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23 results for “binding free energy”
Associated Data: RASPD+: Fast protein-ligand binding free energy prediction using simplified physicochemical features
<p>Additional digital data to "RASPD+: Fast protein-ligand binding free energy prediction using simplified physicochemical features" (ChemRxiv preprint:<a href="https://doi.org/10.26434/chemrxiv.12636704.v1">https://doi.org/10.26434/chemrxiv.12636704</a>).</p> <p>Associated code can be found at: <a href="https://github.com/HITS-MCM/RASPDplus">https://github.com/HITS-MCM/RASPDplus</a></p> <p>Files:</p> <ul> <li>weights.tar.gz: contains the model weights of one random dataset split and its associated crossvalidation folds. Used for standard RASPD+ evaluation.</li> <li>additional_model_replicates.tar.gz: contains the remaining models trained on the full set of descriptors.</li> <li>external_test_sets.tar.gz: contains the descriptor tables for all external test sets used</li> <li>dude.tar.gz: contains the descriptor tables for and several identifier lists for evaluation on the Directory of Useful Decoys - Enhanced (DUD-E)</li> <li>run_outputs.tar.gz: Performance metric data and predicted values created during the model training and evaluation runs. Basis for the figures and metrics in the manuscript.</li> </ul> <p> </p>
Binding Affinity Prediction Workflow - Simulation Input Files and Absolute Binding Free Energies
<p>The Binding Affinity Prediction (BAP) workflow calculates absolute binding free energies for protein-ligand complexes by taking their crystal structures, converting them into input files for molecular dynamics (MD) simulations with GROMACS after they have passed extensive quality checks, and analysing the resulting trajectories with the Generalised Born model of implicit solvation as implemented in gmx_MMPBSA to obtain the free-energy estimates. The workflow was designed for soluble proteins without post-translational modifications, co-factors and non-standard amino acids, and it has limited support for coordinated ions.</p> <p>For the dataset published here, the BAP workflow was run on the PDBbind 2020 (http://www.pdbbind.org.cn/index.php) refined set. This entry contains the MD simulation input files (BAPSimulationInputFiles.tar.gz) and the ABFE estimates (BAPBindingFreeEnergyEstimates.csv) obtained from four 250 ns trajectories for each complex. The MD simulations for more than 4000 complexes were run on the Leonardo supercomputer while the implicit-solvent calculations were carried out on Galileo, both operated by Cineca (Italy). The MD trajectories will be stored at Cineca for approx. 1 year after publication of this entry; contact Cineca's user support if you are interested in the trajectories.</p> <p>The README file describes how to reproduce the MD trajectories and the subsequent implicit-solvent calculations yielding the free-energy estimates. The workflow scripts can be downloaded from GitHub (https://github.com/LigateProject/Binding-Affinity-Prediction-workflow). The MD simulations were run with GROMACS 2023.2 (https://manual.gromacs.org/2023.2/index.html), and the implicit-solvent calculations were carried out with gmx_MMPBSA 1.6.1 (https://valdes-tresanco-ms.github.io/gmx_MMPBSA/v1.6.1/).</p>
Absolute Binding Free Energies with OneOPES
<h1>Supporting Material: Absolute Binding Free Energies with OneOPES</h1> <p>Further information about the content of the directory can be found in the README file included</p> <p>The Plumed input files can also be found on Plumed-Nest https://www.plumed-nest.org/eggs/24/017/</p> <p> </p> <h3>Aknowledgements</h3> <p>The authors acknowledge PRACE and the Swiss National Supercomputing Centre (CSCS) for large supercomputer time allocations on Piz Daint, project IDs: pr126, s1107, s1169, s1228. FLG acknowledges the Swiss National Science Foundation and Bridge for financial support (projects number: 200021_204795, CRSII5_216587 and 40B2-0_203628). The authors are grateful to Nicola Piasentin for helping in devising the error-informed stopping strategy and for carefully reading the manuscript.</p> <p> </p> <h3>Reference</h3> <p>Absolute Binding Free Energies with OneOPES<br>Maurice Karrenbrock, Alberto Borsatto, Valerio Rizzi, Dominykas Lukauskis, Simone Aureli, and Francesco Luigi Gervasio<br>The Journal of Physical Chemistry Letters 0, <em> 15<br>DOI: 10.1021/acs.jpclett.4c02352 </em></p> <p> </p> <h3>Versions :</h3> <ul> <li>1.0.0 First version</li> <li>1.0.1 Bugfix: added the missing topology files (top.top)</li> <li>1.1.0 Bugfix: added missing index files (index.ndx) and missing Slurm files (run.slr). New: added a directory with what is needed to equilibrate the systems</li> </ul>
Free energy simulations of receptor-binding domain opening in the SARS-CoV-2 spike indicate a barrierless transition with slow conformational motions
<p>This online data set accompanies the manuscript entitled "Free energy<br> simulations of receptor-binding domain opening in the SARS-CoV-2 spike<br> indicate a barrierless transition with slow conformational motions."</p> <p>The dataset is composed of the following files:</p> <p>* pmf0-now.dcd -- pmf63-now.dcd : molecular dynamics trajectory frames in<br> each of the 64 umbrella sampling windows, from which water has been<br> removed to save space</p> <p>* s1am_0-now.pdb -- s1am_63-now.pdb : initial coordinates in each of the 64<br> umbrella sampling windows, from which water has been removed,<br> corresponding to the trajectory data above</p> <p>* view -- Visual Molecular Dynamics command script to load a trajectory, <br> e.g., in Linux, use "vmd -e view"</p> <p>* s1am_0-cg.dcd -- s1am_63-cg.dcd : molecular dynamics<br> trajectory frames in each of the 64 umbrella sampling windows, coarse-grained to<br> 1 bead per residue.</p> <p>* s1am_0-cg.pdb -- s1am_63-cg.pdb : initial coordinates in each of the 64<br> umbrella sampling windows, corresponding to the coarse-grained trajectory<br> data above.</p> <p>* viewcg -- Visual Molecular Dynamics command script to load a<br> coarse-grained trajectory, e.g., in Linux, use "vmd -e viewcg"</p> <p>* 0readme -- brief instructions on how to view the trajectories</p> <p>* colors.vmd -- utility script for VMD</p> <p>* covmacros.vmd -- VMD script to define coronavirus spike subdomains</p> <p>* fe.zip -- ZIP archive that contains data and Matlab analysis files to<br> reproduce the free energy profiles</p> <p>* diff.zip -- ZIP archive that contains data and Matlab analysis files to<br> reproduce the diffusion and mean first passage times calculations</p> <p>* pca-qha.zip -- ZIP archive that contains the data and Matlab analysis files<br> to compute the autocorrelation functions of trajectory displacements<br> along principal/quasiharmonic modes</p> <p>Each ZIP archive contains a "0readme" file with brief instructions, and also the <br> results of the calculations<br> </p>
Pose Selector Workflow - Docking Poses, Absolute Binding Free Energy Estimates and Structure Input Files for Machine Learning
<p>The Pose Selector (PS) workflow calculates absolute binding free energies (ABFEs) for binding poses of protein-ligand complexes. First, it converts the binding poses (both docking poses as well as experimentally observed ligand binding poses), which are provided as a combination of protein PDB file and ligand MOL2 file, into input files for molecular dynamics (MD) simulations with GROMACS after they have passed extensive quality checks and repair steps. Next, the PS workflow post-processes and analyses the last frame of the resulting eight 100 ps trajectories per binding pose with the Generalised Born model of implicit solvation as implemented in gmx_MMPBSA to obtain the ABFE estimates. The workflow was designed for soluble proteins without post-translational modifications, co-factors and non-standard amino acids, and it has limited support for coordinated ions.</p> <p>For the dataset published here, the PS workflow was run on docking poses generated for the PDBbind 2020 dataset (http://www.pdbbind.org.cn/index.php), shared in dockingPosesPDBBind2020.tar.gz. This entry and its partner entry 10.5281/zenodo.11397486 also share the intial coordinates used in the MD simulations of >800,000 docking poses of 4022 protein-ligand complexes (structureFiles_dockingPoses1.tar.gz in this entry and structureFiles_dockingPoses2.tar.gz in 10.5281/zenodo.11397486) and of the experimental ligand binding pose of 4549 complexes (structureFiles_experimentalStructures.tar.gz) as well as the corresponding ABFE estimates (absoluteBindingFreeEnergyEstimates.tar.gz). The MD simulations were run on the LUMI and MeluXina supercomputers while the implicit-solvent calculations were carried out on Galileo (Cineca).</p> <p>The README file describes the structure of the shared data in more detail and points out how to reproduce the MD trajectories and the subsequent implicit-solvent calculations yielding the free-energy estimates as well as how to use the data provided in this entry to train a machine-learning model predicting the ABFE of binding poses of protein-ligand complexes. The workflow scripts can be downloaded from GitHub (https://github.com/LigateProject/Pose-Selector-workflow). The MD simulations were run with GROMACS 2023.2 (https://manual.gromacs.org/2023.2/index.html), and the implicit-solvent calculations were carried out with gmx_MMPBSA 1.6.1 (https://valdes-tresanco-ms.github.io/gmx_MMPBSA/v1.6.1/).</p>
Supporting material of "A nonequilibrium alchemical method for drug-receptor absolute binding free energy calculations: the role of restraints"
<p>Supporting material of the paper "A nonequilibrium alchemical method for drug-receptor absolute binding free energy calculations: the role of restraints".</p> <p>The directory is fully documented with README files.</p> <p>Differences of V2.0 with V1.0:</p> <ul> <li>Due to a software bug we had to re-parametrize the ligands whose torsions were parametized with ANI-2.X (ligand 6 and 7).</li> <li>During the peer reviewing process we also parametrized with ANI-2.X and docked ligand 8.</li> </ul>
Benchmark set inputs for absolute binding free energy calculations of fragment optimisations
<p>Supplementary Information: "Evaluating the use of absolute binding free energy in the fragment optimization process"</p> <p>Provided here are the various scripts, input files, and results necessary to reproduce the outcomes of the above mentioned publication. Please see the provided README.md files for further information on the contents of this dataset.</p>
Relative binding free energy between chemically distant compounds using a bidirectional non-equilibrium approach
<p>Data from "Relative binding free energy between chemically distant compounds using a bidirectional nonequilibrium approach"</p> <p>Submitted to J Chem Theory Comput, February 2022.</p> <p>This archive contains the following directories:</p> <p><br> traj -> This directory contains trajectory files for SAMPL9:</p> <p> g??_w.pdb.gz file contains ~ 160 snapshots from the 48 ns HREM sampling<br> of the target bound state of G1-G13 including water solvent.</p> <p> g??_1.pdb.gz file contains ~ 3500 snapshots (no solvent included) of the<br> target bound state of G1-G13</p> <p><br> work -> This trajectory contains the work data (in kJ/mol) obtained in<br> the NE trajectories for SAMPL9:</p> <p> gxx-gyy_b_TIME.wrk is the work sample obtained for the xx->yy transmutation<br> in the bound state with a duration time of TIME.</p> <p> gxx-gyy_u_TIME.wrk is the work sample obtained for the xx->yy transmutation<br> in the unbound state with a duration time of TIME.</p> <p>pdb -> This directory contains the pdb initial structures of the G1-G18 guests and the WP6 host</p> <p>ff -> This directory contains the force field specification for SAMPL9:</p> <p> the PrimaDORAC generated (http://www1.chim.unifi.it/orac/primadorac/)<br> tpg and prm files for the guests and the host in orac format. (see<br> http://ftp.chim.unifi.it/orac/MAN/orac-manual.html)</p> <p>bin -> This directory contains the script files to compute all bidirectional and unidirectional RBFE estimates as reported in Table 2 of the paper.<br> In order to compute an RBFE using the work data in the work dir, do the following:<br> 1) cd into the bin directory<br> 2) issue the command<br> source source_this_file.bash<br> (N.B.: gfortran must be installed)<br> 3) cd ../<br> 4) from the main dir, issue the command:<br> RBFE.bash -b 720 -u 360 B A<br> where B and A are the ghost and physical compound, respectively<br> Results for DG(A->B) are printed to the standard output</p> <p> Example: <br> RBFE.bash -b 720 -u 360 g01 g03 > g03g01<br> In the g03g01 file, estimates and properties of the work data are<br> printed in the format<br> "g05->g02 DG_bar= -5.13 0.37 DG_ff_G= -4.2 0.8 DG_ff_J= -3.0 0.4 sig_AB_u= 0.90 sig_AB_b= 2.16 ADT_AB_u= 0.26 ADT_AB_b= 0.23 DG_rr_G= 130.5 33.5 DG_rr_J= -7.2 0.8 sig_BA_u= 1.10 sig_BA_b= 13.61 ADT_BA_u= 0.48 ADT_BA_b= 3.40 DG_fr_G= -4.27 1.04 DG_fr_J= -3.23 0.58 bias_fr= 0.2 DG_rf_G= 127.88 30.30 DG_rf_J= -7.16 0.66 bias_rf= 0.0 DG_BAR= -5.26 0.0 tb= 720 tu= 360"</p> <p> To compute all DDG estimates of Table 2 launch the script<br> "do_all.bash" from the main dir </p> <p>shift-pot -> This directory contains two gnuplot scripts showing the evolution of the<br> Beutler LJ and elec soft-core potentials (soft.gplt) and of the shifted LJ and<br> elec soft-core potentials used in this work (shift.gplt)<br> </p>
Does Hamiltonian Replica Exchange via lambda-hopping enhance the sampling in alchemical binding free energy calculations?
<p>t-REM HREM lamba-hopping/FEP+ tests on the APA molecule with ORAC<br> (www.chim.unifi.it/orac) </p> <p>The untarred archive contains the following directories: </p> <p>t-rem -> contains input for gas-phase tests<br> st-hrem -> contains input for solvated APA with solute tempering<br> lam-hop -> contains input for solvated APA with lambda-hopping<br> bin -> scripts for REM analysis <br> lib -> APA starting conf and potential parameters for the runs </p> <p>see also README files inside each dir for further details </p>
Data from " Allostery can convert binding free energies into concerted domain motions in enzymes"
<p>Data from " Allostery can convert binding free energies into concerted domain motions in enzymes"</p> <p> </p> <p>Electrophysiology data corresponding to the main text figures and supporting information figures. One representative set was chosen for each triplicate and included in this data set. For details are found in the ‘read me explanation.txt’</p> <p>PDB used for this paper can be found at; 4ake [http://doi.org/10.2210/pdb4AKE/pdb] and 1ake [http://doi.org/10.2210/pdb1AKE/pdb]</p> <p>Full uncropped scans of any cropped gel/blot images are provided.</p> <p>The zip folder contains the MATLAB code package HMM inference, specifically tailored to nanopore ionic current flow data, as analyzed in the publication and can also be found at: https://github.com/yulanvanoppen/nanopore-HMM</p> <pre><br> </pre>
Data Set Accompanying "Free Energy Decompositions Illuminate Synergistic Effects in Interfacial Binding Thermodynamics of Mixed Surfactant Systems"
<p>This data set accompanies "Free Energy Decompositions Illuminate Synergistic Effects in Interfacial Binding<br> Thermodynamics of Mixed Surfactant Systems" by Colin K. Egan and Ali Hassanali. It includes example GROMACS<br> input files for all simulations analyzed in the paper, as well as example data sets and analysis scripts. See<br> https://doi.org/10.26434/chemrxiv-2023-h11k5 for the preprint manuscript.</p>
Data for "Absolute binding free energy calculation based on the fragment molecular orbital method and its application in designing novel SHP-2 allosteric inhibitors"
<p>Data for publication "Absolute binding free energy calculation based on the fragment molecular orbital method and its application in designing novel SHP-2 allosteric inhibitors".All structures of complex and input files for FMO , FMO/SMD , FMO/PCM , and COSMO calculation are provided .</p>
Supplementary Data for "Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation"
<p>Supplementary data for publication "Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation".</p><p>Includes:</p><ul><li>All input structures used in the the retrospective benchmark dataset as well as the (at most) 5 best scoring output models.</li><li>Input structures and output models for IFD-MD predictions of SSTR2, SSTR4, and SSTR5 complexes.</li><li>Output FEP+ maps (in fmp format) for SSTR2, SSTR4, and SSTR5 best models (representative runs shown in publication).</li></ul>
Data for: Accurate sequence-to-affinity models for SH2 domains from multi-round peptide binding assays coupled with free-energy regression
Open the record for dataset details and reuse information.
Comprehensive Evaluation of End-Point Free Energy Techniques in Carboxylated-Pillar[6]arene Host-guest Binding: I. Standard Procedure
<p>All the initial structures as well as the Autodock docked ligand poses used and the computational results.</p>
Molecular simulations to investigate the impact of N6-methylation in RNA recognition: Improving accuracy and precision of binding free energy prediction
<p>Dataset relative to Molecular dynamics simulation performed for the work "Molecular simulations to investigate the impact of N6-methylation in RNA recognition: Improving accuracy and precision of binding free energy prediction".<br><br>The dataset contains data of 42 alchemical simulations and is subdivided in 4 zip files.<br><br>Folders are named following the scheme: system_configuration_forcefield.<br>Zip file C1 contains .mdp files used for all the simulations.<br><br>Folders corresponding to simulations performed with the fit5_AC ff contains:<br>- topology files (topol.top, topol_RNA_chain_A.itp, topol_RNA_chain_B.itp)<br>- index files needed to reconstruct the demuxed trajectories (replica_index.xvg , replica_index.xvg)<br>- 16 folders, one for each replica (lam0 ... lam15), containing:<br> - final configuration (confout.gro)<br> - log file (md.log)</p> <p> - input file for md run (md.tpr)<br> - energies for the concatenated trajectories recomputed for the realtive replica hamiltonian (ener_trj_conc.edr)<br><br>Folders corresponding to simlations performed with fit_A parametrization only contains .edr files corresponding to energies for the concatenated trajectory computed for 14 set of DeQs drawn from a gaussian distribution, with the relative topologies.<br><br>Supplementary materials relative to simlations performed with fit_A parametrizationcan be found in: https://zenodo.org/records/6498021</p>
Dataset 3 for paper: "Estimation of free energy of ligand binding using Multi-eGO"
<p>Dataset 3 contains Abeta42 dataset:</p> <ul> <li>APO: reference and multi-eGO simulations</li> <li>HOLO: reference and multi-eGO simualtions</li> <li>Titration at multiple concentrations with two different multi-eGO parameters</li> </ul>
Dataset 2 for paper "Esimation of free energy of ligand binding using Multi-eGO"
<p>Dataset2 contains Kinase dataset and part of Lysozyme-Benzene dataset:</p> <p>LYZ-BNZ:</p> <ul> <li>Unbiased binding simulations</li> </ul> <p>Kinase:</p> <ul> <li>APO trainng, reference, and multi-eGO simulations</li> <li>HOLO training, reference and multi-eGO simulations of both Dasatinib and PP1</li> <li>Thermodynamic integration of both Dasatinib and PP1</li> </ul> <p> </p> <p> </p>
Dataset 1 for paper: "Estimation of free energy of ligand binding using Multi-eGO"
<p>The dataset 1 contains Lysozyme-Benzene dataset:</p> <ul> <li>APO training, reference and multi-eGO simulations</li> <li>HOLO training, reference and multi-eGO simulations</li> <li> Thermodynamic integration calculations, Volume based metadynamics</li> </ul>
A computational workflow for binding free energies in Python
<p>Dataset of distances between a host and six different ligands. The host was beta-cyclodextrin (bCD), while the ligands were phenol, benzene, aspirin, toluene, chlorobenzene and 1,3-dichlorobenzene. No bonds were frozen. </p> <p>The ligand were set to move with a step of 0.25 angstrom from -26 to 26 relative to the bCD (a total of 208 distances). At each distance, a energy biasing potential <span class="math-tex">\(E_{bias}\)</span> was applied the keep two molecules in place. </p> <p><span class="math-tex">\(E_{bias} = \frac{1}{2}\cdot K \cdot (R - R_0)^2\)</span></p> <p>The parameters of the ligands were taken from OpenFF while GLYCAM were used for the host bCD. All of it were applied in Python and the OpenMM framework. Starting parameters, pdb-, and sdf-files can be found in the start folder.</p>
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