We used quantum-chemical methods to study seven possible mechanisms of monoamine oxidase (MAO) inhibition by acetylenic inhibitors, considering neutral, cationic, anionic and radical mechanisms. MAO is a flavoenzyme responsible for the metabolism of the important neurotransmitters noradrenaline, serotonin and dopamine. It exists in two isoforms: MAO A and MAO B. Selective MAO A inhibitors are used in the treatment of depression, whereas selective MAO B inhibitors such as rasagiline and selegiline are used to relieve symptoms of Parkinson disease. Rasagiline and selegiline are irreversible MAO B inhibitors, each forming a covalent bond with the enzyme's flavin adenine dinucleotide (FAD) cofactor upon inhibition. Although widely used, they both exhibit numerous adverse effects. Our calculations, performed at the B3LYP/6-311++G(2d,2p)//B3LYP/6-31+G(d) level of theory, with application of the CPCM solvent reaction field with a dielectric constant of 4 to mimic the polar environment, found that a polar anionic mechanism, involving deprotonation of the inhibitor molecule at the terminal acetylene carbon atom, is the most plausible. The calculated free energies of activation for rasagiline and selegiline by this mechanistic pathway are 19.9 and 23.7 kcal/mol, respectively, in very good agreement with experimentally determined values of 20.8 and 21.3 kcal/mol, respectively. Together with additional experimental and theoretical work, the results presented here could lead to better understanding of the nature of MAO inhibition and possible design of new antiparkinsonians as improved MAO B inhibitors. Some ideas on the strategy to achieve that and perspectives for future work are also given.
COBISS.SI-ID: 4788506
Hydration of histamine was examined by infrared spectroscopy and Car-Parrinello molecular dynamics simulation. Histamine is a neurotransmitter and inflammation mediator, which at physiological pH conditions is present mainly in monocationic form. Our focus was on the part of vibrational spectra that corresponds to histamine N-H stretching, since these degrees of freedom are essential for its interactions with either water molecules or transporters and receptors. Assignment of the experimental spectra revealed a broad feature between 3350 and 2300 cm(-1), being centered at 2950 cm(-1), which includes a mixed contribution from the ring N-H and the aminoethyl N-H stretching vibrations. Computational analysis was performed in two ways: first, by making Fourier transformation on the autocorrelation function of all four N-H bond distances recorded during CPMD run, and second, and most importantly, by incorporating quantum effects through applying an a posteriori quantization of all N-H stretching motions utilizing our snapshot analysis of the fluctuating proton potential. The one-dimensional vibrational Schroedinger equation was solved numerically for each snapshot, and the N-H stretching envelopes were calculated as a superposition of the 0)1 transitions. The agreement with the experiment was much better in the case of the second approach. Our calculations clearly demonstrated that the ring amino group absorbs at higher frequencies than the remaining three amino N-H protons of the protonated aminoethyl group, implying that the chemical bonding in the former group is stronger than in the three amino N-H bonds, thus forming weaker hydrogen bonding with the surrounding solvent molecules. In this way the results of the simulation complemented the experimental spectrum that cannot distinguish between the two sets of protons. The effects of deuteration were also considered. The resulting N-D absorption is narrower and red-shifted. The presented methodology is of general applicability to strongly correlated systems, and it is particularly tuned to provide computational support to vibrational spectroscopy. Perspectives are given for its future applications in computational studies of tunneling in enzyme reactive centers and for receptor activation.
COBISS.SI-ID: 4658202
Quantitative structure-activity relationship study on three diverse sets of structurally similar fluoroquinolones was performed using a comprehensive set of molecular descriptors. Multiple linear regression technique was applied as a preprocessing tool to find the set of relevant descriptors (10) which are subsequently used in the artificial neural networks approach (non-linear procedure). The biological activity in the series (minimal inhibitory concentration ([mu]g/mL) was treated as negative decade logarithm, [rho]MIC). Using the non-linear technique counter propagation artificial neural networks, we obtained good predictive models. All models were validated using cross validation leave-one-out procedure. The results (the best models: Assay1, R = 0.8108; Assay2, R = 0.8454, and Assay3, R = 0.9212) obtained on external, previously excluded test datasets show the ability of these models in providing structure-activity relationship of fluoroquinolones. Thus, we demonstrated the advantage of non-linear approach in prediction of biological activity in these series. Furthermore, these validated models could be proficiently used for the design of novel structurally similar fluoroquinolone analogues with potentially higher activity.
COBISS.SI-ID: 4355354