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Copper cation interactions with biologically essential types of ligands: a computational DFT study
Matej Pavelka1, Milan Simanek, Jirí Sponer
1Department of Chemical Physics and Optics, Faculty of Mathematics and Physics, Charles University, Ke Karlovu 3, 121 16 Prague 2, Czech Republic.
Copper ions (Cu) in biological settings were studied using theoretical methods. Ammine ligands stabilize copper complexes most, while coordination numbers vary by ion state and ligand type, offering insights into copper
Area of Science:
- Computational chemistry
- Bioinorganic chemistry
- Quantum chemistry
Background:
- Copper ions (Cu) are essential in biological systems, playing critical roles in various metalloenzymes.
- Understanding copper coordination chemistry in biologically relevant ligand environments is crucial for deciphering its functions.
- Hydrogen sulfide (H2S), aqua (H2O), and ammine (NH3) ligands model common biological coordination spheres for copper.
Purpose of the Study:
- To conduct a systematic theoretical investigation of Cu(I) and Cu(II) complexes.
- To explore the influence of hydrogen sulfide, aqua, and ammine ligands on copper ion stability and coordination.
- To elucidate the electronic structure and bonding characteristics of these copper complexes.
Main Methods:
- Density Functional Theory (DFT) was employed for molecular structure optimization.
- Energy analyses were performed to determine complex stability trends.
- Natural Population Analysis (NPA) and molecular orbital analyses were used to study charge distribution and bonding.
Main Results:
- Ammine complexes exhibited the highest stability, followed by aqua and hydrogen sulfide complexes.
- Cu(I) preferred 2-coordination in ammine/aqua fields but 4-coordination with H2S ligands.
- Cu(II) complexes showed stability in 4- and 5-coordinated structures across all ligand types.
Conclusions:
- Ligand type significantly impacts copper ion stability and coordination preferences.
- The study provides fundamental insights into copper-ligand interactions in biological contexts.
- Results can aid in calibrating computational models for bioinorganic systems.
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