Therapeutic and analytical applications of arsenic binding to proteins
Bibliographic record
Abstract
Arsenic binding to proteins plays a pivotal role in the health effects of arsenic. Further knowledge of arsenic binding to proteins will advance the development of bioanalytical techniques and therapeutic drugs. This review summarizes recent work on arsenic-based drugs, imaging of cellular events, capture and purification of arsenic-binding proteins, and biosensing of arsenic. Binding of arsenic to the promyelocytic leukemia fusion oncoprotein (PML-RARα) is a plausible mode of action leading to the successful treatment of acute promyelocytic leukemia (APL). Identification of other oncoproteins critical to other cancers and the development of various arsenicals and targeted delivery systems are promising approaches to the treatment of other types of cancers. Techniques for capture, purification, and identification of arsenic-binding proteins make use of specific binding between trivalent arsenicals and the thiols in proteins. Biarsenical probes, such as FlAsH-EDT2 and ReAsH-EDT2, coupled with tetracysteine tags that are genetically incorporated into the target proteins, are used for site-specific fluorescence labelling and imaging of the target proteins in living cells. These allow protein dynamics and protein-protein interactions to be studied. Arsenic affinity chromatography is useful for purification of thiol-containing proteins, and its combination with mass spectrometry provides a targeted proteomic approach for studying the interactions between arsenicals and proteins in cells. Arsenic biosensors evolved from the knowledge of arsenic resistance and arsenic binding to proteins in bacteria, and have now been developed into analytical techniques that are suitable for the detection of arsenic in the field. Examples in the four areas, arsenic-based drugs, imaging of cellular events, purification of specific proteins, and arsenic biosensors, demonstrate important therapeutic and analytical applications of arsenic protein binding.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".