X3DNA-DSSR: a resource for structural bioinformatics of nucleic acidsCover image provided by X3DNA-DSSR, an NIGMS National Resource for Structural Bioinformatics of Nucleic Acids (R24GM153869; skmatics.x3dna.org). Image generated using DSSR and PyMOL (Lu XJ. 2020. Nucleic Acids Res 48: e74).
As the developer of DSSR, I am thrilled to see its application in cutting-edge research across multiple disciplines. Below is a list of four recent publications that highlight how DSSR has been utilized, underscoring its versatility and significance in structural bioinformatics.
In the Geng et al. (2025) Nucleic Acids Research (NAR) paper, titled 'Revealing hidden protonated conformational states in RNA dynamic ensembles', DSSR is simply cited as follows:
All bp geometries, hydrogen-bond, backbone, stacking, and sugar dihedral angles were calculated using X3DNA-DSSR [77].
In the preprint by Gordan et al. (2025), titled 'High-throughput characterization of transcription factors that modulate UV damage formation and repair at single-nucleotide resolution', DSSR is cited as follows:
Step base stacking, base pair shift, base pair slide, interbase angle, pseudorotation angle, and sugar puckering classifications of nucleobases were computed using X3DNA-DSSR (v2.5.0)75. Base stacking was defined as the overlapping polygon area in Å2 when projecting the dipyrimidine base ring atoms (excluding exocyclic atoms) into the mean base pair plane76. The sugar ring pseudorotation phase angle of each pyrimidine was also calculated using X3DNA-DSSR as described by Altona, C. & Sundaralingam, M.77 Interbase angle was defined as sqrt(propeller2+buckle2) per the X3DNA-DSSR documentation.
Figure 6: TF Binding Induces Structural Distortion Favorable to UV Dimerization is highly informative, particularly panel (a), which illustrates the ensemble of structural parameters that predispose dipyrimidines to cyclobutane pyrimidine dimers (CPD) or 6-4 pyrimidine-pyrimidones (6-4 PP) formation. DSSR is designed as an integrated software tool, offering a comprehensive suite of structural parameters not found in any other single tool I am aware of. Despite this, the innovative use of DSSR by Gordan et al. exceeds my expectations and demonstrates its versatility.
In the preprint by Kubaney et al. (2025) from the Baker group, titled 'RNA sequence design and protein-DNA specificity prediction with NA-MPNN', DSSR is cited as follows:
On the pseudoknot subset, we evaluate additional structure‐ and reactivity‐based metrics. DSSR v2.3.241 is used to extract the ground‐truth secondary structure from the native crystal structures. For each designed sequence, RibonanzaNet predicts 2A3 reactivity profiles, from which we compute predicted OpenKnot scores (see https://github.com/eternagame/OpenKnotScore)31 using the predicted reactivity together with the DSSR ground truth.
In a recent NSMB paper from the Baker group, titled 'Computational design of sequence-specific DNA-binding proteins', 3DNA is cited as follows:
RIF docking of scaffolds onto DNA targets (DBP design step 1) Structures of B-DNA for each target (Supplementary Table 2) were generated by (1) using the DNA portion of PDB 1BC8 (ref. 60), PDB 1YO5 (ref. 61), PDB 1L3L (ref. 51) or PDB 2O4A (ref. 62) or (2) using the software X3DNA63, followed by a constrained Rosetta relax of the DNA structure.
Please note that 3DNA has been replaced by DSSR. The functionality for constructing B-DNA models, previously provided by 3DNA, is now directly available in DSSR via its fiber and rebuild modules.
In the preprint by Si et al. (2025), titled 'End-to-End Single-Stranded DNA Sequence Design with All-Atom Structure Reconstruction', DSSR is cited as follows:
Since ViennaRNA and NUPACK require secondary structures as input, we used DSSR35 to extract secondary structures from the corresponding ssDNA three-dimensional structures.
The above use cases are merely a sample of how DSSR is utilized in the scientific literature. It is reasonable to state that DSSR has emerged as a de facto standard tool within the field of nucleic acid structural bioinformatics. Overall, DSSR is a mature, robust, and efficient software product that is actively developed and maintained. I am committed to making DSSR synonymous with quality and value. Its unmatched functionality, usability, and support save users significant time and effort compared to alternative solutions.
DSSR is available free of charge for academic users. Additionally, it has been integrated into other high-profile bioinformatics resources, including NAKB, PDB-redo, and N•ESPript.
On December 9, 2021, at 15:00 CET, I will present a BioExcel webinar titled “X3DNA-DSSR, a resource for structural bioinformatics of nucleic acids.”
Today, I released a video overview of DSSR (http://docs.x3dna.org/dssr-overview/).
DSSR has a sizable user base. However, in my opinion, DSSR is still underutilized for what it has to offer. This overview video is intended not only to attract new DSSR users, but also to highlight features that even experienced users may overlook.
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.
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.
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.
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.
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.
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:
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.
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.
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
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.
March 2026
February 2026402 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, 5108–5121.201 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, 1213–1227.127 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, W240-6.115 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, 229–237.66 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, e142.51 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:
Thank you for printing this article from http://x3dna.org/. Please do not forget to visit back for more 3DNA-related information. — Xiang-Jun Lu