Accurate Bond Lengths to Hydrogen Atoms from Single-Crystal X-ray Diffraction by Including Estimated Hydrogen ADPs and Comparison to Neutron and QM/MM Benchmarks.
Birger DittrichJens LübbenStefan MebsArmin WagnerPeter LugerRalf FlaigPublished in: Chemistry (Weinheim an der Bergstrasse, Germany) (2017)
Amino acid structures are an ideal test set for method-development studies in crystallography. High-resolution X-ray diffraction data for eight previously studied genetically encoding amino acids are provided, complemented by a non-standard amino acid. Structures were re-investigated to study a widely applicable treatment that permits accurate X-H bond lengths to hydrogen atoms to be obtained: this treatment combines refinement of positional hydrogen-atom parameters with aspherical scattering factors with constrained "TLS+INV" estimated hydrogen anisotropic displacement parameters (H-ADPs). Tabulated invariom scattering factors allow rapid modeling without further computations, and unconstrained Hirshfeld atom refinement provides a computationally demanding alternative when database entries are missing. Both should incorporate estimated H-ADPs, as free refinement frequently leads to over-parameterization and non-positive definite H-ADPs irrespective of the aspherical scattering model used. Using estimated H-ADPs, both methods yield accurate and precise X-H distances in best quantitative agreement with neutron diffraction data (available for five of the test-set molecules). This work thus solves the last remaining problem to obtain such results more frequently. Density functional theoretical QM/MM computations are able to play the role of an alternative benchmark to neutron diffraction.
Keyphrases
- high resolution
- amino acid
- crystal structure
- electron microscopy
- mass spectrometry
- electronic health record
- visible light
- tandem mass spectrometry
- machine learning
- big data
- high speed
- electron transfer
- magnetic resonance
- combination therapy
- emergency department
- magnetic resonance imaging
- artificial intelligence
- smoking cessation
- replacement therapy
- deep learning
- finite element