Omicron favors neuropilin1 binding over ACE2: Increased infectivity and available drugs

COVID-19 pandemic is continue with thousands of new cases every day around the world, even then different vaccines have been developed and proven efficacious against SARS-CoV-2. Several know antivirals drugs have been repurposed or tested against SARS-CoV-2 but we still don’t have an effective therapeutic strategy to control this viral infection. Moreover, in the race of finding out an efficient antiviral, excess uses of antiviral drugs developed a selective pressure on the virus that results in the high frequency of mutations and the possible emergence of antiviral drug resistance against SARS-CoV-2. Omicron is a recently emerged, highly mutated variant of SARS-CoV-2, reported for high infectivity. In the present study, we performed molecular docking analysis between available potential antiviral drugs (remdesivir, nirmatrelvir, molnupiravir, EIDD-1931, GS-441524, and favipiravir) and omicron S protein including S protein/ACE2 complex. Our results suggest high infectivity of omicron, however, the known antiviral drugs were found efficacious against omicron variant. Further, to investigate the high infectivity of omicron, we performed a docking experiment between omicron S protein and neuropilin1 (NRP1). Surprisingly, results suggest high affinities with NRP1 than ACE2. Overall, results suggest that omicron favors NRP1 binding over ACE2, the possible reason behind improved infectivity of omicron variant.

AN OVERVIEW OF EPIGENETIC DRUGS, AND THEIR VIRTUAL SCREENING STUDY RETRIEVED FROM ZINC DATABASE ALONG WITH AN AUTODOCK STUDY OF THE BEST INHIBITOR

Objective: Over the last 30 y cancer epigenetics research has grown extensively. It is note-worthy to recognize that epigenetic misregulation could substantiate the development of cancer and we need to continue to look for anti-neoplastic epi-drugs. Taking into consideration this phenomenon, our first aim is to search for an effective epi-drugs by virtual screening from ZINC database and to explore the validity of the virtual screening. The second aim is to explore a binding conformation of the top affinity ligands against macromolecules, by docking experiment. Methods: The virtual screening was conducted by our Virtual Screening by Docking (VSDK) algorithm and procedure. Small molecules were randomly downloaded by ZINC database. For docking experiment, AutoDock 4.2.6 and AutoDock Tool were used. Results: It took eight to ten hours for the successful virtual screening of the 2778 small compounds retrieved at random from ZINC database. Among histone H2B E76K mutant (HHEM) inhibitors and DNA methyltransferase (DNMT) inhibitors, the first ranked inhibitors were 1H-1,2,4-triazole-3,5-diamine and 2-ethyl-1,3,4-oxadiazole respectively. Conclusion: As for the molecular structures obtained from virtual screening, most of the top ten HHEM and DNMT inhibitors contained 5-member rings. More than two times in affinity difference between the top and bottom ten compounds would indicate a successful virtual screening experiment. The histogram chart of AutoDock4 runs appeared in the lowest affinity region with two or three hydrogen bonds indicating a reliable conformation docking.

Identification and Interaction Analysis of Molecular Markers in Pancreatic Ductal Adenocarcinoma by Integrated Bioinformatics Analysis and Molecular Docking Experiments

The current investigation aimed to mine therapeutic molecular targets that play an key part in the advancement of pancreatic ductal adenocarcinoma (PDAC). The expression profiling by high throughput sequencing dataset profile GSE133684 dataset was downloaded from the Gene Expression Omnibus (GEO) database. Limma package of R was used to identify differentially expressed genes (DEGs). Functional enrichment analysis of DEGs were performed. Protein-protein interaction (PPI) networks of the DEGs were constructed. An integrated gene regulatory network was built including DEGs, microRNAs (miRNAs), and transcription factors. Furthermore, consistent hub genes were further validated. Molecular docking experiment was conducted. A total of 463 DEGs (232 up regulated and 231 down regulated genes) were identified between very early PDAC and normal control samples. The results of Functional enrichment analysis revealed that the DEGs were significantly enriched in vesicle organization, secretory vesicle, protein dimerization activity, lymphocyte activation, cell surface, transferase activity, transferring phosphorus-containing groups, hemostasis and adaptive immune system. The PPI network and gene regulatory network of up regulated genes and down regulated genes were established, and hub genes were identified. The expression of hub genes (CCNB1, FHL2, HLA-DPA1 and TUBB1) were also validated to be differentially expressed among PDAC and normal control samples. Molecular docking experiment predicted the novel inhibitory molecules for CCNB1 and FHL2. The identification of hub genes in PDAC enhances our understanding of the molecular mechanisms underlying the progression of this disease. These genes may be potential diagnostic biomarkers and/or therapeutic molecular targets in patients with PDAC.

Building Science Gateways for Analysing Molecular Docking Results Using a Generic Framework and Methodology

Molecular docking and virtual screening experiments require large computational and data resources and high-level user interfaces in the form of science gateways. While science gateways supporting such experiments are relatively common, there is a clearly identified need to design and implement more complex environments for further analysis of docking results. This paper describes a generic framework and a related methodology that supports the efficient development of such environments. The framework is modular enabling the reuse of already existing components. The methodology, which proposes three techniques that the development team can use, is agile and encourages active participation of end-users. Based on the framework and methodology, two prototype implementations of science-gateway-based docking environments are presented and evaluated. The first system recommends a receptor-ligand pair for the next docking experiment, and the second filters docking results based on ligand properties.

Accuracy Improvement Calibrations for the Double-Position 4-PPPS Aircraft Docking System

A double-positions 4-PPPS parallel mechanism is used for the aircraft fuselage assembly process to improve the docking efficiency and reduce the labor intensity. However, the accuracy is hard to guarantee, for the mechanism is large and redundant and has manufacturing and assembly errors. To improve the accuracy of the 4-PPPS parallel aircraft fuselage docking system, firstly, an averaging iteration method is proposed to calibrate the datum points in the airplane coordinate which are the references of the entire docking system. And secondly, a kinematic calibration method based on the derivative of the spatial pose transformation is proposed to calibrate up to 42 kinematic parameters. By these two methods, the final maximum position error reduced from 2.2 mm to 0.035 mm and the maximum pointing error reduced from 0.08 degree to 0.018 degree. The accuracy measurement and docking experiment prove the efficiency of the proposed methods.

Activating organ’s immunizing power against COVID–19–learning from SARS

Background Coronaviruses cause respiratory diseases in many animals, including humans. Spike protein is an important component of coronavirus structure and the formation of ACE2 (angiotensin converting enzyme 2)–spike complex mediates virus entry to host cells. C–type lectin family are widely distribute on the surface of human cells and have been shown to activate the immune system. In this article, we first illustrate why we can “learn from SARS” with phylogenetic analysis. Then, we use SARS spike protein structure, to inferring our molecular docking experiment, revealing the potential capacity of C–type lectin to directly interact with spike protein obstructs the formation of spike–ACE2 complex. Considering the expression profile of C–type lectin family changing significantly during infection, we predict certain members of this kind of protein as potential therapeutic target and verify their assumed function by inferring an C–type lectin–dependent CD4/CD28 T cell survival molecular network with endogenous molecular network theory (EMT) and comparing the predicted expression trend corresponding to each molecular with experiment data. Methods Alignments are inferred by MAFFT V7 ( G–ins–i, Blosom). Maximum likelihood analyses and bootstrap test carried out by RAXML V8.2 ML+BP online platform. Protein structure is predicted by SWISSMODELLING online platform. Molecular docking experiment is carried out by Z–dock Version 3.0.2. C–type lectin–dependent CD4/CD28 T cell Network is inferred by EMT theory. Result Our molecular docking experiment revealing the potential capacity of C–type lectin to directly interact with spike protein obstructs the formation of spike–ACE2 complex. Based on the expression profile of C–type lectin family during infection, we predicting certain member of this kind of protein as potential therapeutic target such as Clec7a, Clec12a and Clec11a, corresponding immune cell types such as CD4/CD28 T cell simulated by EMT theory and verified by experiment data, antigen adjuvant with similar C–type lectin receptor–TDM and some immune–boosting drugs–radix sophorae, lactoferrin and Astragalus membranaceus, for future testing. Conclusions C–type lectin and their corresponding immune cells predicted in this work may be the potential therapeutic targets for the disease caused by COVID–19. C–lectin with the capacity of directly interact with spike protein inhibiting the formation of ACE2–spike complex may be the way they execute anti–virus function. The corresponding cell type such as CD4/CD28 T cell may participate and against virus while Clec7a, Clec12a and Clec11a presumed capacity for facilitating CD4/CD28 T cell survival during infection being verified by EMT combining with experiment data. Our prediction at least suggest the possibility of activating organ’s immunizing power to prevent from COVID–19 and the drugs we suggested are all need to be further tested. Trial registration Retrospectively registered. Keywords C–type lectin, spike protein, coronavirus, COVID–19, TDM.

Exploring the Role of Water Molecules in the Ligand Binding Domain of PDE4B and PDE4D: Virtual Screening Based Molecular Docking of Some Active Scaffolds

Background: The phosphodiesterase (PDE) is a superfamily represented by four genes: PDE4A, B,C, and D which cause the hydrolysis of phosphodiester bond of cAMP to yield inactive AMP. c-AMP catalyzing enzyme is predominant in inflammatory and immunomodulatory cells. Therapy to treat Chronic Obstructive Pulmonary Disease (COPD) with the use of PDE4 inhibitors is highly envisaged. Objective: A molecular docking experiment with large dataset of diverse scaffolds has been performed on PDE4 inhibitors to analyze the role of amino acid responsible for binding and activation of the secondary transmitters. Apart from the general docking experiment, the main focus was to discover the role of water molecules present in the ligand-binding domain. Methods: All the compounds were docked in the PDE4B and PDE4D active cavity to produce the free binding energy scores and spatial disposition/orientation of chemical groups of inhibitors around the cavity. Under uniform condition, the experiments were carried out with and without water molecules in the LBD. The exhaustive study was carried out on the Autodock 4.2 software and explored the role of water molecules present in the binding domain. Results: In presence of water molecule, Roflumilast has more binding affinity (-8.48 Kcal/mol with PDE4B enzyme and -8.91 Kcal/mol with PDE4D enzyme) and forms two hydrogen bonds with Gln443 and Glu369 and amino acid with PDE4B and PDE4D enzymes respectively. While in absence of water molecule its binding affinity has decreased (-7.3 Kcal/mol with PDE4B enzyme and -5.17 Kcal/mol with PDE4D enzyme) as well as no H-bond interactions were observed. Similar observation was made with clinically tested molecules. Conclusion: In protein-ligand binding interactions, appropriate selection of water molecules facilitated the ligand binding, which eventually enhances the efficiency as well as the efficacy of ligand binding.