three major compounds, DB00826 (Natamycin), DB11274 (Dihydro-alphaergocryptine), and DB11275 (Epicriptine), with the DB11274 having a role against idiopathic Parkinson's disease, and thus may have possible important roles in the prevention of dengue-associated Parkinsonism. These compounds may act as prospective drugs against dengue, by preventing the oligomerization of the NS1 protein, thereby preventing disease progression and pathogenesis.

Anti-quorum sensing activity of α-amidoamides against Agrobacterium tumefaciensNT1: Insights from molecular docking and dynamic investigations to synergistic approach of metronidazole release from gel formulations

A unique approach is imperative for the development of drugs aimed at inhibiting various stages of infection, rather than solely focusing on bacterial viability. Among the array of unconventional targets explored for formulating novel antimicrobial medications, blocking the quorum-sensing (QS) system emerges as a highly effective and promising strategy against a variety of pathogenic microbes. In this investigation, we have successfully assessed nine α-aminoamides for their anti-QS activity using Agrobacterium tumefaciensNT1 as a biosensor strain. Among these compounds, three (2, 3and, 4) have been identified as potential anti-QS candidates. Molecular docking studies have further reinforced these findings, indicating that these compounds exhibit favorable pharmacokinetic profiles. Additionally, we have assessed the ligand's stability within the protein's binding pocket using molecular dynamics (MD) simulations and MMGBSA analysis. Further, combination of antiquorum sensing properties with antibiotics viaself-assembly represents a promising approach to enhance antibacterial efficacy, overcome resistance, and mitigate the virulence of bacterial pathogens. The release study also reflects a slow and gradual release of the metronidazole at both pH 6.5 and pH 7.4, avoiding the peaks and troughs associated with more immediate release formulations.

LVPocket: integrated 3D global-local information to protein binding pockets prediction with transfer learning of protein structure classification

BackgroundPrevious deep learning methods for predicting protein binding pockets mainly employed 3D convolution, yet an abundance of convolution operations may lead the model to excessively prioritize local information, thus overlooking global information. Moreover, it is essential for us to account for the influence of diverse protein folding structural classes. Because proteins classified differently structurally exhibit varying biological functions, whereas those within the same structural class share similar functional attributes.ResultsWe proposed LVPocket, a novel method that synergistically captures both local and global information of protein structure through the integration of Transformer encoders, which help the model achieve better performance in binding pockets prediction. And then we tailored prediction models for data of four distinct structural classes of proteins using the transfer learning. The four fine-tuned models were trained on the baseline LVPocket model which was trained on the sc-PDB dataset. LVPocket exhibits superior performance on three independent datasets compared to current state-of-the-art methods. Additionally, the fine-tuned model outperforms the baseline model in terms of performance.Scientific contributionWe present a novel model structure for predicting protein binding pockets that provides a solution for relying on extensive convolutional computation while neglecting global information about protein structures. Furthermore, we tackle the impact of different protein folding structures on binding pocket prediction tasks through the application of transfer learning methods.Graphical

4D-QSAR, ADMET properties, and molecular dynamics simulations for designing N-substituted urea/thioureas as human glutaminyl cyclase inhibitors

Human glutaminyl cyclase (hQC) inhibitors have great potential to be used as anti- Alzheimer's disease (AD) agents by reducing the toxic pyroform of β-amyloid in the brains of AD patients. The four-dimensional quantitative structure activity relationship (4D-QSAR) model of N-substituted urea/thioureas was established with satisfying predictive ability and statistical reliability (Q2 = 0.521, R2 = 0.933, R2prep = 0.619). By utilizing the developed 4D-QSAR model, a set of new N-substituted urea/thioureas was designed and evaluated for their Absorption Distribution Metabolism Excretion and Toxicity (ADMET) properties. The results of molecular dynamics (MD) simulations, Principal component analysis (PCA), free energy landscape (FEL), dynamic cross-correlation matrix (DCCM) and molecular mechanics generalized Born Poisson-Boltzmann surface area (MM-PBSA) free energy calculations, revealed that the designed compounds were remained stable in protein binding pocket and compounds b ∼ f (−35.1 to −44.55 kcal/mol) showed higher binding free energy than that of compound 14 (−33.51 kcal/mol). The findings of this work will be a theoretical foundation for further research and experimental validation of urea/thiourea derivatives as hQC inhibitors.

Small-Molecule Disruptors of the Interaction between Calcium- and Integrin-Binding Protein 1 and Integrin αIIbβ3 as Novel Antiplatelet Agents.

Thrombosis, a key factor in most cardiovascular diseases, is a major contributor to human mortality. Existing antithrombotic agents carry a risk of bleeding. Consequently, there is a keen interest in discovering innovative antithrombotic agents that can prevent thrombosis without negatively impacting hemostasis. Platelets play crucial roles in both hemostasis and thrombosis. We have previously characterized calcium- and integrin-binding protein 1 (CIB1) as a key regulatory molecule that regulates platelet function. CIB1 interacts with several platelet proteins including integrin αIIbβ3, the major glycoprotein receptor for fibrinogen on platelets. Given that CIB1 regulates platelet function through its interaction with αIIbβ3, we developed a fluorescence polarization (FP) assay to screen for potential inhibitors. The assay was miniaturized to 1536-well and screened in quantitative high-throughput screening (qHTS) format against a diverse compound library of 14,782 compounds. After validation and selectivity testing using the FP assay, we identified 19 candidate inhibitors and validated them using an in-gel binding assay that monitors the interaction of CIB1 with αIIb cytoplasmic tail peptide, followed by testing of top hits by intrinsic tryptophan fluorescence (ITF) and microscale thermophoresis (MST) to ascertain their interaction with CIB1. Two of the validated hits shared similar chemical structures, suggesting a common mechanism of action. Docking studies further revealed promising interactions within the hydrophobic binding pocket of the target protein, particularly forming key hydrogen bonds with Ser180. The compounds exhibited a potent antiplatelet activity based on their inhibition of thrombin-induced human platelet aggregation, thus indicating that disruptors of the CIB1- αIIbβ3 interaction could carry a translational potential as antithrombotic agents.

Druggability Analysis of Protein Targets for Drug Discovery to Combat Listeria monocytogenes.

Extensive research has been conducted to identify key proteins governing stress responses, virulence, and antimicrobial resistance, as well as to elucidate their interactions within Listeria monocytogenes. While these proteins hold promise as potential targets for novel strategies to control L. monocytogenes, given their critical roles in regulating the pathogen's metabolism, additional analysis is needed to further assess their druggability-the chance of being effectively bound by small-molecule inhibitors. In this work, 535 binding pockets of 46 protein targets for known drugs (mainly antimicrobials) were first analyzed to extract 13 structural features (e.g., hydrophobicity) in a ligand-protein docking platform called Molsoft ICM Pro. The extracted features were used as inputs to develop a logistic regression model to assess the druggability of protein binding pockets, with a value of one if ligands can bind to the protein pocket. The developed druggability model was then used to evaluate 23 key proteins from L. monocytogenes that have been identified in the literature. The following proteins are predicted to be high-potential druggable targets: GroEL, FliH/FliI complex, FliG, FlhB, FlgL, FlgK, InlA, MogR, and PrfA. These findings serve as an initial point for future research to identify specific compounds that can inhibit druggable target proteins and to design experimental work to confirm their effectiveness as drug targets.

Glutamate Receptor-like (GLR) Family in Brassica napus: Genome-Wide Identification and Functional Analysis in Resistance to Sclerotinia sclerotiorum.

Plant glutamate receptor-like channels (GLRs) are homologs of animal ionotropic glutamate receptors. GLRs are critical in various plant biological functions, yet their genomic features and functions in disease resistance remain largely unknown in many crop species. Here, we report the results on a thorough genome-wide study of the GLR family in oilseed rape (Brassica napus) and their role in resistance to the fungal pathogen Sclerotinia sclerotiorum. A total of 61 GLRs were identified in oilseed rape. They comprised three groups, as in Arabidopsis thaliana. Detailed computational analyses, including prediction of domain and motifs, cellular localization, cis-acting elements, PTM sites, and amino acid ligands and their binding pockets in BnGLR proteins, unveiled a set of group-specific characteristics of the BnGLR family, which included chromosomal distribution, motif composition, intron number and size, and methylation sites. Functional dissection employing virus-induced gene silencing of BnGLRs in oilseed rape and Arabidopsis mutants of BnGLR homologs demonstrated that BnGLR35/AtGLR2.5 positively, while BnGLR12/AtGLR1.2 and BnGLR53/AtGLR3.2 negatively, regulated plant resistance to S. sclerotiorum, indicating that GLR genes were differentially involved in this resistance. Our findings reveal the complex involvement of GLRs in B. napus resistance to S. sclerotiorum and provide clues for further functional characterization of BnGLRs.

Correlation of protein binding pocket properties with hits’ chemistries used in generation of ultra-large virtual libraries

Although the size of virtual libraries of synthesizable compounds is growing rapidly, we are still enumerating only tiny fractions of the drug-like chemical universe. Our capability to mine these newly generated libraries also lags their growth. That is why fragment-based approaches that utilize on-demand virtual combinatorial libraries are gaining popularity in drug discovery. These à la carte libraries utilize synthetic blocks found to be effective binders in parts of target protein pockets and a variety of reliable chemistries to connect them. There is, however, no data on the potential impact of the chemistries used for making on-demand libraries on the hit rates during virtual screening. There are also no rules to guide in the selection of these synthetic methods for production of custom libraries. We have used the SAVI (Synthetically Accessible Virtual Inventory) library, constructed using 53 reliable reaction types (transforms), to evaluate the impact of these chemistries on docking hit rates for 40 well-characterized protein pockets. The data shows that the virtual hit rates differ significantly for different chemistries with cross coupling reactions such as Sonogashira, Suzuki–Miyaura, Hiyama and Liebeskind–Srogl coupling producing the highest hit rates. Virtual hit rates appear to depend not only on the property of the formed chemical bond but also on the diversity of available building blocks and the scope of the reaction. The data identifies reactions that deserve wider use through increasing the number of corresponding building blocks and suggests the reactions that are more effective for pockets with certain physical and hydrogen bond-forming properties.

Identification of new potent NLRP3 inhibitors by multi-level in-silico approaches

Nod-like receptor protein 3 (NLRP-3), is an intracellular sensor that is involved in inflammasome activation, and the aberrant expression of NLRP3 is responsible for diabetes mellitus, its complications, and many other inflammatory diseases. NLRP3 is considered a promising drug target for novel drug design. Here, a pharmacophore model was generated from the most potent inhibitor, and its validation was performed by the Gunner-Henry scoring method. The validated pharmacophore was used to screen selected compounds databases. As a result, 646 compounds were mapped on the pharmacophore model. After applying Lipinski's rule of five, 391 hits were obtained. All the hits were docked into the binding pocket of target protein. Based on docking scores and interactions with binding site residues, six compounds were selected potential hits. To check the stability of these compounds, 100 ns molecular dynamic (MD) simulations were performed. The RMSD, RMSF, DCCM and hydrogen bond analysis showed that all the six compounds formed stable complex with NLRP3. The binding free energy with the MM-PBSA approach suggested that electrostatic force, and van der Waals interactions, played a significant role in the binding pattern of these compounds. Thus, the outcomes of the current study could provide insights into the identification of new potential NLRP3 inflammasome inhibitors against diabetes and its related disorders.

In silico identification of phytochemical inhibitors for multidrug-resistant tuberculosis based on novel pharmacophore generation and molecular dynamics simulation studies

BackgroundMultidrug-resistant tuberculosis (particularly resistant to pyrazinoic acid) is a life-threatening chronic pulmonary disease. Running a marketed regime specifically targets the ribosomal protein subunit-1 (RpsA) and stops trans-translation in the non-mutant bacterium, responsible for the lysis of bacterial cells. However, in the strains of mutant bacteria, this regime has failed in curing TB and killing pathogens, which may only because of the ala438 deletion, which inhibit the binding of pyrazinoic acid to the RpsA active site. Therefore, such cases of tuberculosis need an immediate and effective regime.ObjectiveThis study has tried to determine and design such chemotypes that are able to bind to the mutant RpsA protein.MethodsFor these purposes, two phytochemical databases, i.e., NPASS and SANCDB, were virtually screened by a pharmacophore model using an online virtual screening server Pharmit.ResultsThe model of pharmacophore was developed using the potential inhibitor (zr115) for the mutant of RpsA. Pharmacophore-based virtual screening results into 154 hits from the NPASS database, and 22 hits from the SANCDB database. All the predicted hits were docked in the binding pocket of the mutant RpsA protein. Top-ranked five and two compounds were selected from the NPASS and SANCDB databases respectively. On the basis of binding energies and binding affinities of the compounds, three compounds were selected from the NPASS database and one from the SANCDB database. All compounds were found to be non-toxic and highly active against the mutant pathogen. To further validate the docking results and check the stability of hits, molecular dynamic simulation of three compounds were performed. The MD simulation results showed that all these finally selected compounds have stronger binding interactions, lesser deviation or fluctuations, with greater compactness compared to the reference compound.ConclusionThese findings indicate that these compounds could be effective inhibitors for mutant RpsA.

Screening of Potential Phytochemicals for the Identified Candidate Virulence Proteins in Enterobacter huaxiensis

ABSTRACT: Enterobacter huaxiensis, a novel gram-negative bacterium of the family Enterobacteriaceae, was recovered from the blood of the patients at West China Hospital. The present study aims to predict potential lead molecules against the identified virulence-associated antibiotic-resistant protein for drug designing. Two virulence-associated antibiotic-resistance proteins belonging to the OqxAB efflux protein family of the RND superfamily were identified in the pathogen using bioinformatics tools/databases. Based on the structure prediction by homology modeling and validations, the RND transporter permease subunit, OqxB was selected as the potential target for lead identification. The binding pocket of the target protein was calculated using CASTp. A total of 204 phytochemicals were screened virtually to obtain compounds that had better binding affinity, drug-likeness and pharmacokinetic potential to be used as safe ligands against the target protein. Among these, Chrysoeriol, Isopimaric acid, Baicalein and Biochanin A were found to be within the permissible range of Lipinski rule of five for drug-likeness, possessing better ADMET properties, and lower target-protein binding energy (less than -8.0 kcal/mol). Ligand-protein docking showed stable non-covalent interactions between active site residues and ligands. Thus, these compounds may be considered potential inhibitors of the target protein that may inactivate the efflux pump and restore antibiotic sensitivity.