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Chirality in Nature

Chirality in Nature

Chirality is the most intriguing yet essential facet of nature, governing life’s biochemical processes and precision. It can be observed from a snail shell pattern in a macroscopic world to an amino acid, the minutest building block of life. Most of the snails around the world have right-coiled shells because of the intrinsic chirality in their genes. All the amino acids present in the human body exist in an enantiomerically pure state, except for glycine - the sole achiral amino acid.

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Related Experiment Video

Updated: Jun 3, 2025

Engineering Molecular Recognition with Bio-mimetic Polymers on Single Walled Carbon Nanotubes

Engineering Molecular Recognition with Bio-mimetic Polymers on Single Walled Carbon Nanotubes

Published on: January 10, 2017

Accurate DNA Sequence Prediction for Sorting Target-Chirality Carbon Nanotubes and Manipulating Their

Xuan Zhou¹, Pengbo Wang¹, Yinong Li¹

¹South China Advanced Institute for Soft Matter Science and Technology, School of Emergent Soft Matter, South China University of Technology, Guangzhou 510640, China.

|January 7, 2025

Summary

Researchers developed a deep learning strategy to accurately predict DNA sequences for sorting single-wall carbon nanotubes (SWCNTs) by chirality. This method overcomes trial-and-error limitations, enabling precise nanotube purification and property manipulation.

Keywords:

DNA deep learning photoluminescence sequence prediction single-chirality carbon nanotubes

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Last Updated: Jun 3, 2025

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Published on: January 10, 2017

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Assembly of Gold Nanorods into Chiral Plasmonic Metamolecules Using DNA Origami Templates

Assembly of Gold Nanorods into Chiral Plasmonic Metamolecules Using DNA Origami Templates

Published on: March 5, 2019

Area of Science:

Materials Science
Nanotechnology
Biotechnology

Background:

Synthetic single-wall carbon nanotubes (SWCNTs) possess diverse chiralities, complicating their applications.
DNA is known to sort SWCNTs, but identifying effective DNA sequences is challenging and often relies on trial-and-error.
Predicting DNA sequences for specific SWCNT chiralities remains a significant hurdle.

Purpose of the Study:

To develop a deep learning (DL) strategy for accurate prediction of DNA sequences that can sort target-chirality SWCNTs.
To create a comprehensive dataset for training DL models through experimental screening.
To enable precise control over SWCNT properties by understanding DNA-SWCNT interactions.

Main Methods:

Experimental screening of 216 DNA sequences using aqueous two-phase (ATP) separation to identify resolving sequences.
Utilizing the Uni-Mol 3D molecular representation learning framework to build a DL workflow.
Mapping atomistic structural information of DNA sequences into a feature space for model training.

Main Results:

Identified 116 resolving DNA sequences capable of purifying 17 distinct single-chirality SWCNTs.
Achieved high prediction accuracy rates for resolving sequences: 87.5% for (6,5), 90% for (6,6), and 70% for (7,4) SWCNTs.
Discovered numerous resolving sequences for (6,5) SWCNTs, facilitating systematic manipulation of DNA-(6,5) hybrid properties.

Conclusions:

The DL-enhanced strategy accurately predicts DNA sequences for sorting specific SWCNT chiralities.
This approach significantly advances the ability to purify SWCNTs and understand DNA-nanotube interactions.
The findings pave the way for tailored SWCNT applications by enabling precise control over their properties.