Structural computational modeling of RNA aptamers

Xu, Xiaojun; Dickey, David D.; Chen, Shi‐Jie; Giangrande, Paloma H.

doi:10.1016/j.ymeth.2016.03.004

Cited by 20 publications

(9 citation statements)

References 32 publications

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“…Then, the 3D structure of these templates is assembled, and energy of the structures is minimized to construct the 3D structure of the whole RNA aptamer. In a study about selecting an aptamer targeting the prostate-specific membrane antigen, the 3D structure of the RNA aptamer was predicted by the Vfold3D online web server [ 41 ].…”

Section: Aptamer Affinity Prediction Through Structural Informatiomentioning

confidence: 99%

Artificial Intelligence in Aptamer–Target Binding Prediction

Chen

Zhang

et al. 2021

IJMS

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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.

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Section: Aptamer Affinity Prediction Through Structural Informatiomentioning

confidence: 99%

Artificial Intelligence in Aptamer–Target Binding Prediction

Chen

Zhang

et al. 2021

IJMS

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“…In addition, the secondary structure prediction results indicated that conservative sequences (i.e. ‘CAGG’ and ‘CGAG’), which might consist of the functional sequences of aptamer , located at the loop area (Figure A; ). Conservative sequence (CAGG) was present in No.…”

Section: Resultsmentioning

confidence: 99%

Label‐ and modification‐free‐based in situ selection of bovine serum albumin specific aptamer

Wang

Xie

et al. 2019

J of Separation Science

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Systematic evolution of ligands by exponential enrichment is a traditional approach to select aptamer, which has a great potential in biosensing field. However, chemical modifications of DNA library or targets before selection might block the real recognition and binding sites between aptamers and their targets. In this study, a labeland modification-free-based in situ selection strategy was developed to overcome this limitation. The strategy is an attempt to screen bovine serum albumin aptamers according to the principle of electrophoretic mobility shift assay, and allowed singlestranded DNA sequence to be fully exposed to interact with bovine serum albumin which was mixed with the agarose gel beforehand. After eight rounds of selection, specific aptamer with low dissociation constant (K d ) value of 69.44 ± 7.60 nM was selected and used for subsequent establishment of fluorescence biosensor. After optimization, the optimal aptasensor exhibited a high sensitivity toward bovine serum albumin with a limit of detection of 0.24 ng/mL (linear range from 1 to 120 ng/mL).These results indicated that the label-and modification-free-based in situ selection strategy proposed in this work could effectively select specific aptamer to develop aptasensor for sensitive detection of bovine serum albumin or other targets in actual complicated samples. K E Y W O R D Sactual sample analysis, aptamers, aptasensors, bovine serum albumin, in situ selection

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“…As demonstrated, their binding affinities are comparable or superior to those of most antibodies. In addition, aptamers have further advantages over antibodies, such as small size, low production cost, facile chemical modification, low immunogenicity, low batch‐to‐batch variation, high chemical stability, rapid tissue penetration, and no toxicity . Because of these advantages, aptamers have attracted tremendous attention in the areas of biosensing, imaging, and drug delivery .…”

Section: Introductionmentioning

confidence: 99%

“…In addition, aptamers have further advantages over antibodies, such as small size, low production cost, facile chemical modification, low immunogenicity, low batch-to-batch variation, high chemical stability, rapid tissue penetration, and no toxicity. [9][10][11][12][13][14][15][16][17] Because of these advantages, aptamers have attracted tremendous attention in the areas of biosensing, imaging, and drug delivery. 10,[18][19][20][21][22] To date, numerous high-affinity aptamers have been selected for a broad range of target molecules, including metal ions, small molecules, peptides, proteins, and even whole cells or viruses.…”

Section: Introductionmentioning

confidence: 99%

Advances in the development of aptamer drug conjugates for targeted drug delivery

Chen

Liu

et al. 2016

WIREs Nanomed Nanobiotechnol

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A key goal of modernmedicine is target-specific therapeutic intervention.However, most drugs lackselectivity, resulting in“off-target” side effects.To address the requirements of “targeted therapy,”aptamers, which are artificial oligonucleotides,have beenusedas novel targeting ligands to construct aptamer drug conjugates (ApDC)that can specifically bind to a broad spectrum of targets, including diseased cells. Accordingly, the applicationof aptamers in targeted drug delivery has attracted broad interest due to their impressive selectivity and affinity, low immunogenicity, easy synthesis with high reproducibility, facile modification, and relatively rapid tissue penetration with no toxicity. Functionally, aptamers themselves can be used as macromolecular drugs, and they are also commonly used in biomarker discovery and targeted drug delivery. In this review,we will highlight the most recent advances in the development of aptamers and aptamer conjugates, and discuss their potential in targeted therapy.

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Structural computational modeling of RNA aptamers

Cited by 20 publications

References 32 publications

Artificial Intelligence in Aptamer–Target Binding Prediction

Artificial Intelligence in Aptamer–Target Binding Prediction

Label‐ and modification‐free‐based in situ selection of bovine serum albumin specific aptamer

Advances in the development of aptamer drug conjugates for targeted drug delivery

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