Quickstart guide to model structures and interactions of artificial molecular muscles with efficient computational methods.
Julia KohnSebastian SpicherMarkus BurschStefan GrimmePublished in: Chemical communications (Cambridge, England) (2021)
Artificial molecular muscles (AMMs) represent an important group of molecular machines. Their theoretical treatment is challenging due to size, element composition, and complex interaction motifs. Moreover, experimentally determined structures often only yield insights into the covalent connectivity of atoms rather than their 3D structure. Accordingly, a reproducible computational modeling of such structures is complicated. In this work we present a standardized, mostly quantum chemical protocol on how to obtain reliable structures from scratch and to compute contraction free energies ΔGc for daisy-chain rotaxane AMMs efficiently. In this protocol, the recently developed force-field (GFN-FF) and extended tight-binding methods (GFNn-xTB) are employed. For comparison, dispersion-corrected density functional theory (DFT-D) based reference ΔGc were computed. In one case for which data are available, excellent agreement between theoretical and experimental ΔGc values within 1-2 kcal mol-1 is obtained.
Keyphrases
- density functional theory
- molecular dynamics
- high resolution
- single molecule
- randomized controlled trial
- gas chromatography
- white matter
- computed tomography
- molecular docking
- big data
- multiple sclerosis
- machine learning
- binding protein
- diffusion weighted imaging
- functional connectivity
- deep learning
- smoking cessation
- tandem mass spectrometry
- crystal structure
- energy transfer