<i>In silico</i>post-SELEX screening and experimental characterizations for acquisition of high affinity DNA aptamers against carcinoembryonic antigen

Wang, Qiong-Lin; Cui, Hui‐Fang; Du, Jiangfeng; Lv, Qi‐Yan; Song, Xiaojie

doi:10.1039/c8ra10163a

Cited by 37 publications

(11 citation statements)

References 45 publications

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“…3D structure prediction methods for RNAs are commonly used for DNA structure prediction. RNAComposer could be used to generate 3D structures of RNAs and then transformed to DNA structures [ 44 , 45 ]. For example, Iman et al introduced a workflow for predicting 3D structures for DNA aptamers [ 20 ].…”

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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“…DNA aptamers were designed against carcinoembryonic antigen (CEA; PDB ID 2QSQ) [ 43 ]. A DNA mutant library was developed via nucleobase substitution or addition to the parent (P) sequence (5′-ATACCAGCTTATTCAATT-3′).…”

Section: Proteins As Targets Of Aptamer Designmentioning

confidence: 99%

Methods and Applications of In Silico Aptamer Design and Modeling

Buglak

Самохвалов

Zherdev

et al. 2020

IJMS

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Aptamers are nucleic acid analogues of antibodies with high affinity to different targets, such as cells, viruses, proteins, inorganic materials, and coenzymes. Empirical approaches allow the design of in vitro aptamers that bind particularly to a target molecule with high affinity and selectivity. Theoretical methods allow significant expansion of the possibilities of aptamer design. In this study, we review theoretical and joint theoretical-experimental studies dedicated to aptamer design and modeling. We consider aptamers with different targets, such as proteins, antibiotics, organophosphates, nucleobases, amino acids, and drugs. During nucleic acid modeling and in silico design, a full set of in silico methods can be applied, such as docking, molecular dynamics (MD), and statistical analysis. The typical modeling workflow starts with structure prediction. Then, docking of target and aptamer is performed. Next, MD simulations are performed, which allows for an evaluation of the stability of aptamer/ligand complexes and determination of the binding energies with higher accuracy. Then, aptamer/ligand interactions are analyzed, and mutations of studied aptamers made. Subsequently, the whole procedure of molecular modeling can be reiterated. Thus, the interactions between aptamers and their ligands are complex and difficult to understand using only experimental approaches. Docking and MD are irreplaceable when aptamers are studied in silico.

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“…The B1-4, as a parent aptamer, was randomly mutated through an in silico base mutagenesis program designed by our team. The number of mutants in this study was about 1000 in each round, much higher than before [ 9 ]. The higher number of mutants means more possibilities of obtaining the sequence with better performance.…”

Section: Methodsmentioning

confidence: 59%

“…Hsieh et al successfully found one new sequence that could produce a relatively superior binding reaction with a prostate-specific antigen based on the known aptamers [ 8 ]. Wang et al identified two aptamer mutants against the carcinoembryonic antigen exhibiting significantly high binding ability [ 9 ]. Thus far, the in silico post-SELEX strategy has been performed to engineer high-affinity aptamers for several targets.…”

Section: Introductionmentioning

confidence: 99%

Evolution of Interferon-Gamma Aptamer with Good Affinity and Analytical Utility by a Rational In Silico Base Mutagenesis Post-SELEX Strategy

et al. 2022

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The Systematic Evolution of Ligands by EXponential enrichment (SELEX) is conventionally an effective method to identify aptamers, which are oligonucleotide sequences with desired properties to recognize targets specifically and sensitively. However, there are some inherent limitations, e.g., the loss of potential high-affinity sequences during biased iterative PCR enrichment processes and the limited structural diversity of the initial library, which seriously restrict their real-world applications. To overcome these limitations, the in silico base mutagenesis post-SELEX strategy based on the low Gibbs free energy (ΔG) and genetic algorithm was developed for the optimization of the interferon-gamma aptamer (B1-4). In the process of evolution, new sequences were created and the aptamer candidates with low ΔG values and advanced structures were produced. After five rounds of selection, systematic studies revealed that the affinity of the newly developed evolutionary aptamer (M5-5) was roughly 10-fold higher than that of the parent aptamer (B1-4), and an aptasensor detection system with a limit-of-detection (LOD) value of 3.17 nM was established based on the evolutionary aptamer. The proposed approach provided an efficient strategy to improve the aptamer with low energy and a high binding ability, and the good analytical utility thereof.

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In silicopost-SELEX screening and experimental characterizations for acquisition of high affinity DNA aptamers against carcinoembryonic antigen

Abstract: High affinity DNA aptamers against carcinoembryonic antigen were selected and verified by using an in silico approach and experimental characterizations.

Cited by 37 publications

References 45 publications

Artificial Intelligence in Aptamer–Target Binding Prediction

Artificial Intelligence in Aptamer–Target Binding Prediction

Methods and Applications of In Silico Aptamer Design and Modeling

Evolution of Interferon-Gamma Aptamer with Good Affinity and Analytical Utility by a Rational In Silico Base Mutagenesis Post-SELEX Strategy

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