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Palladium N-Heterocyclic Carbene Complexes: Synthesis from Benzimidazolium Salts and Catalytic Activity in Carbon-carbon Bond-forming Reactions
Published on: July 30, 2017
Aliphatic C-H/pi interactions: Methane-benzene, methane-phenol, and methane-indole complexes
Ashley L Ringer1, Michelle S Figgs, Mutasem O Sinnokrot
1Center for Computational Molecular Science and Technology, School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, USA.
This study quantifies noncovalent C-H/pi interactions, crucial for molecular recognition in biochemistry. We modeled interactions between C-H bonds and aromatic amino acids using advanced computational methods.
Area of Science:
- Biochemistry
- Computational Chemistry
- Molecular Interactions
Background:
- Noncovalent C-H/pi interactions are fundamental in biological systems, influencing molecular recognition.
- Understanding these interactions is key to deciphering complex biochemical processes.
Purpose of the Study:
- To investigate the potential energy curves of methane-aromatic complexes as models for C-H/pi interactions.
- To provide accurate interaction energies for systems mimicking interactions involving phenylalanine, tyrosine, and tryptophan.
Main Methods:
- Utilized second-order Møller-Plesset perturbation theory (MP2) with augmented correlation-consistent basis sets (aug-cc-pVDZ and aug-cc-pVTZ).
- Employed coupled-cluster theory with perturbative triples [CCSD(T)] for higher-order electron correlation corrections.
- Applied symmetry-adapted perturbation theory (SAPT) to dissect the components of the interaction energy.
Main Results:
- Calculated potential energy curves for methane-benzene, methane-phenol, and methane-indole complexes.
- Estimated highly accurate interaction energies at the CCSD(T)/aug-cc-pVTZ level through additive approximations.
- Decomposed interaction energies into physically meaningful components using SAP তত্ত্ব.
Conclusions:
- The study provides valuable quantitative data on C-H/pi interactions relevant to biochemistry.
- The computational methods employed offer a robust framework for studying similar noncovalent interactions.
- Findings contribute to a deeper understanding of molecular recognition in biological systems.
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