RESUMEN
The gram-positive bacterium Listeria monocytogenes is an important foodborne intracellular pathogen that is widespread in the environment. The functions of hypothetical proteins (HP) from various pathogenic bacteria have been successfully annotated using a variety of bioinformatics strategies. In this study, a HP Imo0888 (NP_464414.1) from the Listeria monocytogenes EGD-e strain was annotated using several bioinformatics tools. Various techniques, including CELLO, PSORTb, and SOSUIGramN, identified the candidate protein as cytoplasmic. Domain and motif analysis revealed that the target protein is a PemK/MazFlike toxin protein of the type II toxin-antitoxin system (TAS) which was consistent with BLASTp analysis. Through secondary structure analysis, we found the random coil to be the most frequent. The Alpha Fold 2 Protein Structure Prediction Database was used to determine the three-dimensional (3D) structure of the HP using the template structure of a type II TAS PemK/MazF family toxin protein (DB ID_AFDB: A0A4B9HQB9) with 99.1% sequence identity. Various quality evaluation tools, such as PROCHECK, ERRAT, Verify 3D, and QMEAN were used to validate the 3D structure. Following the YASARA energy minimization method, the target protein's 3D structure became more stable. The active site of the developed 3D structure was determined by the CASTp server. Most pathogens that harbor TAS create a crucial risk to human health. Our aim to annotate the HP Imo088 found in Listeria could offer a chance to understand bacterial pathogenicity and identify a number of potential targets for drug development.
RESUMEN
Background: Meaningful underestimation of low-density lipoprotein (LDL) cholesterol is an important shortcoming of Friedewald’s formula (FF) at higher triglyceride (TG) levels. Recently a regression equation (RE) has been developed using lipid profiles in one setting and validated externally for the calculation of LDL cholesterol. This newly developed RE requires more studies in different settings. Objective: The aim of this study was to evaluate the performance of the regression equation against direct measurement. Materials and Methods: Lipid profiles of 600 subjects attending a tertiary healthcare center were included in this study. Specimens were collected and lipid profiles were measured by standard methods. Sixty two lipid profiles with TG above 400 mg/dL were excluded. Calculated LDL cholesterol values using FF and RE were compared with measured LDL cholesterol by Pearson’s correlation test, Passing & Bablok regression, accuracy within ±5% and ±12% of measured LDL cholesterol and two-tailed paired t test at various TG ranges. Results: The mean value of LDL cholesterol was 148.6 ± 37.2 mg/dL for direct measurement, 146.9 ± 42.4 mg/dL for FF and 148.6 ± 34.7 mg/dL for RE. The correlation coefficients of calculated LDL cholesterol values with measured LDL cholesterol were 0.949 (p<0.001) for FF and 0.959 (p<0.001) for RE. Passing & Bablok regression equation against measured LDL cholesterol was y = 0.897x + 16.2 for FF and y = 1.0842x – 13.1 for RE. Accuracy within ±5% of measured LDL cholesterol was 45% for FF, 57% for RE and within ±12% of measured LDL cholesterol was 84% for FF, 93% for RE. When calculated LDL cholesterol was compared with measured LDL cholesterol at different TG ranges, FF significantly underestimated LDL cholesterol at TG concentrations above 200 mg/dL whereas no significant difference was observed for RE. Conclusion: This study reveals that RE equation has similar performance to direct measurement for calculation of LDL cholesterol.