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      Molecular imaging: design mechanism and bioapplications

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          Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase

          High-affinity nucleic acid ligands for a protein were isolated by a procedure that depends on alternate cycles of ligand selection from pools of variant sequences and amplification of the bound species. Multiple rounds exponentially enrich the population for the highest affinity species that can be clonally isolated and characterized. In particular one eight-base region of an RNA that interacts with the T4 DNA polymerase was chosen and randomized. Two different sequences were selected by this procedure from the calculated pool of 65,536 species. One is the wild-type sequence found in the bacteriophage mRNA; one is varied from wild type at four positions. The binding constants of these two RNA's to T4 DNA polymerase are equivalent. These protocols with minimal modification can yield high-affinity ligands for any protein that binds nucleic acids as part of its function; high-affinity ligands could conceivably be developed for any target molecule.
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            In vitro selection of RNA molecules that bind specific ligands.

            Subpopulations of RNA molecules that bind specifically to a variety of organic dyes have been isolated from a population of random sequence RNA molecules. Roughly one in 10(10) random sequence RNA molecules folds in such a way as to create a specific binding site for small ligands.
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              Near-infrared fluorophores for biomedical imaging

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                Author and article information

                Journal
                Science China Chemistry
                Sci. China Chem.
                Springer Science and Business Media LLC
                1674-7291
                1869-1870
                May 2023
                March 03 2023
                May 2023
                : 66
                : 5
                : 1336-1383
                Article
                10.1007/s11426-022-1461-3
                5f8e9398-8e06-4b75-905f-59ca8fd5d464
                © 2023

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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