Dataset Information

Elucidation and Structural Modeling of CD71 as a Molecular Target for Cell-Specific Aptamer Binding.

ABSTRACT: Pancreatic cancer is a highly lethal malignancy associated with tissues of the pancreas. Early diagnosis and effective treatment are crucial to improving the survival rate of patients with pancreatic cancer. In a previous study, we employed the cell-SELEX strategy to obtain an ssDNA aptamer termed XQ-2d with high binding affinity for pancreatic cancer. Here, we first identify CD71 as the XQ-2d-binding target. We found that knockdown of CD71 abolished the binding of XQ-2d and that the binding affinity of XQ-2d is associated with membrane-bound CD71, rather than total CD71 levels. Competitive analysis revealed that XQ-2d shares the same binding site on CD71 with transferrin (Tf), but not anti-CD71 antibody. We then used a surface energy transfer (SET) nanoruler to measure the distance between the binding sites of XQ-2d and anti-CD71 antibody, and it was about 15 nm. Furthermore, we did molecular dynamics simulation to clarify that the spatial structure of XQ-2d and Tf competitively binding to CD71. We also engineered XQ-2d-mediated targeted therapy for pancreatic cancer, using an XQ-2d-based complex for loading doxorubicin (Dox). Because CD71 is overexpressed not only in pancreatic cancer but also in a variety of tumors, our work provides a systematic novel way of studying a potential biomarker and also promising tools for cancer diagnosis and therapy, opening new doors for effective cancer theranostics.

SUBMITTER: Wu X

PROVIDER: S-EPMC6625514 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Elucidation and Structural Modeling of CD71 as a Molecular Target for Cell-Specific Aptamer Binding.

Wu Xiaoqiu X Liu Honglin H Han Dongmei D Peng Bo B Zhang Hui H Zhang Lin L Li Jianglin J Liu Jing J Cui Cheng C Fang Senbiao S Li Min M Ye Mao M Tan Weihong W

Journal of the American Chemical Society 20190701 27

Pancreatic cancer is a highly lethal malignancy associated with tissues of the pancreas. Early diagnosis and effective treatment are crucial to improving the survival rate of patients with pancreatic cancer. In a previous study, we employed the cell-SELEX strategy to obtain an ssDNA aptamer termed XQ-2d with high binding affinity for pancreatic cancer. Here, we first identify CD71 as the XQ-2d-binding target. We found that knockdown of CD71 abolished the binding of XQ-2d and that the binding aff ...[more]

PMID: 31185171

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Project description:RNA aptamers are oligonucleotides that bind with high specificity and affinity to target ligands. In the absence of bound ligand, secondary structures of RNA aptamers are generally stable, but single-stranded and loop regions, including ligand binding sites, lack defined structures and exist as ensembles of conformations. For example, the well-characterized theophylline-binding aptamer forms a highly stable binding site when bound to theophylline, but the binding site is unstable and disordered when theophylline is absent. Experimental methods have not revealed at atomic resolution the conformations that the theophylline aptamer explores in its unbound state. Consequently, in the present study we applied 21 microseconds of molecular dynamics simulations to structurally characterize the ensemble of conformations that the aptamer adopts in the absence of theophylline. Moreover, we apply Markov state modeling to predict the kinetics of transitions between unbound conformational states. Our simulation results agree with experimental observations that the theophylline binding site is found in many distinct binding-incompetent states and show that these states lack a binding pocket that can accommodate theophylline. The binding-incompetent states interconvert with binding-competent states through structural rearrangement of the binding site on the nanosecond to microsecond timescale. Moreover, we have simulated the complete theophylline binding pathway. Our binding simulations supplement prior experimental observations of slow theophylline binding kinetics by showing that the binding site must undergo a large conformational rearrangement after the aptamer and theophylline form an initial complex, most notably, a major rearrangement of the C27 base from a buried to solvent-exposed orientation. Theophylline appears to bind by a combination of conformational selection and induced fit mechanisms. Finally, our modeling indicates that when Mg2+ ions are present the population of binding-competent aptamer states increases more than twofold. This population change, rather than direct interactions between Mg2+ and theophylline, accounts for altered theophylline binding kinetics.

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

Elucidation and Structural Modeling of CD71 as a Molecular Target for Cell-Specific Aptamer Binding.

Publications

Elucidation and Structural Modeling of CD71 as a Molecular Target for Cell-Specific Aptamer Binding.

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