RESUMEN
FtsQBL is a transmembrane protein complex in the divisome of Escherichia coli that plays a critical role in regulating cell division. Although extensive efforts have been made to investigate the interactions between the three involved proteins, FtsQ, FtsB, and FtsL, the detailed interaction mechanism is still poorly understood. In this study, we used hydrogen-deuterium exchange mass spectrometry to investigate these full-length proteins and their complexes. We also dissected the structural dynamic changes and the related binding interfaces within the complexes. Our data revealed that FtsB and FtsL interact at both the periplasmic and transmembrane regions to form a stable complex. Furthermore, the periplasmic region of FtsB underwent significant conformational changes. With the help of computational modeling, our results suggest that FtsBL complexation may bring the respective constriction control domains (CCDs) in close proximity. We show that when FtsBL adopts a coiled-coil structure, the CCDs are fixed at a vertical position relative to the membrane surface; thus, this conformational change may be essential for FtsBL's interaction with other divisome proteins. In the FtsQBL complex, intriguingly, we show only FtsB interacts with FtsQ at its C-terminal region, which stiffens a large area of the ß-domain of FtsQ. Consistent with this, we found the connection between the α- and ß-domains in FtsQ is also strengthened in the complex. Overall, the present study provides important experimental evidence detailing the local interactions between the full-length FtsB, FtsL, and FtsQ protein, as well as valuable insights into the roles of FtsQBL complexation in regulating divisome activity.
Asunto(s)
Proteínas de Ciclo Celular , Proteínas de Escherichia coli , Escherichia coli , Proteínas de la Membrana , Proteínas de Ciclo Celular/metabolismo , División Celular , Escherichia coli/metabolismo , Proteínas de Escherichia coli/metabolismo , Proteínas de la Membrana/metabolismo , Conformación ProteicaRESUMEN
In this paper, we aim to use odor fingerprint analysis to identify and detect various odors. We obtained the olfactory sensory evaluation of eight different brands of Chinese liquor by a lab-developed intelligent nose. From the respective combination of the time domain and frequency domain, we extract features to reflect the samples comprehensively. However, the extracted feature combined time domain and frequency domain will bring redundant information that affects performance. Therefore, we proposed data by Principal Component Analysis (PCA) and Variable Importance Projection (VIP) to delete redundant information to construct a more precise odor fingerprint. Then, Random Forest (RF) and Probabilistic Neural Network (PNN) were built based on the above. Results showed that the VIP-based models achieved better classification performance than PCA-based models. In addition, the peak performance (92.5%) of the VIP-RF model had a higher classification rate than the VIP-PNN model (90%). In conclusion, odor fingerprint analysis using a feature mining method based on the olfactory sensory evaluation can be applied to monitor product quality in the actual process of industrialization.