Find research datasets worth reusing

opencc-by-4.0Mar 2021View details →

Discovery of Natural Dual Inhibitors from ZINC Database Targeting Thymic Stromal Lymphopoietin (TSLP) and Interleukin-33 (IL-33) as Potential Anti-Allergy Agents - MD Simulation XVG Files and Complex PDB Files (Docking Results)

This repository contains input and output files of MD simulations along with MM/PBSA related files Manuscript Title: Discovery of Natural Dual Inhibitors from ZINC Database Targeting Thymic Stromal Lymphopoietin (TSLP) and Interleukin-33 (IL-33) as Potential Anti-Allergy Agents

opencc-by-4.0Jul 2024View details →

Molecular Dynamics Simulation and Docking Studies Reveals Inhibition of NF-kB signaling as a Promising Therapeutic Drug Target for reduction in Cytokines Storms

The complexes of the top identified molecules with NF-kB-kB site, as well as all the designed molecules used in the screening process.

opencc-by-4.0Feb 2019View details →

Interaction of N-3-oxododecanoyl homoserine lactone with transcriptional regulator LasR of Pseudomonas aeruginosa: Insights from molecular docking and dynamics simulations

Dataset and supplementary files of the research: Interaction of N-3-oxododecanoyl homoserine lactone with transcriptional regulator LasR of Pseudomonas aeruginosa: Insights from molecular docking and dynamics simulations (https://doi.org/10.1101/121681) - Supporting Information - Input: Parameters and initial structures - Output: Trajectories, Docking poses Gromacs (multi-core with CUDA) was used for the simulations. Autodock Vina, FlexAid and rDock were used for molecular docking.

opencc-by-4.0Aug 2023View details →

Identification of Potential JNK3 Inhibitors Through Virtual Screening, Molecular Docking And Molecular Dynamics Simulation as Therapeutics for Alzheimer's Disease

Alzheimer's disease (AD) is a complex neurological disorder without effective treatment. One factor in its development is c-Jun N-terminal kinases (JNKs), a type of protein related to brain function. JNK3, found mainly in the brain, contributes to AD by promoting brain abnormalities. Current research aims to create new JNK3 inhibitors for AD treatment using a virtual screening method. A database of compounds was filtered, and five potential compounds were identified with better scores than a reference. These compounds underwent simulations and energy calculations, showing stability and potential as JNK3 inhibitors.

opencc-by-4.0Oct 2020View details →

An in silico docking simulation of SARS Coronavirus2 and Ivermectin

ABSTRACT     COVID-19 is spreading and infecting in the world. And that is occurring a death very much. Of course, I want to cooperate for save peoples. And I was simulating a docking about proteins of SARS Coronavirus 2 in silico. That a papain-like protease, a karyopherin importin α,β and RNA Polymerase. One hypothesis [1] said Ivermectin can destabilises about a bind to an importin and the virus cargo proteins. And I got a significantly results from in silico simulation that at catalytic center of RNA Polymerase. Remdesivir is docking at here. [2] I dedicate this report to a current patients and a future patients. 1. Introduction     The virus is called the bacteriophage. You know that the virus infect the bacteria. Therefore, the bacteria need a counter plan. I think, Ivermectin(Avermectin) is that. One hypothesis, the Coronavirus2 is replicating by using a main protease [4], papain-like protease [4] and this RNA Polymerase. And Ivermectin destablishes about bind to an importin and the virus cargo proteins [1]. I checked for these case. But I seems that Importin is too big for Ivermectin. And netxt, I checked for RNA Polymerase. and I report this result simply. 2. A software for docking simulation     This case is using a software that “Autodock vina” [5]. This software is better performance than other docking simulation softwares. But a simulation accuracy isn’t a high quality more than a real phenomenon yet. This software is using an affinity score [kcal/mol]. See also vina web-site [5]. 3. A docking parameters and Dataset     This case is using following parameters for docking simulations. (1).exhaustiveness: 8 (2).num modes: 10 (3).energy range: 1 and I was using a protein data PDB:7bzf for RNA Polymerase. And PDB:IVM for Ivermectin. I chosen a chain A from PDB:7bzf by using pymol [8]. And I converted PDB:IVM from .sdf to .pdb by using “PDB format-PDBx/mmCIF conversion service” web-site [9]. 4. Contents    Figure 2: Front view of RNA Polymerase and Ivermectin I tried to a docking simulation during a four monthes about RNA Polymerase (PDB:7bzf) of SARS Coronavirus2 and Ivermectin [6]. And I got a significantly results. I chosen a few higher score to following. mode |   affinity | dist from best mode      | (kcal/mol) | rmsd l.b.| rmsd u.b. -----+------------+----------+----------    1        -11.5      0.000      0.000    2        -11.1      1.975      3.233    3        -11.0      1.895      2.482 mode |   affinity | dist from best mode      | (kcal/mol) | rmsd l.b.| rmsd u.b. -----+------------+----------+----------    1        -11.4      0.000      0.000    2        -11.0      1.572      2.041    3        -10.8      4.406     14.971 mode |   affinity | dist from best mode      | (kcal/mol) | rmsd l.b.| rmsd u.b. -----+------------+----------+----------    1        -11.4      0.000      0.000    2        -10.8      1.550      2.430    3        -10.6      1.648      2.197 These are a near points. You can see a number of rmsd (Root Mean Square Deviation). These are a catalytic center of RNA Polymerase. One paper said [2], this point can combine Remdesivir too. Figure 1,2,3 are a point of best of affinity score that -11.5[kcal/mol]. Of course, If you want to know detail of results then you can download an all eleven data about this simulation from my web-site. 5. Conclusion     I seems that is very significantly result. Because, One, here are a higher score point (Figure 4). Second, here are a catalytic center. Third, Remdesivir can combine at same point [2]. I consider about an accuracy of Autodoc Vina and a conformation will change, probably. You know, this is a computer simulation absolutely. I want to wait a result of cryo-EM and a crystal structure complex. Acknowledge     Thank you for the NIG supercomputer at ROIS National Institute of Genetics. Because I’m using this computer system everyday.

opencc-by-4.0Nov 2021View details →

Tabulation Result of Molecular Docking Simulation

Tabulation Result of Molecular Docking Simulation

opencc-zeroMar 2022View details →

Figures S1–S10 from: Shoman ME, Abd El-Hafeez AA, Khobrani M, Assiri AA, Al Thagfan SS, Othman EM, Ibrahim ARN (2022) Molecular docking and dynamic simulations study for repurposing of multitarget coumarins against SARS-CoV-2 main protease, papain-like protease and RNA-dependent RNA polymerase. Pharmacia 69(1): 211-226. https://doi.org/10.3897/pharmacia.69.e77021

Molecular docking and Dynamic simulations study for repurposing of multitarget Coumarins against SARS-CoV-2 main protease, papain like protease and RNA-Dependent RNA polymerase.

opencc-by-4.0Jul 2024View details →

Molecular dynamics simulations of 20 complexes from the Protein-Protein Docking Benchmark

We selected 20 complexes from the Protein-Protein Docking Benchmark 5.0 dataset based on structure resolution and parameterization difficulty. For each complex, we conducted a standard 1 µs-long molecular dynamics (MD) simulation in the NPT ensemble (at 1 atm and 300 K, following a 2 ns NVT equilibration) for the bound receptor, unbound receptor, bound ligand and unbound ligand. We set up all systems using Amber ff14SB and its recommended TIP3P water model, running MD simulations with Amber 16. For the 80 (single chain structure) MD, we sampled 500 frames for each simulation and computed the average prediction confidence.

opencc-by-4.0Aug 2024View details →

Molecular dynamics simulation data of the manuscript "KnowVolution of an efficient polyamidase through molecular dynamics simulations of incrementally docked oligomeric substrates"

This repository provides the simulation data as well as the input and parameters files to reproduce our findings. Acknowledgments The authors gratefully acknowledge the computing time provided by RWTH Aachen University. Computations were performed with computing resources granted by RWTH Aachen University under project rwth1584.

opencc-by-4.0Sep 2024View details →

Dataset for protein docking and MD simulations of CARD-CARD interactions of the apoptosome

Protein docking complexes and MD simulation trajectories for the Apaf1-Caspase9 CARD-CARD interactions and the cross-dockings between Apaf1 CARD and Caspase2 CARD, and RAIDD CARD and Caspase9 CARD, respectively (RAIDD and Caspase2 CARD domains interacting in the PIDDosome). Further details in the README file.

opencc-by-4.0Oct 2024View details →

Protein preparation (1LPB), docked structures of Bromhexine and Orlistat to Pancreatic Lipase, and MD simulations trajectories in 3 replicas.

Data set contains 3 folders: 1) Protein preparation (1LPB) 2) XP Docking of Bromhexine and Orlistat 3) MD Simulation of Bromhexine and Orlistat (3 replicates)

opencc-by-4.0Nov 2024View details →

All-atom molecular dynamics simulations for Targeting Human Prostaglandin Reductase 1 with Licochalcone A: Insights from Molecular Dynamics and Covalent Docking Studies

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. 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. PTGR1-NADPH.tar.xz - PTGR1 dimer in complex with NADPH PTGR1-apo.tar.xz - PTGR1 apo dimer PTGR1-monomer_NADPH.tar.xz - PTGR1 monomer in complex with NADPH PTGR1-LicA_covalent.tar.xz - PTGR1 dimer with covalently bound licochalcone A PTGR1-LicA_NO_covalent.tar.xz - PTGR1 dimer with covalently bound licochalcone A   All folders contain: *.parm7 - dry topology in amber format *.nc - dry trajectories in netcdf format   Plain molecular dynamics simulations. 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.   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. 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).   Solvent-site identification and guided docking. 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). LicA docking. 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.

Fig. 8 in PTP1B and α-glucosidase inhibitory activities of the chemical constituents from Hedera rhombea fruits: Kinetic analysis and molecular docking simulation

Fig. 8. Molecular docking related α-glucosidase inhibition by acarbose (green stick), BIP (red stick), 20 (cyan and yellow stick), and 26 (magenta stick) (a). 2D diagram of α-glucosidase inhibition by 20 [b (at the catalytic site) and c (at the allosteric site)] and 26 (d). The figure was generated using PyMOL and Discovery Studio Visualizer. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 7. Molecular docking related PTP1B in PTP1B and α-glucosidase inhibitory activities of the chemical constituents from Hedera rhombea fruits: Kinetic analysis and molecular docking simulation

Fig. 7. Molecular docking related PTP1B inhibition by compound C (magenta stick) and compound A (red stick), 20 (green stick), and 26 (yellow and blue stick) (a). 2D diagram of PTP1B inhibition by 20 (b) and 26 [c (at the catalytic site) and d (at the allosteric site)]. The figure was generated using PyMOL and Discovery Studio Visualizer. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 6 in PTP1B and α-glucosidase inhibitory activities of the chemical constituents from Hedera rhombea fruits: Kinetic analysis and molecular docking simulation

Fig. 6. Dixon plots (a and c), Lineweaver Burk plots (d and f), and secondary plots (b and e) for the α-glucosidase inhibition by 20 (a, b, d, and e) and 26 (c and f).

Fig. 5 in PTP1B and α-glucosidase inhibitory activities of the chemical constituents from Hedera rhombea fruits: Kinetic analysis and molecular docking simulation

Fig. 5. Lineweaver plots [a and b], Dixon plots [d and e], and secondary plots [c and f] for the inhibition of PTP1B enzyme by 20 (a and d) and 26 (b, c, e, and f), respectively.