Computational Characterization of the Inhibition Mechanism of Xanthine Oxidoreductase by Topiroxostat

Xanthine oxidase (XO) is a member of the molybdopterin-containing enzyme family. It interconverts xanthine to uric acid as the last step of purine catabolism in the human body. The high uric acid concentration in the blood directly leads to human diseases like gout and hyperuricemia. Therefore, drugs that inhibit the biosynthesis of uric acid by human XO have been clinically used for many years to decrease the concentration of uric acid in the blood. In this study, the inhibition mechanism of XO and a new promising drug, topiroxostat (code: FYX-051), is investigated by employing molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) calculations. This drug has been reported to act as both a noncovalent and covalent inhibitor and undergoes a stepwise inhibition by all its hydroxylated metabolites, which include 2-hydroxy-FYX-051, dihydroxy-FYX-051, and trihydroxy-FYX-051. However, the detailed mechanism of inhibition of each metabolite remains elusive and can be useful for designing more effective drugs with similar inhibition functions. Hence, herein we present the computational investigation of the structural and dynamical effects of FYX-051 and the calculated reaction mechanism for all of the oxidation steps catalyzed by the molybdopterin center in the active site. Calculated results for the proposed reaction mechanisms for each metabolite’s inhibition reaction in the enzyme’s active site, binding affinities, and the noncovalent interactions with the surrounding amino acid residues are consistent with previously reported experimental findings. Analysis of the noncovalent interactions via energy decomposition analysis (EDA) and noncovalent interaction (NCI) techniques suggests that residues L648, K771, E802, R839, L873, R880, R912, F914, F1009, L1014, and A1079 can be used as key interacting residues for further hybrid-type inhibitor development

Computational Characterization of the Inhibition Mechanism of Xanthine Oxidoreductase by Topiroxostat

Xanthine oxidase (XO) is a member of the molybdopterin-containing enzyme family. It interconverts xanthine to uric acid as the last step of purine catabolism in the human body. The high uric acid concentration in the blood directly leads to human diseases like gout and hyperuricemia. Therefore, drugs that inhibit the biosynthesis of uric acid by human XO have been clinically used for many years to decrease the concentration of uric acid in the blood. In this study, the inhibition mechanism of XO and a new promising drug, topiroxostat (code: FYX-051), is investigated by employing molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) calculations. This drug has been reported to act as both a noncovalent and covalent inhibitor and undergoes a stepwise inhibition by all its hydroxylated metabolites, which include 2-hydroxy-FYX-051, dihydroxy-FYX-051, and trihydroxy-FYX-051. However, the detailed mechanism of inhibition of each metabolite remains elusive and can be useful for designing more effective drugs with similar inhibition functions. Hence, herein we present the computational investigation of the structural and dynamical effects of FYX-051 and the calculated reaction mechanism for all of the oxidation steps catalyzed by the molybdopterin center in the active site. Calculated results for the proposed reaction mechanisms for each metabolite’s inhibition reaction in the enzyme’s active site, binding affinities, and the noncovalent interactions with the surrounding amino acid residues are consistent with previously reported experimental findings. Analysis of the noncovalent interactions via energy decomposition analysis (EDA) and noncovalent interaction (NCI) techniques suggests that residues L648, K771, E802, R839, L873, R880, R912, F914, F1009, L1014, and A1079 can be used as key interacting residues for further hybrid-type inhibitor development

Computational Characterization of the Inhibition Mechanism of Xanthine Oxidoreductase by Topiroxostat

Xanthine oxidase (XO) is a member of the molybdopterin-containing enzyme family. It interconverts xanthine to uric acid as the last step of purine catabolism in the human body. The high uric acid concentration in the blood directly leads to human diseases like gout and hyperuricemia. Therefore, drugs that inhibit the biosynthesis of uric acid by human XO have been clinically used for many years to decrease the concentration of uric acid in the blood. In this study, the inhibition mechanism of XO and a new promising drug, topiroxostat (code: FYX-051), is investigated by employing molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) calculations. This drug has been reported to act as both a noncovalent and covalent inhibitor and undergoes a stepwise inhibition by all its hydroxylated metabolites, which include 2-hydroxy-FYX-051, dihydroxy-FYX-051, and trihydroxy-FYX-051. However, the detailed mechanism of inhibition of each metabolite remains elusive and can be useful for designing more effective drugs with similar inhibition functions. Hence, herein we present the computational investigation of the structural and dynamical effects of FYX-051 and the calculated reaction mechanism for all of the oxidation steps catalyzed by the molybdopterin center in the active site. Calculated results for the proposed reaction mechanisms for each metabolite’s inhibition reaction in the enzyme’s active site, binding affinities, and the noncovalent interactions with the surrounding amino acid residues are consistent with previously reported experimental findings. Analysis of the noncovalent interactions via energy decomposition analysis (EDA) and noncovalent interaction (NCI) techniques suggests that residues L648, K771, E802, R839, L873, R880, R912, F914, F1009, L1014, and A1079 can be used as key interacting residues for further hybrid-type inhibitor development

The effect of contact frequency <i>A</i>_<i>0</i> on the prevalence and threshold of epidemic.

<p>In each of the 6 panels, I^∞(the final epidemic prevalence) as a function of the effective spreading rate λ on a scale-free network of 2000 nodes with degree distribution <i>p</i>(<i>k</i>) ∼ <i>k</i>^−2.35. In each subpanel, there are three color lines, the black, red and blue curves represent contact number <i>A</i>_<i>0</i> = 4,5,6, respectively.</p

The effect of three strategies on the epidemic dynamics.

<p>In each of the 3 panels, I^∞(the final epidemic prevalence) as a function of the effective spreading rate λ on a scale-free network of 2000 nodes with degree distribution <i>p</i>(<i>k</i>) ∼ <i>k</i>^−2.35. In each subpanel, there are three color lines: the black, red and blue line, represent strategy 1 without any contact behavior adjustment, strategy 2 (adjustment of contact number) and strategy 3 (adjustment of both contact number and contact patterns), respectively. The other parameters are set to: <i>A</i>₀ = 5,<i>μ</i> = 0.6,<i>a</i> = 0.2,<i>b</i> = 0.2,<i>c</i> = 0.2,<i>δ</i> = 0.5 in the panel 3–1, <i>A</i>₀ = 5,<i>μ</i> = 0.6,<i>a</i> = 0.5,<i>b</i> = 0.2,<i>c</i> = 0.2,<i>δ</i> = 0.5 in the panel 3–2, <i>A</i>₀ = 5,<i>μ</i> = 0.6,<i>a</i> = 1,<i>b</i> = 0.2,<i>c</i> = 0.2,<i>δ</i> = 0.5 in the panel 3–3.</p

The effect of the local information influencing factor <i>b</i> on the epidemic spreading dynamics under strategy 2.

<p>In this figure, I^∞(final prevalence) as a function of the local information influencing factor <i>b</i> under strategy 2 on a scale-free network of 2000 nodes with degree distribution <i>p</i>(<i>k</i>) ∼ <i>k</i>^−2.35. The other parameters are set to <i>A</i>₀ = 5,<i>β</i> = 0.3,<i>γ</i> = 1, <i>μ</i> = 0.6,<i>a</i> = 0.2,<i>c</i> = 0.2,<i>δ</i> = 0.5.</p

The effect of acquaintance contacts ratio <i>μ</i> on the prevalence and threshold of epidemic.

<p>In each of the 3 panels, I^∞(the final epidemic prevalence) as a function of the effective spreading rate <i>λ</i> on a scale-free network of 2000 nodes with degree distribution <i>p</i>(<i>k</i>) ∼ <i>k</i>^−2.35. In each subpanel, there are 6 color lines, that represent different acquaintance contacts ratios <i>μ</i> = 0, 0.2, 0.4, 0.6, 0.8, and 1.</p

The effect of the global information influencing factor <i>c</i> on the epidemic spreading dynamics.

<p>In this figure, I^∞ (the final epidemic prevalence) as a function of the global information influencing factor <i>c</i> on a scale-free network of 2000 nodes with degree distribution <i>p</i>(<i>k</i>) ∼ <i>k</i>^−2.35. The other parameters are set to <i>A</i>₀ = 5,<i>β</i> = 0.3,<i>γ</i> = 1, <i>μ</i> = 0.6,<i>a</i> = 0.2,<i>b</i> = 0.2,<i>δ</i> = 0.5.</p

Pyranopterin Dithiolene Distortions Relevant to Electron Transfer in Xanthine Oxidase/Dehydrogenase

The reducing substrates 4-thiolumazine and 2,4-dithiolumazine have been used to form Mo^IV-product complexes with xanthine oxidase (XO) and xanthine dehydrogenase. These Mo^IV-product complexes display an intense metal-to-ligand charge-transfer (MLCT) band in the near-infrared region of the spectrum. Optical pumping into this MLCT band yields resonance Raman spectra of the Mo site that are devoid of contributions from the highly absorbing FAD and 2Fe2S clusters in the protein. The resonance Raman spectra reveal in-plane bending modes of the bound product and low-frequency molybdenum dithiolene and pyranopterin dithiolene vibrational modes. This work provides keen insight into the role of the pyranopterin dithiolene in electron-transfer reactivity

Relation between response magnitude and BF.

<p>A and B: mean response magnitude of all 15 vocalizations versus the BF of AAF and PAF neurons, respectively. C: Energy distribution of our vocalization stimuli. Solid line indicates the mean SPL of the 15 vocalizations. Dotted line indicates the mean+SD. Each spectrum of vocalization was normalized by its maximum as 0, before calculating the mean and SD.</p