Understanding Interactions of Functionalized Nanoparticles with Proteins: A Case Study on Lactate Dehydrogenase
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nanomaterials in biological solutions are known to interact with proteins and have been documented to affect protein function, such as enzyme activity. Understanding the interactions of nanoparticles with biological components at the molecular level will allow for rational designs of nanomaterials for use in medical technologies. Here we present the first detailed molecular mechanics model of functionalized gold nanoparticle (NP) interacting with an enzyme (L-lactate dehydrogenase (LDH) enzyme). Molecular dynamics (MD) simulations of the response of LDH to the NP binding demonstrate that although atomic motions (dynamics) of the main chain exhibit only a minor response to the binding, the dynamics of side chains are significantly constrained in all four active sites that predict alteration in kinetic properties of the enzyme. It is also demonstrated that the 5 nm gold NPs cause a decrease in the maximal velocity of the enzyme reaction (V(max)) and a trend towards a reduced affinity (increased K(m)) for the β-NAD binding site, while pyruvate enzyme kinetics (K(m) and V(max)) are not significantly altered in the presence of the gold NPs. These results demonstrate that modeling of NP:protein interactions can be used to understand alterations in protein function.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it