Molecular modeling was addressed to understand different substrate-binding modes and clarify the role of two positively charged residues of the penicillin G acylase active site -bR263 and aR145 -in binding of negatively charged substrates. Although the electrostatic contribution to productive substrate binding was dominated by bR263 rather than aR145, it was found that productive binding was not the only possible mode of substrate placement in the active site. Two extra binding modes -nonproductive and preproductive -were located by means of molecular docking and dynamics with binding affinities comparable with the productive one. A unique feature of nonproductive and preproductive complexes was that the substrate's acyl group did not penetrate the hydrophobic pocket, but occupied a patch on the protein interface spanning from bR263 to aR145. Nonproductive and preproductive complexes competed with each other and productive binding mode, giving rise to increased apparent substrate binding. Preproductive complex revealed an ability to switch to a productive one during molecular dynamics simulations, and conformational plasticity of the penicillin G acylase active site was shown to be crucial for that. Nonproductive binding observed at molecular modeling corresponded well with experimentally observed substrate inhibition in penicillin acylase catalysis. By combining estimated free energies of substrate binding in each mode, and accounting for two possible conformations of the penicillin G acylase active site (closed and open) quantitative agreement with experimentally measured K M values was achieved. Calculated near-attack conformation frequencies from corresponding molecular dynamics simulations were in a quantitative correlation with experimental k cat values and demonstrated adequate application of molecular modeling methods.
Recently, mass-spectrometry methods show its utility in tumor boundary location.The effect of differences between research and clinical protocols such as low-and high-resolution measurements and sample storage have to be understood and taken into account to transfer methods from bench to bedside. In this study, we demonstrate a simple way to compare mass spectra obtained by different experimental protocols, assess its quality, and check for the presence of outliers and batch effect in the dataset. We compare the mass spectra of both fresh and frozen-thawed astrocytic brain tumor samples obtained with the inline cartridge extraction prior to electrospray ionization. Our results reveal the importance of both positive and negative ion mode mass spectrometry for getting reliable information about sample diversity. We show that positive mode highlights the difference between protocols of mass spectra measurement, such as fresh and frozen-thawed samples, whereas negative mode better characterizes the histological difference between samples. We also show how the use of similarity spectrum matrix helps to identify the proper choice of the measurement parameters, so data collection would be kept reliable, and analysis would be correct and meaningful. K E Y W O R D Sbrain tumor, data conversion, inline cartridge extraction, low-and high-resolution comparison, spectra stability and reproducibility | INTRODUCTIONFast tissue profiling methods for mass spectrometers allowed developing methods of rapid analysis of biological substances. [1][2][3][4][5][6][7] There are a lot of attempts to incorporate mass spectrometry into the clinical routine for surgery assistance purposes. [8][9][10][11][12] Mass spectra of complex mixtures of biological molecules, even those whose mass is up to 1000 Da, is still rather a difficult task, because of the enormous diversity of molecules contained in biological tissues. 13,14 Despite the common practice of LC-MS/MS to be used as an instrument for accurate identification of complex mixture components, it could not be used as a routine technique for rapid analyses. 15 Therefore, rapid analyses
Many enzymatic reactions are near-equilibrium reactions. This often limits final yield and hence application of biocatalyzed processes in the industrial production. The most widely applied strategy to overcome this issue is solvent selection. It must be underlined that measuring the equilibrium position experimentally is a difficult and time-consuming procedure and any tool for predicting the solvent effect on the reaction equilibrium can be very valuable. The present work reports on the development of BESSICC, an algorithm to calculate the effect of medium composition on biocatalyzed reactions equilibrium. It is based on COSMO-RS calculation of activity coefficients of all the species in the reaction mixture and minimization of Gibbs free energy of the reaction. Starting from one single experimental measurement of the equilibrium position for a given biocatalyzed reaction it can predict the yield of the reaction in any other solvent or solvent mixture. Predictions are accurate, the errors of prediction are in average below 25% for the esterification of dodecanoic acid with menthol and below 65% for esterification of 1-dodecanoic acid with 1-dodecanol. The best predictions show an error well below 5%.
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