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December 1, 2020

Agreement Factors Crystallography

Filed under: Uncategorized — Mark Baker @ 9:21 pm

Bacterial resistance is a natural result of evolution and environmental pressure. Resistance factors can be coded on plasmids or inside the bacterial chromosome. The etiology of antimicrobial resistance may include mechanisms limiting the entry of the drug, changes in the receptor (objective) of the drug, or metabolic inactivation of the drug. Bacteria acquire genes that confer multiple ways of resisting antimicrobials, including spontaneous DNA mutation, bacterial transformation and plasmid transfer. Because of the complex possibilities that processing can remove some sources of error and not others, the only way to definitively assess the influence of all sources of error is to simulate the experience of bending and calibrate the size and frequency of all sources of error on a real instrument. To do this, we developed MLFSOM, a program to create simulated bending images with a series of structural factors and a set-up list of experimental variables. A detailed description of the implementation of this program is provided in the form of Doc S1. The name MLFSOM was chosen because it does the opposite of data processing programs such as MOSFLM 18, but the simulation is based on the first principles and is not specific to MOSFLM, HKL2000 19 or XDS 20. Unlike previous 21-23 bending image simulators, MLFSOM`s implementation focused on setting up signal and noise on an absolute scale and allowing direct comparison juxtaposed with real data. Two sets of simulated data were generated by MLFSOM using the same parameters as the actual data, some of which were refined values from the reference dataset processing. Other parameters such as flow fluids, sensor calibration errors and the size of all error sources were calibrated from independent experiments (Tables 22 and 3).3). The first simulated dataset (multi-configured simulator) was generated from a coordinate model containing face chains of alternative conformations and gd-ligand positions, which were refined with the actual data collected here. This model was generated from Protein Data Bank ID 1:87 and refined alternately with phenix.refine.29 and REFMAC 28, with regular manual recalcishing rounds with Coot 50.51.

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