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96 results for “free energy”
Free Energy-based Refinement of DNA Force Field via Modification of Multiple Non-bonding Energy Terms
<p>Relevant files of the benchmark DNA simulations.</p> <p>*.xtc --> truncated OPESE trajectory file.</p> <p>*.tar --> compressed initial structures.</p> <p>*.mdp --> GROMACS molecular dynamics parameter file.</p> <p>*.dat --> PLUMED input file for OPESE simulation.</p> <p> </p>
Dataset and R code support the manuscript titled "Streamlining Linear Free Energy Relationships of Proteins through Dimensionality Analysis and Linear Modeling"
<p>This dataset and R code support the manuscript titled "Streamlining Linear Free Energy Relationships of Proteins through Dimensionality Analysis and Linear Modeling" submitted to the Journal of Chemical Information and Modeling.</p> <p>Table S 1: Chemicals with their experimental values of logKch , and values of logKow and<span> </span>logKaw used to calibrate chicken muscle protein-water 2p-LFER model.</p> <p>Table S 2: Chemicals with their experimental values of logKfish and values of logKow and<span> </span>logKaw used to calibrate fish muscle protein-water 2p-LFER model.</p> <p>Table S 3: Chemicals with their experimental values of logKBSA and values of logKow and<span> </span>logKaw used to calibrate bovine serum albumin-water 2p-LFER model.</p> <p>Table S 4: Chemicals with their experimental values of logKpw and values of logKow and<span> </span>logKaw used to calibrate combined chicken and fish muscle protein-water 2p-LFER model.</p> <p>Table S 5: Diversity of data for logKpw.</p> <p>Table S 6: Diversity of data for logKBSA.</p> <p>Table S 7: Comparison of Experimental and 2p-LFER Predicted Partition Coefficients for ionizable PFAS Compounds.</p> <p>Table S 8: List of neutral fluorotelomer PFAS Compounds.</p> <p>Table S 9: List of experimental in vivo and in vitro partitioning data for different tissues and species.</p> <p>Table S 10: List of experimental Milk-water partition coefficient and predicted values of Milk-water partitioning.</p> <p>Table S 11: Training set for logKpw</p> <p>Table S 12: Validation set for log Kpw</p> <p>Table S 13: Training set for log KBSA</p> <p>Table S 14: Validation set for log KBSA</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>
``HiPen'': a new dataset for validating (S)QM/MM free energy simulations
<p>Calculating free energy differences between levels of theory (i.e., <span class="math-tex">\(\Delta A^{low \to high}\)</span>) is integral to performing indirect (S)QM/MM free energy simulations. However, connecting levels of theory via free energy simulations has proved difficult due to (1) bond/angle degrees of freedom, (2) dihedral degrees of freedom, and (3) solvent arrangement differences between levels of theory, largely due to partial charge differences between levels of theory. In order to improve calculation of (S)QM/MM free energy simulations, the free energy simulation community should begin to compare methods based on convergence success relative to overall computational time and resource requirements. We have begun to compile such a dataset by calculating <span class="math-tex">\(\Delta A^{MM \to SCC-DFTB}\)</span> in gas phase for 22 drug-like molecules, as seen in our recent publication, Kearns, et al. <strong>2018</strong>, <em>Molecules</em>, Submitted, and we hope that future practitioners will do the same. With this work we hope to provide a standard for comparison for future FES methodologies; additionally, in the near future we hope to continue to add to this dataset including results in more complicated environments such as in solution and in enzyme. All data can be found in our publication and in the accompanying Supporting Information; raw data (such as simulation trajectories and raw data files) can be made available upon request. The purpose of this dataset publication is to make available all starting coordinates, topologies, parameter sets, and input files necessary to replicating the results published in our work.</p>
Machine Learning-Assisted Discovery of Hidden States in Expanded Free Energy Space
<p>Collective variables (CVs) are crucial parameters in enhanced sampling calculations and strongly impact the quality of the obtained free energy surface. However, many existing CVs are unique to and dependent on the system they are constructed with, making the developed CV non-transferable to other systems. Herein, we develop a non-instructor-led deep autoencoder neural network (DAENN) for discovering general-purpose CVs. The DAENN is used to train a model by learning molecular representations upon unbiased trajectories that contain only the reactant conformers. The prior knowledge of nonconstraint reactants coupled with the here-introduced topology variable and loss-like penalty function are only required to make the biasing method able to expand its configurational (phase) space to unexplored energy basins. Our developed autoencoder is efficient and relatively inexpensive to use in terms of <em>a priori</em> knowledge, enabling one to automatically search for hidden CVs of the reaction of interest.</p>
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
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Data from: Accelerometers can measure total and activity-specific energy expenditure in free-ranging marine mammals only if linked to time-activity budgets
Energy expenditure is an important component of foraging ecology, but is extremely difficult to estimate in free-ranging animals and depends on how animals partition their time between different activities during foraging. Acceleration data have emerged as a new way to determine energy expenditure at a fine scale but this needs to be tested and validated in wild animals. This study investigated whether vectorial dynamic body acceleration (VeDBA) could accurately predict the energy expended by marine predators during a full foraging trip. We also aimed to determine whether the accuracy of predictions of energy expenditure derived from acceleration increased when partitioned by different types of at-sea activities (i.e. diving, transiting, resting and surface activities). To do so, we equipped 20 lactating northern (Callorhinus ursinus) and 20 lactating Antarctic fur seals (Arctocephalus gazella) with GPS, time-depth recorders and tri-axial accelerometers and obtained estimates of field metabolic rates using the doubly labelled water (DLW) method. VeDBA was derived from tri-axial acceleration, and at-sea activities (diving, transiting, resting and surface activities) were determined using dive depth, tri-axial acceleration and travelling speed. We found that VeDBA did not accurately predict the total energy expended by fur seals during their full foraging trips (R2 = 0·36). However, the accuracy of VeDBA as a predictor of total energy expenditure increased significantly when foraging trips were partitioned by activity and when activity-specific VeDBA was paired with time-activity budgets (R2 = 0·70). Activity-specific VeDBA also accurately predicted the energy expenditures of each activity independent of each other (R2 > 0·85). Our study confirms that acceleration is a promising way to estimate energy expenditures of free-ranging marine mammals at a fine scale never attained before. However, it shows that it needs to be based on the time-activity budgets that make up foraging trips rather than being derived as a single measure of VeDBA applied to entire foraging trips. Our activity-based method provides a cost-effective means to accurately calculate energy expenditures of fur seals using acceleration and time-activity budgets, that can be transfered to studies on other species.
Data from: Temporal and spatial activity-associated energy partitioning in free-swimming sea snakes
1. Partitioning energy between critical basal functions and activity-associated behaviours is a primary determinant of animal survival. Consequently, habitat selection is likely to be driven by the efficiency with which resources can be acquired from a heterogeneous energy landscape. 2. Determining how energy partitioning is achieved across temporal and spatial scales is particularly challenging in aquatic animals due to the logistical limitations in estimating field metabolic rates (FMR) while simultaneously examining habitat choice. 3. Here, accelerometry telemetry and bimodal respirometry were used to correlate vectorial dynamic body acceleration (<i>VeDBA</i>) with oxygen consumption rates (<i>V̇o<sub>2</sub></i>) of sea snakes (<i>Hydrophis curtus</i> and <i>H. elegans</i>) across an ecologically-relevant temperature range. Subsequently, <i>VeDBA</i> of free-roaming snakes was used to estimate activity-associated FMR within a near-shore environment over diel, seasonal and spatial scales. 4. Diel changes in activity explained short-term patterns in FMR, whereas seasonal changes in water temperature drove long-term patterns. Spatial analyses demonstrated that activity-associated FMR was elevated in productive seagrass and mudflat habitats, indicative of a concentration of foraging efforts. 5. Our findings illustrate for the first time how sea snakes partition activity-associated FMR across time and space, providing an approach by which we can monitor the impacts of, and vulnerabilities to, natural and anthropogenic disturbances like warming and trawl fisheries.
Underlying data for "Evaluation of solvation free energies for small molecules with the AMOEBA polarizable force field"
<p>This dataset provides the parameters and results generated for the study "Evaluation of solvation free energies for small molecules with the AMOEBA polarizable force field"</p> <p>Contents of dataset:<br /> PARAMETERS.tar.gz<br /> A gzipped and tar'd directory with the parameters of all solutes sorted by solvent for AMOEBA and GAFF force field. This parameters directory is distributed to 4 different subdirectories: a)chloroform (21 solutes) b)toluene (21 solutes) c)dmso (6 solutes) d)acetonitrile (6 solutes). Each solvent includes two set of parameters for each solute (AMOEBA and GAFF).</p> <p>RESULTS.tar.gz<br /> A gzipped and tar'd directory with the solvation free energy results of all solutes in kcal/mol, with AMOEBA and GAFF force fields, for each solvent, in text format. A single set of conditions for a single solute is one row in the text files. The first column represents the experimental data, the remaining columns (2, 3 and 4) correspond to the three repeat simulations.</p>
Data for A weak coupling mechanism for the early steps of the recovery stroke of myosin VI: a free energy simulation and string method analysis
<p>Representative frames, topology for MD simulation with NAMD and GROMACS, data and notebook to reproduce analyses in the paper "A weak coupling mechanism for the early steps of the recovery stroke of myosin VI: a free energy simulation and string method analysis" (Blanc, Houdusse, Cecchini, PLOS Computational Biology 2024).</p>
Application of JumpCount on free energy profiles (preliminary data)
<p>JumpCount method applied on free energy profiles from [1].</p> <p>Ref:<br>1 - Science 2011, 334, 517-520.</p> <p> </p>
Multiple Parameter Replica Exchange Gaussian Accelerated Molecular Dynamics for Enhanced Sampling and Free Energy Calculation of Biomolecular Systems
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Modelling and Comparing Converter Architectures and Energy Harvesting ICs for Battery-Free Systems
<p>Artifacts containing measurement scripts, data sets, and simulations for the paper "Modelling and Comparing Converter Architectures and Energy Harvesting ICs for Battery-Free Systems" (currently submitted and under review).</p>
Free Energy of Membrane Pore Formation and Stability from Molecular Dynamics Simulations
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Datasets for: Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy
<p>The datasets and supplementary materials for the manuscript "Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy". <strong>Citations should refer directly to the manuscript (refer to the DOI </strong><a href="https://doi.org/10.1021/acs.jcim.1c01103">10.1021/acs.jcim.1c01103</a><strong>)</strong>.</p> <p>The preprint version of of the manuscript is also available at: <a href="https://doi.org/10.33774/chemrxiv-2021-djd3d-v2">10.33774/chemrxiv-2021-djd3d-v2</a></p> <p> </p> <p>Regarding "<strong>Solvation_data-1.0.0.zip</strong>":</p> <p>The datasets include the curated data for: (1) Abraham solute parameters, (2) solvation free energy, (3) solvation enthalpy, (4) gas-water partition coefficient (logKw), (5) water-1-octanol partition coefficient (logPow). The fitted Abraham and Mintz solvent parameters are also included.</p> <p>Detailed information can be found in the "README.txt" file of the zip file.</p> <p> </p> <p>Regarding "<strong>ML_model_files.zip</strong>":</p> <p>This contains the machine learning model files for SoluteML and DirectML. For the instruction on how to use it, please refer to the <em>chemprop_solvation</em> git repository (<a href="https://github.com/fhvermei/chemprop_solvation">https://github.com/fhvermei/chemprop_solvation</a>)</p> <p> </p>
example perturbation free energy data (GROMOS format)
<p>example perturbation free energy data used to show the usage of the simulation update scheme feature of the SMArt toolkit - available at https://github.com/drazen-petrov/SMArt</p> <p>data contains:</p> <p>GROMOS format: perturbation of a histone tail H3K4 lysine trimethylation mark bound to the Sp100C PHD finger</p>
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>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.