Computer Aided Development of Nucleic Acid Applications in Nanotechnologies

Paloncýová, Markéta; Pykal, Martin; Kührová, Petra; Banáš, Pavel; Šponer, Jiřı́; Otyepka, Michal

doi:10.1002/smll.202204408

Cited by 7 publications

(6 citation statements)

References 357 publications

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“…[53] The advancements in DNA origami have significantly enhanced the scalability and complexity of DNA nanostructures, thereby offering a multifunctional nanoscale platform for chemical, biological, physical, and computational sciences. [54] The programmable self-assembly property of DNA origami structure makes it possible to apply a defined prestress DNA origami tensegrity to the system. [33a,55] Tim Liedl's group used ssDNA to link the target system to two immobile anchors.…”

Section: Molecular Tension Sensor Systems With Dna Nanotechnologymentioning

confidence: 99%

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Decoding Biomechanical Cues Based on DNA Sensors

Huang,

Chen,

Chen

et al. 2024

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Biological systems perceive and respond to mechanical forces, generating mechanical cues to regulate life processes. Analyzing biomechanical forces has profound significance for understanding biological functions. Therefore, a series of molecular mechanical techniques have been developed, mainly including single‐molecule force spectroscopy, traction force microscopy, and molecular tension sensor systems, which provide indispensable tools for advancing the field of mechanobiology. DNA molecules with a programmable structure and well‐defined mechanical characteristics have attached much attention to molecular tension sensors as sensing elements, and are designed for the study of biomechanical forces to present biomechanical information with high sensitivity and resolution. In this work, a comprehensive overview of molecular mechanical technology is presented, with a particular focus on molecular tension sensor systems, specifically those based on DNA. Finally, the future development and challenges of DNA‐based molecular tension sensor systems are looked upon.

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Section: Molecular Tension Sensor Systems With Dna Nanotechnologymentioning

confidence: 99%

Section: Molecular Tension Sensor Systemsmentioning

confidence: 99%

Decoding Biomechanical Cues Based on DNA Sensors

Huang,

Chen,

Chen

et al. 2024

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“…To gain some preliminary insights into the mechanism of the ampicillin-binding DNA aptamer, we used computational chemistry tools that can describe the biomolecular systems with unprecedented temporal and spatial resolution. [42,43] We used several sets of classical and enhanced-sampling all-atom MD simulations to study the conformational behavior of DNA aptamer (see Table S4, Supporting Information for a complete list of simulations). We performed T-REMD simulations starting from the unfolded state of the DNA single-stranded aptamer in the presence or absence of ampicillin.…”

Section: Mechanism Of Actionmentioning

confidence: 99%

Click and Detect: Versatile Ampicillin Aptasensor Enabled by Click Chemistry on a Graphene–Alkyne Derivative

Flauzino

Martin-Alex

Chronopoulos

et al. 2023

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Tackling the current problem of antimicrobial resistance (AMR) requires fast, inexpensive, and effective methods for controlling and detecting antibiotics in diverse samples at the point of interest. Cost‐effective, disposable, point‐of‐care electrochemical biosensors are a particularly attractive option. However, there is a need for conductive and versatile carbon‐based materials and inks that enable effective bioconjugation under mild conditions for the development of robust, sensitive, and selective devices. This work describes a simple and fast methodology to construct an aptasensor based on a novel graphene derivative equipped with alkyne groups prepared via fluorographene chemistry. Using click chemistry, an aptamer is immobilized and used as a successful platform for the selective determination of ampicillin in real samples in the presence of interfering molecules. The electrochemical aptasensor displayed a detection limit of 1.36 nM, high selectivity among other antibiotics, the storage stability of 4 weeks, and is effective in real samples. Additionally, structural and docking simulations of the aptamer shed light on the ampicillin binding mechanism. The versatility of this platform opens up wide possibilities for constructing a new class of aptasensor based on disposable screen‐printed carbon electrodes usable in point‐of‐care devices.

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“…Furthermore, interested readers may refer to several recent reviews on the application of ML in different research directions of materials science. [ 3 – 5 ] AI, and more specifically ML, are exciting prospects for improving the quality and speed of the development of materials science and, in particular, chiral nanomaterials (NMs). This is reflected in the sizeable number of recent reviews and perspectives which are devoted to the discovery of novel NMs, for example 2D materials, [ 6 ] plasmonic NMs, [ 7 ] carbon-based NMs, [ 8 ] and beyond.…”

Section: Introductionmentioning

confidence: 99%

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Kuznetsova,

Coogan,

Botov

et al. 2024

Advanced Materials

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Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating the design of novel materials, and reducing the need for time‐consuming and labour‐intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials and nanostructures is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarised luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, we provide an analysis of machine learning methods used in studying achiral nanomaterials, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. We present an overview of chiral nanomaterials within the framework of synthesis‐structure‐property‐application relationships and provide insights on how to leverage machine learning for the study of these highly complex relationships. We also review and discuss some key recent publications on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.This article is protected by copyright. All rights reserved

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Computer Aided Development of Nucleic Acid Applications in Nanotechnologies

Cited by 7 publications

References 357 publications

Decoding Biomechanical Cues Based on DNA Sensors

Decoding Biomechanical Cues Based on DNA Sensors

Click and Detect: Versatile Ampicillin Aptasensor Enabled by Click Chemistry on a Graphene–Alkyne Derivative

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

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