No abstract

Polyethylene glycol (PEG) surface conjugations are widely employed to render passivating properties to nanoparticles in biological applications. The benefits of surface passivation by PEG are reduced protein adsorption, diminished non-specific interactions, and improvement in pharmacokinetics. However, the limitations of PEG passivation remain an active area of research, and recent examples from the literature demonstrate how PEG passivation can fail. Here, we study the adsorption amount of biomolecules to PEGylated gold nanoparticles (AuNPs), focusing on how different protein properties influence binding. The AuNPs are PEGylated with three different sizes of conjugated PEG chains, and we examine interactions with proteins of different sizes, charges, and surface cysteine content. The experiments are carried out in vitro at physiologically relevant timescales to obtain the adsorption amounts and rates of each biomolecule on AuNP-PEGs of varying compositions. Our findings are relevant in understanding how protein size and the surface cysteine content affect binding, and our work reveals that cysteine residues can dramatically increase adsorption rates on PEGylated AuNPs. Moreover, shorter chain PEG molecules passivate the AuNP surface more effectively against all protein types.

Thermosensitive chitosan hydrogels—renewable, biocompatible materials—have many applications as injectable biomaterials for localized drug delivery in the treatment of a variety of diseases. To combat infections such as Staphylococcus aureus osteomyelitis, localized antibiotic delivery would allow for higher doses at the site of infection without the risks associated with traditional antibiotic regimens. Fosfomycin, a small antibiotic in its own class, was loaded into a chitosan hydrogel system with varied beta-glycerol phosphate (β-GP) and fosfomycin (FOS) concentrations. The purpose of this study was to elucidate the interactions between FOS and chitosan hydrogel. The Kirby Bauer assay revealed an unexpected concentration-dependent inhibition of S. aureus, with reduced efficacy at the high FOS concentration but only at the low β-GP concentration. No effect of FOS concentration was observed for the planktonic assay. Rheological testing revealed that increasing β-GP concentration increased the storage modulus while decreasing gelation temperature. NMR showed that FOS was removed from the liquid portion of the hydrogel by reaction over 12 h. SEM and FTIR confirmed gels degraded and released organophosphates over 5 days. This work provides insight into the physicochemical interactions between fosfomycin and chitosan hydrogel systems and informs selection of biomaterial components for improving infection treatment.

No abstract

Liquid-liquid phase separation (LLPS) of proteins is thought to be a primary driving force for the formation of membraneless organelles, which control a wide range of biological functions from stress response to ribosome biogenesis. LLPS of proteins in cells is primarily, though not exclusively, driven by intrinsically disordered (ID) domains. Accurate identification of ID regions (IDRs) that drive phase separation is important for testing the underlying mechanisms of phase separation, identifying biological processes that rely on phase separation, and designing sequences that modulate phase separation. To identify IDRs that drive phase separation, we first curated datasets of folded, ID, and phase-separating (PS) ID sequences. We then used these sequence sets to examine how broadly existing amino acids scales can be used to distinguish between the three classes of protein regions. We found that there are robust property differences between the classes and, consequently, that numerous combinations of amino acid property scales can be used to make robust predictions of LLPS. This result indicates that multiple, redundant mechanisms contribute to the formation of phase-separated droplets from IDRs. The top-performing scales were used to further optimize our previously developed predictor of PS IDRs, ParSe. We then modified ParSe to account for interactions between amino acids and obtained reasonable predictive power for mutations that have been designed to test the role of amino acid interactions in driving LLPS.