Any analysis of nucleic acid structures start with the identification of nucleotides (nts), the basic building unit. As per the PDB convention, each nt (like any other ligands) is specified by a three-letter identifier. For example, the four standard RNA nts are ..A, ..C, ..G, and ..U, respectively. The four corresponding standard DNA nts are .DA, .DC, .DG, and .DT, respectively. Note that here, for visualization purpose, each space is represented by a dot (.). In practice, the following codes for the five standard DNA/RNA nts — ADE, CYT, GUA, THY, and URA — are also commonly encountered, among other variants.

On top of the standard nts, there are numerous modified ones, each assigned a unique three-letter code. In the classic yeast phenylalanine tRNA (PDB id: 1ehz), 14 out of the 76 nts are modified, as shown in Fig. 1 below.

Fig. 1: Modified nucleotides in yeast phenylalanine tRNA 1ehz

It is challenging to maintain a comprehensive and updated list of ever-inceasing nts encountered in the PDB and molecular dynamics (MD) simulation packages (e.g., AMBER, GROMACS, and CHARMM). Thus, as of today, some well-known DNA/RNA structural bioinformatics tools can handle only standard nts or a limited list of modified ones.

From early on in the development of 3DNA, I observed that all recognized nts have a core six-membered ring, with atoms named N1,C2,N3,C4,C5,C6 consecutively (see Fig. 2 below). Purines have three additional atoms, named N7,C8,N9. So it is feasible to automatically identify nts, and classify them as pyrimidines and purines, based on the common core skeleton shared by all of them. Moreover, the ‘skeleton’ is not effected by any possible tautomeric or protonation state.

Fig. 2: Identification of nts in 3DNA/DSSR based on atomic names and planar geometry

Early versions of 3DNA employed only three atoms (N1, C2 and C6) and three distances to decide a nt. Purines were further discriminated by the N9 atom, and the N1–N9 distance. While developing DSSR, I revised the nt-identification algorithm by using a least-squares fitting procedure that makes use of all available base ring atoms instead of selected ones. The same new algorithm has also been adapted into the find_pair/analyze etc programs in 3DNA, as of v2.2.

As always, the idea can be best illustrated with a worked example. Guanine in its standard base reference frame, with the following list of nine ring atoms coordinates, is chosen for the least-squares fitting. See file Atomic_G.pdb in the 3DNA distribution, and also Table 1 of the report A Standard Reference Frame for the Description of Nucleic Acid Base-pair Geometry.

ATOM      2  N9    G A   1      -1.289   4.551   0.000
ATOM      3  C8    G A   1       0.023   4.962   0.000
ATOM      4  N7    G A   1       0.870   3.969   0.000
ATOM      5  C5    G A   1       0.071   2.833   0.000
ATOM      6  C6    G A   1       0.424   1.460   0.000
ATOM      8  N1    G A   1      -0.700   0.641   0.000
ATOM      9  C2    G A   1      -1.999   1.087   0.000
ATOM     11  N3    G A   1      -2.342   2.364   0.001
ATOM     12  C4    G A   1      -1.265   3.177   0.000

By using a ls-fitting procedure, only (any) three atoms are needed. We no longer need to make explicit selection, as we did previously (N1,C2,C6 and N9), thus allowing for possible modification on these atoms.

Using four nts (G1, 2MG10, H2U16, and PSU39, see Fig. 1 above top) of 1ehz as examples, the following list gives the atomic coordinates of base ring atoms, and root-mean-squres devisions (rmsd) of the least-squares fit. Of course, when performing least-squares fitting, the names of corresponding atoms must match (note the different ordering of atoms for H2U and PSU in the list vs the above standard G reference).

#G1, rmsd=0.008
ATOM     14  N9    G A   1      51.628  45.992  53.798  1.00 93.67           N  
ATOM     15  C8    G A   1      51.064  46.007  52.547  1.00 92.60           C  
ATOM     16  N7    G A   1      51.379  44.966  51.831  1.00 91.19           N  
ATOM     17  C5    G A   1      52.197  44.218  52.658  1.00 91.47           C  
ATOM     18  C6    G A   1      52.848  42.992  52.425  1.00 90.68           C  
ATOM     20  N1    G A   1      53.588  42.588  53.534  1.00 90.71           N  
ATOM     21  C2    G A   1      53.685  43.282  54.716  1.00 91.21           C  
ATOM     23  N3    G A   1      53.077  44.429  54.946  1.00 91.92           N  
ATOM     24  C4    G A   1      52.356  44.836  53.879  1.00 92.62           C  

#2MG10, rmsd=0.018
HETATM  207  N9  2MG A  10      61.581  47.402  18.752  1.00 42.14           N  
HETATM  208  C8  2MG A  10      62.199  48.621  18.635  1.00 40.38           C  
HETATM  209  N7  2MG A  10      63.494  48.534  18.422  1.00 40.70           N  
HETATM  210  C5  2MG A  10      63.745  47.167  18.395  1.00 43.82           C  
HETATM  211  C6  2MG A  10      64.965  46.449  18.205  1.00 43.45           C  
HETATM  213  N1  2MG A  10      64.767  45.086  18.293  1.00 44.71           N  
HETATM  214  C2  2MG A  10      63.541  44.482  18.486  1.00 47.21           C  
HETATM  217  N3  2MG A  10      62.411  45.125  18.614  1.00 45.85           N  
HETATM  218  C4  2MG A  10      62.574  46.451  18.582  1.00 43.27           C  

#H2U16, rmsd=0.188
HETATM  336  N1  H2U A  16      77.347  53.323  34.582  1.00 91.19           N  
HETATM  337  C2  H2U A  16      76.119  52.865  34.160  1.00 92.39           C  
HETATM  339  N3  H2U A  16      75.123  52.894  35.107  1.00 93.28           N  
HETATM  340  C4  H2U A  16      75.289  52.711  36.458  1.00 93.34           C  
HETATM  342  C5  H2U A  16      76.696  52.479  36.909  1.00 93.77           C  
HETATM  343  C6  H2U A  16      77.717  53.238  36.039  1.00 93.22           C  

#PSU39, rmsd=0.004
HETATM  845  N1  PSU A  39      74.080  36.066   5.459  1.00 75.82           N  
HETATM  846  C2  PSU A  39      74.415  36.835   4.354  1.00 75.59           C  
HETATM  847  N3  PSU A  39      75.735  36.769   3.984  1.00 76.29           N  
HETATM  848  C4  PSU A  39      76.728  36.038   4.591  1.00 77.28           C  
HETATM  849  C5  PSU A  39      76.307  35.280   5.732  1.00 77.93           C  
HETATM  850  C6  PSU A  39      75.025  35.316   6.112  1.00 76.07           C  

As noted in the DSSR paper, the rmsd is normally <0.1 Å since base rings are rigid. To account for experimental error and special non-planar cases, such as H2U in 1ehz, the default rmsd cutoff is set to 0.28 Å by default.

With the above detailed algorithm, DSSR (and the 3DNA find_pair/analyze programs) can automatically identify virtually all ‘recognizable’ nts in the PDB. A survey performed in June 2015 detected 630 different types of modified nucleotides in the PDB.

It is worth noting the following points:

• The choice of standard G instead of A as the reference base has no impact on the results. As a matter of fact, the rmsd between G and A is only 0.04 Å. Note also the generous default cutoff of 0.28 Å.
• The method obviously depends on proper naming of the ring atoms. Specially, the base ring atoms must be named N1,C2,N3,C4,C5,C6 consecutively, with purines having three additional atoms named N7,C8,N9. Thus, under this scheme, TPP (thiamine diphosphate) would not be recognized as a nt by default, simply because of the extra prime (′) of atoms in the six-membered ring. In nucleic acid structures, the prime symbol is normally associated with atoms of the sugar moiety (e.g., the C5′ atom).

Fig. 3: TPP (thiamine diphosphate) would not be recognized as a nt.

• On the other hand, nt cofactors in an otherwise ‘pure’ protein structure will also be recognized. One example is the two AMP (adenosine monophosphate) ligands in PDB entry 12as. This extra identification of nts does no harm in such cases. As shown in the analysis of the SAM-I riboswitch in the DSSR paper, taking the SAM ligand as a nt in base triplet recognition is a neat feature.

• Once a nucleotide has been identified and classified into purines and pyrimidines, exocyclic atoms can be used for further assignment: O6 or N2 distinguishes guanine from adenine, N4 separates cytosine from thymine and uracil, and C7 (or C5M, the methyl group) differentiates thymine from uracil. For some modified nts, the distinctions within purines or pyrimidines may not be that obvious. For example, inosine may be taken as a modified guanine or adenine. However, this ambiguity does not pose any significant effect on the calculated base-pair parameters.

• In DSSR and 3DNA, each identified nt is assigned a one-letter shorthand code: the standard ..A, .DA, and ADE (among a few other common variations) is shortened to upper-case A, and similarly for C, G, T, and U. Modified nts, on the other hand, are shortened to their corresponding lower-case symbol. For example, modified guanine such as 2MG and M2G in the yeast phenylalanine tRNA (see Fig. 1 above) is assigned g. So in 3DNA/DSSR output, the upper and lower cases of bases (e.g., nts=3 gCG A.2MG10,A.C25,A.G45) convey special meanings.

Related topics:

• Least-squares fitting procedures with illustrated examples
• Fitting of base reference frame
• A Standard Reference Frame for the Description of Nucleic Acid Base-pair Geometry

As of v2.3-2016jan01, the 3DNA analyze program outputs a list of new ‘simple’ base-pair and step parameters, by default. Shown below is a sample output for PDB entry 1xvk. This echinomycin-(GCGTACGC)2 complex has a single DNA strand as the asymmetric unit. 3DNA needs the the biological unit (1xvk.pdb1) to analyze the duplex (with the -symm option). This structure contains two Hoogsteen base pairs, and has popped up on the 3DNA Forum for the zero or negative Rise values. Note that the ‘simple’ Rise values are all positive; for the middle (#4) TA/TA step, it is now 3.09 Å instead of 0.

# find_pair -symm 1xvk.pdb1 1xvk.bps
# analyze -symm 1xvk.bps
#   OR by combing the above two commands:
# find_pair -symm 1xvk.pdb1 | analyze -symm
# The output is in file '1xvk.out'
This structure contains 4 non-Watson-Crick (with leading *) base pair(s)
----------------------------------------------------------------------------
Simple base-pair parameters based on RC8--YC6 vectors
      bp        Shear    Stretch   Stagger    Buckle  Propeller  Opening
*    1 G+C      -3.07      1.55     -0.35     -6.98      0.29     67.33
     2 C-G       0.27     -0.17      0.35    -22.34      3.33     -2.80
     3 G-C      -0.39     -0.17      0.41     22.91      1.81     -2.73
*    4 T+A      -3.29      1.56      0.31     -8.03      1.59    -70.46
*    5 A+T      -3.29      1.56     -0.31     -8.03      1.59     70.46
     6 C-G       0.39     -0.17      0.41    -22.91      1.81     -2.72
     7 G-C      -0.27     -0.17      0.35     22.34      3.32     -2.80
*    8 C+G      -3.07      1.55      0.35     -6.98      0.30    -67.33
           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
       ave.     -1.59      0.69      0.19     -3.75      1.75     -1.38
       s.d.      1.72      0.92      0.32     17.57      1.15     52.11
----------------------------------------------------------------------------
Simple base-pair step parameters based on consecutive C1'-C1' vectors
      step       Shift     Slide      Rise      Tilt      Roll     Twist
*    1 GC/GC     -0.55      0.39      7.41      6.40     -4.22     23.36
     2 CG/CG     -0.05      0.87      2.44     -0.55      3.94     -0.81
*    3 GT/AC      0.38      0.47      7.23     -8.62      3.75     25.70
*    4 TA/TA     -0.00      4.73      3.09     -0.00      7.49     25.67
*    5 AC/GT     -0.38      0.47      7.23      8.62      3.75     25.70
     6 CG/CG      0.05      0.87      2.44      0.55      3.94     -0.82
*    7 GC/GC      0.55      0.39      7.41     -6.40     -4.22     23.36
            ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        ave.     -0.00      1.17      5.32     -0.00      2.06     17.45
        s.d.      0.39      1.59      2.50      6.21      4.49     12.52

The simple parameters are ‘intuitive’ for non-Watson-Crick base pairs and associated base-pair steps, where the existing standard-reference-frame-based 3DNA parameters may look weird. Note that these simple parameters are for structural description only, not to be fed into the ‘rebuild’ program. Overall, they complement the rigorous characterization of base-pair geometry, as demonstrated by the original analyze/rebuild pair of programs in 3DNA.

In short, the ‘simple’ base-pair parameters employ the YC6—RC8 vector as the y-axis whereas the ‘simple’ step parameters use consecutive C1’—C1’ vectors. As before, the z-axis is the average of two base normals, taking consideration of the M–N vs M+N base-pair classification. In essence, the ‘simple’ parameters make geometrical sense by introducing an ad hoc base-pair reference frame in each case. More details will be provided in a series of blog posts shortly.

Overall, this new section of ‘simple’ parameters should be taken as experimental. The output can be turned off by specifying the analyze -simple=false command-line option explicitly. As always, I greatly appreciate your feedback.

In DSSR (and find_pair -p from the original 3DNA suite), multiplets is defined as “three or more bases associated in a coplanar geometry via a network of hydrogen-bonding interactions. Multiplets are identified through inter-connected base pairs, filtered by pair-wise stacking interactions and vertical separations to ensure overall coplanarity.”

DSSR detects multiplets automatically, and outputs a corresponding MODEL/ENDMDL delineated PDB file (dssr-multiplets.pdb by default) where each multiplet is laid in the most extended view in terms of base planes. The DSSR Nucleic Acids Research (NAR) paper contains four examples (in supplemental Figures 1, 3, 4, and 7) to illustrate this functionality. Please refer to Reproducing results published in the DSSR-NAR paper on the 3DNA Forum for details.

Recently, I read the article titled InterRNA: a database of base interactions in RNA structures by Appasamy et al. in NAR. In Figure 2 of the paper, the authors showcased a sextuple (hexaplet) identified in the E. coli ribosome (PDB id: 4tpe), along with six base-base H-bonds contained therein.

With interest, I tried to run DSSR on the PDB entry 4tpe. As it turns out, ‘4tpe’ has been merged into 4u27 in mmCIF format. I ran DSSR (v1.4.6-2015dec16) in its default settings on ‘4u27’ and get the following summary of results.

# x3dna-dssr -i=4u27.cif -o=4u27.out
    total number of base pairs: 4822
    total number of multiplets: 680
    total number of helices: 264
    total number of stems: 566
    total number of isolated WC/wobble pairs: 193
    total number of atom-base capping interactions: 615
    total number of hairpin loops: 215
    total number of bulges: 137
    total number of internal loops: 244
    total number of junctions: 108
    total number of non-loop single-stranded segments: 83
    total number of kissing loops: 14
    total number of A-minor (type I and II) motifs: 246
    total number of ribose zippers: 127
    total number of kink turns: 15

Among the 680 DSSR-identified multiplets, two hexaplets (one on chain “AA”, and another on “CA”) match those reported by Appasamy et al., as shown below:

 678 nts=6 GUUAAA 1:AA.G404,1:AA.U438,1:AA.U439,1:AA.A496,1:AA.A498,1:AA.A499
 679 nts=6 GUUAAA 1:CA.G404,1:CA.U438,1:CA.U439,1:CA.A496,1:CA.A498,1:CA.A499

For illustration, the hexaplet #678 is extracted from dssr-multiplets.pdb to file 4u27-hexaplet.pdb (download the coordinates) and shown below. The figure is generated by DSSR and PyMOL, as detailed in Reproducing results published in the DSSR-NAR paper on the 3DNA Forum.

x3dna-dssr -i=4u27-hexaplet.pdb -o=4u27-hexaplet.pml --hbfile-pymol 

DSSR-identified hexaplet GUUAAA in 4u27.

DSSR identifies 6 base pairs in the hexaplet:

# x3dna-dssr -i=4u27-hexaplet.pdb --idstr=short
List of 6 base pairs
      nt1            nt2           bp  name        Saenger    LW  DSSR
   1 G404           A498           G+A --          n/a       tSS  tm+m
   2 G404           A499           G+A --          n/a       cWH  cW+M
   3 U438           A496           U-A rHoogsteen  24-XXIV   tWH  tW-M
   4 U439           A496           U-A --          n/a       cH.  cM-.
   5 U439           A498           U-A WC          20-XX     cWW  cW-W
   6 A496           A498           A+A --          n/a       cWH  cW+M

It detects a total of 9 H-bonds as shown below. In addition to the 6 base-base H-bonds noted by Appasamy et al., DSSR also finds 3 sugar-base H-bonds (#1, #2, and #4, labeled in green) that obviously play a role in stabilizing the high-order base association.

# x3dna-dssr -i=4u27-hexaplet.pdb --get-hbonds --idstr=short
   11    59  #1     o    3.017 O:N O2'@G404 N3@U439
   11   104  #2     o    2.578 O:N O2'@G404 N1@A498
   18   125  #3     p    3.089 O:N O6@G404 N6@A499
   21    96  #4     o    3.289 N:O N2@G404 O2'@A498
   21   106  #5     p    2.797 N:N N2@G404 N3@A498
   39    78  #6     p    2.944 N:N N3@U438 N7@A496
   61    81  #7     p    3.167 O:N O4@U439 N6@A496
   61   103  #8     p    2.662 O:N O4@U439 N6@A498
   82   103  #9     p    3.152 N:N N1@A496 N6@A498

Over the years, I have played quite a few computer programming languages. ANSI C has become my top choice for ‘serious’ software projects, due to its small size, efficiency, flexibility, and ubiquitous support. Moreover, C is a mature language, with a rich ecosystem. As it turns out, C has also been consistently rated as one of the most popular computer languages (#1 or #2) over the past thirty years.

Needless to say, ANSI C has its own quirks, and it takes a steep learning curve. However, once you get over the hurdles, the language serves you. I cannot remember when, but it has been a long while that coding in ANSI C is no longer an issue. It is the understanding of scientific questions that takes most of my time, and coding helps greatly in refining my thoughts.

Not surprisingly, ANSI C was chosen as the sole language for DSSR (and SNAP, or 3DNA in general). The ensure the overall quality of the DSSR codebase, I have taken the following steps:

• The whole project is under git.
• The ANSI C source code is compiled with strict GCC options for full compliance to the standard:

-ansi -pedantic -W -Wall -Wextra -Wunused -Wshadow -Werror -O3

• The executable is checked with valgrind for any memory leak:

valgrind --leak-check=full x3dna-dssr -i=1ehz.pdb -o=1ehz.out --quiet
==19624== Memcheck, a memory error detector
==19624== Copyright (C) 2002-2013, and GNU GPL'd, by Julian Seward et al.
==19624== Using Valgrind-3.10.1 and LibVEX; rerun with -h for copyright info
==19624== Command: x3dna-dssr -i=1ehz.pdb -o=1ehz.out --quiet
==19624==
==19624==
==19624== HEAP SUMMARY:
==19624==     in use at exit: 0 bytes in 0 blocks
==19624==   total heap usage: 52,829 allocs, 52,829 frees, 92,878,578 bytes allocated
==19624==
==19624== All heap blocks were freed -- no leaks are possible
==19624==
==19624== For counts of detected and suppressed errors, rerun with: -v
==19624== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 0 from 0)

• Extensive tests (with the simple diff command) to ensure the program is working as expected.

The above four measures combined allow me to add new features, refactor the code, and fix bugs, without worrying about accidentally breaking existing functionality. Reading literature (including citations to 3DNA/DSSR) and responding to user feedback on the 3DNA Forum keep me continuously improve DSSR. Some of the recent refinements to DSSR came about this way.

The other day, I came across an article titled Different duplex/quadruplex junctions determine the properties of anti-thrombin aptamers with mixed folding by Krauss et al. published in Nucleic Acids Research (NAR). This NAR article draw my attention via Google Scholar alert because of its citation to the 2008 3DNA Nature Protocols paper, as shown below (in the Structural analysis section):

3DNA-dssr (41) was used to calculate local and overall geometric parameters of the aptamer. Superpose program from CCP4 package (42) was used to calculate root mean square deviations. Features of the thrombin–RE31 interface were calculated using Cocomaps server (43), whereas contacts between the two molecules, as well as packing interactions between the aptamer and symmetry related thrombin molecules, were found by using 3DNA-snap (41) and Pisa (44) programs. All the results were veri ed by visual inspec- tion of the structure with WinCoot (39).

Moveover, Table 2 lists Stacking interactions as calculated by 3DNA-DSSR (41) among residues belonging to the duplex, the junction and the quadruplex of RE31, with a note on the definition of base-stacking interactions:

Base-stacking is quantified as the area of the overlapped polygon de ned by the two bases of the interacting nucleotides, where the base atoms are projected onto the mean base plane.

To the best of my knowledge, this is the first time SNAP is mentioned in a peer-reviewed journal article. This paper also made good use of DSSR for the analysis of a complicated DNA structure (like RNA), with three non-canonical base pairs at the duplex/quadruplex junction (Figure 3) and extensive stacking interactions (Figure 4).

Figure 3. The duplex/quadruplex junction in RE31 aptamer.

Figure 4. Ribbon representation of RE31 highlighting the continuous stacking of bases from the duplex to the quadruplex region.

As this paper and those by Paul Paukstelis illustrate, DNA can adopt far more complicated 3D structures enabled by non-canonical base pairing schemes than the simple Watson-Crick paired double helices. 3DNA (including DSSR and SNAP) is well suited for the analysis of such extraordinary structures. On a different perspective, following 3DNA citations has become an effective way for me to keep in pace with relevant literature.

Recently, I noticed via Google Scholar the first citation to the paper DSSR, an integrated software tool for dissecting the spatial structure of RNA, recently published in Nucleic Acids Research (NAR). The citation is from Srinivas Somarowthu, in a review article titled Progress and current challenges in modeling large RNAs in the Journal of Molecular Biology. The JMB review article is concise, and overall a nice reading.

Specifically, in the section “Model Evaluation and Refinement”, DSSR is listed along with RNAView and MC-Annotate for the characterization of the secondary from 3D atomic coordinates, as below:

After building a model, it is essential to evaluate the quality, find any errors and refine the accordingly. First, it is important to make sure that all the base-pairs and the overall secondary structure is maintained correctly in the model. Tools such as RNAview [82], MC-Annotate [83], and DSSR [84] can calculate the secondary structure from a given 3D structure and thereby allow identification of problematic base-pairs. Recently, Antczak et al [85], developed a web server, RNApdbee, which integrates RNAview, MC-Annotate and DSSR, and extracts not only secondary structures but also kissing-loops and pseudoknots from a target tertiary model. Problematic base pairs can be fixed or rebuilt using interactive tools such as S2S/ASSEMBLE [45].

I am glad to see the first citation to the 2015 DSSR paper per se shortly after its publication in NAR. Looking forward, I can only expect more DSSR citations in diverse fields related to RNA structures.

Recently, I became aware of the metallo-base pairs, such as T-Hg-T (PDB id: 4l24) and C-Ag-C (5ay2) from the work of Kondo et al (Pubmed: 24478025 and 26448329). As of v1.4.3-2015oct23, DSSR can detect such metallo-bps automatically, as shown below:

# x3dna-dssr -i=4l24.pdb -o=4l24.out
List of 12 base pairs
      nt1            nt2           bp  name        Saenger    LW  DSSR
   1 A.DC1          B.DG24         C-G WC          19-XIX    cWW  cW-W
   2 A.DG2          B.DC23         G-C WC          19-XIX    cWW  cW-W
   3 A.DC3          B.DG22         C-G WC          19-XIX    cWW  cW-W
   4 A.DG4          B.DC21         G-C WC          19-XIX    cWW  cW-W
   5 A.DA5          B.DT20         A-T WC          20-XX     cWW  cW-W
   6 A.DT6          B.DT19         T-T Metal       n/a       cWW  cW-W
   7 A.DT7          B.DT18         T-T Metal       n/a       cWW  cW-W
   8 A.DT8          B.DA17         T-A WC          20-XX     cWW  cW-W
   9 A.DC9          B.DG16         C-G WC          19-XIX    cWW  cW-W
  10 A.DG10         B.DC15         G-C WC          19-XIX    cWW  cW-W
  11 A.DC11         B.DG14         C-G WC          19-XIX    cWW  cW-W
  12 A.DG12         B.DC13         G-C WC          19-XIX    cWW  cW-W

and

# x3dna-dssr -i=5ay2.pdb -o=5ay2.out
List of 24 base pairs
      nt1            nt2           bp  name        Saenger    LW  DSSR
   1 A.G1           B.C12          G-C WC          19-XIX    cWW  cW-W
   2 A.G2           B.C11          G-C WC          19-XIX    cWW  cW-W
   3 A.A3           B.U10          A-U WC          20-XX     cWW  cW-W
   4 A.C4           B.C9           C-C Metal       n/a       cWW  cW-W
   5 A.U5           B.A8           U-A WC          20-XX     cWW  cW-W
   6 A.CBR6         B.G7           c-G WC          19-XIX    cWW  cW-W
   7 A.G7           B.CBR6         G-c WC          19-XIX    cWW  cW-W
   8 A.A8           B.U5           A-U WC          20-XX     cWW  cW-W
   9 A.C9           B.C4           C-C Metal       n/a       cWW  cW-W
  10 A.U10          B.A3           U-A WC          20-XX     cWW  cW-W
  11 A.C11          B.G2           C-G WC          19-XIX    cWW  cW-W
  12 A.C12          B.G1           C-G WC          19-XIX    cWW  cW-W
  13 C.G1           D.C12          G-C WC          19-XIX    cWW  cW-W
  14 C.G2           D.C11          G-C WC          19-XIX    cWW  cW-W
  15 C.A3           D.U10          A-U WC          20-XX     cWW  cW-W
  16 C.C4           D.C9           C-C Metal       n/a       cWW  cW-W
  17 C.U5           D.A8           U-A WC          20-XX     cWW  cW-W
  18 C.CBR6         D.G7           c-G WC          19-XIX    cWW  cW-W
  19 C.G7           D.CBR6         G-c WC          19-XIX    cWW  cW-W
  20 C.A8           D.U5           A-U WC          20-XX     cWW  cW-W
  21 C.C9           D.C4           C-C Metal       n/a       cWW  cW-W
  22 C.U10          D.A3           U-A WC          20-XX     cWW  cW-W
  23 C.C11          D.G2           C-G WC          19-XIX    cWW  cW-W
  24 C.C12          D.G1           C-G WC          19-XIX    cWW  cW-W

Note the name “Metal” for the metallo-bps. Moreover, the corresponding entries in the ‘dssr-pairs.pdb’ file also include the metal ions, as shown below:

It is worth noting that in a metallo-bp, the metal ion lies approximately in the bp plane. Moreover, it is in the middle of the two bases, which would otherwise not form a pair in the conventional sense.

On October 29, 2015, I performed a survey of citations to the following three 3DNA papers, using the Web of Science. The total number of citations are: NAR03 (787) + NP08 (184) + NAR09 (78) = 1049, spanning a diverse set of 191 journals in biology, chemistry, and material sciences. On the same date, Google Scholar reported 1360 citations for the same three papers.

1. [NAR03] Lu, Xiang‐Jun, and Wilma K. Olson. “3DNA: a software package for the analysis, rebuilding and visualization of three‐dimensional nucleic acid structures.” Nucleic acids research 31.17 (2003): 5108-5121.
2. [NP08] Lu, Xiang-Jun, and Wilma K. Olson. “3DNA: a versatile, integrated software system for the analysis, rebuilding and visualization of three-dimensional nucleic-acid structures.” Nature protocols 3.7 (2008): 1213-1227.
3. [NAR09] Zheng, Guohui, Xiang-Jun Lu, and Wilma K. Olson. “Web 3DNA—a web server for the analysis, reconstruction, and visualization of three-dimensional nucleic-acid structures.” Nucleic acids research 37.suppl 2 (2009): W240-W246.

Among the 1049 citations in 191 journals, 694 citations (66%) are from the following 24 journals (~13%). The remaining 355 citations are from 167 other journals, including Cell (5 times), Science (2), Nature (3) and six additional Nature Publishing Group sub-journals (17).

1 Nucleic Acids Res (167)
2 J Phys Chem B (64)
3 Biochemistry (45)
4 J Am Chem Soc (45)
5 J Mol Biol (41)
6 Phys Chem Chem Phys (28)
7 Biophys J (25)
8 J Biol Chem (23)
9 PLoS One (23)
10 Acta Crystallogr D Biol Crystallogr (22)
11 J Chem Theory Comput (22)
12 Proc Natl Acad Sci U S A (22)
13 Bioinformatics (18)
14 Biopolymers (18)
15 J Biomol Struct Dyn (18)
16 J Chem Phys (18)
17 J Phys Chem A (16)
18 Structure (13)
19 RNA (12)
20 Biochem Biophys Res Commun (11)
21 Chem Res Toxicol (11)
22 J Comput Chem (11)
23 J Mol Model (11)
24 Nat Struct Mol Biol (10)

It is worth noting that while the Web of Science citation report is comprehensive, it is certainly not complete. In particular, citations in the online methods section seem not to be covered. For example, two 3DNA citations (on the DSSR program) in “Materials and Methods” (the Supplementary Materials) of two Science articles by the Ramakrishnan lab are missing from the list. Specifically, the Science papers employed DSSR for the characterization of RNA secondary structural features in crystal structures of the large ribosomal subunit and the whole ribosome of human mitochondria.

For those why are interested in knowing the details, click the link for the full reports of 3DNA citations. In the file, the citations are sorted in two ways: by citation numbers per journal, and by journal names.

From the Jmol mailing list, I noticed Jmol 14.4.0 was released yesterday (October 13, 2015) by Dr. Bob Hanson. Among the development highlights is the following item:

biomolecule annotations including DSSR, RNA3D, EBI sequence domains, and PDB validation data

I am glad to see that DSSR has been integrated into Jmol, one of the most popular molecular graphics visualization programs. To enable easy access to the DSSR functionality from Jmol, I’ve set up two websites with easy-to-remember URLs: http://jmol.x3dna.org and http://jsmol.x3dna.org. They both point to the same jsmol/ folder extracted from jsmol.zip of the Jmol distribution.

In retrospect, I first met Bob at the Workshop on the PDBx/mmCIF Data Exchange Format for Structural Biology held at Rutgers University during October 21-22, 2013. I approached him during a lunch break, asking for a possible collaboration on integrating DSSR into Jmol. The name DSSR may have played a role in convincing Bob, since it matches the well-known DSSP program for proteins. In the end, we were both excited about the project, talked into details after the meeting, and continued our conversation the next morning while I drove him to the airport.

Nothing real happened until early April 2014. Once getting started, however, we moved forward rapidly: it took less then three weeks to get the first functional version ready for the community to play. See Bob’s announcement RNA/DNA Secondary Structure, anyone? in the Jmol mailing list on April 9, 2014. During this process, we communicated extensively via email, up to 30 messages per day, on technical details for better communication between the two programs. The integration works by using Jmol as a front-end, which calls a web-serivce hosted at Columbia University for DSSR analysis. Jmol’s parsing of the DSSR output is facilitated by the dedicated --jmol option.

The above preliminary, yet functional, DSSR-Jmol integration had be in service without infrastructural changes until two months ago. In August 10, 2015, Bob contacted me:

I might make a significant request though. That would be for the server to deliver all this in JSON format. This is really the way to go. It is what people want and it is perfect for Jmol as well.

I’d played around with JSON or SQLite as a structured data exchange format for quite some time, and Bob’s request finally convinced me that JSON is the (better) way to go. And that began another around of intensive collaborative work that has switched the exchange format between DSSR and Jmol from plain text output to JSON. From August 10 to September 22, we had a total of over 170-email exchanges, plus Skype. JSON has really simplified lives of both parties, especially in the long run.

Overall, collaborating with Bob has been truly an enjoyable and rewarding experience. The DSSR-Jmol integration also serves as a concrete example of what can be achieved by two dedicated minds with complementary expertise.