RESUMEN
Human arginase I (HARGI) is a metalloprotein highly expressed in the liver cytosol and catalyzes the hydrolysis of l-arginine to form l-ornithine and urea. Understanding the reaction mechanism would be highly helpful to design new inhibitor molecules for HARGI as it is a target for heart- and blood-related diseases. In this study, we explored the hydrolysis reaction mechanism of HARGI with antiferromagnetic and ferromagnetic coupling between two Mn(II) ions at the catalytic site by employing molecular dynamics simulations coupled with quantum mechanics and molecular mechanics (QM/MM). The spin states, high-spin ferromagnetic couple (S Mn1 = 5/2, S Mn2 = 5/2), low-spin ferromagnetic couple (S Mn1 = 1/2, S Mn2 = 1/2), high-spin antiferromagnetic couple (S Mn1 = 5/2, S Mn2 = -5/2), and low-spin antiferromagnetic couple (S Mn1 = 1/2, S Mn2 = -1/2) are considered, and the calculated energetics for the complex of the substrate and HARGI are compared. The results show that the high-spin antiferromagnetic couple (S Mn1 = 5/2, S Mn2 = -5/2) is more stable than other spin states. The low-spin ferromagnetic and antiferromagnetic coupled states are highly unstable compared with the corresponding high-spin states. The high-spin antiferromagnetic couple (S Mn1 = 5/2, S Mn2 = -5/2) is stabilized by 0.39 kcal/mol compared with the ferromagnetic couple (S Mn1 = 5/2, S Mn2 = 5/2). The reaction mechanism is independent of spin states; however, the energetics of transition states and intermediates are more stable in the case of the high-spin antiferromagnetic couple (S Mn1 = 5/2, S Mn2 = -5/2) than the corresponding ferromagnetic state. It is evident that the calculated coupling constants are higher for antiferromagnetic states and, interestingly, superexchange coupling is found to occur between Mn(II) ions via hydroxide ions in a reactant. The hydroxide ion enhances the coupling interaction and initiates the catalytic reaction. It is also noted that the first intermediate structure where there is no superexchange coupling is similar to the known inhibitor 2(S)-amino-6-boronohexanoic acid.
RESUMEN
This work proposes a soft-sensor design for real-time estimation of glucose concentration under mixotrophic conditions using Raman spectroscopy. The suggested approach applies a Rolling-Circle Filter (RCF), Partial Least Squares (PLS), and a successive Savitzky-Golay (SG) smoothing filter. RCF is used to remove the background effects of Raman spectrum in the pre-processing step. PLS is used to reduce the dimensionality of spectrum data and relate them to the concentration. The SG filter is further employed as a post-processing step in a successive manner to adjust predicted glucose concentrations. Two sets of experiments using artificial assays and samples from a microalgae cultivation system were performed for verification. The proposed approach showed improved prediction performances compared to other data processing and regression techniques.