Theoretical Investigation of the Electronic Structure in the Reaction Center of Acaryochloris marina

Abstract

This thesis explores the electronic structure of photosystem I (PSI) in far-red lightutilizing organism Acaryochloris marina through density functional theory (DFT), with a particular focus on the spin density distribution around the P740 reaction center and its surrounding residues. While the final hyperfine couplings show excellent agreement with experimental values, they reveal a nearly symmetric spin density between the A and B branches, rather than the anticipated localization on the B branch. Various computational parameters, including the choice of functional, optimization method, and model size, were systematically examined to determine their influence on the final results. Among the tested functionals, TPssh produced the most pronounced asymmetry while keeping the hyperfine coupling values close to the experiment. CHARMM-based optimizations yielded lower RMSD values, indicating greater structural accuracy compared to quantum-based optimizations. A detailed residue analysis identified specific amino acids such as Trp660 and Leu623 that play a significant role in modulating spin density, particularly due to their proximity to the reaction center. Interestingly, some residues like Phe653 showed minimal influence despite their size, suggesting that spatial positioning is more critical than steric bulk alone. Additionally, hydrogen bonding networks involving selected residues may further influence electronic asymmetry by stabilizing charge distribution pathways. These computational insights provide a deeper understanding of how local residue environments affect PSI function and may help explain the evolutionary adaptations seen in organisms like Acaryochloris marina, which perform photosynthesis efficiently under far-red light. The study concludes by recommending future QM/MM calculations with expanded QM regions to better capture the protein environment and resolve discrepancies between computational models and experimental data.