3DNA is a versatile, integrated software system for the analysis, rebuilding, and visualization of three-dimensional nucleic-acid-containing structures. The software is applicable not only to DNA (as the name 3DNA may imply) but also to complicated RNA structures and DNA-protein complexes. In 3DNA, structural analysis and model rebuilding are two sides of the same coin: the description of the structure is rigorous and reversible, thus allowing for its exact reconstruction based on the derived parameters. 3DNA automatically detects all non-canonical base pairs, base triplets and higher-order associations (collectively termed multiplets), and coaxially stacked helices; provides a comprehensive collection of fiber models of regular DNA and RNA helices; generates highly effective schematic presentations that reveal key features of nucleic-acid structures; performs undisturbed base mutations, and have facilities for the analysis of molecular dynamics simulation trajectories.

DSSR is an integrated software tool for dissecting the spatial structure of RNA. It is a representative of what would become the brand new version 3 of 3DNA. DSSR consolidates, refines, and significantly extends the functionality of 3DNA v2.x for RNA structural analysis. Among other features, DSSR denotes base-pairs by common names (e.g., WC, reverse WC, Hoogsteen A+U, reverse Hoogsteen A—U, wobble G—U, sheared G—A), the Saenger classification of 28 H-bonding types, and the Leontis-Westhof nomenclature of 12 basic geometric classes; determines double-helical regions, differentiates stems from helices, and provides a pragmatic definition of coaxial stacking interactions; identifies hairpin loops, bulges, internal loops, and multi-branch (junction) loops; characterizes pseudoknots of arbitrary complexity; outputs RNA secondary structure in commonly used formats (including the dot-bracket notation and connectivity table); identifies A-minor interactions, splayed-apart dinucleotide conformations, base-capping interactions, ribose zippers, G quadruplexes, i-motifs, kissing loops, U-turns, and k-turns etc. By connecting dots in RNA structural bioinformatics, it makes many common tasks simple and advanced applications feasible. DSSR comes with a professional User Manual, and some of its features have been integrated into Jmol and PyMOL. Moreover, the DSSR-Jmol paper, titled DSSR-enhanced visualization of nucleic acid structures in Jmol, has been featured in the cover image of the 2017 Web-server issue of Nucleic Acids Research (NAR).

3DNA version 3 is under active development. The SNAP program has been created from scratch for an integrated characterization of the three-dimensional Structures of Nucleic Acid-Protein complexes. Sharing the same new codebase as DSSR, SNAP works for DNA-protein as well as RNA-protein interactions. Other 3DNA v2.x programs (e.g., fiber, rebuild etc) are gradually distilled into version 3, and a new atomic coordinates-based homology searching tool is also being developed. In the end, 3DNA version 3 will consist of a suite of fully independent (as DSSR and SNAP) yet closely related programs, serving as cornerstones of DNA/RNA structural bioinformatics.

All 3DNA-related questions are welcome and should be directed to the 3DNA Forum. For the benefit of the community at large, I do not provide private support of 3DNA via email or personal message. As a general rule, I strive to provide a prompt and concrete response to each and every question posted on the Forum.

More info · Seeing is believing · Cover image · What’s new · 3DNA Forum · Download

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ONZ classification of G-tetrads

Recently I read the article Topology-based classification of tetrads and quadruplex structures in Bioinformatics by Popenda et al. In this work, the authors proposed an ONZ classification scheme of G-tetrads in intramolecular G-quadruplexes (G4) as shown below (Fig. 2 in the publication):

ONZ classification of G-tetrads in intramolecular G-quadruplexes

I am glad to find that DSSR has been used as a component in their computational tool ElTetrado to automatically identify and classify tetrads and quadruplexes.

Structures from both sets were analysed using self-implemented programs along with DSSR software from the 3DNA suite (Lu et al. (2015)). From DSSR, we acquired the information about base pairs and stacking.

I like the ONZ classification scheme: it is simple in concept yet provides a new perspective for the topologies of G-tetrads in intramolecular G4 structures. So I implemented the idea in DSSR v1.9.8-2019oct16, with this feature available via the --g4-onz option. Note that ElTetrado, according to the authors, is applicable to ONZ classifications of general types of tetrads and quadruplexes. The DSSR implementation of ONZ classifications, on the other hand, is strictly limited to G-tetrads in intramolecular G4 structures.

The DSSR ONZ classification results match the ones reported in Figs. 1, 5, and 6 of the Popenda et al. paper. For example, for PDB entry 6H1K (Fig. 6), the relevant results with the --g4-onz option and without it are listed below:

# x3dna-dssr -i=6h1k.pdb --g4-onz
List of 3 G-tetrads
   1 glyco-bond=s--- groove=w--n planarity=0.149 type=planar Z- nts=4 GGGG A.DG1,A.DG20,A.DG16,A.DG27
   2 glyco-bond=-sss groove=w--n planarity=0.136 type=planar Z+ nts=4 GGGG A.DG2,A.DG19,A.DG15,A.DG26
   3 glyco-bond=--s- groove=-wn- planarity=0.307 type=other  O+ nts=4 GGGG A.DG17,A.DG21,A.DG25,A.DG28
# ---------------------------------------
# x3dna-dssr -i=6h1k.pdb 
#   without option --g4-onz
List of 3 G-tetrads
   1 glyco-bond=s--- groove=w--n planarity=0.149 type=planar nts=4 GGGG A.DG1,A.DG20,A.DG16,A.DG27
   2 glyco-bond=-sss groove=w--n planarity=0.136 type=planar nts=4 GGGG A.DG2,A.DG19,A.DG15,A.DG26
   3 glyco-bond=--s- groove=-wn- planarity=0.307 type=other  nts=4 GGGG A.DG17,A.DG21,A.DG25,A.DG28

With the --json option, the ONZ classification results are always available. An example is shown below for PDB entry 6H1K (Fig. 6):

# x3dna-dssr -i=6h1k.pdb --json | jq -c '.G4tetrads[] | [.nts_long, .topo_class]'
["A.DG1,A.DG20,A.DG16,A.DG27","Z-"]
["A.DG2,A.DG19,A.DG15,A.DG26","Z+"]
["A.DG17,A.DG21,A.DG25,A.DG28","O+"]

Comment

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H-bonds reported by DSSR and SNAP

I recently read a short communication by Pavel Afonine, titled phenix.hbond: a new tool for annotation hydrogen bonds in the July 2019 issue of the Computational Crystallography Newsletter (CCN). It appears that every bioinformatics tool (e.g., PyMOL or Jmol) has its own implementation of an algorithm on calculating H-bonds, one of the fundamental stabilizing forces of proteins and DNA/RNA structures. So does 3DNA/DSSR, as noted in my 2014-04-11 blogpost Get hydrogen bonds with DSSR.

Both DSSR and SNAP have the --get-hbond option, and they use the same underlying algorithm. However, the default output from the two programs differs: DSSR reports the H-bonds within nucleic acids, and SNAP covers only those at the DNA/RNA-protein interface. Using the PDB entry 1oct as an example, Running DSSR on it with the --get-hbond option gives 33 H-bonds in the DNA duplex, while SNAP reports 38 H-bonds at the DNA-protein interface. By design, the default output caters for the most-common use case of each program.

Under the scene, however, there exist variations in the seemingly simple --get-hbond option. One can attach text ‘nucleic’ (or ‘nuc’, ‘nt’), as in --get-hbond-nucleic, to output H-bonds within nucleic acids. Similarly, --get-hbond-protein (or ‘amino’, ‘aa’) would output H-bonds within proteins. Not surprisingly, the --get-hbond-nt-aa option would list H-bonds in nucleic acids and proteins, including those at their interface. These variations apply to both DSSR and SNAP, even though some are redundant with the default.

Notably, in combination with --json, the --get-hbond option by default would output all H-bonds, as if --get-hbond-nt-aa has been set. For PDB entry 1oct, DSSR or SNAP would report 208 H-bonds. Moreover, the JSON output has a residue_pair field for each identified H-bond, with values like "nt:nt", "nt:aa", or "aa:aa". Using 1oct as an example,

# x3dna-dssr -i=1oct.pdb --get-hbond --json | jq '.hbonds[0]'
{
  "index": 1,
  "atom1_serNum": 34,
  "atom2_serNum": 608,
  "donAcc_type": "standard",
  "distance": 3.304,
  "atom1_id": "O6@A.DG202",
  "atom2_id": "N4@B.DC230",
  "atom_pair": "O:N",
  "residue_pair": "nt:nt"
}
# x3dna-dssr -i=1oct.pdb --get-hbond --json | jq '.hbonds[60]'
{
  "index": 61,
  "atom1_serNum": 462,
  "atom2_serNum": 1187,
  "donAcc_type": "standard",
  "distance": 3.692,
  "atom1_id": "O2@B.DT223",
  "atom2_id": "NH2@C.ARG102",
  "atom_pair": "O:N",
  "residue_pair": "nt:aa"
}
# x3dna-dssr -i=1oct.pdb --get-hbond --json | jq '.hbonds[100]'
{
  "index": 101,
  "atom1_serNum": 791,
  "atom2_serNum": 818,
  "donAcc_type": "standard",
  "distance": 2.871,
  "atom1_id": "N@C.THR26",
  "atom2_id": "OD2@C.ASP29",
  "atom_pair": "N:O",
  "residue_pair": "aa:aa"
}

In the above three cases, using SNAP instead of DSSR would give the same results.

Also, one can take advantage of the residue_pair value to filter H-bonds by type. For example, the following command would extract only H-bonds at the DNA-protein interface (38 occurrences, same as the number noted above):

x3dna-snap -i=1oct.pdb --get-hbond --json | jq '.hbonds[] | select(.residue_pair=="nt:aa")'

Back to the phenix.hbond tool, the author noted that:

Running phenix.hbond requires atomic model in PDB or mmCIF format with all hydrogen atoms added, as well as ligand restraint files if the model contains unknown to the library items.

While there is no particular reason why this should not work for all bio-macromolecules, currently phenix.hbond is only optimized and tested to work with proteins, which is the limitation that will be removed in future.

In contrast, the H-bond identification algorithm in DSSR/SNAP does not require hydrogen atoms. In fact, hydrogen atoms are simply ignored if they exist. As shown above, the H-bond method as implemented in DSSR/SNAP works for DNA, RNA, protein, or their complexes. This does not necessarily mean that the 3DNA way is superior to other similar tools. It just works well in my hand, and it may serve as a pragmatic choice for other users.

Comment

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5CM and 5MC, two forms of 5-methylcytosine in the PDB

In the PDB, the ligand identifiers 5MC and 5CM all refer to 5-methylcytosine, but differ in the sugar moieties the base is attached to. Chemically, 5MC is 5-methyl-2’-deoxycytidine-5’-monophosphate as in DNA, and 5MC is 5-methylcytidine-5’-monophosphate. See the molecular images shown below.

Web 3DNA 2.0 highlighted in the cover of the NAR'19 webserTwo forms of 5-methylcytosine in PDB: 5CM and 5MC

The 5-methyl group is named C5A in 5CM and CM5 in 5MC, respectively, for non-obvious reasons other than conventions. For comparison, the methyl-group in thymine of DNA is named C7, as for example in PDB id 355d. It is worth noting that DSSR is able to handle all such variations in atom or residue names.

Comment

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DNA conformational changes play a force-generating role during bacteriophage genome packaging

A paper titled DNA Conformational Changes Play a Force-Generating Role during Bacteriophage Genome Packaging has just been officially published in the Biophysical Journal (Volume 116, Issue 11, P2172-2180, June 04, 2019). I am glad to have the opportunity to collaborate with Kim Sharp, Gino Cingolani and Stephen Harvey on this interesting project that has big implications in understanding the mechanism of bacteriophage genome packaging. The abstract of the paper is shown below:

Motors that move DNA, or that move along DNA, play essential roles in DNA replication, transcription, recombination, and chromosome segregation. The mechanisms by which these DNA translocases operate remain largely unknown. Some double-stranded DNA (dsDNA) viruses use an ATP-dependent motor to drive DNA into preformed capsids. These include several human pathogens as well as dsDNA bacteriophages—viruses that infect bacteria. We previously proposed that DNA is not a passive substrate of bacteriophage packaging motors but is instead an active component of the machinery. We carried out computational studies on dsDNA in the channels of viral portal proteins, and they reveal DNA conformational changes consistent with that hypothesis. dsDNA becomes longer (“stretched”) in regions of high negative electrostatic potential and shorter (“scrunched”) in regions of high positive potential. These results suggest a mechanism that electrostatically couples the energy released by ATP hydrolysis to DNA translocation: The chemical cycle of ATP binding, hydrolysis, and product release drives a cycle of protein conformational changes. This produces changes in the electrostatic potential in the channel through the portal, and these drive cyclic changes in the length of dsDNA as the phosphate groups respond to the protein’s electrostatic potential. The DNA motions are captured by a coordinated protein-DNA grip-and-release cycle to produce DNA translocation. In short, the ATPase, portal, and dsDNA work synergistically to promote genome packaging.

Significantly, our work is highlighted in a “New and Notable” article, May the Road Rise to Meet You: DNA Deformation May Drive DNA Translocation by Paul Jardine (Volume 116, Issue 11, Pages 2060-2061, 4 June 2019):

Regardless of what drives conformational change in the portal, the idea that the linear DNA substrate is deformed in a way that makes it an energetic participant in its own movement opens new possibilities for how motors work. Large paddling or rotational motions by motor components may not be required if linear motion can be achieved by stretching or compressing the linear substrate, with rectified, cyclic conformational changes in the DNA rather than lever motions doing the work. If borne out by experiments, further simulation, and more structural information, this proposed mechanism may require a reappraisal of how we think about translocating motors.

For this project, I developed the x3dna-search program to survey similar fragments of single-stranded or double helical structures in the PDB.

Comment [2]

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The article on G.A pairs in ACS Biochemistry

After many years of efforts, it is a great pleasure to see our paper Effects of Noncanonical Base Pairing on RNA Folding: Structural Context and Spatial Arrangements of G·A Pairs published in ACS Biochemistry. The abstract is shown below:

Noncanonical base pairs play important roles in assembling the three-dimensional structures critical to the diverse functions of RNA. These associations contribute to the looped segments that intersperse the canonical double-helical elements within folded, globular RNA molecules. They stitch together various structural elements, serve as recognition elements for other molecules, and act as sites of intrinsic stiffness or deformability. This work takes advantage of new software (DSSR) designed to streamline the analysis and annotation of RNA three-dimensional structures. The multiscale structural information gathered for individual molecules, combined with the growing number of unique, well-resolved RNA structures, makes it possible to examine the collective features deeply and to uncover previously unrecognized patterns of chain organization. Here we focus on a subset of noncanonical base pairs involving guanine and adenine and the links between their modes of association, secondary structural context, and contributions to tertiary folding. The rigorous descriptions of base-pair geometry that we employ facilitate characterization of recurrent geometric motifs and the structural settings in which these arrangements occur. Moreover, the numerical parameters hint at the natural motions of the interacting bases and the pathways likely to connect different spatial forms. We draw attention to higher-order multiplexes involving two or more G·A pairs and the roles these associations appear to play in bridging different secondary structural units. The collective data reveal pairing propensities in base organization, secondary structural context, and deformability and serve as a starting point for further multiscale investigations and/or simulations of RNA folding.

Sample G.A pair characterized by DSSR

This work represents a multifaceted, fundamental application enabled by DSSR. Even at the base-pair (bp) level, DSSR provides unique features that complement the Leontis-Westhof (LW) notation of 12 geometric types.

At the review stage, we were asked by a referee to comment on the differences between DSSR and LW on bp classifications. The following paragraph in the “DISCUSSION” section of the paper is our response, expanded on the original writing that focused on DSSR’s capabilities:

Qualitative descriptions of noncanonical RNA base pairing, pioneered by Leontis and Westhof9,41 and linked in this work to the rigid-body parameters of interacting bases, have proven valuable in deciphering the connections between RNA primary, secondary, and tertiary structures. The present categorization is based on the positions of the hydrogen-bonded atoms with respect to a standard, embedded base reference frame30 defined in terms of an idealized Watson−Crick base pair. The major- and minor-groove base edges used here correspond in most cases to what are termed the Hoogsteen and sugar edges in the Leontis−Westhof scheme (one can compare the two classification schemes in Table S2). The + and − symbols introduced in 3DNA24 and DSSR27 unambiguously distinguish the relative orientations of the two bases. The trans and cis designations used in the earlier literature, however, are qualitative in nature and often uncertain. There are many “nc” (near cis, as in ncWW) and “nt” (near trans, as in ntSH) annotations listed in the RNA Structure Atlas; see, for example, the base-pair interactions in the sarcin−ricin domain of E. coli 23S rRNA found by entering PDB entry 1msy at http://rna.bgsu.edu/rna3dhub/pdb. The assignment of qualitative descriptors of RNA associations on the basis of atomic identity alone is generally not clear-cut. Numerical differences in the rigid-body parameters are critical to differentiating pairing schemes that share a common hydrogen bond, e.g., the G(N3)···A(N6) interaction found in m−WII and m−MI arrangements of G and A (Table 1 and Figures 4 and S3). The numerical data also provide a basis for following conformational transitions and may potentially be of value in making functional and other meaningful distinctions among RNA base pairs.

See also a recent thread Noncanonical base pair standards on the 3DNA Forum and the section titled “3.2.2 Base pairs” in the DSSR User Manual.

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Web 3DNA 2.0 paper published in NAR

It is a great pleasure to announce the publication of Web 3DNA 2.0 for the analysis, visualization, and modeling of 3D nucleic acid structures in Nucleic Acids Research (NAR). The paper will appear in the web server issue of NAR in July 2019. At nine-page in length and with several new structural parameters, this w3DNA 2.0 paper is certainly not a typical NAR web-server publication. It represents a significant contribution to the field of 3D nucleic acids structural bioinformatics, and will undoubtedly push the popularity of 3DNA to a new level.

The abstract is shown below:

Web 3DNA (w3DNA) 2.0 is a significantly enhanced version of the widely used w3DNA server for the analysis, visualization, and modeling of 3D nucleic-acid-containing structures. Since its initial release in 2009, the w3DNA server has continuously served the community by making commonly-used features of the 3DNA suite of command-line programs readily accessible. However, due to the lack of updates, w3DNA has clearly shown its age in terms of modern web technologies and it has long lagged behind further developments of 3DNA per se. The w3DNA 2.0 server presented here overcomes all known shortcomings of w3DNA while maintaining its battle-tested characteristics. Technically, w3DNA 2.0 implements a simple and intuitive interface (with sensible defaults) for increased usability, and it complies with HTML5 web standards for broad accessibility. Featurewise, w3DNA 2.0 employs the most recent version of 3DNA, enhanced with many new functionalities, including: the automatic handling of modified nucleotides; a set of ‘simple’ base-pair and step parameters for qualitative characterization of non-Watson–Crick double- helical structures; new structural parameters that integrate the rigid base plane and the backbone phosphate group, the two nucleic acid components most reliably determined with X-ray crystallography; in silico base mutations that preserve the backbone geometry; and a notably improved module for building models of single-stranded RNA, double- helical DNA, Pauling triplex, G-quadruplex, or DNA structures ‘decorated’ with proteins. The w3DNA 2.0 server is freely available, without registration, at http://web.x3dna.org.

Moreover, details on reproducing our reported results are available in a dedicated section ‘web 3DNA 2.0 (http://web.x3dna.org)’ on the 3DNA Forum.

Graphical abstract of web 3DNA 2.0

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DSSR on PDB entry 6neb

Via PDB weekly update, I recently came across PDB entry 6neb, which is solved by NMR and described as an “MYC promoter G-quadruplex with 1:6:1 loop length”. I downloaded the atomic coordinates of the entry and ran DSSR on it. Indeed, DSSR readily identifies a three-layered parallel G-quadruplex (G4) with three propeller-type loops of 1, 6 and 1 nucleotides (i.e., 1:6:1), as shown below.

List of 1 G4-stem
  Note: a G4-stem is defined as a G4-helix with backbone connectivity.
        Bulges are also allowed along each of the four strands.
  stem#1[#1] layers=3 INTRA-molecular loops=3 descriptor=3(-P-P-P) note=parallel(4+0) UUUU parallel
   1  glyco-bond=---- groove=---- WC-->Major nts=4 GGGG A.DG3,A.DG7,A.DG16,A.DG20
      pm(>>,forward)  area=14.54 rise=3.36 twist=24.7
   2  glyco-bond=---- groove=---- WC-->Major nts=4 GGGG A.DG4,A.DG8,A.DG17,A.DG21
      pm(>>,forward)  area=9.67  rise=3.48 twist=30.3
   3  glyco-bond=---- groove=---- WC-->Major nts=4 GGGG A.DG5,A.DG9,A.DG18,A.DG22
    strand#1  U DNA glyco-bond=--- nts=3 GGG A.DG3,A.DG4,A.DG5
    strand#2  U DNA glyco-bond=--- nts=3 GGG A.DG7,A.DG8,A.DG9
    strand#3  U DNA glyco-bond=--- nts=3 GGG A.DG16,A.DG17,A.DG18
    strand#4  U DNA glyco-bond=--- nts=3 GGG A.DG20,A.DG21,A.DG22
    loop#1 type=propeller strands=[#1,#2] nts=1 A A.DA6
    loop#2 type=propeller strands=[#2,#3] nts=6 TTTTAA A.DT10,A.DT11,A.DT12,A.DT13,A.DA14,A.DA15
    loop#3 type=propeller strands=[#3,#4] nts=1 T A.DT19

I then read the associated paper titled Solution Structure of a MYC Promoter G-Quadruplex with 1:6:1 Loop Length lately published in the new, open-access ACS Omega journal. The reported structure 6neb has a 27-nt sequence (termed Myc1245) of bases 5’-TTGGGGAGGGTTTTAAGGGTGGGGAAT-3’. Myc1245 is based on the 27-nt long, purine-rich MycPu27 which has 5 tracts of guanines of G4-forming motif within the MYC promoter. In Myc1245, the third G-tract of MycPu27 has been replaced by TTTA, thus it uses only G-tracts 1, 2, 4, 5 for G4 formation. Previously, it was shown that Myc2345 (using G-tracts 2-5 of MycPu27) adopts a parallel G4 structure with three propeller loops of 1:2:1 nt length.

The MycPu27 sequence is representative of the G4-forming nuclease hypersensitive element (NHE III1) within the promoter region of the MYC oncogene. Formation of G4 structures suppresses MYC transcription, thus ligand-induced G4 stabilization in the DNA level is a promising strategy for cancer therapy. The NHE III1 motif can fold into multiple G4 structures depending on factors such as protein binding. The paper on 6neb illustrates that nucleolin, a protein shown to bind MYC G4 and repress MYC transcription, preferably binds the 1:6:1 loop length conformer than the 1:2:1 conformer (the major form under physiological conditions).

The DSSR analysis of 6neb shows that the two G-tetrad steps have different overlapping areas and twist angles. The top step comprising G3 and G4 (Fig. 1A) has better stacking interactions (14.5 Å2) and smaller twist (25º) than the bottom step containing G4 and G5 (9.7 Å2 and 30º, respectively).

area=14.54 rise=3.36 twist=24.7
area=9.67  rise=3.48 twist=30.3

The analysis characterizes the T1–A15 pair as a reverse Hoogsteen pair (rHoogsteen), which is distinct from the T+A Hoogsteen pair. In DSSR, the rHoogsteen pair is of type M–N (anti-parallel), whilst the Hoogsteen pair is of type M+N (parallel). With the local base-reference frames attached (Fig. 1B), it is easy to visualize that the z-axis of T1 is pointing out of the base-pair plane, and the z-axis of A15 is pointing inwards. See also my blog post Hoogsteen and reverse Hoogsteen base pairs.

Fig. 1C shows the ATG-triad automatically identified by DSSR. As is clear in Fig. 1A, the ATG-triad stacks on the 3-layered G4 structure on the 5’ side. Moreover, with color-coded base blocks (G in green, T in blue, and A in red), the two stacks (T10–T11, and T12–T13–A14) in the 6-nt central propeller loop is immediately obvious.


Figure 1. DSSR-derived structural features in PDB entry 6neb. The images were created using DSSR and PyMOL.

In the 6neb paper, the author stated that “The central loop of 6 nt connects the outer tetrads by spanning the G-core. A single nucleotide is the minimal length of this structural motif, so the five additional residues can significantly increase the loop’s conformational flexibility.” (p.2536) It is worth noting that in PDB entry 2m53, described in G-rich VEGF aptamer with locked and unlocked nucleic acid modifications exhibits a unique G-quadruplex fold, it was observed that:

An unprecedented all parallel-stranded monomeric G-quadruplex with three G-quartet planes exhibits several unique structural features. Five consecutive guanine residues are all involved in G-quartet formation and occupy positions in adjacent DNA strands, which are bridged with a no-residue propeller-type loop.

The G4 structure is polymorphic. It seems every imaginable or even unexpected form is possible, depending on the context.

Comment

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Non-G base tetrads

In addition to the well-known G-tetrad serving as the building block of G-quadruplexes (G4), other types of homogeneous or heterogeneous base-tetrads are also possible. In DSSR, all these base tetrads are generally termed multiplets where three or more bases associate in a co-planar fashion via H-bonding interactions.

In the context of G4 structures, U-tetrads are the most common. Fig. 1A shows an example of U-tetrads in PDB entry 4rne reported in the paper titled Structural Variations and Solvent Structure of r(UGGGGU) Quadruplexes Stabilized by Sr2+ Ions.. In the structure (Fig. 1B), two terminal U-tetrads cap the six-layered G4 structure in the middle. The four U’s in the U-tetrad are paired in parallel orientation (i.e., U+U), just as the G+G pairs in the G-tetrad of G4 structures (Fig. 1C). On the other hand, there is only one H-bond (O4…N3) in the U+U pair of the U-tetrad, in contrast to the two H-bonds in the G+G pair of the G-tetrad (Fig. 1C). In the PDB entry 4rne, DSSR also detects two octads where the middle G-tetrad is surrounded by four U’s in anti-parallel orientation (G’s filled in green vs. U’s empty, see Fig. 1C for an example).

Similarly orientated C-tetrad (C+C pair, Fig. 1D) or A-tetrad (A+A pair, Fig. 1E) are also possible. PDB entry “6a85”, associated with the paper High-resolution DNA quadruplex structure containing all the A-, G-, C-, T-tetrads., reported a high-resolution crystal structure of sequence 5'-AGAGAGATGGGTGCGTT-3' which contains all the homogeneous A-, G-, C-, T-tetrads, and the heterogeneous A:T:A:T tetrads. As of this writing (Feb. 19, 2019), the status for the PDB entry “6a85” is still “HPUB” (‘processing complete, entry on hold until publication’) even though the paper was published several months ago. Using mutate_bases in 3DNA, I generated a C-tetrad and an A-tetrad as shown in Fig. 1D and 1E. As the U-tetrad (Fig. 1A), the C- and A-tetrads also have only one H-bond in their M+N type pairs. The G-tetrad, with two H-bonds in its connecting pairs, is more stable than the other homogeneous base tetrads, leading to wide-spread G4 structures.

In the homogeneous base tetrads shown in Fig.1A-E, pairs are of the parallel M+N type and the bases are associated via their Watson-Crick and major-groove (Hoogsteen) edges. Two canonical (WC or G—U wobble) pairs can also associate via their minor-groove edges, as seen in PDB entries 2hk4 and 2lsx. Fig. 1F gives an example with two G—U wobble pairs (of anti-parallel M—N type, filled U in blue vs. empty G) in PDB entry 2lsx reported in the paper titled A minimal i-motif stabilized by minor groove G:T:G:T tetrads..

DSSR-derived non-G base tetrads
Figure 1. Non-G base tetrads automatically identified or modeled by 3DNA-DSSR. The images were created using DSSR and PyMOL.

Comment

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G-tetrad and pseudo G-tetrads

A G-quadruplex (G4) is composed of stacks of G-tetrad where four guanines form four G•G pairs in a circular, planar fashion. Specifically, the G•G pairs of the G-tetrad (see Fig. 1A below) in G4 are of type M+N according to 3DNA/DSSR: i.e., G+G with the local z-axes of pairing guanines in parallel. Moreover, the G+G pair is uniquely quantified by three base-pair parameters: Shear, Stretch, and Opening with mean values [+1.6 Å, +3.5 Å, –90º] or [–1.6 Å, –3.5 Å, +90º], corresponding to the cWH (cW+M) or cHW (cM+W) types of LW (DSSR) classifications, respectively. This pair is numbered VI in the list of 28 base pairs with two or more H-bonds between base atoms, compiled by Saenger.

In addition to the standard G-tetrad configuration as normally seen in G4 structures, a so-called pseudo-G-tetrad form (see Fig. 1B below) is reported in a 2013 paper titled Duplex-quadruplex motifs in a peculiar structural organization cooperatively contribute to thrombin binding of a DNA aptamer. (PDB entry 4i7y). In a 2017 publication from the same group, Through-bond effects in the ternary complexes of thrombin sandwiched by two DNA aptamers, another form of pseudo-G-tetrad (Fig. 1C) is found in PDB entries 5ew1 and 5ew2.

Clearly, pseudo-G-tetrads are very different from the normal G-tetrad, in terms of base pairing patterns. The G-tetrad is highly regular with the same type of G+G pairs, with the O6 atoms pointing to the middle of the circle. The two pseudo-G-tetrads are less regular, and they differ from each other as well, by flipping G12 from syn (Fig. 1B) to trans (Fig. 1C).

These distinctions stand out even more by filling the up-face (+z-axis outwards) of a guanine base in green while leaving the down-face (+z-axis inwards) empty (G5 in Fig. 1B, G5 and G12 in Fig. 1C). So in G-tetrad (Fig. 1A), all four guanines have their positive z-axis point towards the viewer, corresponding to all four G+G pairs. In one pseudo-G-tetrad (Fig. 1B), G5 has its positive z-axis pointing away from the viewer. So G5–G7 and G5–G16 pairs are of the M–N type. The other type of pseudo-G-tetrad (Fig. 1C) has the opposite orientation for G12. Finally, Fig. 1D shows schematically PDB entry 4i7y where the G-tetrad and a pseudo-G-tetrad are directly stacked, creating a two-layered pseudo-G-quadruplex.

DSSR-derived G-tetrads
Figure 1. (A) G-tetrad, (B-C) two types of pseudo-G-tetrads, and (D) the complex of a DNA-apatmer with thrombin. G-tetrads were automatically identified by 3DNA-DSSR. The images were created using DSSR and PyMOL.

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