Analysis of Bacterial Amyloid Interaction with Lipidic Membrane by Orientated Circular Dichroism and Infrared Spectroscopies

Unveiling protein corona composition: predicting with resampling embedding and machine learning

Biomaterials with surface nanostructures effectively enhance protein secretion and stimulate tissue regeneration. When nanoparticles (NPs) enter the living system, they quickly interact with proteins in the body fluid, forming the protein corona (PC). The accurate prediction of the PC composition is critical for analyzing the osteoinductivity of biomaterials and guiding the reverse design of NPs. However, achieving accurate predictions remains a significant challenge. Although several machine learning (ML) models like Random Forest (RF) have been used for PC prediction, they often fail to consider the extreme values in the abundance region of PC absorption and struggle to improve accuracy due to the imbalanced data distribution. In this study, resampling embedding was introduced to resolve the issue of imbalanced distribution in PC data. Various ML models were evaluated, and RF model was finally used for prediction, and good correlation coefficient (R2) and root-mean-square deviation (RMSE) values were obtained. Our ablation experiments demonstrated that the proposed method achieved an R2 of 0.68, indicating an improvement of approximately 10%, and an RMSE of 0.90, representing a reduction of approximately 10%. Furthermore, through the verification of label-free quantification of four NPs: hydroxyapatite (HA), titanium dioxide (TiO2), silicon dioxide (SiO2) and silver (Ag), and we achieved a prediction performance with an R2 value >0.70 using Random Oversampling. Additionally, the feature analysis revealed that the composition of the PC is most significantly influenced by the incubation plasma concentration, PDI and surface modification.

Interactions and Insertion of Escherichia coli Hfq into Outer Membrane Vesicles as Revealed by Infrared and Orientated Circular Dichroism Spectroscopies

The possible carrier role of Outer Membrane Vesicles (OMVs) for small regulatory noncoding RNAs (sRNAs) has recently been demonstrated. Nevertheless, to perform their function, these sRNAs usually need a protein cofactor called Hfq. In this work we show, by using a combination of infrared and circular dichroism spectroscopies, that Hfq, after interacting with the inner membrane, can be translocated into the periplasm, and then be exported in OMVs, with the possibility to be bound to sRNAs. Moreover, we provide evidence that Hfq interacts with and is inserted into OMV membranes, suggesting a role for this protein in the release of sRNA outside the vesicle. These findings provide clues to the mechanism of host-bacteria interactions which may not be defined solely by protein-protein and protein-outer membrane contacts, but also by the exchange of RNAs, and in particular sRNAs.