Dataset Information

High-Affinity Host-Guest Recognition for Efficient Assembly and Enzymatic Responsiveness of DNA Nanostructures.

ABSTRACT: The combination of multiple orthogonal interactions enables hierarchical complexity in self-assembled nanoscale materials. Here, efficient supramolecular polymerization of DNA origami nanostructures is demonstrated using a multivalent display of small molecule host-guest interactions. Modification of DNA strands with cucurbit[7]uril (CB[7]) and its adamantane guest, yielding a supramolecular complex with an affinity of order 10¹⁰ m^-1 , directs hierarchical assembly of origami monomers into 1D nanofibers. This affinity regime enables efficient polymerization; a lower-affinity β-cyclodextrin-adamantane complex does not promote extended structures at a similar valency. Finally, the utility of the high-affinity CB[7]-adamantane interactions is exploited to enable responsive enzymatic actuation of origami nanofibers assembled using peptide linkers. This work demonstrates the power of high-affinity CB[7]-guest recognition as an orthogonal axis to drive self-assembly in DNA nanotechnology.

SUBMITTER: Narayanan RP

PROVIDER: S-EPMC10922742 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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High-Affinity Host-Guest Recognition for Efficient Assembly and Enzymatic Responsiveness of DNA Nanostructures.

Narayanan Raghu Pradeep RP Prasad Abhay A Buchberger Alex A Zou Lei L Bernal-Chanchavac Julio J MacCulloch Tara T Fahmi Nour Eddine NE Yan Hao H Zhang Fei F Webber Matthew J MJ Stephanopoulos Nicholas N

Small (Weinheim an der Bergstrasse, Germany) 20231017 9

The combination of multiple orthogonal interactions enables hierarchical complexity in self-assembled nanoscale materials. Here, efficient supramolecular polymerization of DNA origami nanostructures is demonstrated using a multivalent display of small molecule host-guest interactions. Modification of DNA strands with cucurbit[7]uril (CB[7]) and its adamantane guest, yielding a supramolecular complex with an affinity of order 10<sup>10</sup> m<sup>-1</sup> , directs hierarchical assembly of origa ...[more]

PMID: 37849034

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Project description:Accurately predicting the binding affinities of small organic molecules to biological macromolecules can greatly accelerate drug discovery by reducing the number of compounds that must be synthesized to realize desired potency and selectivity goals. Unfortunately, the process of assessing the accuracy of current computational approaches to affinity prediction against binding data to biological macromolecules is frustrated by several challenges, such as slow conformational dynamics, multiple titratable groups, and the lack of high-quality blinded datasets. Over the last several SAMPL blind challenge exercises, host-guest systems have emerged as a practical and effective way to circumvent these challenges in assessing the predictive performance of current-generation quantitative modeling tools, while still providing systems capable of possessing tight binding affinities. Here, we present an overview of the SAMPL6 host-guest binding affinity prediction challenge, which featured three supramolecular hosts: octa-acid (OA), the closely related tetra-endo-methyl-octa-acid (TEMOA), and cucurbit[8]uril (CB8), along with 21 small organic guest molecules. A total of 119 entries were received from ten participating groups employing a variety of methods that spanned from electronic structure and movable type calculations in implicit solvent to alchemical and potential of mean force strategies using empirical force fields with explicit solvent models. While empirical models tended to obtain better performance than first-principle methods, it was not possible to identify a single approach that consistently provided superior results across all host-guest systems and statistical metrics. Moreover, the accuracy of the methodologies generally displayed a substantial dependence on the system considered, emphasizing the need for host diversity in blind evaluations. Several entries exploited previous experimental measurements of similar host-guest systems in an effort to improve their physical-based predictions via some manner of rudimentary machine learning; while this strategy succeeded in reducing systematic errors, it did not correspond to an improvement in statistical correlation. Comparison to previous rounds of the host-guest binding free energy challenge highlights an overall improvement in the correlation obtained by the affinity predictions for OA and TEMOA systems, but a surprising lack of improvement regarding root mean square error over the past several challenge rounds. The data suggests that further refinement of force field parameters, as well as improved treatment of chemical effects (e.g., buffer salt conditions, protonation states), may be required to further enhance predictive accuracy.

Dataset Information

High-Affinity Host-Guest Recognition for Efficient Assembly and Enzymatic Responsiveness of DNA Nanostructures.

Publications

High-Affinity Host-Guest Recognition for Efficient Assembly and Enzymatic Responsiveness of DNA Nanostructures.

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