Functional Nucleic Acids as Molecular Recognition Elements for Small Organic and Biological Molecules
Bibliographic record
Abstract
The abundance of small molecules in biological systems, the wide-spread usage of synthetic small molecules as drugs and biological probes, and the escalation of industrial pollutants in our environment, underscore the importance of detection of small molecules for many disciplines. Functional nucleic acids (FNAs) are single-stranded DNA or RNA sequences that are capable of carrying out ligand binding (aptamers), catalysis (nucleic acid enzymes) or both functions (aptazymes). Many FNAs have been shown to be suitable molecular recognition elements for small-molecule targets. In this article, we will present a focused review on FNAs for small molecule binding and detection. First, we will discuss the technique of “in vitro selection” by which artificial FNAs can be isolated from random-sequence DNA or RNA pools. This will be followed by a survey of aptamers for small molecules isolated to date. Next, the diverse functions of natural aptamers, as part of riboswitches (metabolite-sensing RNA regulatory systems that exist in many organisms) will be explored. Efforts in creating aptazymes will also be presented. Finally, we will examine numerous applications of aptamers and aptazymes in the development of fluorescent, colorimetric and electrochemical biosensors and discuss some emerging applications concerning FNAs. Keywords: Functional nucleic acid, aptamer, ribozyme, DNAzyme, aptazyme, riboswitch, biosensor, in vitro selection, SELEX, amino acids, steroids, carbohydrates, nucleotides, organic drugs, dyes, carcinogens, IN VITRO SELECTION (SELEX) TECHNIQUE, RT-PCR (for RNA-based selec-tion), RNA-cleaving DNAzyme, elution, structure-switching, L-arginine, L-citrulline, L-dopamine, L-isoleucine, D-tryptophan, L-tyrosine, L-valine, L-tyrosinamide, 7-methyl-guanosine, 8-oxo-deoxyguanosine, adenosine 5'-triphosphate, guanosine 5'-triphosphate (GTP), cyclic adenosine mono-phosphate, biotin, coenzyme A, cya-nocobalamin (vitamin B12), riboflavin, flavin adenine dinucleo-tide, flavin mononucleotide, nicotina-mide mononucleotide, S-adenosylmethionine, S-adenosylhomocysteine, steroids 17-estradiol, cholic acid, thyroxine hormone, ochratoxin A (a mycotoxin), chloramphenicol, kanamycin, livi-domycin, neomycin, streptomycin, tetracycline, tobramycin, viomycin
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".