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18 results for “All-atom molecular dynamics”
All-atom 500-nano seconds Molecular Dynamics Simulations of SARS-CoV-2 Spike Receptor-binding Domain bound with ACE2
<p>Data includes all of the trajectories (1000) of classical all-atom molecular dynamics (MD) simulations of of SARS-CoV2 Spike Protein/ACE2 complex (PDB ID: 6M0J). In order to decrease the size of the file only protein rajectories were provided. Simulation has been performed with Desmond. Protein was placed in the cubic boxes with explicit TIP3P water models that have 10.0 Å thickness from surfaces of protein. The system is neutralized by adding counter ions, and salt solution of 0.15M NaCl was also used to adjust the concentration of the systems. The long-range electrostatic interactions were calculated by the particle mesh Ewald method. A cutoff radius of 9.0 Å was used for both van der Waals and Coulombic interactions. The temperature was set as 310K initially, and Nose–Hoover thermostat was used for adjustment. Martyna–Tobias–Klein protocol was employed to control the pressure, which was set at 1.01325 bar. The time-step was assigned as 2.0 fs. The default values were used for minimization and equilibration steps, and finally 500 nano-seconds (ns) production run was performed for the simulation.</p>
All-atom molecular dynamics simulations of Synechocystis halorhodopsin (SyHR)
<p>The trajectories of all-atom MD simulations of:<br> 1) Cl<sup>-</sup>-bound SyHR in the ground (GR) state (SyHR_monomer_GR_POPC_CHARMM36_200ns)<br> 2) Cl<sup>-</sup>-bound SyHR in the K state (SyHR_monomer_K_POPC_CHARMM36_200ns)<br> in the monomeric form in a POPC bilayer.<br> 3) SO<sub>4</sub><sup>2-</sup>-bound SyHR in the GR state (SyHR_trimer_GR_POPC_CHARMM36_500ns)<br> in the trimeric form in a POPC bilayer.</p> <p>Simulations have been performed using the CHARMM36 force field, running with the GROMACS 2022 package.</p>
All-atom Molecular Dynamics Simulations of SARS-CoV-2 Spike Receptor-binding Domain bound with ACE2
<p>Data includes all of the trajectories (1000) of classical all-atom molecular dynamics (MD) simulations of of SARS-CoV2 Spike Protein/ACE2 complex (PDB ID: 6M0J). In order to decrease the size of the file only protein rajectories were provided. Simulation has been performed with Desmond. Protein was placed in the cubic boxes with explicit TIP3P water models that have 10.0 Å thickness from surfaces of protein. The system is neutralized by adding counter ions, and salt solution of 0.15M NaCl was also used to adjust the concentration of the systems. The long-range electrostatic interactions were calculated by the particle mesh Ewald method. A cutoff radius of 9.0 Å was used for both van der Waals and Coulombic interactions. The temperature was set as 310K initially, and Nose–Hoover thermostat was used for adjustment. Martyna–Tobias–Klein protocol was employed to control the pressure, which was set at 1.01325 bar. The time-step was assigned as 2.0 fs. The default values were used for minimization and equilibration steps, and finally 100 ns production run was performed for the simulation.</p>
All-atom Molecular Dynamics Simulations of Meiosis 1-associated protein (M1AP) to Investagate the Impact of Known Missense Mutations Associated with Male Infertility through Non-obstructive Azoospermia
<p>Protein structure of meiosis 1-associated protein (M1AP) was modelled by using GalaxyWeb (from Seok Lab). We used this model to investigate the impact of variants (i.e., S50P, R266Q, P389L, G317R, and L430P) in M1AP which were recently associated with non-obstructive azoospermia (NOA). NOA is a male infertility-related condition causing absence of sperm in the seminal fluid due to meiosis failure. We aimed to elucidate the pathogenicity mechanisms of these five missense NOA-related mutations on M1AP by performing molecular modeling and molecular dynamics (MD) simulations. This dataset includes the results of 1000 ns MD simulations (two repeats, each 500 ns) for each of the mutant and wild-type systems.</p> <p>Systems were prepared in Visual Molecular Dynamics (VMD 1.9.3) by placing them in a TIP3P water box with approximately 20 Å thickness from the protein surface and neutralizing the system charge with 0.15 M KCl. Of note, only protein parts were kept for the submission to reduce the size of files. Nanoscale Molecular Dynamics (NAMD 2.13-CUDA) was used to perform MD simulations with CHARMM36m force field. For pressure and temperature controls, Nosé-Hoover Langevin barostat and Langevin thermostat were used. ShakeH algorithm of NAMD was applied for water molecule constraints. 12 Å cut-off distance was used for van der Waals interactions. Switching function starts at 10 Å and reaches zero at 14 Å. Integration time-step was 2 fs. To compute the long-range Coulomb interactions, the particle-mash Ewald method was used. NPT ensemble was applied for whole simulations. Two step minimization & equilibration procedure was performed: (1) 5,000-step minimization and 1 ns equilibrium with constraints on the protein; (2) 5,000-step minimization and 1 ns equilibrium without the constraints on the protein. All related configuration files for wild-type system were also included to the dataset. Production simulations were run twice along 500 ns by using different random seeds to assign the velocities from Boltzmann distribution (total simulation time for each system was 1000 ns, which are given as 500 ns repeat 1, and 500 ns repeat 2). The production simulations were supplied in the dataset. "out" and "log" files were used for energy analysis.</p> <p>For all analysis scripts, see https://github.com/ugerlevik/M1AP_analysis.</p>
All-atom molecular dynamics simulations of phenylalanine-4-hydroxylase (PAH) tetramer to investigate the impact of two novel heterozygous mutations, p.Y198N and p.Y204F, observed in a classical phenylketonuria patient
<p>Phenylalanine-4-hydroxylase (PAH) tetramer system (Robetta modelling to complete the structure with template PDB ID: 6hyc) with parametrised BH<sub>4</sub> ligand (parameters are available in the dataset) and Fe(II) metal ions in a TIP3P water box ionised with 0.15 M KCl were presented as wild-type and carrying two novel mutations as Y198N on dimeric chains A and B, and Y204F on dimeric chains C and D. In addition, E353 and E422 are protonated as predicted by PROPKA. BH<sub>4</sub> molecule parametrization was performed by using GAFF, Antechamber and “amb2chm_par.py” program of Amber2018.</p> <p>5,000-step minimization and 1 ns equilibration were performed by fixing the protein to relax the system. Then, another 5,000-step minimization and 1 ns equilibration were performed without any constraints, except the SHAKE algorithm on water molecules, to relax the protein and system. The production simulations were performed along 100 ns trajectory at 310 K collected under NpT ensemble.</p> <p>All system preparation and simulation details for this dataset is available with the related background, results and conclusions in the following article:</p> <p>Tolga Aslan, Aslı Yenenler-Kutlu, Umut Gerlevik, Ayşe Çiğdem Aktuğlu Zeybek, Ertuğrul Kıykım, Osman Uğur Sezerman & Necla Birgul Iyison (2021) Identifying and elucidating the roles of Y198N and Y204F mutations in the PAH enzyme through molecular dynamic simulations, Journal of Biomolecular Structure and Dynamics, DOI: <a href="https://doi.org/10.1080/07391102.2021.1921619">10.1080/07391102.2021.1921619</a></p>
All-atom molecular dynamics simulations of incomplete ATP synthase rotor rings with unusually high stoichiometry predicted by the AlphaFold2-based method
<p>The trajectories of all-atom MD simulations of <span>AlphaFold2 4, 11, 16 or 18-mer structures of the subunit <em>c</em> from<br></span><span><em>Candidatus Kryptonium thompsoni</em></span><span> (CKt_Nmer_lipid_mix_CHM36m_303K_500ns) and <br></span><span><em>Thalassoglobus polymorphus </em>(Tp_Nmer_lipid_mix_CHM36m_303K_500ns), and <br>AlphaFold2 11-mer structure of the subunit <em>c</em> from <em>Spinacia oleracea</em> (So_11mer-c20_POPC_CHM36m_303K_300ns) </span><span>in a lipid bilayer.</span></p> <p><span>Simulations have been performed using the CHARMM36m force field, running with the GROMACS 2022 package.</span></p>
All-atom molecular dynamics simulations of synaptic vesicle fusion I: a glimpse at the primed Synaptotagmin-SNARE-complexin complex
<p>Synaptic vesicles are primed into a state that is ready for fast neurotransmitter release upon Ca<sup>2+</sup>-binding to Syt1. This state likely includes trans-SNARE complexes between the vesicle and plasma membranes that are bound to Syt1 and complexins. However, the nature of this state and the steps leading to membrane fusion are unclear, in part because of the difficulty of studying this dynamic process experimentally. To shed light into these questions, we performed all-atom molecular dynamics simulations of systems containing trans-SNARE complexes between two flat bilayers or a vesicle and a flat bilayer with or without fragments of Syt1 and/or complexin-1. Our results need to be interpreted with caution because of the limited simulation times and the absence of key components, but suggest mechanistic features that may control release and help visualize potential states of the primed Syt1-SNARE-complexin-1 complex. In particular, the simulations suggest that SNAREs alone induce formation of extended membrane-membrane contact interfaces that may fuse slowly, and that the primed state contains macromolecular assemblies of trans-SNARE complexes bound to the Syt1 C<sub>2</sub>B domain and complexin-1 in a spring-loaded configuration that prevents premature membrane merger and formation of extended interfaces but keeps the system ready for fast fusion upon Ca<sup>2+</sup> influx.</p>
All-atom molecular dynamics simulations of iRFP713/C15S/V254C/N136R
<p>The trajectories of all-atom MD simulations of monomeric and dimeric iRFP713/C15S/V254C/N136R with PCB (phycocyanobilin) and BV (biliverdin).</p> <p> </p> <p>Simulations have been performed using the CHARMM36 force field, running with the GROMACS 2022 package.</p>
Molecular dynamics trajectories of pYEEI:SH2 recognition, unbiased, at all-atom resolution.
<div> </div> <p>Set of 772 all-atom trajectories simulated from an unbound (apo) configuration of the human p56 -lck tyrosine kinase SH2 domain with its high-specificity phosphopeptide recognition substrate pYEEI (initial structure based on PDB:<a href="https://www.rcsb.org/structure/1LKK">1LKK</a> ). Approximately 24 trajectories spontaneously reach a bound state with ligand RMSD < 2 Â from the crystal. System building and run details are described in [1].</p> <p>A preliminary version of this dataset have been analyzed and discussed in [1] (approx 200 ns per trajectory were available and used in [1]). </p> <p>The trajectories provided here are extended to ~800 ns each, for a total of ~640 μs sampled time. The full dataset is analyzed in [2] with a SOM-based technique.</p> <div> <h2>Notes</h2> </div> <div> <ul> <li>These are all-atom simulations (with TIP3P water). Water molecules have been stripped off from these files (filtered).</li> <li>Not all trajectories have the same length. Some are cut short due to the distributed computing setup.</li> <li>Frame-to-frame interval is 1 ns.</li> </ul> </div> <h2>Acknowledgments</h2> <p>We thank the volunteers of the GPUGRID.net project for donating computing time.</p> <p> </p> <h2>References</h2> <p>[1] T. Giorgino, I. Buch, and G. De Fabritiis. <a href="https://pubs.acs.org/doi/10.1021/ct300003f">Visualizing the Induced Binding of SH2-Phosphopeptide</a>, J. Chem. Theory Comput. 2012, 8, 4, 1171-1175. doi:10.1021/ct300003f</p> <p>[2] Lara Callea, Camilla Caprai, Laura Bonati, Toni Giorgino, Stefano Motta. Self-Organizing Maps of Unbiased Ligand-Target Binding Pathways and Kinetics. J. Chem. Phys, 2024. https://doi.org/10.1063/5.0225183 </p> <p> </p> <div> </div> <div> <p> </p> </div>
Molecular dynamics simulation input files: Dynamics of amphiphilic poly($\varepsilon$-caprolactone) micelles with doxorubicin and transition temperature predictions using all-atom molecular dynamics simulation
<p>The files uploaded contain the input files for simulations:<br><br>1) P10_Solv: Input files for drug-free micelles.<br>2) Micelle_Solv: Input files for drug-loaded micelles.</p>
Data from: All-atom molecular dynamics simulation and rate calculation for norepinephrine binding beta adrenergic receptor
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Molecular mechanism underlying SNARE-mediated membrane fusion enlightened by all-atom molecular dynamics simulations
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A lever hypothesis for Synaptotagmin-1 action in neurotransmitter release and Studies of Synaptotagmin-1 action by all-atom molecular dynamics simulations
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All-atom molecular dynamics simulations of synaptic vesicle fusion I: a glimpse at the primed Synaptotagmin-SNARE-complexin complex
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The dynamics of protein-RNA interfaces using all-atom molecular dynamics simulations
<p>We investigated to characterize the dynamics of protein-RNA complexes and their interfaces at molecular level by performing a more systematic analysis. To get insights on the dynamics of protein-RNA complexes, all-atom MD simulations were generated for the manuscript "The dynamics of protein-RNA interfaces using all-atom molecular dynamics simulations". Nine protein-RNA complexes are studied in this work: 1ASY (an aspartyl-tRNA synthase/tRNA), 1JBS (a ribotoxin restrictocin/SRD RNA), 1MMS (a ribosomal protein L11/23S), 1OOA (a nuclear factor NF-kappaB p105 subunit/RNA aptamer), 1RKJ (a nucleolin/pre-rRNA), 2R8S (a FAB/P4-P6 RNA ribozyme domain), 2VPL (a 50S ribosomal protein/mRNA), 2ZM5 (a tRNA delta(2)-isopentenylpyrophosphate transferase/tRNA), 3IEV (a GTP-binding protein era/3' end of 16S rRNA). </p><p>Each folder for a complex is organised as followed:</p><ul><li>in <strong>complex</strong> there are the dry MD simulations for the complex protein-RNA with the starting structure</li><li>in <strong>protein</strong> there are the dry MD simulations for the unbound protein with the starting structure</li><li>in <strong>rna</strong> there are the dry MD simulations for the unbound RNA with the starting structure</li></ul><p>In each folder, all the trajectory files are named : <strong>md_(times of simulations).xtc</strong> and the starting structure called : <strong>start.gro</strong>.</p>
Tracking conformational transitions of the gonadotropin hormone receptors in a bilayer of (SDPC) poly-unsaturated lipids from all-atom molecular dynamics simulations.
<p>In the present study, we describe the results from a computational microscopy perspective (also known as molecular dynamics simulation) at the atomistic resolution for the two gonadotropin hormone receptors, the follicle-stimulant hormone receptor and the luteinizing/chorionic gonadotropin hormone receptor, which are essential for reproduction in humans.</p>
All-atom accelerated molecular dynamics (aMD) simulations of Filamin-A (FLNa) actin-binding Domain, immunoglobulin-like Domains 3, 4, 5, 21 and 24 to investagate the impact of known missense mutations associated with periventricular nodular heterotopia in the liveborn males
<p>Data includes all of the wild-type and mutant trajectories of accelerated all-atom molecular dynamics (aMD) simulations of Filamin-A (FLNa, the product of <em>FLNA</em> gene located on chromosome X). Wild-type proteins are from the PDB structures with IDs: 4M9P, 3HOP, 3CNK. The mutations, including R484Q that we discovered in a Turkish family, were formerly found in the liveborn males with <em>FLNA</em>-associated periventricular nodular heterotopia (PNH), who survived with the only copy of mutated <em>FLNA</em>. To understand how these mutations lead to the PNH and simultaneously allow their survival, we performed these MD simulations for the wild-type and mutant systems.</p> <p>Systems were prepared in Visual Molecular Dynamics (VMD 1.9.3) by placing them in a TIP3P water box with approximately 20 Å thickness from the protein surface and neutralizing the system by adding counter ions in the form of NaCl. Of note, only protein parts were kept for the submission to reduce the size of files. Nanoscale Molecular Dynamics (NAMD 2.13-CUDA) was used to perform MD simulations with CHARMM36m force field. For pressure and temperature controls, Nosé-Hoover Langevin barostat and Langevin thermostat were used. ShakeH algorithm of NAMD was applied for water molecule constraints. 12 Å cut-off distance was used for van der Waals interactions. Switching function starts at 10 Å and reaches zero at 14 Å. Integration time-step was 2 fs. To compute the long-range Coulomb interactions, the particle-mash Ewald method was used. After a 10000-step minimization with conjugate gradient algorithm and an equilibration for 1 ns at 298 K under NVT ensemble, production simulations were run along 100 ns. Only the production simulations were supplied in this dataset. Further details are available in the regarding configuration files.</p> <p>Resulting analysis files and scripts are included with the carbon alpha-containing dcd files of the simulations.</p> <p>This dataset is not used directly for any study, but they are preliminary results for the usage of aMD to understand rare disease mechanisms.</p> <p>Related publications:</p> <pre>Zenodo repo of classical MD for these variants: https://doi.org/10.5281/zenodo.4483108</pre> <p>Journal article based on classical MD:</p> <p>Gerlevik U, Saygı C, Cangül H, Kutlu A, Çaralan EF, Topçu Y, et al. (2022) Computational analysis of missense filamin-A variants, including the novel p.Arg484Gln variant of two brothers with periventricular nodular heterotopia. PLoS ONE 17(5): e0265400. https://doi.org/10.1371/journal.pone.0265400</p>
All-atom molecular dynamics simulations for Targeting Human Prostaglandin Reductase 1 with Licochalcone A: Insights from Molecular Dynamics and Covalent Docking Studies
<p>The dataset comprises simulations of the PTGR1 protein under four different conditions: in its apo (unbound) form, bound to the cofactor NADH, and in complex with both covalently and non-covalently bound licochalcone A. Each simulation was conducted using the ff19SB force field and the OPC water model, with water molecules excluded from the trajectories.</p> <p>For the apo form, NADH-bound, and non-covalently bound licochalcone A conditions, each trajectory consists of 5000 snapshots, representing a total of 500 nanoseconds of simulation. However, the trajectory for the no covalently bound licochalcone A condition includes only 1000 frames, corresponding to 150 nanoseconds. This discrepancy in frame count and simulation length across conditions is important to consider when comparing dynamics and structural behavior within the dataset.</p> <p><strong>PTGR1-NADPH.tar.xz</strong> - PTGR1 dimer in complex with NADPH<br><strong>PTGR1-apo.tar.xz</strong> - PTGR1 apo dimer<br><strong>PTGR1-monomer_NADPH.tar.xz</strong> - PTGR1 monomer in complex with NADPH<br><strong>PTGR1-LicA_covalent.tar.xz </strong>- PTGR1 dimer with covalently bound licochalcone A<br><strong>PTGR1-LicA_NO_covalent.tar.xz</strong> - PTGR1 dimer with covalently bound licochalcone A</p> <p> </p> <p>All folders contain:</p> <p><em>*.parm7</em> - dry topology in amber format</p> <p><em>*.nc</em> - dry trajectories in netcdf format</p> <p> </p> <p><strong>Plain molecular dynamics simulations.</strong> Two structures of human PTGR1 have been deposited in the PDB: one bound to NADPH and the raloxifene inhibitor (PDB ID 2Y05, 2.2 Å resolution), and another in apo form (PDB ID 1ZSV, 2.3 Å resolution). In the first structure, it is reported as a monomer, whereas in the second as a dimer. However, the protomers exhibit highly similar conformations in both structures (backbone RMSD ~0.5 Å). Experimental evidence, akin to PTGR1 orthologs and many other MDR enzymes, indicates that the functional form of human PTGR1 is a homodimer (Mesa et al., 2015). Thus, to construct the dimeric form of the coenzyme complex, we duplicated the NADPH-bound protomer from 2Y05 and aligned the two subunits with the dimer from 1ZSV, deleting the raloxifene molecule. In the resulting structure, no steric clashes between the protomers were observed. This structure was used as the starting point for the simulations. Additionally, the apo dimer was generated by removing the NADPH from both subunits, and the monomeric form in complex with NADPH was derived from the initial structure. </p> <p>The pmemd.cuda module of AMBER 22 was used to perform the MD simulations, employing the force field FF19SB and the OPC water model (Case et al., 2022; Izadi, Anandakrishnan, & Onufriev, 2014; Salomon-Ferrer, Götz, Poole Duncan and Le Grand, & Walker, 2013; Tian et al., 2020). NADPH parameters were taken from (Cummins, Ramnarayan, Singh, & Gready, 1991). The system was protonated at pH 7.4 with PDBfixer (Eastman et al., 2017a) and placed in a truncated octahedral box, initially spanning 12 Å further from the solute in each direction using the AMBER tLeap module. The overall charge of the system was neutralized by the addition of four sodium ions. ParmEd (Eastman et al., 2017b) was used to implement the hydrogen mass repartitioning scheme (Hopkins, Le Grand, Walker, & Roitberg, 2015). Local clashes and solvent orientation were corrected using the steepest descent algorithm for 5,000 cycles. During the initial NVT equilibration, the velocities gradually increased through five steps of 200 ps each. The temperature progression started at 150 K and was raised to 200 K, 250 K, 300 K, and finally, 310 K. Position restraints were applied to heavy atoms of the protein, with the restraining forces progressively decreasing at each step. The spring constants were set at 4, 5, 3, and 1 kcal/mol Å2, respectively, to allow for the gradual relaxation of the protein. The system was further equilibrated for 1 ns in the NPT ensemble with no restraints. For treating long-range electrostatic interactions, periodic boundary conditions and Ewald sums were used with a 9 Å cutoff for direct interactions (Darden, York, & Pedersen, 1993; Simmonett & Brooks, 2021). The same cutoff was used for Lennard-Jones interactions. The Langevin thermostat (Sindhikara, Kim, Voter, & Roitberg, 2009) with a collision frequency of 4 ps-1 and the Monte Carlo barostat(Åqvist, Wennerström, Nervall, Bjelic, & Brandsdal, 2004) with a pressure relaxation time of 2 ps were used to control temperatures and pressures, respectively. The SHAKE algorithm was used to fix any bond involving hydrogen atoms (Ryckaert, Ciccotti, & Berendsen, 1977), and a 4-fs time step integration was used. This protocol was taken from (Cofas-Vargas et al., 2022; Medrano‐Cerano et al., 2024) Unless otherwise stated, no other constraints were used. Five replicas of 500 ns each per system were produced.</p> <p>The topology and parameter files for a LicA molecule and for this inhibitor covalently bound to the sulfur atom of a cysteine residue were generated with Antechamber suite (J. Wang, Wang, Kollman, & Case, 2006), using the general Amber force field (GAFF2) for organic molecules (He, Man, Yang, Lee, & Wang, 2020). Atomic charges were derived using the AM1-BB method (Jakalian, Jack, & Bayly, 2002). The parameters are documented in Supplementary Tables SI-1 and SI-2. Trajectories for PTGR1 covalently and noncovalently bound to LicA were run using the same conditions as described above. All molecular structure representations were created using UCSF ChimeraX v1.8 (Meng et al., 2023; Pettersen et al., 2021).</p> <p> </p> <p><strong>Solvent-site identification and guided docking.</strong> Determination of solvent sites (SS) for ethanol and water molecules was conducted by employing the MDmix method. After removing both NADPH molecules from the enzyme dimer, the system was protonated at pH 7.4 with PDBfixer (Eastman et al., 2017a) and placed in a truncated octahedral box of water/ethanol 80/20% v/v, extending12 Å beyond the solute in each direction using the AMBER tLeap module. Five 20 ns replicas were run, using the same conditions described above, but applying Cartesian restrictions of 0.01 kcal/mol A2 over all heavy atoms. After the alignment of trajectories, density maps for probe atoms were generated by constructing a static mesh with cubic grids (0.5 Å edge length) over the entire simulation box. The occurrence of probe atoms within each grid were tracked across the trajectories. These density distributions were then converted into binding free energy using the Boltzmann relationship, comparing observed probe atom distributions against the expected bulk solvent distribution at 1.0 M. Solvent sites were then filtered by applying an energy threshold of 1 kcal/mol, as previously described (Alvarez-Garcia & Barril, 2014; Avila-Barrientos et al., 2022).</p> <p><strong>LicA docking. </strong>For covalent docking, LicA, bound through its Cb atom to the sulfur atom of C239, was docked onto the NADPH-binding site of human PTGR1 employing the covalent docking module of AutoDock4 v4.2.6 (Bianco, Forli, Goodsell, & Olson, 2016; Morris et al., 2009). The flexible side-chain methodology was used. In a subsequent non-covalent docking, solvent sites previously identified for ethanol and water were used as pharmacophoric element for rDock (Ruiz-Carmona et al., 2014). This docking involved defining the receptor system and generating a binding cavity using the NADPH as a reference molecule. During the non-covalent docking, a penalty score proportional to the square of the distance from each ligand conformation to a solvent site (SS) was applied when the separation exceeded 2 Å. The docking run included 100 simulations, generating a set of potential binding modes for LicA within the NADPH site. </p>
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Allen Brain Atlas
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