Computed surface and chemical potentials, expansion coefficients, structures, models and results for the PMFPredictor Toolkit Article Swipe

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Ian Rouse , Vladimir Lobaskin · YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.5281/zenodo.7253565

The PMFPredictor toolkit enables the prediction of the potentials of mean force describing the interaction between a surface and a small molecule in aqueous solution, which would otherwise be obtained from lengthy metadynamics simulations. This repository contains files to enable the operation of the toolkit, with source code available at https://github.com/ijrouse/PMFPredictor-Toolkit and corresponding to release v0.5-alpha. In PMFPredictor-Repository.zip we provide supplementary data necessary for the operation of the PMFPredictor Toolkit including: Structures of surfaces ("Structures/Surfaces") and chemicals ("Structures/Chemicals") in a united tabulated (.csv) format, listing x/y/z co-ordinates, atom IDs, mass (in amu), charge (in elementary units), Lennard Jones 6-12 parameters: sigma (in nm) and epsilon (in kJ/mol). Interaction potentials of surfaces ("SurfacePotentials") and chemicals ("ChemicalPotentials") with probe atoms and molecules in tabulated format with distances relative to reference points in nm and energies in kJ/mol. Also included in these folders are the potentials with the molecular probes in individual files. Hypergeometric expansion coefficients of the interaction potentials ("Datasets/SurfacePotentialCoefficientsNoise-1-oct12.csv" and "Datasets/ChemicalPotentialCoefficients-oct10.csv") in tabulated form, corresponding to potentials with units of nm for distance and kJ/mol for energy. Descriptions of the headers are provided in DatasetHeaderDescription.txt, included in the archive. Trained TensorFlow models for the prediction of potentials of mean force from HG interaction coefficients, suitable for loading via the Keras backend. PMFs generated for a range of surfaces and chemicals as output from the trained model, in both text format and figures showing comparisons to training PMFs where available. PMFs are supplied as tabulated data with comma separated values of distance in nm and interaction energies in kJ/mol. Adsorption energies in kJ/mol evaluated at T=300K extracted from all PMFs and compared to the values obtained from known PMFs where available. The surface_pmfpredictor.zip archive contains PMFs selected for the operation of the UnitedAtom software package for the calculation of protein-nanoparticle interactions. This data is included in the main repository file and provided separately to avoid the download of unnecessary data if only the final PMFs are required. As with the main set, these are provided in tabulated form with distance [nm], energy [kJ/mol] pairs. This repository also contains the sets of figures illustrating these PMFs for each surface. Both archives contain further information on the contents, including descriptions of the surfaces and chemicals for which PMFs are computed. We also supply the training data used to build the model in a separate archive, PMFPredictor-TrainingData.zip, along with a text file containing descriptions of all headers in this file. This training data is quite large when uncompressed, c.a. 7 Gb, hence its exclusion from the main archive. If you use results from this repository please cite the following paper in addition to the repository itself: I. Rouse, V. Lobaskin, Machine-learning based prediction of small molecule -- surface interaction potentials, arXiv:2211.07999 https://arxiv.org/abs/2211.07999

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dataset

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en

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https://doi.org/10.5281/zenodo.7253565

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green

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10

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https://openalex.org/W4393663554

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https://openalex.org/W4393663554

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DOI

https://doi.org/10.5281/zenodo.7253565

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Title

Computed surface and chemical potentials, expansion coefficients, structures, models and results for the PMFPredictor Toolkit

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dataset

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en

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2022

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2022-10-26

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Ian Rouse, Vladimir Lobaskin

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https://doi.org/10.5281/zenodo.7253565

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Yes

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green

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https://doi.org/10.5281/zenodo.7253565

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Surface (topology), Mathematics, Statistical physics, Geometry, Physics

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0

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10

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