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DNA Conformation

DNA conformation refers to the three-dimensional shape or structure adopted by a DNA molecule.
This can include various forms such as the classic double-helix, as well as more complex conformations like cruciform, triplex, and quadruplex structures.
The conformation of DNA is critical for its biological functions, including replication, transcription, and interactions with proteins.
Studying DNA conformation is essential for understanding fundamentsal genetic processes and can provide insights into disease pathways and potential therapeutic targets.
PubCompare.ai's AI-driven research protocol optimizaton can enhance the reproducibility of DNA conformation analyses by helping researchers identify the most effective protocols from literature, preprints, and patents, while using intelligent comparisons to locate the best protocols and products.
This can streamline research and improve results for studies focusing on the conformation of DNA.

Most cited protocols related to «DNA Conformation»

Efficient Analysis of Molecular Dynamics Trajectories

Since MD trajectories now typically produce tens of thousands of conformational snapshots it is necessary to be able to analyse such large datasets quickly. Curves+ can read MD trajectory files directly without the need for creating PDB format files. It currently deals with AMBER format trajectory files. It could be adapted to other formats, but it is also relatively easy to modify trajectory files to the AMBER format (see, for example, Simulaid by M. Mezei http://atlas.physbio.mssm.edu/∼mezei/simulaid/or CatDCD by J. Gullingsrud http://www.ks.uiuc.edu/Development/MDTools/catdcd/).
Curves+ can be used to pick out and analyse a single snapshot or to analyse snapshots at chosen time intervals for the whole trajectory or extract information on a given sequence fragment from an ensemble of trajectories. The present version of Curves+ can analyse roughly 100 conformational snapshots of a 20-bp double-stranded DNA oligomer per second on a 2.5 GHz processor. When multiple snapshots are treated, printed output is suppressed and the program creates an unformatted file which can be treated with a supplementary program named Canal (Curves+ analysis). This program calculates the maxima, minima, mean and standard deviations of any chosen conformational parameters which are output in a list file. It can also generate time series and histograms which are output in flat files that can be used for producing graphics and will optionally calculate linear correlation coefficients between all helical, backbone and groove parameters, which can again be output in files for checking correlations graphically. The simple format of all files output by Canal makes them useable in any common graphic program. We have used both Gnuplot and MatLab (Mathworks Inc.) in preparing the illustrations of Canal data for this article. Canal can lastly be applied to the analysis of files produced by Curves+ from single structures. In this case, it can be used to plot the variation of chosen parameters along an oligomer or, as with trajectories, to look at parameter distributions and correlations. The use of Canal will be further illustrated in an article (manuscript in preparation) concerning the analysis of multiple MD trajectories from the ABC dataset (40 (link),41 (link)). A full user guide for Canal is available at the web site cited below.

Lavery R., Moakher M., Maddocks J.H., Petkeviciute D, & Zakrzewska K. (2009). Conformational analysis of nucleic acids revisited: Curves+. Nucleic Acids Research, 37(17), 5917-5929.

Publication 2009

Amber DNA Conformation Helix (Snails) Pulp Canals Toxic Epidermal Necrolysis Vertebral Column

Modeling Oligomer Ion Dynamics Using Molecular Simulations

We illustrate the ion analysis developed here using microsecond long molecular dynamics trajectories for two 18-mer oligomers belonging to the so-called ABC (Ascona B-DNA Consortium) database (24–26 (link)). Both oligomers are based on tetranucleotide repeats, respectively, AGCT and ATCG, flanked by GC ends to reduce fraying during the simulations. Their sequences are GCCTAGCTAGCTAGCTGC and GCGCATGCATGCATGCGC (where the tetranucleotide repeat used to refer to the oligomers has been underlined).
Each oligomer was simulated using the AMBER program suite (27 ) and with the current ABC protocol (24 (link)). This involves the parm99 force field (28 (link),29 (link)) with the bsc0 modifications (30 ). The oligomers were simulated using periodic boundary conditions with a truncated octahedral cell and a solvent environment consisting of SPC/E water molecules (31 ) (with a minimum thickness of 10 Å around the solute), leading to 11 621 and 11 546 water molecules for AGCT and ATCG, respectively). The DNA net charge is neutralized with K⁺ ions and then KCl ion pairs are added to reach a concentration of 150 mM KCl. The ions were represented using Dang parameters (32 ). Counterions were initially placed at random, at least 5 Å from the solute and 3.5 Å from one another. Electrostatic interactions were treated with the particle mesh Ewald method (33 ), using a real-space cutoff of 9 Å and cubic B-spline interpolation onto a charge grid with a 1-Å spacing. Lennard–Jones interactions were truncated at 9 Å. The pair list was built with a buffer region and updated whenever a particle moved by more than 0.5 Å.
The oligomers were initially built with a canonical B-DNA conformation. Initial equilibration involved energy minimization of the solvent, followed by a slow thermalization. Production simulations were carried out using a 2 fs time step in an NPT ensemble. The length of chemical bonds involving hydrogen were restrained using SHAKE (34 ) and the Berendsen algorithm (35 ) was used to control the temperature and the pressure, with a coupling constant of 5 ps. Center of mass motion was removed every ps to limit the translational kinetic energy of the solute (36 ).
Both oligomers were simulated for a total of 1 μs and conformational snapshots were saved for analysis every 1 ps (leading to 10⁶ data sets per oligomer).

Lavery R., Maddocks J.H., Pasi M, & Zakrzewska K. (2014). Analyzing ion distributions around DNA. Nucleic Acids Research, 42(12), 8138-8149.

Publication 2014

ABC protocol Amber Buffers Cells Cuboid Bone DNA Conformation Electrostatics Familial Mediterranean Fever Hydrogen Ions Kinetics Molecular Dynamics Pressure Protein Biosynthesis Solvents Tetranucleotide Repeats Tremor

Protein-DNA Docking Protocol with Explicit Solvent Refinement

Our docking protocol consists of (i) rigid-body docking, (ii) semi-flexible refinement stage and (iii) final refinement in explicit solvent.
Rigid-body docking. A total of 100 structures were generated for each protein−DNA combination from the ensembles of starting structures. Each docking attempt was performed 10 times and the solution with the lowest HADDOCK score was kept. For each protein we used an ensemble of 10 NMR structures; thus 1000 rigid-body docking solutions were generated for each of the three canonical B-DNA docking runs and 5000 structures were generated for each of the DNA library docking runs (5 different pre-bent and twisted DNA structures and 10 protein structures resulting in 50 different combinations). For the docking of the protein and DNA in their bound conformation a total of 1000 structures were generated. Systematic sampling of 180° rotated solutions was used in the rigid-body docking stage to minimize the occurrence of false positives (principles described in Results). This basically doubled the number of docking trials bringing the total to 20 000 and 100 000 evaluations for docking from canonical B-DNA and DNA libraries, respectively.
Semi-flexible refinement. Of all structures generated in the rigid-body docking stage the best 20% based on the HADDOCK score were further refined in the semi-flexible refinement stage consisting of three parts: rigid-body torsion angle dynamics (500 MD steps at 2000 K and 500 MD cooling steps to 500 K with a 8 fs time step), semi-flexible simulated annealing stage (1000 MD steps from 1000 to 50 K with 4 fs time steps) with the side chains of the protein residues at the interface and the complete DNA (excluding terminal base pairs) allowed to move and a final semi-flexible simulated annealing stage (1000 MD steps from 300 to 50 K with 2 fs time steps) with both side chains and backbone of the protein residues at the interface and the complete DNA (excluding terminal base pairs) allowed to move.
Water refinement. This final stage consists of a gentle refinement (100 MD heating steps at 100, 200 and 300 K followed by 750 sampling steps at 300 K and 500 MD cooling steps at 300, 200 and 100 K all with 2 fs time steps) in an 8 Å shell of TIP3P water molecules (33 ).
Semi-flexible segments for the proteins were defined as residues 7–20, 24–37 for Cro, residues 6–30, 50–56 for Lac and residues 1–17, 54–70 for Arc. In all cases the complete DNA, excluding the terminal base pairs, were defined as semi-flexible.

van Dijk M., van Dijk A.D., Hsu V., Boelens R, & Bonvin A.M. (2006). Information-driven protein–DNA docking using HADDOCK: it is a matter of flexibility. Nucleic Acids Research, 34(11), 3317-3325.

Publication 2006

Decompression Sickness DNA Conformation DNA Library HSP40 Heat-Shock Proteins Human Body Muscle Rigidity Proteins Solvents Torsional Force Vertebral Column

Nucleosome-Linker Histone Docking Simulation

Both gH5A and gH5B were docked using rigid body BD based docking simulations to eight nucleosome structures with different L-DNA1 conformations and L-DNA2 fixed in a specific, highly-populated conformation. These were selected from the CMD simulation without LH based on the γ₁ and γ₂ angles (Figure 7C and D). In addition, we docked gH5B in the nucleosome structure taken from the recent chromatosome structure by Zhou et al. (pdb ID 4qlc, 3.5 Å resolution) (36 (link)) using the protocol of Pachov et al. (32 (link)). In short, NMA was applied using the NOMAD-Ref web-server (62 (link)) to generate nucleosome conformations with different degrees of L-DNA opening. The original structure (conformation 0), as well as two conformations with RMSD of 1 and 2 Å, respectively (all non-hydrogen atoms superimposed) along the first mode (‘conformation 1’ and ‘conformation 2’), were selected. The RMSD of the L-DNAs in these two structures from the original structure was 4.7 and 9.2 Å (the non-hydrogen atoms of the core histones superimposed), respectively.
First, polar hydrogen atoms were added to the structures by using PDB2PQR 1.8 (63 (link)) and partial atomic charges and atomic radii were assigned from the AMBER99 force field. The electrostatic potential was calculated for all structures by solving the non-linear Poisson–Boltzmann equation on a grid with a 1 Å spacing and dimension of 193³ in APBS 1.4 (64 ) at temperature 298.15 K. The solvent and solute dielectric constants were 78.54 and 2, respectively and the ionic strength was 100 mM. Higher solute dielectric constants of 4, 6 and 8 were also tested for docking gH5 to the highly populated conformation of the nucleosome from snapshot 5 (Figure 7C and D). The results were insensitive to varying the solute dielectric constant in this range. To define dielectric boundary conditions, the van der Waals surface was used.
The BD simulations were performed with SDA7 (Simulation of Diffusional Association) (65 (link)) using electrostatic interaction forces. Short-range interactions were neglected, and a 0.5 Å excluded volume criterion to prevent overlap was applied. Effective charges were assigned to charged residues on the protein and to P atoms on the DNA using the ECM program (66 ). The trajectories were started randomly on a sphere at a center-to-center distance of b = 280 Å and stopped at a center-to-center distance of c = 500 Å. The time step was set to 1 ps for center-to-center distances up to 160 Å and increased linearly up to 100 ps at a distance of 260 Å. A total of 20 000 trajectories were generated for each pair of LH-nucleosome conformations simulated. The diffusional encounter complex was considered formed when the following two geometric conditions were satisfied: (i) the center-to-center distance of gH5 and the nucleosome <73 Å, and (ii) the nucleosome dyad point and gH5 separation <40 Å. The interaction energies and the coordinates of a complex were recorded if the RMSD to previously recorded complexes was >1 Å and the interaction energy was within the 5000 lowest (most favorable) energy complexes recorded. A complex with RMSD < 1 Å to a previously recorded complex but lower energy was recorded as a substitute of that complex. The 5000 recorded complexes were clustered into 10 groups according to the backbone RMSD values between them. Upon ranking the clusters by their population during the BD simulations, representative structures of the clusters were generated.

Öztürk M.A., Pachov G.V., Wade R.C, & Cojocaru V. (2016). Conformational selection and dynamic adaptation upon linker histone binding to the nucleosome. Nucleic Acids Research, 44(14), 6599-6613.

Publication 2016

Diffusion DNA2 protein, human DNA Conformation Electrostatics Histones Human Body Hydrogen Migrants Muscle Rigidity Nucleosomes Proteins Radius Solvents Vertebral Column

Recombinant RSV F Protein Production

Expression plasmids encoding the recombinant prefusion RSV F protein were generated as follows. Parental construct based on RSV A2 (Genbank ACO83301.1) was ordered from GeneArt (Life Technologies). It consists of residues 1–513 of the F protein and fibritin trimerization domain. Amino acid substitutions were introduced by site-directed mutagenesis (QuickChange II Site-Directed Mutagenesis kit, Agilen Biotechnologies). For RSV F in postfusion conformation a DNA construct encoding RSV F residues 1–136 and 146–524 (corresponding to the F protein ectodomain without the fusion peptide)13 (link) was synthesized (GeneArt). Recombinant proteins were expressed in 293 Freestyle cells (Life Technologies). The cells were transiently transfected using 293Fectin (Life Technologies) according to the manufacturer's instructions and cultured in a shaking incubator at 37 °C and 10% CO₂. The culture supernatants containing F protein were harvested on 5th day after transfection. Sterile-filtered supernatants were stored at 4 °C until use. The recombinant polypeptides were purified by a two-step protocol applying a cation exchange chromatography followed by SEC. For the ion-exchange step the culture supernatant was diluted with two volumes of 50 mM NaOAc, pH 5.0, and passed over a 5 ml HiTrap Capto S (GE Healthcare) column at 5 ml min⁻¹. Subsequently the column was washed with 10 column volumes of 20 mM NaOAc, 50 mM NaCl, 0.01% (v/v) Tween20, pH 5, and eluted with 2 column volume of 20 mM NaOAc, 1 M NaCl, 0.01% (v/v) Tween20, pH 5. The eluate was concentrated and the protein was further purified on a Superdex200 column (GE Healthcare) using 40 mM Tris buffer, 500 mM NaCl, 0.01% (v/v) Tween20, and pH 7.4 as running buffer. A reduced SDS–PAGE analysis was used to determine purity of the final protein preparation; only protein with purity >95% was used for further analysis. The identity of the band was verified using western blot with CR9503.

Krarup A., Truan D., Furmanova-Hollenstein P., Bogaert L., Bouchier P., Bisschop I.J., Widjojoatmodjo M.N., Zahn R., Schuitemaker H., McLellan J.S, & Langedijk J.P. (2015). A highly stable prefusion RSV F vaccine derived from structural analysis of the fusion mechanism. Nature Communications, 6, 8143.

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Publication 2015

Amino Acid Substitution Buffers Cells Chromatography DNA Conformation Ion Exchange Mutagenesis, Site-Directed Parent Peptides Plasmids Polypeptides Proteins Recombinant Proteins SDS-PAGE Sodium Chloride Sterility, Reproductive Transfection Tromethamine Tween 20 Western Blot

Most recents protocols related to «DNA Conformation»

Multiscale DNA Origami Nanostructure Design

Using caDNAno,²² (link) we designed the following three DNA origami nanostructures (Figure S1): a flat DOS with a size of 14 nm × 15 nm × 2 nm; a DNA tube (DTU) bent by the flat DOS through the red folding axis (Figure S2); and a DNA tetrahedron (DTE) folded by DOS through green folding axes (Figure S2). These DNA designs were firstly predicted using the mrdna framework proposed by Aksimentiev and colleagues.²⁴ (link) The original caDNAno documents were imported into the framework, respectively. We employed the multiresolution simulations method as previously described.²¹ (link) The whole process is divided into three stages. At first, 4 base pair (bp)/bead models of these objects were created and used in 20 μs simulations to reveal the fluctuations of the structures, assuming the self-assembly and folding direction occurred as designed. The structural configurations at the end of the simulations were used to update splines representing the centerline of each helix. Secondly, the higher resolution 2 beads/bp models with an explicit, local representation of the twist in each helix were generated and relaxed during 400 ns simulations. In the third stage, the conformations from the last 5 ns of the simulations were averaged and used to update the configuration of the beads for the final simulations with the linking number of each helix held fixed by harmonic twist dihedral angle potentials. The NAMD³⁵ (link) conformations of our three DNA nanostructures at the end of the final mrdna stage were performed by the Elastic Network of Restraints Guided molecular dynamics simulations.²⁵ (link) In the study, base pairs were considered intact when the bases fell within a 5 Å cutoff. All simulated animations were monitored by VMD.³⁶ (link)

Liu F., Liu X., Gao W., Zhao L., Huang Q, & Arai T. (2023). Transmembrane capability of DNA origami sheet enhanced by 3D configurational changes. iScience, 26(3), 106208.

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Publication 2023

ARID1A protein, human DNA, A-Form DNA Conformation Epistropheus Helix (Snails)

Identifying Native-like Protein-DNA Binding

In this approach, we seek to explore multiple binding modes and rely on statistical mechanics to identify the most native-like one (e.g. the most compatible with our physical model). We presume knowledge of (i) the protein structure, (ii) the DNA sequence to bind and (iii) the DNA-binding domain. We generate a B-DNA structure using Chimera (50 (link)) and create a dummy atom at the N1 position of each purine base (see Figure 1). We define the binding data as the possible interactions between Cα atoms in the binding domain and the N1 atoms (Figure 1A). We produce a list of potential contacts, where only some might be satisfied during binding (noisy data) (Figure 1B). We reduce the amount of possible combinatorics by taking into consideration geometric considerations (e.g. residues far away in the binding site are unlikely to interact with the same DNA base simultaneously). Clustering on the MELD ensemble, we identify native-like poses in the ensemble (see Figure 1). The current set-up has two advantages: (i) by using dummy particles at the N1 site, we do not favor the protein approaching through either major or minor groove orientations and (ii) the information added is not exhaustive of all possibilities—and it does not need to be, as the force field will sample the most likely conformations given the available data (Figure 1C).
We chose 15 protein–DNA systems to apply this approach (see Table 1). The systems include complexes with little or no deformation of the DNA from its canonical B-DNA form, others that induce moderate deformation upon binding and complexes where the DNA is far from its canonical B-DNA conformation. The dataset also contains systems that have been solved experimentally with two different sequences, resulting in binding mode variations (e.g. different spacing between binding domains: 1R4R and 1R4O). Finally, we include two types of systems intended to challenge our approach: binding occurs through either flexible (disordered) tails (1ZME) or where large conformational changes are needed for accessing the binding site (1BGB and 2B0D).

Esmaeeli R., Bauzá A, & Perez A. (2023). Structural predictions of protein–DNA binding: MELD-DNA. Nucleic Acids Research, 51(4), 1625-1636.

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Publication 2023

Binding Sites Chimera DNA, A-Form DNA Conformation HSP40 Heat-Shock Proteins Mechanics Mental Orientation Physical Examination Proteins purine Tail

Bayesian Inference for Protein-DNA Binding

We use the MELD Bayesian inference approach [p(x|D) αp(D|x) · p(x)] to incorporate ambiguous and noisy data to enhance binding/unbinding events (41 (link),42 (link)). The prior distribution [p(x)] is given by the Boltzmann distribution based on the chosen force field, while the likelihood [p(D|x)] comes from the agreement of the sampled conformations (x) with a subset of the data (D, the one with the lowest restraint energy). As MELD samples the energy landscape, different subsets of data are also explored, exploiting regions compatible with some subset of data, and the force field (41 (link),42 (link)) gives rise to the posterior distribution [p(x|D)]. In practical terms, MELD uses a Hamiltonian and Temperature replica exchange molecular dynamics approach in which some replica conditions are compatible with unbound states and some with bound states. As ‘walkers’ sample different conditions in the replica ladder, they go through cycles of binding and unbinding. We identify bound states by clustering the lowest temperature ensembles, where each cluster represents a different binding mode and is compatible with varying subsets of data. We will showcase here three protocols to address three questions: (i) general binding (applied to any protein–DNA system); (ii) specific binding (applied to many DNA sequences binding a particular protein where additional information is known); and (iii) relative binding affinities. The type of data used to guide simulations depends on the questions we ask. Examples are accessible from Zenodo (see Data Availability section). MELD simulations use 30 replicas, the parmBSC1 force field for nucleic acids (43–45 (link)), the ff14SB side force field for the protein (46 (link),47 ) and the GBneck2Nu implicit solvent model (48 (link),49 (link)). Throughout all protocols, we include restraints to keep the protein and DNA from unfolding at high temperatures. For proteins, we enforce secondary structure and flat-bottom harmonic restraints on native Cα–Cα contacts; the initial coordinates for simulations and to set up the restraints are based on its bound conformation. For DNA, we implement restraints that maintain hydrogen-bonding patterns at each base pair to prevent DNA melting. All simulations were initialized with the protein far away (at least 30 Å) from the DNA. The initial DNA conformation is generated in its canonical B-form based on the sequence (50 (link)).

Esmaeeli R., Bauzá A, & Perez A. (2023). Structural predictions of protein–DNA binding: MELD-DNA. Nucleic Acids Research, 51(4), 1625-1636.

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Publication 2023

Base Pairing Base Sequence Cold Temperature DNA Conformation DNA Sequence Fever HSP40 Heat-Shock Proteins Molecular Dynamics Nucleic Acids Proteins Solvents Walkers

Solving Spiegelmer Crystal Structure

The structure was solved by molecular replacement using Phaser (McCoy et al., 2007 ▸ ). The postdoc performing the structure-solution step was unaware that the crystal contained the l-d(CGCGCG)₂ ‘spiegelmer’ of the oligonucleotide and he used, quite naturally, the DNA part of PDB entry 7atg, corresponding to our earlier model of the d-d(CGCGCG)₂/Put²⁺/K⁺ complex (Drozdzal et al., 2021 ▸ ), as the molecular probe. Since Friedel’s law makes the diffraction pattern centrosymmetric (with the exception of the, usually small, deviations caused by anomalous scattering), a noncentrosymmetric crystal structure can be solved equally well, of course, by both enantiomers of the molecular model.
In the initial stages of the refinement, the model was refined using REFMAC5 (Murshudov et al., 2011 ▸ ) from the CCP4 suite (Winn et al., 2011 ▸ ). The final anisotropic refinement was carried out with SHELXL (Sheldrick, 2015 ▸ ) using the full resolution of the diffraction data. The details of the SHELXL refinement were the same as described for our previous Z-DNA structures (Drozdzal et al., 2013 ▸ , 2015 ▸ ). At this resolution, no stereochemical restraints are necessary to supplement the experimental observations (Jaskolski, 2017 ▸ ). However, restraints may still be needed for some disordered or highly mobile fragments. In the present structure, restraints were only applied to the cadaverinium dication and to the bonds and angles of dual-conformation Z-DNA fragments. The ideal geometry targets for Cad²⁺ were taken from a high-quality X-ray structure of cadaverinium dichloride (Pospieszna-Markiewicz et al., 2006 ▸ ). Conformation-dependent geometrical restraints on bond lengths (DFIX) and bond angles (DANG) for the polynucleotide chains were generated using the RestraintLib server (http://achesym.ibch.poznan.pl/restraintlib/) as described by Kowiel et al. (2016 ▸ , 2020 ▸ ) and Gilski et al. (2019 ▸ ). The CSD-derived conformation-dependent RestraintLib dictionary supersedes the classic nucleic acid restraints compiled by Clowney et al. (1996 ▸ ), Gelbin et al. (1996 ▸ ) and Parkinson et al. (1996 ▸ ). The final cycles of CGLS (conjugate-gradient least-squares) refinement converged with an R and R_free of 10.32% and 12.83%, respectively. The very last round of refinement, calculated with the test reflections included in the working set, converged with R = 10.39%. In order to provide estimations of standard uncertainties in all individual refined parameters and all derived geometrical parameters, in the final stage of the refinement one cycle of full-matrix least-squares minimization was calculated. The placement of the model in the unit cell was standardized using the ACHESYM server (Kowiel et al., 2014 ▸ ).
At the wavelength used in the diffraction experiment (0.7085 Å), the imaginary components of the anomalous scattering (f′′) of K and P atoms are 0.252 and 0.098 electron units, respectively (Cromer, 1983 ▸ ). Anomalous signal is visible in the diffraction data up to ∼0.9 Å resolution and therefore the refinement was carried out against unmerged anomalous data. The signal was quite weak, however, as no clear peaks were located in the anomalous electron-density map contoured at the 3σ level that could correspond to the positions of the K⁺ ion and P atoms.
Coot (Emsley et al., 2010 ▸ ) was used for visualization of the electron-density maps and manual rebuilding of the atomic model.

Drozdzal P., Manszewski T., Gilski M., Brzezinski K, & Jaskolski M. (2023). Right-handed Z-DNA at ultrahigh resolution: a tale of two hands and the power of the crystallographic method. Acta Crystallographica. Section D, Structural Biology, 79(Pt 2), 133-139.

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Publication 2023

Anisotropy calcium chromate(VI) Cells Debility Dietary Supplements DNA, Z-Form DNA Conformation Electrons Microtubule-Associated Proteins Molecular Probes Nucleic Acids Oligonucleotides Polynucleotides Radiography Reflex

Measuring DNA Linker Arm Angles in NCPs

The angles of the DNA linker arms that extended from the discoidal-shaped NCP surface were measured by the following steps. i) Defining the X-, Y-, and Z-axes of each NCP model. The Z-axis was defined along the helical axis of the wrapping DNA measured from its rotational symmetry. The Y-axis was defined by the dyad axis of the NCP. The cross-point of these two axes was used as the center of NCP and defined the X-axis, where it simultaneously passed the NCP center and was perpendicular to both Z- and Y-axis. ii) Defining the relative DNA arm angle θ. θ measured the angle between the previously defined entry and exit DNA linker arm vectors on the same NCP. Because DNA arm conformational states (“open and close”) must be defined relative to the NCP, the projections of the θ angle on the NCP discoidal plane (X-Y plane) and its perpendicular plane (Y-Z plane) were measured as θ∥ and θ⊥, respectively. iii) Defining the in-plane wrapping angle α of an NCP arm vector. α calculated the angle formed by two vectors within the X-Y plane. One vector is the projection of the NCP linker arm vector on the X-Y plane, and the other vector is defined by the tangential direction of the discoidal-shaped projection of NCP on X-Y plane, which crosses the origin of the corresponding NCP linker arm. iv) Defining the out-of-plane bending angle β of an NCP arm vector. β calculated the angle formed by the NCP arm vector and the X-Y plane. The measured angle distribution was fitted with either one gaussian or two gaussians with the sklearn.mixture.Gaussian Mixture package.

Zhang M., Celis C.D., Liu J., Bustamante C, & Ren G. (2023). Conformational Change of Nucleosome Arrays prior to Phase Separation. Research Square.

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Publication Preprint 2023

Cloning Vectors DNA Conformation Epistropheus Helix (Snails)

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What are the main types or variations of DNA conformation?

DNA can adopt several different conformational states beyond the classic double-helix form. These include cruciform structures, triplex DNA, and quadruplex DNA. Cruciform structures involve the formation of a four-way DNA junction, while triplex DNA has three strands and quadruplex DNA has four strands. Each of these conformations serves specialized biological functions and can be important in processes like replication, transcription, and gene regulation.

How does DNA conformation impact biological processes?

The conformation of DNA is critical for its proper functioning in the cell. The double-helix structure facilitates efficient DNA replication and storage of genetic information. Other conformations like cruciforms and quadruplexes can regulate gene expression, influence DNA repair mechanisms, and even play a role in disease pathways. Understanding the relationships between DNA conformation and biological activity is essential for advancing genetic research and developing potential therapies.

What are some common challenges in studying DNA conformation?

Analyzing DNA conformation can be technically challenging, as different techniques are required to detect and characterize the various conformational states. Factors like pH, ionic conditions, and the presence of specific proteins can all influence DNA structure. Reproducibility is also a key concern, as small changes in experimental protocols can lead to inconsistent results. This is where PubCompare.ai's AI-driven research protocol optimization can be helpful - it allows researchers to more efficiently screen the literature, identify the most effective protocols, and make well-informed choices to improve the reliability of their DNA conformation studies.

How can PubCompare.ai assist in DNA conformation research?

PubComapre.ai's AI-driven platform can greatly enhance the reproducibility and efficiency of DNA conformation analyses. The tool allows researchers to more quickly and thoroughly screen the scientific literature, preprints, and patents to identify the most effective protocols related to their specific DNA conformation research goals. The platform's intelligent comparison capabilities can highlight key differences in protocol effectiveness, enabling researchers to choose the best option to ensure accurate and reproducible results. By streamlining the protocol selection process, PubCompare.ai can help researchers focus more time on the actual science and less on the challenges of identifying the optimal experimental approach.

More about "DNA Conformation"

DNA structure, DNA topology, nucleic acid conformation, genetic processes, disease pathways, therapeutic targets, DNA replication, DNA transcription, protein-DNA interactions, double helix, cruciform, triplex, quadruplex, DNA analysis, research protocol optimization, reproducibility, literature review, preprint analysis, patent search, intelligent comparisons, research streamlining, mouse anti-V5 antibody, PacBio Sequel platform, SanPrep Column Plasmid Mini-Preps Kit, Cary 5000 spectrophotometer, Labspec software, AutoDock Tools 1.5.6, PEG 8000, SYBR Green II, Alexa Fluor 488-conjugated goat anti-mouse IgG.