A new paper titled Analyzing and Building Nucleic Acid Structures with 3DNA has been published in JoVE (Journal of Visualized Experiments). Specifically, the article illustrates 3DNA’s unique capability to characterize and modify DNA structures at the level of the constituent base-pair steps, and highlights a new feature in v2.1 to analyze and align an ensemble of related structures determined with NMR or generated by MD simulations.

Here is the abstract:

The 3DNA software package is a popular and versatile bioinformatics tool with capabilities to analyze, construct, and visualize three-dimensional nucleic acid structures. This article presents detailed protocols for a subset of new and popular features available in 3DNA, applicable to both individual structures and ensembles of related structures. Protocol 1 lists the set of instructions needed to download and install the software. This is followed, in Protocol 2, by the analysis of a nucleic acid structure, including the assignment of base pairs and the determination of rigid-body parameters that describe the structure and, in Protocol 3, by a description of the reconstruction of an atomic model of a structure from its rigid-body parameters. The most recent version of 3DNA, version 2.1, has new features for the analysis and manipulation of ensembles of structures, such as those deduced from nuclear magnetic resonance (NMR) measurements and molecular dynamic (MD) simulations; these features are presented in Protocols 4 and 5. In addition to the 3DNA stand-alone software package, the w3DNA web server, located at http://w3dna.rutgers.edu, provides a user-friendly interface to selected features of the software. Protocol 6 demonstrates a novel feature of the site for building models of long DNA molecules decorated with bound proteins at user-specified locations.

A new section dedicated to the JoVE paper will be set up on the 3DNA Forum soon. It will contain all the data files and scripts so our published results can be strictly reproduced. The section should also serve as a platform for open discussions of related protocols.

Over the past six months or so¹, I’ve been focusing mostly on developing DSSR, a new addition to the 3DNA suite of programs. So what is DSSR, specifically? Why did I bother to create it? How would it be relevant to the nucleic acid structure community?

Literally, DSSR stands for Defining the (Secondary) Structures of RNA². Starting from an RNA structure in PDB format, DSSR employs a set of simple criteria to identify all existent base pairs (bp): both canonical Watson–Crick (WC) pairs and non-canonical pairs with at least one H-bond, made up of normal or modified bases, regardless of tautomeric or protonation state. The classification is based on the six standard rigid-body bp parameters (shear, stretch, stagger, propeller, buckle, and opening), which together rigorously quantify the spatial disposition of any two interacting bases. Moreover, the program characterizes each bp by commonly used names (WC, reverse WC, Hoogsteen, reverse Hoogsteen, wobble, sheared, imino, Calcutta, and dinucleotide platform), the Saenger classification scheme of 28 types, and the Leontis-Westhof nomenclature of 12 basic geometric classes. DSSR also checks for non-pairing interactions (H-bonds or base stacking).

DSSR detects triplets and even higher-order base associations by searching horizontally in the plane of the associated bp for further H-bonding interactions. The program determines helical regions by exploring each bp’s neighborhood vertically for base-stacking interactions, regardless of backbone connection (e.g., coaxial stacking of helices or pseudo helices). Moreover, each helix/stem is characterized by a least-squares fitted helical axis to allow for easy quantification of relative helical geometry. DSSR calculates commonly used backbone (including the virtual η/θ) torsion angles, classifies the main chain backbone into BI/BII conformation and the sugar into C2’/C3’-endo like pucker, identifies A-minor interactions (types I and II), ribose zippers, G quartets, hairpin loops, kissing loops, bulges, internal loops and multi-branch loops (junctions). It also detects the existence of pseudo-knots, and outputs RNA secondary structure in the dot-bracket notation.

Experienced 3DNA users may notice that some of the above outlined functionality (e.g., calculation of torsion angles, identification of all pairs, higher order base associations, and helices) have existed for over a decade. Over the years, I have written several posts (see What can 3DNA do for RNA structures?, and links therein) to advocate 3DNA’s applications in RNA structural analysis. Nevertheless, 3DNA has never been widely used in the RNA structure community, for various possible reasons: (1) the misconception that 3DNA is only for DNA (but not RNA); (2) the basic functionality is split into two programs (find_pair and analyze), and needs to be run several times with different options (default find_pair, and with -s, or -p). Thus even though 3DNA is applicable to RNA structures, it is unnecessarily complicated and confusing (especially to new 3DNA users); (3) 3DNA is command-line driven, consisting of many C programs and scripts, with different styles in specifying options. It has the ‘reputation’ of being powerful, but cryptic and hard to use.

I’ve created DSSR from scratch to take consideration of these factors, by employing my extensive experience in supporting 3DNA, an increased knowledge in RNA structures and refined C programming skills. Implemented in ANSI C as a stand-alone command-line program, DSSR is self-contained. Its executables (on MacOS X, Linux and Windows) have zero runtime dependencies. No setup is necessary; simply put the program into a folder of your choice (preferably one on your command PATH), and it should work. DSSR has sensible default settings and an intuitive output, making it directly accessible to a much broader audience than 3DNA per se. Since its initial release on March 3, 2013, I’ve yet to hear any installation or usage problem. So far, all reported bugs have been verified and fixed promptly. The latest beta release has been checked against all nucleic-acid-containing entries in the PDB, without any known issues.

Overall, DSSR consolidates, refines, and significantly extends 3DNA’s functionality for RNA structural analysis. There are more in DSSR than its simple interface suggests. Piecewise, DSSR may appear nothing new, yet combined together, it has unique features not available anywhere else. Its value will be gradually appreciated as DSSR becomes more widely used by the community. Want to know if your structure contains any Hoogsteen pair, sheared G•A pair, or a dinucleotide platform? DSSR can check it for you, easily.

DSSR-beta already possesses all the basic functionality and has been well tested to serve as a handy tool for RNA structural analysis. I stand firmly behind DSSR, and strive to continuously improve the program. Give it a try, and report back on the 3DNA Forum any issues you have. As always, I respond quickly and concretely to all questions posted there. I hope you enjoying using DSSR as much as I enjoy creating and supporting it!

¹ This post was published on March 29, 2013, shortly after the beta releases of DSSR [note added on March 15, 2014].

² DSSR also works for DNA, or DNA-protein complexes, as far as the basic functionality is concerned. Moreover, the acronym could have two other possible interpretations, as would be obvious when the program gains a wider recognition.

Early on when I started on DNA structures, I read Saenger’s book Principles of Nucleic Acid Structure and became familiar with his classification of the 28 possible base-pairs (bps) for A, G, U(T), and C involving at least two (cyclic) hydrogen bonds (see figure below).

Later on, I read from the 2nd edition of The RNA World book a list of 29 bps compiled by Burkard, Turner & Tinoco. While the one bp discrepancy (28 vs 29) has been in my mind for quite a long while, I had never paid much attention to the issue until recently while adding classifications of RNA bps (among many other functionalities) to 3DNA. A Google search did not help solve the puzzle, so I decided to dig it out by comparing the two lists.

The Burkard et al. list is titled Structures of Base Pairs Involving at Least Two Hydrogen Bonds and it mentions specifically Saenger’s list:

The structures of 29 possible base pairs that involve at least two hydrogen bonds are given in Figures 1–5 (for further descriptions, see Saenger, in Principles of nucleic acid structure, p. 120. Springer-Verlag [1984]).

However, in the five figures, Burkard et al. do not provide the corresponding Saenger numbers (I to XXVIII, 1—28) for the 28 common bps; thus it is not immediately obvious which one (i.e., the new addition by Burkard et al.) is missing from Saenger’s list. Under careful scrutiny, the absent bp turns out to be the “G•C N3-amino, amino-N3” pair in Figure 3: “Six possible flipped purine-pyrimidine mismatches.” One example of such G+C pair is found in the 5S ribosomal RNA (chain 9, G3022—C3026) of Haloarcula marismortui in PDB entry 1vq8.

The above figure shows clearly that the G+C bp does indeed have two canonical H-bonds between base atoms, and it is difficult to speculate how it escaped Saenger’s selection criteria. In the upcoming new 3DNA component, I am listing this bp as number XXIX (29), along with the other 28 base pairs.

Recently, I came across the so-called Calcutta U-U base pair (bp) [see figure below] while reading articles on C-H…O contacts in nucleic acid structures. Not familiar with this named pair before, I was curious to find out what it’s about. After some searching, I traced the origin of the Calcutta U-U bp to the following two papers published by Sundaralingam’s group during the middle 1990s:

• Wahl et al. (1996). The structure of r(UUCGCG) has a 5’-UU-overhang exhibiting Hoogsteen-like trans UU base pairs. Nat. Struct. Biol., 3(1), 24-31. In Note added in proof, the author wrote:

We have called the novel U•U base pair, where the Hoogsteen face of one of the pyrimidines is involved in a C5-H—O4 hydrogen bond, the ‘Calcutta Base Pair’, since it was announced at the International Seminar-cum-School on Macromolecular Crystallographic Data held in Calcutta, November 16-20, 1995.

• Wahl & Sundaralingam (1997). C-H…O hydrogen bonding in biology. Trends Biochem Sci., 22(3), 97-102. In this review article, the authors noted:

We recently discovered a novel U•U base pair, referred to as the Calcutta base pair, in the crystal structure of an RNA hexamer UUCGCG (Ref. 18). The two uracil bases form a conventional N(3)-H…O(4) and an unconventional C(5)-H…O(2) hydrogen bond (Fig. 3a). The C-H…O interaction is entirely ‘voluntary’ and not ‘forced’, underlining its importance in base mispairing.

3DNA has no problem to identify the Calcutta U-U bps (or any pair for that matter); an example is shown below based on the RNA hexamer UUCGCG structure (PDB entry: 1osu) solved by Sundaralingam and colleagues.

In the new 3DNA component I’ve been working on (and to be released soon), the Calcutta U-U pair is characterized as below:

1/A.U1 3/A.U2 [U-U] Calcutta 00-n/a tHW -MW
  anti C3'-endo 8.9 --- anti C3'-endo 30.3
  dcc=11.18  dnn=8.48  dmm=7.58  tor=-174.1
  H-bonds[2]: "O4(carbonyl)-N3(imino)[2.76]; C5-O4(carbonyl)[3.27]"

  Shear=-3.67   Stretch=-0.52     Stagger=-0.89
  Buckle=-1.41  Propeller=-16.03  Opening=-90.67

The Calcutta pair is explicitly named, along with other named base pairs (e.g., Watson-Crick [WC], Wobble, and Hoogsteen bps). It is classified as type tHW (trans with Hoogsteen/WC interacting edges), following the commonly used Leontis-Westhof nomenclature. It does not belong to any of the 28 bps (00-n/a) with at least two conventional H-bonds, as categorized by Saenger. In 3DNA, the Calcutta U-U pair is of M-N type, designated as -MW.

Among the well-known named base pairs, some are after the scientists who discovered them (e.g., WC and Hoogsteen bps), while others are based on chemical/geometrical features (e.g., Wobble and Sheared G-A bps), or a combination of both (e.g., reversed WC/Hoogsteen bps). The Calcutta U-U pair is unique in that it is named after a place in India:

Kolkata, or Calcutta, is the capital of the Indian state of West Bengal. … While the city’s name has always been pronounced Kolkata or Kolikata in Bengali, the anglicized form Calcutta was the official name until 2001, when it was changed to Kolkata in order to match Bengali pronunciation.

Prior to v2.1, 3DNA does not provide any direct support for the analysis of molecular dynamics (MD) simulations trajectories of nucleic acid structures. Nevertheless, over the years, I noticed some significant applications of 3DNA in the active MD field; see my blog post (December 6, 2009) titled 3DNA in the PCCP nucleic acid simulations themed issue. In January 2011, I released a set of two Ruby scripts specifically aimed to facilitate the analysis of MD simulations trajectories. Thereafter (as of 3DNA v2.1), I have significantly refined and expanded the Ruby scripts, and consolidated the functionality under one umbrella, x3dna_ensemble with multiple sub-commands (analyze, block_image, extract, and reorient). I believe x3dna_ensemble would make it straightforward to analyze ensembles (NMR or MD simulations trajectories) of nucleic acid structures.

Under this background, I am glad to read recently an article titled Structure, Stiffness and Substates of the Dickerson-Drew Dodecamer in J. Chem. Theory Comput. where 3DNA was used extensively. This work represents a re-visit of the classic Dickerson−Drew B-DNA dodecamer d-[CGCGAATTCGCG]2 using state-of-the-art MD simulations with different ionic conditions and solvation models, and compares the MD trajectories with modern crystallographic and NMR data. Among the author list (Tomas Drsata, Alberto Perez, Modesto Orozco, Alexandre Morozov, Jiri Sponer, and Filip Lankas) are some well-known figures in the MD field of nucleic acid structures.

Reading through the text, I am not sure if the newly available functionality of x3dna_ensemble was used. From the excerpts of the citations given below, however, it seems obvious that 3DNA is now well-accepted by the MD community.

Snapshots taken in 10 ps intervals were analyzed using the 3DNA program.43 From 3DNA outputs, time series of conformational parameters were extracted. These included the intra-base-pair coordinates (buckle, propeller, opening, shear, stretch, and stagger), inter-base-pair or step coordinates (tilt, roll, twist, shift, slide, and rise) as well as groove widths (based on P−P distances), backbone torsions, and sugar puckers.

Contrary to the original work of Lankas et al.,31 the intra-base-pair and step coordinates used here are those defined by 3DNA.43

Here, we apply this model together with the 3DNA definitions of the intra-base-pair and step coordinates.43

However, important differences remain, and non- negligible differences are in fact observed between individual experimental structures also in the central part of DD, even though the intra-base-pair and step coordinates are computed using the same coordinate definitions64 (we consistently use the 3DNA coordinates in this work).

Thanks to Google scholar, I recently become aware of the article by Mohammed AlQuraishi & Harley McAdams (2012) Three enhancements to the inference of statistical protein-DNA potentials” in Proteins: Structure, Function, and Bioinformatics. Reading through the text, I like it quite a bit. The abstract summarize the work well:

The energetics of protein-DNA interactions are often modeled using so-called statistical potentials, that is, energy models derived from the atomic structures of protein-DNA complexes. Many statistical protein-DNA potentials based on differing theoretical assumptions have been investigated, but little attention has been paid to the types of data and the parameter estimation process used in deriving the statistical potentials. We describe three enhancements to statistical potential inference that significantly improve the accuracy of predicted protein-DNA interactions: (i) incorporation of binding energy data of protein-DNA complexes, in conjunction with their X-ray crystal structures, (ii) use of spatially-aware parameter fitting, and (iii) use of ensemble-based parameter fitting. We apply these enhancements to three widely-used statistical potentials and use the resulting enhanced potentials in a structure-based prediction of the DNA binding sites of proteins. These enhancements are directly applicable to all statistical potentials used in protein-DNA modeling, and we show that they can improve the accuracy of predicted DNA binding sites by up to 21%.

I’m glad to find that the 3DNA mutate_bases program was used in deriving the statistical potentials of protein-DNA interactions:

The relative binding affinity of a protein to two different DNA sequences can be evaluated by computing the binding energy of the protein to those two sequences. This is done by mutating the DNA sequence in silico while keeping the protein fixed. We used the 3DNA software package for mutating DNA^23,24, which maintains the backbone atoms of the DNA molecule but replaces the basepair atoms in a way that is consistent with the backbone orientation of the DNA.

For each base position, in silicon structural mutants are generated using 3DNA^23,24 to mutate the basepair to include all four possibilities.

This is exactly one of the use cases I have in mind while creating the program:

Overall, mutate_bases has been designed to solve the in silica base mutation problem in a practical sense: robust and efficient, getting its job done and then out of the way. The program can have many possible applications: in addition to perform base-pair mutations in DNA-protein complexes, it should also prove handy in RNA modeling and in providing initial structures for QM/MM/MD energy calculations, and in DNA/RNA modeling studies.

With the recent refinement to allow for 3-letter nucleotide name in the standard base-reference frame file, mutate_bases now makes it exceedingly easy to mutate cytosine to 5-methylcytosine.

As more people get to know this 3DNA functionality, I am confident that mutate_bases will be more widely used.

Base-stacking interactions stabilize nucleic acid structures. Many ways exist to account for such interactions, including quantum chemical calculations (see for example the review by Sponer et al. [2008] on Nature and magnitude of aromatic stacking of nucleic acid bases.). In 3DNA, base-stacking interactions are assessed from planar projections of the ring and exocyclic atoms in consecutive bases or base pairs; the larger the overlap area, the stronger the stacking interactions, and vice versa.

Over the years, I’ve seen a few publications taking advantage of this 3DNA parameter. Here are two recent ones:

• Tamjar et al. (2012). Structural dynamics of double-helical RNAs composed of CUG/CUG- and CUG/CGG-repeats. J Biomol Struct Dyn. 30(5), 505-23.

To analyze the role of the sequence regularity for the double-helical structure, we calculated the overall overlapping of base pairs (stacking) at every step of the two duplexes of 20mer pG(CUG)6C and the duplex of 19mer pGG(CGG)3(CUG)2CC using the program 3DNA (Lu & Olson, 2003).

• Halder & Bhattacharyya (2012). Structural variations of single and tandem mismatches in RNA duplexes: a joint MD simulation and crystal structure database analysis. J Phys Chem B. 116(39), 11845-56.

Basepair overlap values are calculated by 3DNA software.35

Hopefully, more 3DNA users would notice this ‘little’ feature and make good use of it.

In the field of nucleic acid structures, especially in the ‘RNA world’, we often hear named base pairs (bp). Among those, the Watson-Crick (WC) A–U and G–C bps (see figure below) are by far the most common.

Reversed WC (rWC) base pairs

Closely related to the WC bps are the so-called reversed WC (rWC) bps, where the relative glycosidic bond are reversed; instead of being on the same side of the bases as in WC bps shown above, they are now on opposite sides in rWC bps as shown below. According to the Leontis-Westhof (LW) bp classification scheme, the rWC bps belong to trans WC/WC. Following Saenger’s numbering, the rWC A+U bp corresponds to XXI, and the rWC G+C bp XXII.

In the figures below, the name of each type of bp and its LW & Saenger designations (separated by ‘;’) are noted under the corresponding image. All images are generated with 3DNA; for easy comparison, each bp is oriented in the reference frame of the leading base.

Reversed WC A+U pair	Reversed WC G+C pair
trans WC/WC; XXI	trans WC/WC; XXII

Hoogsteen and reversed Hoogsteen base pairs

The next most famous one is the Hoogsteen A+U bp, which also has a reverse variant, i.e., the rHoogsteen A–U bp (see figure below). Now the major groove edge of A, termed the Hoogsteen edge by LW, is used for pairing with U.

Hoogsteen A+U pair	Reversed Hoogsteen A–U pair
cis Hoogsteen/WC; XXIII	trans Hoogsteen/WC; XXIV

The G–U Wobble base pair

First proposed by Crick in 1966 to account for the degeneracy in codon–anticodon pairing, the Wobble bp is an essential component (in addition to the WC bps) in forming double helical RNA secondary structures.

Wobble G–U pair

cis WC/WC; XXVIII

The sheared G–A base pair

Sheared G–A is a commonly found non-WC bp in both DNA and RNA structures. Noticeably, tandem sheared G–A bps introduce distinct stacking geometry. Here G uses its minor groove edge, termed the sugar edge by LW, to pair with the Hoogsteen edge of A.

Sheared G–A pair

trans Suger/Hoogsteen; XI

Dinucleotide platforms

Dinucleotide platforms are formed via side-by-side pairing of adjacent bases; the most common of which are GpU and ApA. Here the sugar (minor-groove) edge of the 5′ base interacts with the Hoogsteen (major-groove) edge of the 3′ base. Since there is only one base-base H-bond in dinucleotide platforms, no Saenger classification is available. In 3DNA output, the GpU dinucleotide platform is designated as G+U, and ApA as A+A.

GpU dinucleotide platform	ApA dinucleotide platform
cis Sugar/Hoogsteen; n/a	cis Sugar/Hoogsteen; n/a

Other named base pairs

There exist other named bps in RNA literature, e.g., G⋅A imino, A⋅C reverse Hoogsteen, G⋅U reverse Wobble etc. In the my experience, they are (much) less commonly used than the ones illustrated above.

Glycosidic bond “is a type of covalent bond that joins a carbohydrate (sugar) molecule to another group, which may or may not be another carbohydrate.” In nucleic acid structures, the other group is a nucleobase, and the predominated type is the N-glycosidic bond where the purine (A/G) N9 or pyrimidine (C/T/U) N1 atom connects to the C1′ atom of the five-membered (deoxy) ribose sugar ring. Another well-known type is the C-glycosidic bond in pseudouridine, the most common modified base in RNA structures where the C5 atom instead of N1 is linked to the C1′ atom of the sugar ring.

Recently, I performed a survey of all nucleic-acid-containing structures in the PDB/NDB database to see how many types of glycosidic bond are there. As always, I noticed some inconsistencies in the data: nucleotides with disconnected base/sugar, a base labeled as U but with pseudoU-type C-glycosidic bond. Shown below are a few unusual types of glycosidic bond in otherwise seemingly “normal” structures:

• The residue GN7 (number 28 on chain A) in PDB entry 1gn7 contains a N7-glycosylated guanine.

• The residue UPG (number 501 on chain A) in PDB entry 1y6f has sugar C1C (instead of C1′) atom connects to N1 of U.

• The residue XAE (number 11 on chain B) in PDB entry 2icz contains a benzo-homologous adenine.

• The residue F5H (number 206 on chain B) in PDB entry 3v06 has N1 of U connects to C2′ of a six-membered sugar ring.

The unusual glycosidic bond has implications in 3DNA calculated parameters, for example the chi torsion angle. Identifying such cases would help refine 3DNA to provide sensible parameters and to avoid possible misinterpretations.