Better force fields start with better data -- A data set of cation dipeptide interactions

Code

• github.com/XiaojuanHu/AA_property_calculation
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Abstract

We present a data set from a first-principles study of amino-methylated and acetylated (capped) dipeptides of the 20 proteinogenic amino acids - including alternative possible side chain protonation states and their interactions with selected divalent cations (Ca^2+, Mg^2+ and Ba^2+). The data covers 21,909 stationary points on the respective potential-energy surfaces in a wide relative energy range of up to 4 eV (390 kJ/mol). Relevant properties of interest, like partial charges, were derived for the conformers. The motivation was to provide a solid data basis for force field parameterization and further applications like machine learning or benchmarking. In particular the process of creating all this data on the same first-principles footing, i.e. density-functional theory calculations employing the generalized gradient approximation with a van der Waals correction, makes this data suitable for data-driven force field development. To make the data accessible across domain borders and to machines, we formalized the metadata in an ontology.

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