Dataset Information

Integrating Ligand-Receptor Interactions and In Vitro Evolution for Streamlined Discovery of Artificial Nucleic Acid Ligands.

ABSTRACT: To discover DNA ligands against a predetermined receptor protein complex, we introduce a comprehensive version of ligand-guided selection (LIGS). LIGS is, itself, a variant of systematic evolution of ligands by exponential enrichment (SELEX). Herein, we have optimized LIGS to identify higher affinity aptamers with high specificity. In addition, we demonstrate the expandability of LIGS by performing specific aptamer elution at 25°C, utilizing multiple monoclonal antibodies (mAbs) against cultured cells and primary cells obtained from human donors expressing the same receptor. Eluted LIGS libraries obtained through Illumina high-throughput (HT) DNA sequencing were analyzed by bioinformatics tools to discover five DNA aptamers with apparent affinities ranging from 3.06 ± 0.485 nM to 325 ± 62.7 nM against the target, T cell receptor-cluster of differentiation epsilon (TCR-CD3?) expressed on human T cells. The specificity of the aptamers was validated utilizing multiple strategies, including competitive binding analysis and a double-knockout Jurkat cell line generated by CRISPR technology. The cross-competition experiments using labeled and unlabeled aptamers revealed that all five aptamers compete for the same binding site. Collectively, the data in this report introduce a modified LIGS strategy as a universal platform to identify highly specific multiple aptamers toward multi-component receptor proteins in their native state without changing the cell-surface landscape.

SUBMITTER: Zumrut HE

PROVIDER: S-EPMC6606840 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Integrating Ligand-Receptor Interactions and In Vitro Evolution for Streamlined Discovery of Artificial Nucleic Acid Ligands.

Zumrut Hasan E HE Batool Sana S Argyropoulos Kimon V KV Williams Nicole N Azad Roksana R Mallikaratchy Prabodhika R PR

Molecular therapy. Nucleic acids 20190604

To discover DNA ligands against a predetermined receptor protein complex, we introduce a comprehensive version of ligand-guided selection (LIGS). LIGS is, itself, a variant of systematic evolution of ligands by exponential enrichment (SELEX). Herein, we have optimized LIGS to identify higher affinity aptamers with high specificity. In addition, we demonstrate the expandability of LIGS by performing specific aptamer elution at 25°C, utilizing multiple monoclonal antibodies (mAbs) against cultured ...[more]

PMID: 31255977

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Project description:Computational methods play a pivotal role in drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the last decades, almost all the attention in medicinal chemistry has been directed to protein-ligand binding, and computational tools have been created with this target in mind. With novel discoveries of functional RNAs and their possible applications, RNAs have gained considerable attention as potential drug targets. However, the availability of bioinformatics tools for nucleic acids is limited. Here, we introduce fingeRNAt-a software tool for detecting non-covalent interactions formed in complexes of nucleic acids with ligands. The program detects nine types of interactions: (i) hydrogen and (ii) halogen bonds, (iii) cation-anion, (iv) pi-cation, (v) pi-anion, (vi) pi-stacking, (vii) inorganic ion-mediated, (viii) water-mediated, and (ix) lipophilic interactions. However, the scope of detected interactions can be easily expanded using a simple plugin system. In addition, detected interactions can be visualized using the associated PyMOL plugin, which facilitates the analysis of medium-throughput molecular complexes. Interactions are also encoded and stored as a bioinformatics-friendly Structural Interaction Fingerprint (SIFt)-a binary string where the respective bit in the fingerprint is set to 1 if a particular interaction is present and to 0 otherwise. This output format, in turn, enables high-throughput analysis of interaction data using data analysis techniques. We present applications of fingeRNAt-generated interaction fingerprints for visual and computational analysis of RNA-ligand complexes, including analysis of interactions formed in experimentally determined RNA-small molecule ligand complexes deposited in the Protein Data Bank. We propose interaction fingerprint-based similarity as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of interaction fingerprints for the clustering of molecular complexes. This approach can be used to group ligands that form similar binding networks and thus have similar biological properties. The fingeRNAt software is freely available at https://github.com/n-szulc/fingeRNAt.

Dataset Information

Integrating Ligand-Receptor Interactions and In Vitro Evolution for Streamlined Discovery of Artificial Nucleic Acid Ligands.

Publications

Integrating Ligand-Receptor Interactions and In Vitro Evolution for Streamlined Discovery of Artificial Nucleic Acid Ligands.

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