Understanding Interactions of Functionalized Nanoparticles with Proteins: A Case Study on Lactate Dehydrogenase
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.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".