As documented in the Overview PDF, DSSR can be easily incorporated into other structural bioinformatics pipelines. Working with Robert Hanson and Thomas Holder respectively, I initiated the integrations of DSSR into Jmol and PyMOL, two of the most popular molecular viewers. The DSSR-Jmol and DSSR-PyMOL integrations lead to unparalleled search capabilities and innovative visualization styles of 3D nucleic acid structures. They also exemplify the critical roles that a domain-specific analysis engine may play in general-purpose molecular visualization tools.

On January 27, 2016, I wrote the blogpost Integrating DSSR into Jmol and PyMOL. Four years later, these integrations have led to two peer-reviewed articles, both published in Nucleic Acids Research (NAR). This blogpost (dated 2020-09-15) highlights key features in each case and reflects on my experience in these two exciting collaborations.

The DSSR-Jmol integration

Hanson RM and Lu XJ (2017). DSSR-enhanced visualization of nucleic acid structures in Jmol. The DSSR-Jmol integration excels in its SQL-like, flexible searching capability of structural features, as demonstrated at the website http://jmol.x3dna.org. This work fills a gap in RNA structural bioinformatics by enabling deep analyses and SQL-like queries of RNA structural characteristics, interactively. Here are some simple examples:

SELECT WITHIN(dssr, "nts WHERE is_modified = true") # modified nucleotides
SELECT pairs # all pairs
Select WITHIN(dssr, "pairs WHERE name = 'Hoogsteen'") # Hoogsteen pairs
SELECT WITHIN(dssr, "pairs WHERE name != 'WC'") # non-Watson-Crick pairs
SELECT junctions # all junctions loops
select within(dssr, "junctions WHERE num_stems = 4") # four-way junction loops

In a recently email communication, Bob wrote:

How are you doing? I’m smiling, because I am remembering our incredible, animated discussions and how fun it was to work together with you on Jmol and DSSR.

The DSSR-PyMOL integration

Lu XJ (2020). DSSR-enabled innovative schematics of 3D nucleic acid structures with PyMOL. The DSSR-PyMOL integration brings unprecedented visual clarity to 3D nucleic acid structures, especially for G-quadruplexes. The four interfaces cover virtually all conceivable use cases. The easiest way to get started and quickly benefit from this work is via the web application at http://skmatic.x3dna.org.

I approached Thomas to write the DSSR-PyMOL manuscript together, in a similar way as the DSSR-Jmol paper. He wrote back, saying “I’m not interesting in being co-author of the paper”, adding:

But, if there is anything I can help you with, like revising the `dssr_block.py` script, or proof-reading the PyMOL related parts of the manuscript, I’ll be happy to do so.

Indeed, Thomas helped in several aspects of the DSSR-PyMOL project, as acknowledged in the paper:

I appreciate Thomas Holder (PyMOL Principal Developer, Schrödinger, Inc.) for writing the DSSR plugin for PyMOL, and for providing insightful comments on the manuscript and the web application interface.

Enhanced vs Innovative

Some viewers may noticed the difference in titles of the two NAR papers: “DSSR-enhanced visualization of nucleic acid structures in Jmol” vs. “DSSR-enabled innovative schematics of 3D nucleic acid structures with PyMOL”. As a matter of fact, the initial title of the DSSR-PyMOL paper was DSSR-enhanced visualization of nucleic acid structures in PyMOL, as shown in the December 02, 2019 announcement post on the 3DNA Forum.

In an era where reproducibility of “scientific” publications has become an issue and “break-throughs” are often broken or hardly held, I hesitate to use phrases such as “innovative”, “novel”, “paradigm shift” etc. Instead, I often use the modest words “refinement”, “enhance”, “improved”, “revised” etc, and try to deliver more than claimed. However, reviewers may take solid work but modest writing as “incremental” or “unexciting”. Before submitting the DSSR-PyMOL paper, I changed the title to DSSR-enabled innovative schematics of 3D nucleic acid structures with PyMOL. Does it mean that the DSSR-PyMOL integration is more innovative than the DSSR-Jmol case? Not necessarily. I do have a paper with “innovative” in its title.

Recently, while reading the Miskiewicz et al. review article How bioinformatics resources work with G4 RNAs, I noticed the term DSSR-G4DB under the category Databases with G4-related data. It refers to the website http://G4.x3dna.org (or g4.x3dna.org) that has been there since 2017 and weekly updated with new G-quadruplexes from the PDB. The DSSR-G4 resource, DSSR-Enabled Automatic Identification and Annotation of G-quadruplexes in the PDB, has already been cited several times in literature. However, I have not written up a paper on it yet, and thus have never thought carefully on a name for the resource. The term DSSR-G4DB sounds good to me, and I may well use it in the future.

Given below are the relevant quotations on DSSR and the DSSR-G4DB resource in the Miskiewicz et al. review article and my notes. The underlined headings (e.g., “Conclusion”) are those of the Miskiewicz et al. review article.

Methods: Databases with G4-related data

Currently, there exist 16 databases, which store information concerning quadruplexes. They fall into three categories: databases that collect primary or tertiary structures with experimentally verified G4s (DSSR-G4DB, G4IPDB, G4LDB, G4RNA, Lit392 and Lit638); databases storing data from high-throughput sequencing with mapped quadruplexes (GSE63874, GSE77282, GSE110582 and GSE129281); and databases of sequences with G4s identified in silico (Greglist, GRSDB2, G4-virus, Non-B DB v2.0, Plant-GQ and QuadBase2)

DSSR-G4DB [38] contains quadruplex nucleic acid structures found by DSSR in the Protein Data Bank [30], currently 354 entries. The data are annotated. Users can find information about G-tetrads, G4 helices and G4-stems and visualize the 3D models of G4 structures. Availability: webserver (http://g4.x3 dna.org). Recent update: 5 June 2020.

Note: DSSR-G4DB is updated weekly. The latest update is on 2020-09-09, with 362 G-quadruplexes auto-curated with DSSR from the PDB.

Methods: Tools that analyze and visualize 2D and 3D structure

Currently, four tools can analyze and visualize G4 structures. DSSR [38] … ElTetrado [31] … RNApdbee [66, 69] … 3D-NuS [65]

DSSR [38] processes the 3D structure of the RNA molecule and annotates its secondary structure. It is a part 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, includ- ing pseudoknots and kink-turns. The recent extension, DSSR- PyMOL [68], allows drawing cartoon-block schemes of the 3D structure and responds to the need for simplified visualization of quadruplexes. Input data formats: PDB, mmCIF and PDB ID. Availability: standalone program, web application (http://dssr.x3 dna.org/, http://skmatic.x3dna.org/).

Note: The other three tools all depend on or make use of DSSR and 3DNA:

• ElTetrado “ElTetrado depends on DSSR (Lu, Bussemaker and Olson, 2015) in terms of detection of base pairing and stacking.”
• RNApdbee uses 3DNA/DSSR as the default to identify base pairs.
• 3D-NuS employs 3DNA for structural analysis and model building.
  “These filtrated structures (225 DNA and 166 RNA structures) have been used to derive the local base pair step and base pair parameters (Table S2 for DNA and Table S3 for RNA) using 3DNA software package [35] and are stored in the server for 3D-NuS modeling.”
  “Soon after the user submits input for sequence-specific modeling, the server fetches the appropriate base pair step and base pair parameters from the database and creates a 3DNA style input file. Subsequently, the template model is built using the rebuild module of 3DNA software package and subjected to energy optimization using X-plor [56] to remove steric hindrance, specifically in the mismatch- containing duplexes (Fig. 1).”

Results: Computational experiments with structure-based tools

DSSR and ElTetrado identified quadruplexes in the input PDB files. Both programs focused on structural aspects of the input molecule, explicitly informing about quadruplexes and tetrads within the structure. DSSR provided an extensive analysis of 3D structures and output the data about G-tetrads, G-helices and G4-stems. It computed planarity for each G-tetrad and gave the sections area, rise and twist parameters for G4-helix and G4-stems. The program automatically assigned loop topologies according to the predefined types (P—parallel, D—diagonal and L—lateral) and their orientation (+/−). DSSR-PyMOL generated block schemes of both quadruplexes (Figure 4A3 and B3). ElTetrado also calculated planarity, rise and twist parameters and identified strand directions for both quadruplexes. It classified the quadruplexes and their component tetrads to ONZ classes. Finally, it generated the arc diagram (Figure 4A1 and B1) and two-line dot-bracket encoding of every quadruplex.

Note: DSSR contains an undocumented option --G4. With the ONZ variant, i.e., --g4=onz (case does not matter), DSSR also outputs the ONZ classification of G-tetrads from the same chain.

Conclusion

DSSR comprehensively examines the G4 structure, determines a variety of its parameters and provides the schematic 3D view.

It is worth noting that DSSR has been categorized under “Databases with G4-related data” and “Tools that analyze and visualize 2D and 3D structure” of the Methods section. It is not a tool that predicts G4 location in the sequence. There are 14 tools listed in “Table 2. Selected features of PQS prediction tools”, including G4Hunter and QGRS Mapper etc.

Via Google Scholar, I noticed the following two citations to the DSSR-enabled innovative schematics of 3D nucleic acid structures with PyMOL paper recently published in Nucleic Acids Research (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 N-terminal domain of SARS-CoV-2 nucleocapsid protein drives dsRNA melting in a counterintuitive tweezer-like mechanism. bioRxiv (2020).

Here are the direct quotations on the DSSR-PyMOL paper from these two 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 part 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-block schemes of the 3D structure and responds to the need for simplified visualization of quadruplexes.

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

Figure 4: Visualization of (A) 2RQJ and (B) 6GE1 structures generated by (1) ElTetrado, (2) RNApdbee and (3) DSSR-PyMOL.

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

Next, the structural model of the N-NTD:dsTRS (5’–UCUAAAC–3’) complex was generated from the lowest-energy structure of the N-NTD:dsNS complex, derived from the cluster with the lowest HADDOCK score, by mutating the dsRNA sequence using w3DNA (29). Therefore, both complexes have 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 easy creation of DSSR (Dissecting the Spatial Structure of RNA)-PyMOL schematics (32).

Figure 1: Structural model of the N-NTD:dsRNA complex and its validation from molecular dynamics simulations. (A) Structural model of the N-NTD:dsTRS complex determined by molecular docking calculations and mutation of dsNS nucleotide sequence. N-NTD is presented as purple 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: red, C: yellow, U: cyan, and G: green. The large protruding β2-β3 loop is referred to as the finger. (B) …

p(clean)=.

Figure 3: Analysis of the intramolecular (dsRNAs) and intermolecular (N-NTD:dsRNAs) hydrogen bonds. (A) … (B) … (C) Structural model of the N-NTD:dsTRS complex representative of the MD simulation for run 5. The protein is shown in purple cartoon 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: red, C: yellow, U: cyan, and G: green, while the rectangles refer to the nitrogenous base color of the dsRNA sense strand.

It is really 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 become an effective way for me to become informed of directly relevant references. Reading these citing articles motivates me to further improve DSSR.

Recently, while visiting the NAR website on DSSR-enabled innovative schematics of 3D nucleic acid structures with PyMOL, I noticed a big red circle ① near “View Metrics”. I was quite curious to see what it meant. After a few clicks, I was delighted to read the following recommendation in Faculty Opinions by Quentin Vicens:

I really enjoyed “playing” with the revised and expanded version of Dissecting the Spatial Structure of RNA (DSSR) described by Xiang-Jun Lu in this July issue of NAR. The software is known to generate ‘block view’ representations of nucleic acids that make many parameters more immediately visible, such as base composition, stacking, and groove depth. This new version includes Watson-Crick pairs shown as single rectangles, and G quadruplexes as large squares, making such regions more quickly distinguishable from other regions within an overall tertiary structure. I was amazed at how simple and effective the web interface was, and I liked the possibility to download a PyMOL session to look at molecules under different angles. If need be, blocks can be further edited in PyMOL using the provided plugin (see on page 35). I highly recommend it!

The DSSR-PyMOL schematics paper/website has been rated “Very Good”, and classified as “Good for Teaching”. See Vicens Q: Faculty Opinions Recommendation of [Lu XJ, Nucleic Acids Res 2020 48(13):e74]. In Faculty Opinions, 14 Aug 2020; 10.3410/f.738001682.793577327.

DSSR 2.0 is out. It integrates an unprecedented set of features into one computational tool, including analysis/annotation, schematic visualization, and model building of 3D nucleic acid structures. DSSR 2.0 supersedes 3DNA 2.4, which is still maintained but no additional features other than bug fixes are scheduled. See the DSSR 2.0 overview PDF.

DSSR delivers a great user experience by solving problems and saving time. Considering its usability, interoperability, features, and support, DSSR easily stands out among `competitors’. It exemplifies a `solid software product’. I strive to make DSSR a pragmatic tool that the structural bioinformatics community can count on.

DSSR 2.0 is licensed by Columbia University. The software remains free for academic users, with the basic user manual. The professional user manual (over 230 pages, including 7 appendices) is available for paid academic users or commercial users only. Licensing revenue helps ensure the long-term sustainability of the DSSR project.

Additionally, the paper “DSSR-enabled innovative schematics of 3D nucleic acid structures with PyMOL” has recently been published in Nucleic Acids Research, 48(13):e74. Check the web interface.

The DSSR-PyMOL paper/website has been rated “very good” and classified as “Good for Teaching”. See Vicens Q: Faculty Opinions Recommendation of [Lu XJ, Nucleic Acids Res 2020 48(13):e74]. In Faculty Opinions, 14 Aug 2020; 10.3410/f.738001682.793577327

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: X (JSBX). In the caption to Figure S1, “Secondary structure 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 nice surprise to see that 3DNA-DSSR is cited this way. The LW scheme is based on the three edges of each base with potential for H-bonding interactions (Watson-Crick, Hoogsteen, and Sugar), and the two orientations (cis or trans) of the interacting bases with respect to the glycosidic bonds. The combinations of edges and orientations (3 × 2 × 2) “gives rise 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 balance between 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 classification has inherent limitations. For example, the Sugar edge explicitly includes the 2′-hydroxyl group, rendering it less applicable to DNA structures. Additionally, while the aromatic base can be taken as a rigid body with three fixed edges, the χ (chi) torsion angle characterizes the internal freedom between base and sugar (anti vs. syn). When χ is in the relatively rare (but 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. There are numerous pairs in RNA with only one H-bond or with bifurcated H-bonds, at boundary locations where the LW classification 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 by an RNA-specific H-bond 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 fall into the 12 geometric types. For example, the page for 1msy contains pairing types ncSW, ntSH, and ntHH. Note that the terms nc (in ncSW) and nt (in ntSH/ntHH) do not have the normal meanings in literature; they stands for near cis and near trans respectively.

As shown in the figure above, DSSR adopts a base-centric terminology for the three edges. In principle, M (Major groove) in the DSSR classification corresponds to the Hoogsteen/CH-edge (H) in the LW notation, 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 M/H and m/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 extra ± 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 be ambiguous. 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.

Recently I performed a survey of citations to thirteen 3DNA-related publications using Web of Science from Clarivate Analytics. The time range is from 2015 to 2020 (June 30), for a total of five-and-half years. The 1,050 citations span 223 scientific journals, covering a broad range of research fields such as biology, medicine, chemistry, physics, materials etc. Not surprisingly, the citing journals include Cell, Nature and sub-journals, Science, and PNAS.

Each of following six papers has been cited over 50 times, as detailed below. Adding the six numbers together, there are 962 citations, accounting for 92% of the total 1,050.

1. [402 times in 138 journals] Lu,X.-J. and Olson,W.K. (2003) 3DNA: A software package for the analysis, rebuilding and visualization of three-dimensional nucleic acid structures. Nucleic Acids Res., 31, 5108–5121.
2. [201 times in 81 journals] Lu,X.-J. and Olson,W.K. (2008) 3DNA: A versatile, integrated software system for the analysis, rebuilding and visualization of three-dimensional nucleic-acid structures. Nat. Protoc., 3, 1213–1227.
3. [127 times in 71 journals] Zheng,G., Lu,X.J. and Olson,W.K. (2009) Web 3DNA––a web server for the analysis, reconstruction, and visualization of three-dimensional nucleic-acid structures. Nucleic Acids Res, 37, W240-6.
4. [115 times in 57 journals] Olson,W.K., Bansal,M., Burley,S.K., Dickerson,R.E., Gerstein,M., Harvey,S.C., Heinemann,U., Lu,X.-J., Neidle,S., Shakked,Z., Sklenar,H., Suzuki,M., Tung,C.-S., Westhof,E., Wolberger,C. and Berman,H.M. (2001) A standard reference frame for the description of nucleic acid base-pair geometry. J. Mol. Biol., 313, 229–237.
5. [66 times in 32 journals] Lu,X.-J., Bussemaker,H.J. and Olson,W.K. (2015) DSSR: An integrated software tool for dissecting the spatial structure of RNA. Nucleic Acids Res., 43, e142.
6. [51 times in 41 journals] Lu,X.J., Shakked,Z. and Olson,W.K. (2000) A-form conformational motifs in ligand-bound DNA structures. J. Mol. Biol., 300, 819–40.

The top 21 journals that cite 3DNA papers 10 times or more are listed below. Nucleic Acids Research stands out, with a total of 148 citations, accounting for 14% of the total 1,050 citations.

148	Nucleic Acids Research
84	Journal of Physical Chemistry B
40	Physical Chemistry Chemical Physics
34	Biophysical Journal
33	Journal of Chemical Theory and Computation
29	Biochemistry
29	RNA
24	PLoS One
24	Scientific Reports
22	Journal of Biomolecular Structure & Dynamics
20	Bioinformatics
19	Journal of Chemical Information and Modeling
16	Nature Communications
15	Biopolymers
15	Journal of the American Chemical Society
12	Acta Crystallographica Section D: Structural Biology
12	Journal of Molecular Modeling
11	Chemistry: a European Journal
11	Journal of Chemical Physics
10	Journal of Biological Chemistry
10	Structure

As I am writing this blogpost on June 26, 2020, the registrations on the 3DNA Forum has reached 5,054. The numbers were 3,000 on October 15, 2016, 2,000 on on February 3, 2015, and 1,000 on February 27, 2013 respectively. For year 2020, the monthly registrations are 36 (January), 35 (February), 54 (March), 84 (April), 69 (May). As of June 26, the number is 56, which will more than likely pass 60 by the end of this month. The Covid-19 pandemic does not seem to having a negative effect on the registrations.

The over 5,000 registrations are from users all over the world. The 3DNA Forum remains spam free, and all questions are promptly answered. It is functioning well; certainly better than I originally imagined.

Overall, the Forum serves as a virtual platform for me to interact effectively with the ever-increasing user community. I greatly enjoy answering questions, fixing bugs, and making 3DNA/DSSR/SNAP better tools for real-world applications.

Following my previous post 3DNA/blocview-PyMOL images in covers of the RNA journal in 2019, here is an update for 2020. The cover images of the January to July issues have all been generated with help of 3DNA and provided by the NDB:

RNA is displayed as a red ribbon; block bases use NDB colors: A—red, C—yellow, G—green, U—cyan. The image was generated using 3DNA/blocview and PyMol software. Cover image provided by the Nucleic Acid Database (ndbserver.rutgers.edu).

Here is the composite figure of the seven cover images, with the brand new DSSR-PyMOL schematics for comparison.

Details of the seven structures illustrated in the cover images are described below:

1. January 2020 Pumilio homolog PUF domain in complex with RNA (PDB id: 5yki; Zhao YY, Mao MW, Zhang WJ, Wang J, Li HT, Yang Y, Wang Z, Wu JW. 2018. Expanding RNA binding specificity and affinity of engineered PUF domains. Nucleic Acids Res 46: 4771–4782). Engineered nine-repeat PUF domain binds to its RNA target specifically and with high binding affinity.
2. February 2020 Aprataxin RNA–DNA deadenylase product complex (PDB id: 6cvo; Tumbale P, Schellenberg MJ, Mueller GA, Fairweather E, Watson M, Little JN, Krahn J, Waddell I, London RE, Williams RS. 2018. Mechanism of APTX nicked DNA sensing and pleiotropic inactivation in neurodegenerative disease. EMBO J 37: e98875). Human aprataxin RNA–DNA deadenylase protects genome integrity and corrects abortive DNA ligation arising during ribonucleotide excision repair and base excision DNA repair.
3. March 2020 PreQ1 riboswitch (PDB id: 6e1w; Connelly CM, Numata T, Boer RE, Moon MH, Sinniah RS, Barchi JJ, Ferre-D’Amare AR, Schneekloth Jr JS. 2019. Synthetic ligands for PreQ1 riboswitches provide structural and mechanistic insights into targeting RNA tertiary structure. Nat Commun 10: 1501). Class I PreQ1 riboswitch regulates downstream gene expression in response to its cognate ligand PreQ1 (7-aminomethyl-7-deazaguanine).
4. April 2020 Hatchet ribozyme (PDB id: 6jq6; Zheng L, Falschlunger C, Huang K, Mairhofer E, Yuan S, Wang J, Patel DJ, Micura R, Ren A. 2019. Hatchet ribozyme structure and implications for cleavage mechanism. Proc Natl Acad Sci 116: 10783–10791). This crystal structure of the hatchet ribozyme product features a compact symmetric dimer.
5. May 2020 Adenovirus virus-associated RNA (PDB id: 6ol3; Hood IV, Gordon JM, Bou-Nader C, Henderson FE, Bahmanjah S, Zhang J. 2019. Crystal structure of an adenovirus virus-associated RNA. Nat Commun 10: 2871). Acutely bent viral RNA fragment is a protein kinase R inhibitor and features an unusually structured apical loop, a wobble-enriched, coaxially stacked apical and tetra-stems, and a central domain pseudoknot that resembles codon-anticodon interactions.
6. June 2020 Archeoglobus fulgidus L7Ae bound to cognate K-turn (PDB id: 6hct; Huang L, Ashraf S, Lilley DMJ. 2019. The role of RNA structure in translational regulation by L7Ae protein in archaea. RNA 25: 60–69). 50S archaeal ribosome protein L7Ae binds to a K-turn structure in the 5′-leader of the mRNA of its structural gene to regulate translation.
7. July 2020 Spinach RNA aptamer/Fab complex (PDB id: 6b14; Koirala D, Shelke SA, Dupont M, Ruiz S, DasGupta S, Bailey LJ, Benner SA, Piccirilli JA. 2018. Affinity maturation of a portable Fab-RNA module for chaperone-assisted RNA crystallography. Nucleic Acids Res 46: 2624–2635). Novel Fab-RNA module can serve as an affinity tag for RNA purification and imaging and as a chaperone for RNA crystallography.