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Abstract The orientation adopted by proteins on nanoparticle surfaces determines the nanoparticle’s bioactivity and its interactions with living systems. Here, we present a residue-based affinity scale for predicting protein orientation on citrate-gold nanoparticles (AuNPs). Competitive binding between protein variants accounts for thermodynamic and kinetic aspects of adsorption in this scale. For hydrophobic residues, the steric considerations dominate, whereas electrostatic interactions are critical for hydrophilic residues. The scale rationalizes the well-defined binding orientation of the small GB3 protein, and it subsequently predicts the orientation and active site accessibility of two enzymes on AuNPs. Additionally, our approach accounts for the AuNP-bound activity of five out of six additional enzymes from the literature. The model developed here enables high-throughput predictions of protein behavior on nanoparticles, and it enhances our understanding of protein orientation in the biomolecular corona, which should greatly enhance the performance and safety of nanomedicines used in vivo. 

Biofilms, when formed on medical devices, can cause malfunctions and reduce the efficiency of these devices, thus complicating treatments and serving as a source of infection. The autolysin protein of Staphylococcus epidermidis contributes to its biofilm forming ability, especially on polystyrene surfaces. R2ab and amidase are autolysin protein domains thought to have high affinity to polystyrene surfaces, and they are involved in initial bacterial attachment in S. epidermidis biofilm formation. However, the structural details of R2ab and amidase binding to surfaces are poorly understood. In this study, we have investigated how R2ab and amidase influence biofilm formation on polystyrene surfaces. We have also studied how these proteins interact with polystyrene nanoparticles (PSNPs) using biophysical techniques. Pretreating polystyrene plates with R2ab and amidase domains inhibits biofilm growth relative to a control protein, indicating that these domains bind tightly to polystyrene surfaces and can block bacterial attachment. Correspondingly, we find that both domains interact strongly with anionic, carboxylate-functionalized as well as neutral, non-functionalized PSNPs, suggesting a similar binding interaction for nanoparticles and macroscopic surfaces. Both anionic and neutral PSNPs induce changes to the secondary structure of both R2ab and amidase as monitored by circular dichroism (CD) spectroscopy. These changes are very similar, though not identical, for both types of PSNPs, suggesting that carboxylate functionalization is only a small perturbation for R2ab and amidase binding. This structural change is also seen in limited proteolysis experiments, which exhibit substantial differences for both proteins when in the presence of carboxylate PSNPs. Overall, our results demonstrate that the R2ab and amidase domains strongly favor adsorption to polystyrene surfaces, and that surface adsorption destabilizes the secondary structure of these domains. Bacterial attachment to polystyrene surfaces during the initial phases of biofilm formation, therefore, may be mediated by aromatic residues, since these residues are known to drive adsorption to PSNPs. Together, these experiments can be used to develop new strategies for biofilm eradication, ensuring the proper long-lived functioning of medical devices. 

Citizenry decision-making relies on data for informed actions, and official statistics provide many of the relevant data needed for these decisions. However, the wide, distributed, and diverse datasets available from official statistics remain hard to access, scrutinise and manipulate, especially for non-experts. As a result, the complexities involved in official statistical databases create barriers to broader access to these data, often rendering the data non-actionable or irrelevant for the speed at which decisions are made in social and public life. To address this problem, this paper proposes an approach to automatically generating basic, factual questions from an existing dataset of official statistics. The question generating process, now specifically instantiated for geospatial data, starts from a raw dataset and gradually builds toward formulating and presenting users with examples of questions that the dataset can answer, and for which geographic units. This approach exemplifies a novel paradigm of question-first data rendering, where questions, rather than data tables, are used as a human-centred and relevant access points to explore, manipulate, navigate and cross-link data to support decision making. This approach can automate time-consuming aspects of data transformation and facilitate broader access to data.