March 2026

February 2026

Cover 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 Ų 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.

References

1. Geng A, Roy R, Ganser L, Li L, Al-Hashimi HM. Revealing hidden protonated conformational states in RNA dynamic ensembles. Nucleic Acids Research. 2025;53:gkaf1366. https://doi.org/10.1093/nar/gkaf1366.
2. Gordan R, Wasserman H, Chi B, Bohm K, Duan M, Sahay H, et al. High-throughput characterization of transcription factors that modulate UV damage formation and repair at single-nucleotide resolution. 2025. https://doi.org/10.21203/rs.3.rs-8197218/v1.
3. Kubaney A, Favor A, McHugh L, Mitra R, Pecoraro R, Dauparas J, et al. RNA sequence design and protein–DNA specificity prediction with NA-MPNN. 2025. https://doi.org/10.1101/2025.10.03.679414.
4. Glasscock CJ, Pecoraro RJ, McHugh R, Doyle LA, Chen W, Boivin O, et al. Computational design of sequence-specific DNA-binding proteins. Nat Struct Mol Biol. 2025;32:2252–61. https://doi.org/10.1038/s41594-025-01669-4.
5. Si Y, Xu Y, Chen L. End-to-end single-stranded DNA sequence design with all-atom structure reconstruction. 2025. https://doi.org/10.64898/2025.12.05.692525.