Read Syn and Anti Base Pairs in Pymol

Ii citations to the DSSR-PyMOL NAR paper

Via Google Scholar, I noticed the post-obit two citations to the DSSR-enabled innovative schematics of 3D nucleic acid structures with PyMOL newspaper recently published in Nucleic Acids Enquiry (NAR):

• Miskiewicz, Joanna, Joanna Sarzynska, and Marta Szachniuk. How bioinformatics resources work with G4 RNAs." Briefings in Bioinformatics (2020).
• Caruso, Icaro Putinhon, et al. Dynamics of the Northward-terminal domain of SARS-CoV-2 nucleocapsid protein drives dsRNA melting in a counterintuitive tweezer-like mechanism. bioRxiv (2020).

Hither are the direct quotations on the DSSR-PyMOL paper from these 2 citations.

The Miskiewicz et al. paper, in Briefings in Bioinformatics (2020):

DSSR [38] processes the 3D structure of the RNA molecule and annotates its secondary structure. It is a role of the 3DNA suite [67] designed to work with the structures of nucleic acids. DSSR identifies, classifies and describes base pairs, multiplets and characteristic motifs of the secondary structure; helices, stems, hairpin loops, bulges, internal loops, junctions and others. It can also detect modules and tertiary structure patterns, including pseudoknots and kink-turns. The recent extension, DSSR-PyMOL [68], allows drawing cartoon-cake schemes of the 3D structure and responds to the need for simplified visualization of quadruplexes.

DSSR-PyMOL generated block schemes of both quadruplexes (Effigy 4A3 and B3).

Figure 4: Visualization of (A) 2RQJ and (B) 6GE1 structures generated by (1) ElTetrado, (two) RNApdbee and (three) DSSR-PyMOL.

The Caruso et al. paper in bioRxiv (2020):

Next, the structural model of the Northward-NTD:dsTRS (5'–UCUAAAC–three') complex was generated from the lowest-energy construction of the Due north-NTD:dsNS complex, derived from the cluster with the everyman HADDOCK score, by mutating the dsRNA sequence using w3DNA (29). Therefore, both complexes take identical geometries, varying only the dsRNA sequences. Structural conformation of the constructed model for N-NTD:dsTRS complex was displayed using the web application http://skmatic.x3dna.org for piece of cake creation of DSSR (Dissecting the Spatial Structure of RNA)-PyMOL schematics (32).

Effigy 1: Structural model of the Due north-NTD:dsRNA complex and its validation from molecular dynamics simulations. (A) Structural model of the N-NTD:dsTRS circuitous adamant by molecular docking calculations and mutation of dsNS nucleotide sequence. N-NTD is presented as regal cartoon and dsTRS is denoted as a ribbon model with base pairing as colored rectangles. The color of the rectangles corresponds to the nitrogenous base of the dsRNA sense strand, namely A: scarlet, C: yellow, U: cyan, and G: green. The large protruding β2-β3 loop is referred to as the finger. (B) …

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p(clean)=.

Figure 3: Analysis of the intramolecular (dsRNAs) and intermolecular (Due north-NTD:dsRNAs) hydrogen bonds. (A) … (B) … (C) Structural model of the Due north-NTD:dsTRS circuitous representative of the Doctor simulation for run 5. The poly peptide is shown in imperial drawing and dsTRS is denoted as a ribbon model with nitrogenous bases and base-pairing as colored squares and rectangles, respectively. The color of the squares corresponds to the type of nitrogenous base, namely A: ruddy, C: xanthous, U: cyan, and G: light-green, while the rectangles refer to the nitrogenous base of operations color of the dsRNA sense strand.

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It is actually a pleasure to see the DSSR-PyMOL paper being cited quickly after its publication. I am always curious to see how DSSR is cited in literature. Indeed, over the years following citations to DSSR has go an effective fashion for me to become informed of directly relevant references. Reading these citing articles motivates me to further amend DSSR.

LW base-pair classifications derived using DSSR

I recently come across the article FMN riboswitch aptamer symmetry facilitates conformational switching through mutually exclusive coaxial stacking configurations by Wilt et al. in the Journal of Structural Biology: Ten (JSBX). In the explanation to Figure S1, "Secondary construction map of the FMN riboswitch", the authors wrote:

Base-pairing is annotated using Leontis-Westhoff nomenclature (Leontis and Westhof, 2001), derived using 3DNA-DSSR (Lu and Olson, 2003), and the map was generated using VARNA (Darty et al., 2009).

It is a prissy surprise to see that 3DNA-DSSR is cited this way. The LW scheme is based on the iii edges of each base with potential for H-bonding interactions (Watson-Crick, Hoogsteen, and Carbohydrate), and the two orientations (cis or trans) of the interacting bases with respect to the glycosidic bonds. The combinations of edges and orientations (3 × ii × 2) "gives ascension to 12 basic geometric types with at least two H bonds connecting the bases" (Leontis and Westhof, 2001). This geometry-based approach captures salient features of pairing interactions and strikes a residue betwixt simplicity and expressiveness. The LW scheme is more widely applicable than the Saenger classification, and more intuitive to biologists. As a result, the LW classification has become a standard in RNA structural bioinformatics.

However, the RNA-centric LW nomenclature has inherent limitations. For instance, the Sugar edge explicitly includes the 2′-hydroxyl group, rendering it less applicable to DNA structures. Additionally, while the aromatic base of operations can be taken as a rigid trunk with 3 stock-still edges, the χ (chi) torsion angle characterizes the internal freedom between base and carbohydrate (anti vs. syn). When χ is in the relatively rare (simply not uncommon) syn conformation (especially abundant in G-quadruplexes), the Sugar edge, defined with reference to the common anti conformation, seems to no longer exist. The rich variety of RNA pairs extends beyond the 12 basic LW types. In that location are numerous pairs in RNA with only 1 H-bail or with bifurcated H-bonds, at boundary locations where the LW nomenclature does not strictly apply. Lemieux and Major (2002) were the first to extend the LW classification. We noted the importance of the out-of-plane 'backbone edge' formed past an RNA-specific H-bail between O2′(G) and OP2 (Lu et al., 2010). Finally, the RNA 3D Hub website, hosted by the Leontis-Zirbel team, lists pairing interactions that do not autumn into the 12 geometric types. For example, the page for 1msy contains pairing types ncSW, ntSH, and ntHH. Annotation that the terms nc (in ncSW) and nt (in ntSH/ntHH) do non have the normal meanings in literature; they stands for near cis and almost trans respectively.

As shown in the figure above, DSSR adopts a base-axial terminology for the 3 edges. In principle, M (Major groove) in the DSSR classification corresponds to the Hoogsteen/CH-edge (H) in the LW note, and the DSSR m (minor groove) to the LW Sugar-edge (S) if χ is in the anti conformation. In practice, direct DSSR/LW correspondences Thou/H and grand/S are assumed, regardless of anti/syn base conformation. Moreover, the cis/trans assignment is the same for both notations. Within the DSSR implementation, the LW and DSSR classifications are thus strictly parallel in terms of cis/trans orientation and interacting edges. The DSSR scheme has the actress ± for relative base orientations.

The LW classifications implemented in DSSR may differ from those listed in the RNA 3D Hub website or other resources. These discrepancies normally occur in boundary cases where the assignment of cis/trans and interaction edges can exist cryptic. For 'authentic' LW classification results, users should consult the original publication of Leontis and Westhof (2001) and use the RNAView (Yang et al., 2003) or FR3D (Sarver et al., 2008) tools instead of DSSR.

DSSR is cited in a Nature paper on RIC-seq for profiling RNA–RNA interactions

I recently read the newspaper RIC-seq for global in situ profiling of RNA–RNA spatial interactions published in Nature past the Yuanchao Xue team from the Chinese Academy of Sciences. The abstract is every bit below:

Highly structured RNA molecules usually interact with each other, and associate with various RNA-binding proteins, to regulate disquisitional biological processes. Notwithstanding, RNA structures and interactions in intact cells remain largely unknown. Here, by coupling proximity ligation mediated past RNA-binding proteins with deep sequencing, we report an RNA in situ conformation sequencing (RIC-seq) engineering science for the global profiling of intra- and intermolecular RNA–RNA interactions. This technique not just recapitulates known RNA secondary structures and 3rd interactions, but too facilitates the generation of three-dimensional (3D) interaction maps of RNA in human cells. Using these maps, nosotros identify noncoding RNA targets globally, and discern RNA topological domains and trans-interacting hubs. Nosotros reveal that the functional connectivity of enhancers and promoters can be assigned using their pairwise-interacting RNAs. Furthermore, we show that CCAT1-5L—a super-enhancer hub RNA—interacts with the RNA-binding protein hnRNPK, also as RNA derived from the MYC promoter and enhancer, to boost MYC transcription by modulating chromatin looping. Our study demonstrates the power and applicability of RIC-seq in discovering the 3D structures, interactions and regulatory roles of RNA.

The Methods function contains the following section, where DSSR is cited forth with several other software tools:

Structural analysis of 28S rRNA. The RIC-seq reads aligned to 45S pre-rRNA (NR_046235.three) were collected and used to construct the interaction matrix shown in Fig. 1h. A Knight–Ruiz normalization al- gorithm, widely used in the normalization of Hello-C contact matrices⁵¹, was practical to eliminate sequencing bias along rRNA. For building the physical interaction map of 28S rRNA, the cryo-EM model of homo 80S ribosome (RCSB Protein Data Bank (PDB) ID 4V6X) was down- loaded, and the spatial distances between every 5-nt bin in 28S rRNA were calculated using the hateful spatial coordinates of carbon atoms in each 5-nt bin. Watson–Crick and not-Watson–Crick base pairs were identified using the DSSR software⁵². The 3D structure of the ribosome was visualized past the PyMOL system (Educational version, https:// pymol.org/2/). For the missing structures in 28S rRNA, we combined intramolecular RNA–RNA interactions detected by RIC-seq with the RNAconstruction algorithm53 to deduce their 2d structures.

There are several other well-known programs for identifying and annotating RNA base of operations pairs, including RNAView, FR3D, and MC-Annotate. One may wonder why DSSR is used here. In addition to asking the authors, interested viewers could but test for themselves: effort the different tools on PDB entry 4V6X and encounter what happens.

It is worth mentioning that a new DSSR-related paper " DSSR-enabled innovative schematics of 3D nucleic acid structures with PyMOL" has recently been accepted by publication in Nucleic Acids Research. I will soon write another post on this topic when this newspaper is officially published online. To meet DSSR-PyMOL schematics in action, please visit http://skmatic.x3dna.org. Here is the abstract of the new DSSR-PyMOL article:

Sophisticated analysis and simplified visualization are crucial for agreement complicated structures of biomacromolecules. DSSR (Dissecting the Spatial Structure of RNA) is an integrated computational tool that has streamlined the analysis and annotation of 3D nucleic acid structures. The program creates schematic block representations in diverse styles that can be seamlessly integrated into PyMOL and complement its other pop visualization options. In addition to portraying private base blocks, DSSR can draw Watson-Crick pairs as long blocks and highlight the small-scale-groove edges. Notably, DSSR tin can dramatically simplify the delineation of G-quadruplexes by automatically detecting Grand-tetrads and treating them equally large square blocks. The DSSR-enabled innovative schematics with PyMOL are aesthetically pleasing and highly informative: the base identity, pairing geometry, stacking interactions, double-helical stems, and G-quadruplexes are immediately obvious. These features tin can exist accessed via four interfaces: the command-line interface, the DSSR plugin for PyMOL, the spider web application, and the web application programming interface. The supplemental PDF serves every bit a practical guide, with complete and reproducible examples. Thus, even beginners or occasional users tin get started quickly, especially via the spider web awarding at http://skmatic.x3dna.org.

DSSR is used for the analysis of CRISPR PbuCas13b-crRNA

Recently I read with great interest the article Loftier-Resolution Structure of Cas13b and Biochemical Characterization of RNA Targeting and Cleavage by Slaymaker et al., published in Cell Reports (2019, 26, 3741–3751). This ane.65-Å construction (PDB id: 6dtd) "provides a mechanistic model for Cas13b target RNA recognition and identifies features responsible for target and cleavage specificity."

I am pleased to see that DSSR is listed in the "KEY Resource TABLE" nether the category "Software and Algorithms", and mentioned in the "Structure Assay" section:

RNA construction was analyzed using DSSR (Lu et al., 2015). Protein conservation mapping to the structure was washed using the Consurf server (Ashkenazy et al., 2016). Protein secondary construction was analyzed using the PDBSUM webserver (de Beer et al., 2014) (Figure S1E). APBS as function of the PyMOL visualization plan was used to calculate electrostatics (Jurrus et al., 2018). Structure validation statistics were generated with MolProbity (Chen et al., 2010)

In the main text, the authors cited DSSR for the detection of a base multiplet. Running DSSR on PDB entry 6dtd, I found two base triplets, equally shown below:

In the figure higher up, each of the two adenines is interacting with a Thousand–C pair in the small-groove edge (g) of the pair: A30 (left) is using its Watson-Crick edge (Westward), whilst A23 (right) is employing its major-groove edge (Thousand). Thus they do not belong to the canonical A-minor motifs (types I or 2) where the minor-groove edge of A interacts with the small-groove edge of a WC pair. In DSSR, they are classified equally type=X, a full general category of noncanonical A-pocket-size motifs.

DSSR in the visualization of DNA/RNA structures

Past following DSSR citations, I recently came beyond the article Interactive Visualization of RNA and DNA Structures past Lindow et al. The paper introduced a DNA/RNA visualization tool that integrates 1D sequence, 2D secondary structure in linear and graph representations, and 3D backbone ribbons and base ladders, all in ane package. Notably, the 3D visualization was tailored for Deoxyribonucleic acid/RNA structures and achieved quite impressive results. A nice feature of the second graph representation is the handling of multiple chains.

Reading through the chief text and the supplementary material, I was surprised to run across the and so many locations where DSSR was mentioned, especially the following:

Our approach detects all standard and many modified nucleotides besides equally the most mutual base pairs. Further special cases could be easily added. Yet, the system we adult should not be seen as a replacement for well established tools like DSSR. Rather, it shows what can be achieved with modernistic techniques in terms of both ciphering and rendering.

Overall, DSSR is an analysis/annotation tool that is supposedly agnostic to visualization programs. Information technology derives a huge number of structural features that are unlikely to exist matched elsewhere. I collaborated with Bob Hanson and then that Jmol can directly take advantage of what DSSR has to offer, not just for the visualization of (modified) nucleotides and some common base pairs, but also the interactive option of loops, pseudoknots, coaxial stacks, and various motifs. In particular, the SQL-like selection syntax Bob developed is actually flexible and extremely powerful. I collaborated with Thomas Holder so that PyMOL can gain Deoxyribonucleic acid/RNA domain knowledge. The resultant dssr_block PyMOL plugin is quite useful for creating base of operations/base-pair cake images with many revealing features, especially for small to medium-sized DNA/RNA structures. Information technology is obvious to me that PyMOL (or any other molecular visualization tool) would benefit greatly from SQL-similar selections of DSSR-derived features of nucleic acid structures, but as Jmol does.

In the Lindow et al. paper, some of the references to DSSR are technical in nature. Here, I'd like to respond and clarify each of them. Since DSSR is beingness actively developed and supported, I e'er welcome any feedback on the 3DNA Forum. Following and responding to literature represents another way that I strive to make DSSR a amend tool to serve the community.

Built on their experience from 3DNA, Lu et al. developed DSSR [27], a very powerful tool to clarify RNA structures that uses Jmol for the 3D visualization. Recently, Hanson and Lu described this integration [x], which is based on a JSON-interface that directly couples DSSR and the 3D visualization of Jmol. This is a dandy improvement, but still missing is the integration of 2nd secondary structure visualizations and brushing & linking techniques to enable simple selection with and exploration of the 3D molecular structure. 1 contribution of this paper is to show how a full linking betwixt 3D and 2D visualizations tin exist done and what benefits arise from such a tight coupling (see Sects. 8 and nine).

This is a valid betoken, and the authors did a good job. Actually, 1 of the reviewers of our DSSR-Jmol paper brought up this indicate, and nosotros acknowledged the limitation. While passing DSSR-derived secondary structural features (in DBN or .ct format) to a 2D visualization tool is straightforward, the connection would not be as smooth as nosotros'd like it to be.

For this purpose, other approaches rely on the unique naming and ordering of the atoms [27], for example, N1, C2, N3, C4, C5, C6 etc. We found that this information is not ever reliable.

The naming of the purines and pyrimidines follows the IUPAC standard and is a prerequisite of Deoxyribonucleic acid/RNA structures in the PDB. In my experience, I have never found a single case where such information is non reliable. Encounter below for abasic sites in PDB id 3BWP, and 4SU (4-thiouridine) in PDB id 5AFI.

We compared these results with the latest version of DSSR [27]. Our arroyo is able to correctly discover all regular nucleotides and most of the modified and undefined nucleotides. In the following, nosotros describe the pocket-sized differences.

Information technology is not clear what was the "latest" version of DSSR that was actually used in the paper. Annotation that DSSR has version info as in v1.8.three-2018oct29. I deliberately put the release date along with the version number.

For dataset 4RGE, we detected 3 modified uracil nucleotides that were non labeled every bit modified by DSSR. These nucleotides have a Deoxyribonucleic acid backbone instead of an RNA 1.

DSSR takes A, C, G, T, U as standard nucleotides, even if T is in RNA or U is in DNA. So this upshot is expected.

Dataset 3BWP contains 7 nucleotides that only consist of the courage part without bases. While our approach marks these as undefined, in DSSR they are non detected at all.

The vii nucleotides on 3BWP are abasic sites, i.e., without base atoms (N1, C2, N3, C4, C5, C6 etc), and then they do not possess base reference frames. From early on, DSSR had the --abasic selection for such cases. Every bit of v1.7.3-2017dec26, DSSR directly incorporated abasic sites into the analysis. So thereafter they are detected by DSSR, by default.

Furthermore, in 5AFI we mark 3 nucleotides as undefined, while these are detected as a modified uracil by DSSR. This is due to the base containing sulfur instead of oxygen, and then they possibly are sulfur analogs of uracil.

Presumably, the authors are referring to 4SU, 4-thiouridine, conspicuously a modified nucleotide occurring in 137 PDB entries (as of 2018oct28). DSSR detects three cases in 5AFI, as shown hither: 4SU-u 3 5.4SU8,w.4SU8,y.4SU8

We likewise compared the results of our base of operations pair detection (Suppl. Tab. i). We adamant all Watson-Crick, Hoogsteen, and Wobble pairs, and the opposite versions of the first two. For nearly of the datasets, our method returned the same results equally DSSR. In item, both approaches never created contradicting results, which ways all common base pairs had identical pair type. In general, our geometrical approach generates slightly more base pairs compared to DSSR. However, when investigating both, the base pairs determined past DSSR just not by our approach and vice versa, we plant that nigh of these pairs are border-line cases, where the determination was made depending on the threshold of the geometrical heuristic. Just in a few cases, the differences were not clear for both approaches, see Suppl. Fig. 3.

In Suppl. Fig. three,

… Withal, the hydrogen bonds for classical M-U Wobble pairs seem to exist quite unrealistic for the bottom left pair. Either this is a limitation of DSSR or it is some kind of specific Wobble pair with other hydrogen bonds than the depicted ones that our arroyo does non observe.

I echo the bespeak that border-line cases could crusade discrepancies between different methods. However, things can get easily clarified in concrete examples. Unfortunately, the authors did not specify the cases used in their Suppl. Fig. 3. I finally figured out the DSSR-assigned G-U Wobble pair in PDB id 1S72, U2586—G2592. As shown in the figure below, DSSR detects two H-bonds (dashed pink lines), "N3(imino)*N2(amino)[iii.05],O4(carbonyl)-N1(imino)[ii.77]". Note that one of the H-bonds is betwixt two donors, N3(U) and N2(K), thus the * symbol. The H-bonds are by no ways as those in "classical G-U Wobble pairs". Notwithstanding, the pair is conspicuously Wobble-like, and that's why information technology was assigned "Wobble". To avoid such confusions, I've revised DSSR to tighten the criteria of G-U Wobble pair. As of v1.8.3-2018oct29, this pair is called "~Wobble".

Nevertheless, our evaluation (Sect. 8.one) shows that with the proposed approaches in terms of quality we get very similar results to the ones obtained past tools similar DSSR. In terms of speed, DSSR needs much longer run times. For case, for 4U4O, DSSR needed ~15 min for the secondary and tertiary structure assay [27], while our algorithm only needs ~0.ii s (see Tab. ane).

Equally noted above, DSSR provides far more structural features than simply the identifications of nucleotides and several common base of operations pairs. Even for the identified base pairs, DSSR provides many more annotations and structural parameters than just the named pairs picked past the authors. Not surprisingly, DSSR is slower than the dedicated method for a specific purpose.

As of DSSR v1.8.iii-2018oct29, I've added the --pair-merely option that just outputs a complete listing of base-pairing information and and then stops. Some sample runs are equally below:

x3dna-dssr -i=1ehz.pdb --pair-simply x3dna-dssr -i=1ehz.pdb --pair-only --more x3dna-dssr -i=1ehz.pdb --pair-merely --json x3dna-dssr -i=1ehz.pdb --pair-simply --json | jq '.pairs[] | select(.name=="WC")' x3dna-dssr -i=1ehz.pdb --pair-only --more than --json | jq . x3dna-dssr -i=4u4o.cif --pair-only -o=4u4o-pairs.txt          

Compared to the default settings, DSSR runs ~10 times faster when the --pair-only option is set; 36s vs 5m48s for 4U4O on my MacBook Pro 2017 (2.nine GHz Intel Core i7). Note the timing here is a complete run of the DSSR programme (as shown above), from reading the mmCIF file to writing out all the derived features. In my hand, simply reading and parsing the 85MB 4U4O.cif would take ~5s. As a reference, only loading 4U4O.cif into PyMOL takes >6s. I'm thus more than than surprised (and remain to be convinced) by the claim that their new algorithm "but needs" ~0.2s "for the secondary and tertiary structure assay" of 4U4O.

DSSR-enabled drawing block images for G-quadruplex

Via Google Scholar, I noticed the recent publication in Nucleic Acids Research by Meier et al. titled Structure and hydrodynamics of a Dna K-quadruplex with a cytosine bulge. Reading through the article, I am pleased to see the section "Nucleic acid geometry and visualization" nether MATERIALS AND METHODS:

We used the program DSSR (53) of the 3DNA suite (54) to analyse the nucleic acid backbone and the base of operations pair geometry from the 3D structures. We reported the 'simple' base of operations-pair parameters for buckle, propeller twist and stagger which are more intuitive for non-canonical base-pairs than the classic base-pair parameters as explained in the program manual and the 3DNA website (http://x3dna.org/highlights/details-on-the-simple-base-pair-parameters, http://x3dna.org/articles/simple-parameters-for-non-Watson-Crick-base-pairs). We wrote an R (55) script that automatically creates a backbone angle plot from the output of the DSSR program. The script tin can be downloaded from the 3DNA forum at http://x3dna.org. The nucleic acid was visualized in PyMOL and the dssr block plugin (The PyMOL Molecular Graphics Sys- tem, Version 2.0, Schro ̈dinger, LLC, https://pymol.org/). …

This is the first time (I'm aware of) that the 'simple' base-pair parameters introduced in 3DNA v2.3 is cited in a peer-reviewed journal article. I'm too glad to know that the web log posts on the 3DNA homepage are read, and even referenced in a publication — which surely will prompt me to write more. This is also the outset time that the dssr_block PyMOL plugin is cited. I must say that Figures 1, 5, and six from the newspaper look gorgeous. Amidst other things, the G-tetrads and the surrounding base of operations identity are immediately obvious using the simple color lawmaking: A, red; T, blueish, C, yellowish, and G, greenish. Run across Fig. i below.

In the section on "DATA AVAILABILITY", the authors farther noted:

Our R (55) script that automatically creates courage angle plots from the output of the DSSR plan can be downloaded from the 3DNA forum at http://x3dna.org.

I communicated with Markus Meier (the lead author) on the 3DNA Forum, on the thread DSSR: Analyzing NMR structures – overwritten output files. Checking the thread correct at present, I found that the R script (backbone_torsions_plot-1.0.tar.bz2) has been downloaded 263 times. I appreciate Markus'south effort in contributing the R script with a working example to the DSSR user customs. It has always been my hope that more DSSR users would share their scripts and examples via the 3DNA Forum.

Equally a side notation, I met Markus in Los Angles at the 60th Annual Coming together. Information technology was a overnice feel chatting with him, and had a luncheon together. We've kept in touch following the meeting.

SNAP is cited in the DNAproDB paper

I recently came beyond an article titled DNAproDB: an interactive tool for structural assay of Dna-poly peptide complexes by Sagendorf et al. in Nucleic Acids Research (NAR). Notably, the DNAproDB tool allows users to search the underlying database by combining features of the Deoxyribonucleic acid, poly peptide, or Dna-protein interactions at the interface. Compared to the well-established NUCPLOT tool which generates only 'static' schematic diagrams of protein-nucleic acid interactions, DNAproDB is interactive and more user friendly, with many new features.

Information technology was a pleasant surprise to notice that SNAP was cited in the DNAproDB NAR paper, every bit follows:

Nucleotide-residue interaction geometry (stacking, pseudo-pairing or other) is determined using SNAP, a new component of the 3DNA program suite (35). SNAP also serves as a autumn-back for calculating hydrogen bonds if HBPLUS cannot procedure the file.

I am glad that SNAP has also been used for identifying H-bonds where HBPLUS fails. The H-bonding detection algorithm, initially implemented in 3DNA (v2.3 and before) and refined in DSSR/SNAP, was originally intended to brand the 3DNA software fully independent of third-party tools. I did not expect this feature could one day compete with dedicated H-bond finding tools, such as HBPLUS.

Past the way, 3DNA is also cited in the DNAproDB NAR paper, as beneath:

DNA base pairing, shape parameters and conformation are derived from the 3DNA plan suite (29) with a 10.0 Å cut-off for helix breaking.

DSSR in the structural analysis of an E.coli initiator tRNAfMet A1-U72 variant

While browsing the latest issue (May 2017) of the RNA periodical, I came across the paper titled The structure of an E. coli tRNA_f ^Met A1–U72 variant shows an unusual conformation of the A1–U72 base pair by Monestier et al.. Reading through the text, I am pleasantly surprised by the two references to DSSR as shown below:

An assay using DSSR (Lu et al. 2015) identifies all the secondary structure elements characteristic of the classical cloverleaf secondary structure also as usual tertiary interactions that stabilize the L-shaped tertiary fold of the molecule.

As a outcome, the opening parameter (Lu et al. 2015) of the A1–U72 base pair becomes unusually high (153.42°). The NH2 group of A1 points toward the small-scale groove of the acceptor helix. An interaction betwixt the N1 of A1 and the O2 of U72 (d = 3.0 Å) is observed which requires protonation of the N1 atom of A1.

The PDB id for the deposited structure is 5l4o. Running DSSR on this structure is straightforward: x3dna-dssr -i=5l4o.pdb --more. As with the archetype yeast phenylalanine tRNA (PDB id: 1ehz), DSSR identifies two helices, three hairpin loops, one [2,ane,5,0] four-way junction loop, among other features.

With regard to the unusual A1-U72 pair highlighted in the title of the paper, DSSR provides the post-obit data. Note the * in the unconventional N1*O2 H-bond.

            1 A.A1           A.U72          A+U --          n/a       tWW  tW+W        [-fourteen.4(...) ~C3'-endo lambda=32.nine] [-172.4(anti) ~C3'-endo lambda=65.0]        d(C1'-C1')=10.80 d(N1-N9)=9.19 d(C6-C8)=10.68 tor(C1'-N1-N9-C1')=173.vi        H-bonds[1]: "N1*O2(carbonyl)[ii.99]"        interBase-angle=6  Simple-bpParams: Shear=3.53 Stretch=1.71 Buckle=2.0 Propeller=-half dozen.0        bp-pars: [-0.32   3.91    0.01    6.32    -0.26   153.56]          

This citation is yet another example of DSSR's adoption past experimental biologists. I can only expect to see more such blazon of DSSR usages in the coming years.

DSSR is used in the URS DataBase

Recently, I came across the article URS DataBase: universe of RNA structures and their motifs past Baulin et al. in Database, an online periodical of biological databases and curation. I am glad to meet that DSSR is used in the URSDB, as quoted below.

In the "Input data" subsection of "Materials and methods":

RNA-containing structures were extracted from the PDB in mmCIF format; each file was divided into models. The base pairs (both canonical and non-canonical) and dinucleotide steps were annotated using the DSSR program from 3DNA toolkit (26). We also exploited detailed information provided by DSSR on given elements such as geometric parameters, types according to different classifications and various details on base of operations conformations.

Moreover, nether "Future development", the authors said:

We plan to perform a comparative analysis of programs that annotate base pairs in RNA-containing PDB files. We volition consider the four virtually popular programs, FR3D (35), MC-Comment (36), RNAView (37) and DSSR (26). According to the analysis the annotation of the base pairs will be refined. In addition, we program to include in the database annotations of base-phosphate, base-ribose and base of operations stacking contacts and to implement search of such information.

It is gratifying to run across DSSR listed as one of "the 4 near popular programs" for annotating RNA base pairs. It'd also be interesting to see how DSSR compares with FR3D, MC-Annotate, and RNAView from the user's perspective.

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Source: https://x3dna.org/citations/