Small Surface, Big Effects, and Big Challenges: Toward Understanding Enzymatic Activity at the Inorganic Nanoparticle–Substrate Interface
Bibliographic record
Abstract
Enzymes are important biomarkers for molecular diagnostics and targets for the action of drugs. In turn, inorganic nanoparticles (NPs) are of interest as materials for biological assays, biosensors, cellular and in vivo imaging probes, and vectors for drug delivery and theranostics. So how does an enzyme interact with a NP, and what are the outcomes of multivalent conjugation of its substrate to a NP? This invited feature article addresses the current state of the art in answering this question. Using gold nanoparticles (Au NPs) and semiconductor quantum dots (QDs) as illustrative materials, we discuss aspects of enzyme structure-function and the properties of NP interfaces and surface chemistry that determine enzyme-NP interactions. These aspects render the substrate-on-NP configurations far more complex and heterogeneous than the conventional turnover of discrete substrate molecules in bulk solution. Special attention is also given to the limitations of a standard kinetic analysis of the enzymatic turnover of these configurations, the need for a well-defined model of turnover, and whether a "hopping" model can account for behaviors such as the apparent acceleration of enzyme activity. A detailed and predictive understanding of how enzymes turn over multivalent NP-substrate conjugates will require a convergence of many concepts and tools from biochemistry, materials, and interface science. In turn, this understanding will help to enable rational, optimized, and value-added designs of NP bioconjugates for biomedical and clinical applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".