solvated_protein_fragments_JCTC_2019 ...

• Main Authors: Unke, Oliver T., Meuwly, Markus
• Format: Dataset
• Language: English
• Published: ColabFit 2023
• Subjects: ColabFit; Dataset; AgPd_NPJ_2021; Materials Science; https//id.loc.gov/authorities/subjects/sh85082094.html; Machine Learning; https//id.loc.gov/authorities/subjects/sh85079324.html; Techniques/Computational Techniques/First-principles calculations; Techniques/Computational Techniques/Machine Learning; Techniques/Computational Techniques/Molecular Dynamics; Research Areas/Atomic & molecular structure/Potential energy surfaces; Research Areas/Electronic structure/Interatomic & molecular potentials; Orca
• Online Access: https://dx.doi.org/10.60732/c4731f07; https://materials.colabfit.org/id/DS_ctjgc03xdauc_0

The solvated protein fragments dataset was generated as a partner benchmark dataset, along with SN2, for measuring the performance of machine learning models, in particular PhysNet, at describing chemical reactions, long-range interactions, and condensed phase systems. The dataset contains structures for all possible "amons" (hydrogen-saturated covalently bonded fragments) of up to eight heavy atoms (C, N, O, S) that can be derived from chemical graphs of proteins containing the 20 natural amino acids connected via peptide bonds or disulfide bridges. For amino acids that can occur in different charge states due to (de)protonation (i.e., carboxylic acids that can be negatively charged or amines that can be positively charged), all possible structures with up to a total charge of +-2e are included. In total, the dataset provides reference energies, forces, and dipole moments for 2,731,180 structures calculated at the revPBE-D3(BJ)/def2-TZVP level of theory using ORCA 4.0.1. ...