Dataset Information

Modifying cellular properties using artificial aptamer-lipid receptors.

ABSTRACT: We demonstrate that artificial aptamer-lipid receptors (AR), which anchor on the surface of cells, can modify important cellular functions, including protein binding, enzymatic activity, and intercellular interactions. Streptavidin (SA)-AR-modified CEM cells captured the tetravalent SA with one biotin binding site. The remaining biotin sites captured biotinylated TDO5 aptamers, which target IgM on Ramos cells, to form CEM-Ramos cell assemblies. In another design, thrombin, an enzyme involved in blood clotting, was captured by thrombin-AR-modified cells and clot formation was visualized. Lastly, hematopoietic stem cell (HSC) mimics were modified with a tenascin-C-AR to improve the homing of HSC after an autologous bone marrow transplant. Tenascin-C-AR modified cells aggregated to cells in a tenascin-C expressing stem cell niche model better than library-AR modified cells. Modification of cellular properties using ARs is a one-step, dosable, nontoxic, and reversible method, which can be applied to any cell-type with any protein that has a known aptamer.

SUBMITTER: Altman MO

PROVIDER: S-EPMC3840377 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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Modifying cellular properties using artificial aptamer-lipid receptors.

Altman Meghan O MO Chang Yun Min YM Xiong Xiangling X Tan Weihong W

Scientific reports 20131126

We demonstrate that artificial aptamer-lipid receptors (AR), which anchor on the surface of cells, can modify important cellular functions, including protein binding, enzymatic activity, and intercellular interactions. Streptavidin (SA)-AR-modified CEM cells captured the tetravalent SA with one biotin binding site. The remaining biotin sites captured biotinylated TDO5 aptamers, which target IgM on Ramos cells, to form CEM-Ramos cell assemblies. In another design, thrombin, an enzyme involved in ...[more]

PMID: 24275961

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Project description:Aptamers are short single-stranded DNA, RNA, or synthetic Xeno nucleic acids (XNA) molecules that can interact with corresponding targets with high affinity. Owing to their unique features, including low cost of production, easy chemical modification, high thermal stability, reproducibility, as well as low levels of immunogenicity and toxicity, aptamers can be used as an alternative to antibodies in diagnostics and therapeutics. Systematic evolution of ligands by exponential enrichment (SELEX), an experimental approach for aptamer screening, allows the selection and identification of in vitro aptamers with high affinity and specificity. However, the SELEX process is time consuming and characterization of the representative aptamer candidates from SELEX is rather laborious. Artificial intelligence (AI) could help to rapidly identify the potential aptamer candidates from a vast number of sequences. This review discusses the advancements of AI pipelines/methods, including structure-based and machine/deep learning-based methods, for predicting the binding ability of aptamers to targets. Structure-based methods are the most used in computer-aided drug design. For this part, we review the secondary and tertiary structure prediction methods for aptamers, molecular docking, as well as molecular dynamic simulation methods for aptamer-target binding. We also performed analysis to compare the accuracy of different secondary and tertiary structure prediction methods for aptamers. On the other hand, advanced machine-/deep-learning models have witnessed successes in predicting the binding abilities between targets and ligands in drug discovery and thus potentially offer a robust and accurate approach to predict the binding between aptamers and targets. The research utilizing machine-/deep-learning techniques for prediction of aptamer-target binding is limited currently. Therefore, perspectives for models, algorithms, and implementation strategies of machine/deep learning-based methods are discussed. This review could facilitate the development and application of high-throughput and less laborious in silico methods in aptamer selection and characterization.

Dataset Information

Modifying cellular properties using artificial aptamer-lipid receptors.

Publications

Modifying cellular properties using artificial aptamer-lipid receptors.

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