Structure

Manufactured by YASARA

Sourced in Austria

YASARA Structure is a molecular modeling and visualization software package designed for scientific research. It provides tools for the analysis and manipulation of molecular structures, including proteins, nucleic acids, and small molecules. The software supports a wide range of file formats and can be used for tasks such as structural alignment, energy minimization, and molecular dynamics simulations.

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Lab products found in correlation

9 protocols using structure

Molecular Docking of DNA Structures

Cited 4 times

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Docking studies were performed in YASARA
software (http://www.yasara.org/).^{39 (link)} Structures of the PDB ID: 4DIH (DNA TA)^{40 (link),41 (link)} and PDB ID: 2N3M(42 (link),43 (link)) G-quadruplexes were obtained from the RCSB PDB (https://www.rcsb.org/).^{44 (link)} The structures of amino acids and peptides were
constructed in YASARA Structure (ver. 21.12.19). Prior to docking,
the receptor energy was minimized in YASARA Structure using the built-in
macro ‘em_run’. Docking simulations were performed in
YASARA using the built-in macro ‘dock_run’ with a flexible
ligand. For the DNA TA, 100 docking runs were performed using the
AutoDockLGA algorithm with the AMBER03 force field.^{45 (link)} For 2N3M, 100 docking runs were performed using the AutoDockVINA
algorithm.^{46 (link)} The simulated structures were
clustered with a 5 Å RMSD cutoff, and the dissociation constant
of the energy minimized cluster was obtained from the simulation report.
The calculated binding interactions of the energy minimized cluster
were visualized in the YASARA software, and graphics were produced
using POVRay (www.povray.org).

Fadeev M., O’Hagan M.P., Biniuri Y, & Willner I. (2022). Aptamer–Protein Structures Guide In Silico and Experimental Discovery of Aptamer–Short Peptide Recognition Complexes or Aptamer–Amino Acid Cluster Complexes. The Journal of Physical Chemistry. B, 126(44), 8931-8939.

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Receptor Preparation for Molecular Docking

Cited 1 time

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All molecular modelling was performed using YASARA Structure [24 (link)]. The intended receptor crystal Structure (PDB code: 1QS4) contained two identical chains, A and B. Chain A was chosen as receptor; however, it was missing residues 141–144. Chain B contained residues 143 and 144 and these residues were inserted into chain A by first superimposing chain B onto chain A and then removing all other chain B residues and finally merging the residues into chain A (all other residues were deleted). The two remaining residues were artificially added (Ile 141 and Pro 142). All residues in the Structure, except for residues 140 to 145, were fixed and energy minimization was performed to relax the flexible region. Subsequently, all residues were made flexible and another energy minimization was performed on the entire Structure. The resulting Structure was used as the receptor in the subsequent molecular docking experiments.

Makola M.M., Dubery I.A., Koorsen G., Steenkamp P.A., Kabanda M.M., du Preez L.L, & Madala N.E. (2016). The Effect of Geometrical Isomerism of 3,5-Dicaffeoylquinic Acid on Its Binding Affinity to HIV-Integrase Enzyme: A Molecular Docking Study. Evidence-based Complementary and Alternative Medicine : eCAM, 2016, 4138263.

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Molecular Dynamics Analysis of MdfA

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Analysis of the trajectory of simulation was also conducted in Yasara according to the manufacturer’s instructions. RMSD and RMSF were used to evaluate the total and local flexibilities, respectively. Ionic interaction (salt bridge) was identified if the distance between positively and negatively charged residues was lower than 5 Å and the interaction strength was calculated. Hydrophobic interaction strength, defined using Yasara Structure, was used to determine the hydrophobic interaction between MdfA and membrane. All the plots were made in the Python Matplotlib module, and the Structures were visualized in PyMOL. Data for growth curves are presented as means ± standard errors of means (SEM). Statistical significances are indicated by P values determined by one-way analysis of variance (ANOVA).

Li Y, & Ge X. (2023). Role of Berberine as a Potential Efflux Pump Inhibitor against MdfA from Escherichia coli: In Vitro and In Silico Studies. Microbiology Spectrum, 11(2), e03324-22.

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Molecular Dynamics Simulations of Transcription Complexes

Cited 2 times

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All Structures were prepared to the same specifications to maximize comparability between the simulations. For the ABD1/cAD complex simulation both polypeptide chains of Model 1 of the GCN4-GAL11 complex (PDB#2LPB) were capped (acetyl and N-methylamide groups added to the N- and C-termini, respectively) using Yasara Structure [59 (link)]. For the ABD1/cAD-like96 simulation the GCN4-cAD Structure (PDB 2LPB-Model 1) was mutagenized in silico with Yasara Structure [59 (link)] to create the cAD-like96 sequence. The coordinates were prepared for simulation in LEaP (AmberTools 14/15) with the Amber 14SB forcefield [60 (link)], neutralized and solvated in a TIP3P [61 ] solvent box with a minimum distance of 15 Å between solute and border. The final ionic concentration within the water box was adjusted to a final concentration of 150 mM NaCl. Capped Structures of the GCN4-cAD, GCN4 cAD-like07 and cAD-like96 were built de novo from their primary amino acid in LEaP, and prefolded using 10 ns of GB implicit MD before solvating them under the same conditions described above.

Scholes N.S, & Weinzierl R.O. (2016). Molecular Dynamics of "Fuzzy" Transcriptional Activator-Coactivator Interactions. PLoS Computational Biology, 12(5), e1004935.

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Molecular Dynamics Simulation of CBTS Homology Model

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Molecular dynamics (MD) simulation of our homology model of CBTS with GGPP were embedded in a TIP3P water box (water density 0.997 g/ml, water probe radius 1.4 Å) and neutralized with a 0.9 mM NaCl concentration, and simulated using periodic boundary conditions at T = 298 K using the YASARA2-force field, an 1 fs integration time, a NPT ensemble and a cutoff of 8 Å for non-bonded interactions. Long-range coulomb electrostatics were treated using the Particle-Mesh-Ewald approach (PME) with a grid spacing of < 1 Å. The size of the simulation box was 125.00 × 156.83 × 123.79 Å³^. Charges and force field parameters for GGPP and cations A-C were obtained from the AutoSmiles force field parameter assignment is YASARA Structure [23] (link). QM-optimized structure of cation A from Hong et al. [24] (link) was docked in CBTS using the AutoDockVina program implemented in YASARA Structure using a rectangular 14 × 14 × 14 Å³ simulation cell around residue R316, V340, N347, S451, A487, D499, A764 and Y836. The docking simulations were performed treating all atoms as rigid, and binding modes were characterized based on a cluster analysis. Cations B and C were relaxed within the active site by energy minimization and a 1 ns MD-simulation using the YASARA2 force field [23] (link). MD simulations were performed using YASARA Structure (version 15.8.31, YASARA Biosciences, [23] (link)a).

Schrepfer P., Ugur I., Klumpe S., Loll B., Kaila V.R, & Brück T. (2020). Exploring the catalytic cascade of cembranoid biosynthesis by combination of genetic engineering and molecular simulations. Computational and Structural Biotechnology Journal, 18, 1819-1829.

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Structural Alignment of Cytokine Complexes

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Multiple sequence alignments were performed using Clustal Omega^{61 (link)}. Structural alignments were generated with PyMOL (www.pymol.org) based on crystal structures from the PDB database (1F45 (IL-12)^{62 (link)}, 3DUH (IL-23)^{28 (link)}). Missing loops were modelled with Yasara structure (www.yasara.org) with a subsequent steepest descent energy minimization. Structures were depicted with PyMOL.

Meier S., Bohnacker S., Klose C.J., Lopez A., Choe C.A., Schmid P.W., Bloemeke N., Rührnößl F., Haslbeck M., Bieren J.E., Sattler M., Huang P.S, & Feige M.J. (2019). The molecular basis of chaperone-mediated interleukin 23 assembly control. Nature Communications, 10, 4121.

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Molecular Docking and Dynamics of Naproxen-Polymer Complexes

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The Structures of Naproxen, HPMC, and SLS were downloaded from PubChem while Eudragit was build-up using Chemdraw Professional v15.0 (Figure 2). Energy minimization of all the generated structures was carried out using YASARA-Structure software.27 The structures of polymers and surfactants including HPMC, Eudragit, and SLS were considered as alternative receptors (host) and ligand (guest) to obtain the stable complex of co-polymeric structure, while NP was used as only ligand (guest) structure for the molecular docking simulations. AutoDock Vina was used for molecular docking calculation in PyRx,28 (link) in which the grid box was set to cover the entire polymer to ensure that all possible interactions with the drug were explored.29 (link) The best-docked complex between co-polymer and drug was then subjected to molecular dynamics (MD) to divulge its stability in time and under the influence of explicit solvent molecules. MD simulations were carried out in the YASARA-Structure program using the YASARA force field with knowledge-based components.27 Chimera was used for the visualization and graphical representations of all co-polymer and drug complex.30 (link)Figure 2

Minimized structures of polymers, surfactants, and NP.

Hameed H.A., Khan S., Shahid M., Ullah R., Bari A., Ali S.S., Hussain Z., Sohail M., Khan S.U, & Htar T.T. (2020). Engineering of Naproxen Loaded Polymer Hybrid Enteric Microspheres for Modified Release Tablets: Development, Characterization, in silico Modelling and in vivo Evaluation. Drug Design, Development and Therapy, 14, 27-41.

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Visualizing Nanoparticle-Protein Interactions

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A 3D model of InvA497-decorated spherical and aspherical nanoparticles was generated via a self-written script for PovRay 3.7. To this end, X-ray coordinates of InvA497 (PDB ID: 1CWV) (33 (link)) were exported and scaled to the PovRay format using YASARA structure (YASARA Biosciences) (34 (link)). After modeling of the nanoparticulate shapes, the calculated numbers of 198 and 235 InvA497 molecules were randomly distributed on the aspherical and spherical objects, respectively. The picture was rendered with subsurface light scattering turned on.

Castoldi A., Empting M., De Rossi C., Mayr K., Dersch P., Hartmann R., Müller R., Gordon S, & Lehr C.M. (2018). Aspherical and Spherical InvA497-Functionalized Nanocarriers for Intracellular Delivery of Anti-Infective Agents. Pharmaceutical Research, 36(1), 22.

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In-silico Modeling of BEST1 Variants

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For in-silico modelling, the 3D structure of chicken Best1 (cBest: PDB ID 4rdg) [15 (link)] was implemented in the software YASARA Structure (Version 18.2.7, YASARA Biosciences GmbH, Austria). Autosomal dominant and recessive genetic variants depicted in the model were selected from a list of 1315 BEST1 mutations (i) that have been published between 1997 and March 2018 or (ii) from our patient cohort (Institute of Human Genetics of Wuerzburg (1997–2005) and Regensburg (2006–2018)). Mutations were categorized as autosomal dominant or autosomal recessive according to the following criteria: (I) An unambiguous autosomal dominant or autosomal recessive inheritance has been reported for at least three independent patients; (II) a biallelic inheritance has never been reported for an autosomal dominant mutation; or (III) functional characterization of a mutation was reported. Mutations that have been found exclusively in a homozygous condition were excluded.

Nachtigal A.L., Milenkovic A., Brandl C., Schulz H.L., Duerr L.M., Lang G.E., Reiff C., Herrmann P., Kellner U, & Weber B.H. (2020). Mutation-Dependent Pathomechanisms Determine the Phenotype in the Bestrophinopathies. International Journal of Molecular Sciences, 21(5), 1597.

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