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DNA Ring Motif with Flexible Joints.

ABSTRACT: The invention of DNA origami has expanded the geometric complexity and functionality of DNA nanostructures. Using DNA origami technology, we develop a flexible multi-joint ring motif as a novel self-assembling module. The motif can connect with each other through self-complementary sequences on its segments. The flexible joints can be fixed in a straightened position as desired, thereby allowing the motif to take various shapes. We can adjust the number of flexible joints and the number of connectable segments, thereby enabling programmable self-assembly of the motif. We successfully produced the motif and evaluated several self-assembly patterns. The proposed multi-joint ring motif can provide a novel method for creating functional molecular devices.

SUBMITTER: Liu S

PROVIDER: S-EPMC7693582 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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DNA Ring Motif with Flexible Joints.

Liu Shiyun S Murata Satoshi S Kawamata Ibuki I

Micromachines 20201031 11

The invention of DNA origami has expanded the geometric complexity and functionality of DNA nanostructures. Using DNA origami technology, we develop a flexible multi-joint ring motif as a novel self-assembling module. The motif can connect with each other through self-complementary sequences on its segments. The flexible joints can be fixed in a straightened position as desired, thereby allowing the motif to take various shapes. We can adjust the number of flexible joints and the number of conne ...[more]

PMID: 33142895

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Project description:BackgroundCis-regulatory elements (CREs) are DNA sequence segments that regulate gene expression. Among CREs are promoters, enhancers, Boundary Elements (BEs) and Polycomb Response Elements (PREs), all of which are enriched in specific sequence motifs that form particular occurrence landscapes. We have recently introduced a hierarchical machine learning approach (SVM-MOCCA) in which Support Vector Machines (SVMs) are applied on the level of individual motif occurrences, modelling local sequence composition, and then combined for the prediction of whole regulatory elements. We used SVM-MOCCA to predict PREs in Drosophila and found that it was superior to other methods. However, we did not publish a polished implementation of SVM-MOCCA, which can be useful for other researchers, and we only tested SVM-MOCCA with IUPAC motifs and PREs.ResultsWe here present an expanded suite for modelling CRE sequences in terms of motif occurrence combinatorics-Motif Occurrence Combinatorics Classification Algorithms (MOCCA). MOCCA contains efficient implementations of several modelling methods, including SVM-MOCCA, and a new method, RF-MOCCA, a Random Forest-derivative of SVM-MOCCA. We used SVM-MOCCA and RF-MOCCA to model Drosophila PREs and BEs in cross-validation experiments, making this the first study to model PREs with Random Forests and the first study that applies the hierarchical MOCCA approach to the prediction of BEs. Both models significantly improve generalization to PREs and boundary elements beyond that of previous methods-including 4-spectrum and motif occurrence frequency Support Vector Machines and Random Forests-, with RF-MOCCA yielding the best results.ConclusionMOCCA is a flexible and powerful suite of tools for the motif-based modelling of CRE sequences in terms of motif composition. MOCCA can be applied to any new CRE modelling problems where motifs have been identified. MOCCA supports IUPAC and Position Weight Matrix (PWM) motifs. For ease of use, MOCCA implements generation of negative training data, and additionally a mode that requires only that the user specifies positives, motifs and a genome. MOCCA is licensed under the MIT license and is available on Github at https://github.com/bjornbredesen/MOCCA .

Dataset Information

DNA Ring Motif with Flexible Joints.

Publications

DNA Ring Motif with Flexible Joints.

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