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Format and QC. Automation Solutions. Custom Assay Development. Student Resources. Peer Reviewed Literature. Product Usage Information. Global Support. Medical Affairs. Local Sales Support. About Promega. Join Our Team. Contact Us. Your Cart. The green strand represents the scaffold and the colored strands represent the staples.
Different structures have a different ssDNA length, varying from to bases. Each rod is constructed of seven dsDNA segments and has a length of nm. The different structures have different ssDNA length segments with the number of nucleotides varying in range from to As a scaffold, we used the M13mp18 plasmid, which contains nucleotides.
Each rod is folded with bases and the remaining unpaired bases were divided differently between the edges of the structure and the segment in between the rods. When the structures are imaged with the AFM, the two origami rods precisely denote the edges of the ssDNA polymer; this allows one to measure its end-to-end distance with an accuracy of 4 nm. This therefore leads to a rather direct way of measuring the ssDNA conformation.
In order to ascertain the environmental conditions for the experiments, all samples were prepared under similar conditions using TAEx1 and 14 mM MgCl 2. M13mp18 is a circular plasmid and in order to convert it to a linear DNA, we used a restriction enzyme 32 PstI before the folding Supporting Information Note 1. Before adding the restriction enzyme, we added a complementary segment of DNA to the restriction site to make it double-stranded.
The cutting process was verified by gel electrophoresis Supporting Information Note 2. The linear plasmid and staples were mixed with TAEx1 buffer and 14 mM MgCl 2 and then incubated for about 20 hours at a gradient temperature. A detailed list of the staples and folding protocol is shown in Supporting Information Note 4.
It was previously shown 35 that dsDNA deposited onto freshly cleaved mica can equilibrate on the surface as in an ideal two-dimensional solution, and numerous studies are performed this way. This mode has been used to acquire high-resolution images of the studied objects DNA. The peak force of every curve is used as a feedback signal.
PFTm provides higher resolution than other modes do because of the very small and controlled force that minimizes the deformation depth and consequently decreases the contact area between the tip and the sample.
The images were typically captured in the retrace direction with a scan rate of 1. The scans showed that the DNA origami structures were folded correctly according to their design Figure 2. Figure 2. Folding of the DNA origami structure nt between the rods.
The bar equals nm. B Zoom-in of two structures; the length of each rod provides an exact scale of nm. D Traversal cross section profile of a single rod. To verify that the DNA conformation on the mica substrate follows the expected theoretical predictions, we measured nm long dsDNA in solution on mica.
Although this value is somewhat shorter than the commonly quoted value of 50 nm, 35 it is in good agreement with similar AFM measurements of dsDNA in solution. Figure 2 A,B shows pairs of rods with different angles in between and different end-to-end distances of ssDNA in between them. Because the length of each nucleotide is 0. All measurements were performed with the Matlab script that we wrote; however, the edges of the rods were located manually from structures that are far apart from each other to prevent errors overlapping structures were not measured.
For all structures that we synthesized, we found many structures in the frames measured by AFM. Nevertheless, to fulfill all the conditions described above, we selected from every frame only a few structures Supporting Information Note 9.
Figure 3. Ninety-one structures were measured by picking their edges manually and the distances were calculated from the known AFM image pixel size. It may result from the DNA origami folding process through a slow-temperature gradient.
Such a process may result in the preferred selection of short-range DNA loops that eventually do not allow long-distant loops to form. It may also result from the difference between the nucleotide bond energy of ssDNA and RNA, with which most calculations were performed. In order to extract the persistence length, the number of nucleotides that form loops has to be determined, since it effectively shortens the nominal polymer length.
Although it may be possible to identify some of the loops from the AFM images, in most cases it is still below the resolution limit. Therefore, we used Mfold, a program that calculates the possible loops for a specific sequence as a function of the solution temperature and salt concentration. Furthermore, if we do not limit this distance Mfold indeed finds a conformation where all the nucleotides of ssDNA take part in the formation of stem loops and the end-to-end distance approaches zero, a situation that we know does not occur Supporting Information Note To test different possible loops, as explained above, we started by taking all the loops that are supposedly formed from Mfold.
For most of the structures, Mfold produced a single possible stem-loops combination. For the nt ssDNA structure, however, three possible secondary structures were found, but they only differed slightly regarding the number of nucleotides participating in the loops , , and nt and we took the average number nt. We next fit the distribution to a Gaussian chain model and extracted an effective persistence length for our experimental conditions of 2 mM NaCl.
For example, for the nt structure with no loops at all, we obtained 1. Similar calculations were performed for all the structures Table 1 for different effective loop formation percentages Figure 4 and Supporting Information Note Figure 4. Calculated persistence length for the different DNA origami structures with different nucleotide lengths. Table 1 summarizes the parameters of the four different structures we fabricated, assuming no loops. The calculated persistence length values of most of the structures are in good agreement, except for the sample with nucleotides Figures 4 and 5.
Taken together, the average persistence length from all structures and the possible loop formation efficiencies was found to be 2. In this case, the persistence length was found to be 1. This value is in agreement with most of the values published so far, which are in the range of 1. Figure 5. All possible loops with a maximal distance of 50 nt between their edges are shown as calculated by Mfold.
Our approach for extracting the effective persistence length assumes that the free part of ssDNA which does not participate in the formation of stem loops behaves like an ideal chain. A possible explanation for the differences in the persistence length of the nt sample is based on the fact that the rigidity of ssDNA also depends on the specific sequence of nucleotides along the strand, 39 which is not the same in the structures that we fabricated. The percentages of all the kinds of repetitive nucleotides such as TT, TTT in the structures we measured are presented in Table 2.
As one can see, the nt ssDNA has the lowest percentage of dTs and a more detailed examination shows that this structure has significantly fewer dTs in a row.
Therefore, this structure should be more rigid than the other structures that we found, which may explain our result. We developed a method to measure and characterize the conformation and persistence length of ssDNA by using a DNA origami structure that consists of two rigid rods with an ssDNA segment between them. In contrast to previous methods, this method does not enforce any restrictions on the measured ssDNA, not regarding its length nor its sequence, and therefore serves as an important experimental method for further studies.
The rods provide a means of exact location recognition of the ssDNA ends, which leads to accurate determination of the end-to-end distance for each ssDNA segment. By using this precise recognition capability on structures with various ssDNA segment lengths, we found an end-to-end Rayleigh distribution with an average value of 25—40 nm.
This value is not consistent with theoretical predictions of a zero end-to-end distance for RNA, which was validated experimentally. This demonstrates the capability of this method for studying ssDNA.
Finally, by fitting the measured distribution to the ideal chain polymer model Gaussian chain , we measured an effective persistence length in a range of 1. In the future, this method can be used for measuring stem loops, the influence of repetitive nucleotide sequences and environmental conditions on the mechanical properties of ssDNA, as well as the interaction of proteins with ssDNA.
It can be further extended to nanoprobes for measuring the interactions of specific DNA sequences, because the DNA origami rods or similar structures can handle multiple fluorescent probes that can be easily detected. Such files may be downloaded by article for research use if there is a public use license linked to the relevant article, that license may permit other uses.
The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. More by Efrat Roth. More by Alex Glick Azaria. More by Olga Girshevitz. More by Arkady Bitler. More by Yuval Garini. Compared to the models with single feature, the best performance using more features with an accuracy of 0.
Thus, we further train the classifier on mixed set and predicted the unknown proteins unknowns. The classified results are listed in additional file 2. While the sequence and structural properties of DSBs and SSBs binding interfaces has been studied during the last decade [ 28 , 40 ], computationally distinguishing between the DSBs and SSBs binding interfaces is still a lack of research. In this study, we investigated surface tunnels features of SSBs and DSBs and found that they have different ranges of tunnel lengths and tunnel curvatures; moreover, the alignment results with OB-fold templates have also found to be the discriminative feature of SSBs and DSBs.
Therefore, we made the first try to present a method to computationally distinguish SSBs with DSBs based on the discriminant features and got the satisfactory results. The protein surface features should also be useful for the analysis of other types of molecular interactions, such as protein-ligand, protein-RNA, and protein-protein complexes, and for the study of a variety of proteins, multiple binding sites or a specific family of proteins.
These problems would require modelling interface surfaces of different characteristics such as compatibility, different sizes, and cooperatives between these surfaces, thus new surface features in addition to the solid angle may be needed. Proteins: Structure, Function, and Bioinformatics. Nucleic Acids Research.
PLoS Genetics. The Journal of Biological Chemistry. Genome Integrity. Article Google Scholar. Annu Rev Biophys Biomol Struct. Nature Structural Biology. Protein and Peptide Letters. CAS Google Scholar. J Mol Graph. Article PubMed Google Scholar. BMC Bioinformatics. PLoS Computational Biology. Current Opinion in Structural Biology. The EMBO journal. Journal of Biological Chemistry.
Google Scholar. Proceedings of the National Academy of Sciences. Download references. The publication costs for this article were funded by the National Science Foundation of China You can also search for this author in PubMed Google Scholar. Correspondence to Juan Liu. All authors read and approved the final manuscript. Additional file 2: This file describes the classified results of the unknown proteins by the mixed set classifier. Reprints and Permissions.
Wang, W. Identification of single-stranded and double-stranded dna binding proteins based on protein structure. BMC Bioinformatics 15, S4 Download citation.
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