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Adenita: interactive 3D modelling and visualization of DNA nanostructures.

ABSTRACT: DNA nanotechnology is a rapidly advancing field, which increasingly attracts interest in many different disciplines, such as medicine, biotechnology, physics and biocomputing. The increasing complexity of novel applications requires significant computational support for the design, modelling and analysis of DNA nanostructures. However, current in silico design tools have not been developed in view of these new applications and their requirements. Here, we present Adenita, a novel software tool for the modelling of DNA nanostructures in a user-friendly environment. A data model supporting different DNA nanostructure concepts (multilayer DNA origami, wireframe DNA origami, DNA tiles etc.) has been developed allowing the creation of new and the import of existing DNA nanostructures. In addition, the nanostructures can be modified and analysed on-the-fly using an intuitive toolset. The possibility to combine and re-use existing nanostructures as building blocks for the creation of new superstructures, the integration of alternative molecules (e.g. proteins, aptamers) during the design process, and the export option for oxDNA simulations are outstanding features of Adenita, which spearheads a new generation of DNA nanostructure modelling software. We showcase Adenita by re-using a large nanorod to create a new nanostructure through user interactions that employ different editors to modify the original nanorod.

SUBMITTER: de Llano E

PROVIDER: S-EPMC7470936 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Adenita: interactive 3D modelling and visualization of DNA nanostructures.

de Llano Elisa E Miao Haichao H Ahmadi Yasaman Y Wilson Amanda J AJ Beeby Morgan M Viola Ivan I Barisic Ivan I

Nucleic acids research 20200901 15

DNA nanotechnology is a rapidly advancing field, which increasingly attracts interest in many different disciplines, such as medicine, biotechnology, physics and biocomputing. The increasing complexity of novel applications requires significant computational support for the design, modelling and analysis of DNA nanostructures. However, current in silico design tools have not been developed in view of these new applications and their requirements. Here, we present Adenita, a novel software tool f ...[more]

PMID: 32692355

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Project description:BACKGROUND: Mesopsammic acochlidians are small, and organ complexity may be strongly reduced (regressive evolution by progenesis), especially in microhedylacean species. The marine interstitial hedylopsacean Pseudunela cornuta (Challis, 1970), however, was suggested as having a complex reproductive system resembling that of much larger, limnic and benthic species. The present study aims to reconstruct the detailed anatomy and true complexity of P. cornuta from serial, semithin histological sections by using modern computer-based 3D visualization with Amira software, and to explain it in an evolutionary context. RESULTS: Our results demonstrate considerable discordance with the original species description, which was based solely on paraffin sections. Here, we show that the nervous system of P. cornuta has paired rhinophoral, optic and gastro-oesophageal ganglia, three distinct ganglia on the visceral nerve cord, and a putative osphradial ganglion, while anterior accessory ganglia are absent. The presence of an anal genital cloaca is clearly rejected and the anus, nephropore and gonopore open separately to the exterior; the circulatory and excretory systems are well-differentiated, including a two-chambered heart and a complex kidney with a long, looped nephroduct; the special androdiaulic reproductive system shows two allosperm receptacles, three nidamental glands, a cavity with unknown function, as well as highly complex anterior copulatory organs with two separate glandular and impregnatory systems including a penial stylet that measures approximately a third of the whole length of the preserved specimen. CONCLUSION: In spite of its small body size, the interstitial hermaphroditic P. cornuta shows high complexity regarding all major organ systems; the excretory system is as differentiated as in species of the sister clade, the limnic and much larger Acochlidiidae, and the reproductive system is by far the most elaborated one ever observed in a mesopsammic gastropod, though functionally not yet fully understood. Such organ complexity as shown herein by interactive 3D visualization is not plesiomorphically maintained from a larger, benthic ancestor, but newly evolved within small marine hedylopsacean ancestors of P. cornuta. The common picture of general organ regression within mesopsammic acochlidians thus is valid for microhedylacean species only.

Project description:BackgroundAlthough convolutional neural networks (CNNs) achieve high diagnostic accuracy for detecting Alzheimer's disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap as they allow the visualization of key input image features that drive the decision of the model. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge.MethodsWe trained a CNN for the detection of AD in N = 663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including in total N = 1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps, thereby allowing intuitive model inspection.ResultsAcross the three independent datasets, group separation showed high accuracy for AD dementia versus controls (AUC ≥ 0.91) and moderate accuracy for amnestic MCI versus controls (AUC ≈ 0.74). Relevance maps indicated that hippocampal atrophy was considered the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson's r ≈ -0.86, p < 0.001).ConclusionThe relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels. The high hippocampus relevance scores as well as the high performance achieved in independent samples support the validity of the CNN models in the detection of AD-related MRI abnormalities. The presented data-driven and hypothesis-free CNN modeling approach might provide a useful tool to automatically derive discriminative features for complex diagnostic tasks where clear clinical criteria are still missing, for instance for the differential diagnosis between various types of dementia.

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Adenita: interactive 3D modelling and visualization of DNA nanostructures.

Publications

Adenita: interactive 3D modelling and visualization of DNA nanostructures.

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