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The surface characteristics of stimuli-responsive Pickering emulsifiers can be modified by external environmental triggers, making them highly versatile in various applications. In this study, we report three novel organic–inorganic composite structure emulsifiers. These emulsifiers were designed with a core of magnetic Fe3O4 particles, surrounded by a protective silica layer, and coated on the exterior with three distinct types of modified chitosan (CS). Experimental results demonstrate that these emulsifiers can stabilize emulsion systems consisting of liquid paraffin and deionized water at a concentration of 0.5 wt%. The unique properties of the modified CS coatings allowed for the controlled demulsification of two types of emulsions by adjusting the proton concentration. Additionally, these emulsifiers exhibited magnetic-responsive demulsification under the control of an external magnetic field. The findings of this study provide valuable insights into the design and construction of multi-responsive chitosan-based magnetic Pickering emulsifiers with controllable properties. [ABSTRACT FROM AUTHOR]

Protein-protein interactions are pivotal in numerous biological processes. The computational design of these interactions facilitates the creation of novel binding proteins, crucial for advancing biopharmaceutical products. With the evolution of artificial intelligence (AI), protein design tools have swiftly transitioned from scoring-function-based to AI-based models. However, many AI models for protein design are constrained by assuming complete unfamiliarity with the amino acid sequence of the input protein, a feature most suited for de novo design but posing challenges in designing protein-protein interactions when the receptor sequence is known. To bridge this gap in computational protein design, we introduce ProBID-Net. Trained using natural protein-protein complex structures and protein domain-domain interface structures, ProBID-Net can discern features from known target protein structures to design specific binding proteins based on their binding sites. In independent tests, ProBID-Net achieved interface sequence recovery rates of 52.7%, 43.9%, and 37.6%, surpassing or being on par with ProteinMPNN in binding protein design. Validated using AlphaFold-Multimer, the sequences designed by ProBID-Net demonstrated a close correspondence between the design target and the predicted structure. Moreover, the model's output can predict changes in binding affinity upon mutations in protein complexes, even in scenarios where no data on such mutations were provided during training (zero-shot prediction). In summary, the ProBID-Net model is poised to significantly advance the design of protein-protein interactions., Competing Interests: The authors declare no conflict of interest., (This journal is © The Royal Society of Chemistry.)

The surface characteristics of stimuli-responsive Pickering emulsifiers can be modified by external environmental triggers, making them highly versatile in various applications. In this study, we report three novel organic-inorganic composite structure emulsifiers. These emulsifiers were designed with a core of magnetic Fe 3 O 4 particles, surrounded by a protective silica layer, and coated on the exterior with three distinct types of modified chitosan (CS). Experimental results demonstrate that these emulsifiers can stabilize emulsion systems consisting of liquid paraffin and deionized water at a concentration of 0.5 wt%. The unique properties of the modified CS coatings allowed for the controlled demulsification of two types of emulsions by adjusting the proton concentration. Additionally, these emulsifiers exhibited magnetic-responsive demulsification under the control of an external magnetic field. The findings of this study provide valuable insights into the design and construction of multi-responsive chitosan-based magnetic Pickering emulsifiers with controllable properties.