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Marvin

Manufactured by ChemAxon
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Sourced in Hungary

Marvin is a molecular editor and visualization tool developed by ChemAxon. It provides a comprehensive set of features for drawing, editing, and manipulating chemical structures and reactions. Marvin's core function is to enable users to create, view, and interact with molecular representations.

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Lab products found in correlation

Chemdraw, by PerkinElmer (2 mentions) Cellomics hcs studio, by Thermo Fisher Scientific (2 mentions) Tools, by AutoDock (1 mentions) Vina program, by AutoDock (1 mentions) Jchem calculator plugins, by ChemAxon (1 mentions) Jchembase, by ChemAxon (1 mentions) Spss statistics version 21, by IBM (1 mentions) Chemdraw prime, by PerkinElmer (1 mentions) Instant jchem, by ChemAxon (1 mentions) Mestrenova, by Mestrelab Research (1 mentions) Marvinsketch, by ChemAxon (1 mentions) Prism 8, by GraphPad (1 mentions) Sybyl 8, by Tripos (1 mentions) Acetic acid, by Thermo Fisher Scientific (1 mentions) Ammonium acetate, by Thermo Fisher Scientific (1 mentions) Hplc grade, by Thermo Fisher Scientific (1 mentions)

20 protocols using marvin

1

Nanoparticle Characterization and Transfection Efficacy

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FlowJo was used for flow cytometry analysis and Cellomics HCS Studio (Thermo Fisher) was used for image acquisition based transfection analysis. Polymer structures were characterized in ChemDraw (Perkin Elmer, Boston, MA) and Marvin (ChemAxon, Cambridge, MA) to determine logP and logD values. Calculation of normalized 50% serum transfection efficacy was performed by dividing the percent transfection or geometric mean transfection efficacy achieved in 50% serum media by the same nanoparticle (B8% and w/w ratio) formulation percent transfection or geometric mean transfection efficacy achieved in 10% serum. Confocal microscopy colocalization of plasmid DNA with lysosomes was assessed as intensity weighted colocalization in Zen Blue, then normalized by individual image area of plasmid DNA per image for statistical quantification.
Wilson D.R., Rui Y., Siddiq K., Routkevitch D, & Green J.J. (2019). Differentially Branched Ester Amine Quadpolymers with Amphiphilic and pH Sensitive Properties for Efficient Plasmid DNA Delivery. Molecular pharmaceutics, 16(2), 655-668.
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2

Molecular Docking of SphK2 Inhibitors

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Molecular docking was performed using compounds (2, 20x) to assess potential difference in position in the binding pocket of SphK2. The SphK2 model, with ATP and Mg2+ bound, was generated with Molecular Operating Environment (MOE) and energy minimized as previously described.60 (link) Marvin was used for drawing, displaying and characterizing chemical structures, substructures and reactions for preparation in docking programs, Marvin 17.3.13, 2017, ChemAxon (http://www.chemaxon.com). AutoDock Tools68 (link) was used to prepare the protein and ligand files, while AutoDock Vina69 (link) was used to perform the docking for pose prediction. The grid box was set to 20 × 20 × 28 Angstrom, with a 1.000 Å grid spacing was used. The center of the box was placed at the approximate center of the ligand-binding cavity, with a part of the ATP binding cavity included as previously performed60 (link) and to ensure coverage and interaction with key Asp residues. Up to ten docked poses were predicted for each compound. The number of predicted poses is dependent on the fitness of the sampled compound orientations. The lowest energy pose for each docked ligand in SphK2 was then used for analysis of interactions with key residues in the SphK2 binding pocket. Free energy of binding scores were cataloged for each docked compound and used as one level of comparison between compounds.
Childress E.S., Kharel Y., Brown A.M., Bevan D.R., Lynch K.R, & Santos W.L. (2017). Transforming Sphingosine Kinase 1 Inhibitors into Dual and Sphingosine Kinase 2 Selective Inhibitors: Design, Synthesis, and In Vivo Activity. Journal of medicinal chemistry, 60(9), 3933-3957.
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3

Homology Modeling and Molecular Docking of PKCδ

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Since the structure of human PKCδ has not been characterized, homology modeling was performed to generate the human PKCδ protein structure for molecular docking simulations [23 (link)]. The structures of each of the six iridoid compounds were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov/ accessed on 7 January 2021) and determined using the Marvin program (https://www.chemaxon.com accessed on 10 January 2021). As previously described, the docking simulation between PKCδ and each compound was performed using the Autodock Vina program (http://vina.scripps.edu accessed on 6 February 2021), as presented in Table S1 and Supplementary Figure S5 [23 (link)].
Oh E.S., Ryu H.W., Kim M.O., Lee J.W., Song Y.N., Park J.Y., Kim D.Y., Ro H., Lee J., Kim T.D., Hong S.T., Lee S.U, & Oh S.R. (2023). Verproside, the Most Active Ingredient in YPL-001 Isolated from Pseudolysimachion rotundum var. subintegrum, Decreases Inflammatory Response by Inhibiting PKCδ Activation in Human Lung Epithelial Cells. International Journal of Molecular Sciences, 24(8), 7229.
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4

Computational Physicochemical Profiling

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Physicochemical
partition coefficient (log  D) values
at pH 7.4 were calculated using Marvin and JChem
calculator plugins (ChemAxon, Budapest, Hungary). Molecular orbital,
UV–vis spectra, and electrostatic map calculations were performed
using density functional theory modeling on gas-phase B3LYP/6-31G
optimized geometries using Gaussian 09.23
Wang L., Barth C.W., Sibrian-Vazquez M., Escobedo J.O., Lowry M., Muschler J., Li H., Gibbs S.L, & Strongin R.M. (2017). Far-Red and Near-Infrared Seminaphthofluorophores for Targeted Pancreatic Cancer Imaging. ACS Omega, 2(1), 154-163.
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5

Thermodynamic Curation of Human GEMs

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The thermodynamic curation of the human GEMs Recon 2 and Recon 3D aims to include thermodynamic information, i.e., the Gibbs free energy of formation for the compounds and the corresponding error for the estimation, into the model. The workflow to obtain this information is as follows.
We first used MetaNetX (http://www.metanetx.org)69 (link) to annotate the compounds of the GEMs with identifiers from SEED70 (link), KEGG54 (link), CHEBI71 (link), and HMDB72 (link). We then used Marvin (version 18.1, 2018, ChemAxon http://www.chemaxon.com) to transform the compound structures (canonical SMILES) into their major protonation states at pH 7 and to generate MDL Molfiles. We used the MDL Molfiles and the Group Contribution Method to estimate the standard Gibbs free energy of the formation of the compounds as well as the error of the estimation59 (link).
Since the model for Recon 3D already incorporates the structure for 82% of the metabolites in the form of SMILES, we used those SMILES and followed the previous workflow from the point of obtaining the major forms at pH 7 using Marvin.
Furthermore, we have integrated in the models the thermodynamic properties for the compartments of human cells, including, pH, ionic strength, membrane potentials, and generic compartment concentration ranges from 10 pM to 0.1 M (Supplementary Table 10).
Masid M., Ataman M, & Hatzimanikatis V. (2020). Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN. Nature Communications, 11, 2821.
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6

Molecular Structure Preprocessing for Docking

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Molecules from the NR-DBIND were extracted in SMILES format, and molecules from the Tox21 were extracted in SDF format. The majority of the protonated microspecies at pH 7.4 of each molecule was computed using Marvin (Version 17.22.0, 2017, ChemAxon, http://www.chemaxon.com). For each compound, 3D conformations were generated using iCon as implemented in LigandScout [16 (link)] (Version 4.3.) and only the lowest-energy conformation of each compound was considered. MGLTools was used to convert ligand SDF files into the AutoDock Vina PDBQT format by assigning Gasteiger charges and atom types.
Réau M., Lagarde N., Zagury J.F, & Montes M. (2019). Hits Discovery on the Androgen Receptor: In Silico Approaches to Identify Agonist Compounds. Cells, 8(11), 1431.
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7

Chemical Structure Characterization

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MarvinSketch software was used for drawing, displaying and characterising chemical structures, substructures and reactions, Marvin 17.24.0, 2017, ChemAxon. JChem Base was used for structure searching and chemical database access and management, JChem 18.3.0, 2018, ChemAxon (www.chemaxon.com).
Stankiewicz A., Kaczorowska K., Bugno R., Kozioł A., Paluchowska M.H., Burnat G., Chruścicka B., Chorobik P., Brański P., Wierońska J.M., Duszyńska B., Pilc A, & Bojarski A.J. (2021). New 1,2,4-oxadiazole derivatives with positive mGlu4 receptor modulation activity and antipsychotic-like properties. Journal of Enzyme Inhibition and Medicinal Chemistry, 37(1), 211-225.
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8

pKa Estimation of EGFR TKIs

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Computer programs for pKa estimations of EGFR TKIs and Gefi-2OH were applied in the commonly used package Marvin from ChemAxon.24 (link),25 (link) The structures of gefitinib and Gefi-2OH were drawn by MarvinSketch. The general calculation mode was set as follows: the macro mode was used to estimate the macro acidic dissociation constant; the static acid/base prefix was chosen to estimate the pKa value of neutral acidic and basic sites by red and blue annotation, and the pKa range was set according to the defaults (min basic pKa = −2, max acidic pKa = 16).
Yang L., Ying S., Hu S., Zhao X., Li M., Chen M., Zhu Y., Song P., Zhu L., Jiang T., An H., Yousafzai N.A., Xu W., Zhang Z., Wang X., Feng L, & Jin H. (2019). EGFR TKIs impair lysosome-dependent degradation of SQSTM1 to compromise the effectiveness in lung cancer. Signal Transduction and Targeted Therapy, 4, 25.
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9

Molecular Interactions of Polyphenols with Enamel

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The present study was undertaken to determine the molecular interactions between polyphenols and enamel by computational molecular docking studies. Molecular docking studies were carried out on crystal structures. The data on hydroxyapatite was obtained from http://rruff.geo.arizona.edu/AMS/result.php and drawing done using Marvin, ChemAxon. The crystals were visualised using VESTA 3 for volumetric and morphology data43 . Glide (Standard precision (SP) mode and Extra precision (XP) mode) and induced-Fit docking modules of Schrodinger 2020-2 were used to determine the interactions between bioactive compounds and enamel hydroxyapatite. From the binding energy and interaction studies, all the compounds were determined for interactions. The compounds were prepared for molecular docking studies using protein preparation wizard with OPLS-3e force field at pH 7.20 ± 0.20 and the other default settings. For further confirmation, the binding efficacy of the compounds were done with induced-fit docking studies using ‘Induced-Fit docking’ module within the range of 5 Å of the receptor made flexible. In general, induced-fit docking provides better insights on binding interactions and efficacy. The poses with highest negative docking scores are shown in the results23 (link).
Kohli S., Bhatia S., Banavar S.R., Al-Haddad A., Kandasamy M., Qasim S.S., Kit-Kay M., Pichika M.R, & Daood U. (2023). In-vitro evaluation of the effectiveness of polyphenols based strawberry extracts for dental bleaching. Scientific Reports, 13, 4181.
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10

Predicting OCT2 Inhibition Potential

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Comparison of metformin or MPP+ uptake in HEK-VK and HEK-OCT2 cells was performed using the unpaired two-sample t test. Significant inhibition of OCT2 mediated metformin or MPP+ uptake was determined by the one-sample t test. Univariate relationships were tested by Spearman’s correlation coefficient. Data were presented as means ± standard error of the mean. A value of P < 0.05 was considered statistically significant.
A standard multiple regression was performed to assess the ability of the molecular descriptors topological surface area (TPSA), number of aromatic rings, net charge (at pH 7.4), distribution coefficient (logD at pH 7.4), and molecular weight to predict the percent inhibition of OCT2-mediated MPP+ or metformin uptake. We used the inhibition data set generated with 20 μM drug concentrations. The molecular descriptors (independent variables) were calculated with Marvin (Chemaxon, Budapest, Hungary) and ChemMine [12 (link)]. They were selected based on previously published studies on the structure-activity relationship [7 (link), 8 (link), 13 (link)]. Multiple regression analysis was performed with SPSS Statistics, version 21 (IBM Corporation, Armonk, NY).
Hacker K., Maas R., Kornhuber J., Fromm M.F, & Zolk O. (2015). Substrate-Dependent Inhibition of the Human Organic Cation Transporter OCT2: A Comparison of Metformin with Experimental Substrates. PLoS ONE, 10(9), e0136451.
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