Characterization of Recognition Events between Proteins on a Single Molecule Level with Atomic Force Microscopy
Bibliographic record
Abstract
The application of functional imaging tools and techniques on the molecular level enables investigators to characterize structures of biomolecules and interactions between biomolecules. The characterization of recognition events between biomolecules with atomic force microscopy (AFM) is a new methodology in biological research. Recent advances in the field of scanning probe microscopy especially atomic force microscopy have made it possible to discover the dynamic systems of molecular self-assembly and quantify the molecular forces between proteins. The interaction mechanisms between biomolecules were explored by many researchers to tackle broad-category initiatives of human diseases. This review overviews the advances in characterizing recognition events between proteins on the molecular level, specifically on antigen–antibody interactions and the aggregation of amyloid-β peptides. AFM as an emerging approach for characterizing biomolecular interactions will also be highlighted. Furthermore, the potential and limitations of AFM for the measurement of single molecule interactions and the issues in analysis procedure will be discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".