Nowadays, I’ve been used to google searches as a quick way to solve problems. Once in a while, I come across a tip or trick that fixes an issue at hand and then move on. However, I may late on meet a similar problem, but only vaguely remember how I solved it previously. So I’d need to start googling around again. This list is a remedy for such situations, and it will be continuously updated. While the list is created for my own reference, it may also be useful to other viewers of the post, presumably reaching here via google.
icdiff — show diff with colorscc — strip C commentsTaskwarrior (taks) — manage TODO list from terminalhttpie as a replacement of curl and wgetbyebug and pry for debugging Rubyag to search for PATTERN in source files, replacing grepfd to find files and directoriesbat to view files with syntax highlighting (in place of cat)exa as an alternative to lsbench to benchmark codeasciinema and svg-term to record terminal activity as an SVG animation. Another option is termtosvg. Moreover, the trio ttyrec, ttyplay, ttygif can record, play terminal screen recordings, and convert it into smooth GIFwrk to benchmark HTTP APIshub — git wrapper for GitHubtail -n +2 to skip the first line (starting from the second line)sudo -i -u user_id, the -i or --login option invokes login shell- Understanding Shell Script’s idiom: 2>&1 — redirect ‘stderr’ to ‘stdout’ via ’2>&1’ in
bash shell.
- Ruby one-liners
ruby -pi.bak -e "gsub(/SOME_PATTERN/, 'other_text')" files for global replacement of SOME_PATTERN by other_text in filesruby -pe 'gsub(/_/, ".")' globally replace ‘_’ with ‘.’
Recently, a 3DNA user asked on the Forum about how to perform mutations to 3-methyladenine. The user reported that the procedure described in the FAQ entry How can I mutate cytosine to 5-methylcytosine did not work for the case of 3-methyladenine. This ‘limitation’ is easily understandable: the 3DNA mutate_bases program must have knowledge of the target base, 3-methyladenine, to perform the mutation properly. The program works for the most common 5-methylcytosine mutations since the corresponding 5MC file (Atomic_5MC.pdb, in the standard base-reference frame) is already included within the 3DNA distribution. By supplying a similar file for the target base, mutate_bases runs the same for mutations to 5-methylcytosine (or other bases). This blog post outlines the procedure, using 3-methyladenine as an example.
A ligand name search for 5-methylcytosine on the RCSB PDB led to only two matched entries: 2X6F and 3MAG. The ligand id is 3MA. Since 3MAG has a better resolution (1.8 Å) than 2X6F (3.3 Å), its 3MA ligand was extracted from the corresponding PDB file (3MAG.pdb). The atomic coordinates, excluding those for the two hydrogens, are as below. Note that the 3-methyl carbon atom is named CN3.
HETATM 2960 N9 3MA A 600 16.587 14.258 22.170 1.00 49.87 N
HETATM 2961 C4 3MA A 600 17.123 13.100 21.622 1.00 50.46 C
HETATM 2962 N3 3MA A 600 16.877 11.811 22.009 1.00 50.37 N
HETATM 2963 CN3 3MA A 600 15.983 11.363 23.063 1.00 50.41 C
HETATM 2964 C2 3MA A 600 17.590 10.968 21.241 1.00 50.11 C
HETATM 2965 N1 3MA A 600 18.422 11.217 20.224 1.00 49.27 N
HETATM 2966 C6 3MA A 600 18.627 12.484 19.858 1.00 48.99 C
HETATM 2967 N6 3MA A 600 19.426 12.709 18.829 1.00 46.12 N
HETATM 2968 C5 3MA A 600 17.949 13.503 20.593 1.00 49.89 C
HETATM 2969 N7 3MA A 600 17.929 14.900 20.488 1.00 49.84 N
HETATM 2970 C8 3MA A 600 17.113 15.286 21.434 1.00 49.58 C
After running the 3DNA utility program std_base with options -fit -A, the corresponding atomic coordinates of 3MA are transformed to the standard base reference frame of adenine. The file must be named Atomic_3MA.pdb, and it has the following contents:
HETATM 1 N9 3MA A 1 -1.287 4.521 0.006 1.00 49.87 N
HETATM 2 C4 3MA A 1 -1.262 3.133 0.004 1.00 50.46 C
HETATM 3 N3 3MA A 1 -2.337 2.286 -0.009 1.00 50.37 N
HETATM 4 CN3 3MA A 1 -3.743 2.648 -0.047 1.00 50.41 C
HETATM 5 C2 3MA A 1 -1.905 1.013 0.001 1.00 50.11 C
HETATM 6 N1 3MA A 1 -0.662 0.520 0.004 1.00 49.27 N
HETATM 7 C6 3MA A 1 0.366 1.372 -0.003 1.00 48.99 C
HETATM 8 N6 3MA A 1 1.588 0.867 -0.034 1.00 46.12 N
HETATM 9 C5 3MA A 1 0.068 2.768 0.003 1.00 49.89 C
HETATM 10 N7 3MA A 1 0.875 3.914 -0.003 1.00 49.84 N
HETATM 11 C8 3MA A 1 0.026 4.909 -0.003 1.00 49.58 C
Note that in file Atomic_3MA.pdb, (1) the z-coordinates of the base atoms are close to zeros, (2) the ordering of atoms is as in the original ligand of 3MA shown above.
With Atomic_3MA.pdb in place (in the current working directory, or the $X3DNA/config folder), one can perform 3-methyladenine mutations using mutate_bases. For illustration purpose, let’s generate a B-form DNA with base sequence GACATGATTGCC using the 3DNA fiber program:
fiber -seq=GACATGATTGCC fiber-BDNA.pdb
To mutate A7 to 3MA, one needs to run mutate_bases as following:
mutate_bases "chain=A s=7 m=3MA" fiber-BDNA.pdb fiber-BDNA-A7to3MA.pdb
The result of the mutation is shown in the figure below. Note that the backbone has identical geometry as that before the mutation, and the mutated 3MA-T pair has exactly the same parameters (propeller/buckle etc) as the original A-T. These are the two defining features of the 3DNA mutate_bases program.
Please see the thread mutations to 3-methyladenine on the 3DNA Forum to download files fiber-BDNA.pdb and fiber-BDNA-A7to3MA.pdb.
When visiting the RCSB PDB website today, I am please to notice that the PDB now contains “10015 Nucleic Acid Containing Structures”. Based on “Macromolecule Type” in “Advanced Search” of the RCSB PDB website, I observed the following information:
- The number of DNA-containing structures is
6,384 (reported in 2,997 papers), and the corresponding number for RNA-containing structures is 3,861 (associated with 2,012 publications).
- There are
4,570 structures containing both DNA and protein (potentially forming DNA-protein complexes), and 2,478 RNA-protein complexes.
- The smallest nucleic-acid-containg structures have only two nucleotides (e.g., 3rec), and largest ones are the ribosomes (and virus particles).
- The earliest released DNA structure from the PDB is 1zna (on March 18, 1981), a Z-DNA tetramer. The earliest RNA structure released is 4tna (on April 12, 1978), a refined structure of the yeast phenylalanine transfer RNA.
This landmark achievement is made possible by the world-wide scientific community through decades of efforts solving DNA/RNA 3D structures via experimental approaches (mainly solution NMR, x-ray crystal, and cryo-EM). These over 10K nucleic acid structures present both challenges and opportunities for the field of structural bioinformatics, especially for intricate RNA molecules. DSSR is an integrated software tool for dissecting the spatial structure of RNA. It is my effort in addressing the challenging issues for the analysis/annotation and visualization of RNA structures.
It is textbook knowledge that the Watson-Crick (WC) pairs are specific, forming only between A and T/U (A–T/U or T/U–A) or G and C (G–C or C–G). Furthermore, an A only forms one WC pair with a T, so is G vs. C. The widely used dot-bracket-notation (DBN) of DNA/RNA secondary structure depends crucially on this feature of specificity and uniqueness, by using matched parentheses to represent WC pairs, such as ((....)) for a GCGA (GNRA-type) tetra-loop of sequence GCGCGAGC.
The reality is more complicated, even for what’s presumably to be a ‘simple’ question of deriving RNA secondary structure from 3D coordinates in PDB. One subtlety is related to the ambiguity of atomic coordinates that renders one base apparently forming two WC pairs with two other complementary bases. As always, the case can be best illustrated with a concrete example. The image shown below is taken from PDB entry 1qp5 where C20 (on chain B) forms two WC pairs, each with G4 and G5 (on chain A) respectively.
Clearly, taking both as valid WC G–C pairs would make the resultant DBN illegitimate. DSSR resolves such discrepancies by taking structural context into consideration to ensure that one base can only have a WC pair with another base. Here the G5–C20 WC pair is retained whilst the G4–C20 WC is removed.
This issue, one base can form two WC pairs as derived from the PDB, has been noticed for a long while. Two examples from literature are shown below:
The crystal structure data files were downloaded from the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (Berman et al. 2000). For each crystal structure, the set of canonical base pairs was extracted by selecting all Watson–Crick and standard G-U wobble pairs found by RNAview (Yang et al. 2003). Occasional conflicts in this list, where RNAview annotates two bases, x and y, as a standard base pair and also y and z as another conflicting base pair, were removed manually by visual inspection of the crystal structure in the program PyMOL (http://pymol.sourceforge.net/). The helix-extension data set was created by taking the canonical pairs and adding all additional base–base interactions identified by RNAview (excluding stacked bases and tertiary interactions) for which the direct neighbor was already in the collection. This means each base pair (i,j) was added if both i and j were still unpaired and if either (i + 1, j – 1) or (i –1, j + 1) were already in the set.
… From these complexes, we retrieved all RNA chains also marked as non-redundant by RNA3DHub. Each chain was annotated by FR3D. Because FR3D cannot analyze modified nucleotides or those with missing atoms, our present method does not include them either. If several models exist for a same chain, the first one only was considered. For the rest of this paper, the base pairs extracted from the FR3D annotations are those defined in the Leontis–Westhof geometric classification (24).
For each chain a secondary structure without pseudoknots was deduced from the annotated interactions, as follows. First all canonical Watson–Crick and wobble base pairs (i.e. A-U, G-C and G-U) were identified. Then, since many structures are naturally pseudoknotted, we used the K2N (25) implementation in the PyCogent (26) Python module to remove pseudoknots. Problems arise when a nucleotide is involved in several Watson–Crick base pairs (which is geometrically not feasible), probably due to an error of the automatic annotation. Those discrepancies were removed with a ad hoc algorithm such that if a nucleotide is involved in several Watson–Crick base pairs, we remove the base pair which belongs to the shortest helix.
By design, DSSR takes care of these ‘little details’, among other handy features (such as handling modified nucleotides and removing pseudoknots). By providing a robust infrastructure and comprehensive framework, DSSR allows users to focus on their research topics. If you have experience with other tools, such as RNAView and FR3D cited above, give DSSR a try: it may fit your needs better.
An article titled Simulations and electrostatic analysis suggest an active role for DNA conformational changes during genome packaging by bacteriophages has recently been published in bioRxiv. I was honored to have the opportunity collaborating with fellow researchers from University of Pennsylvania and Thomas Jefferson University in this significant piece of work.
Here is the abstract. Please download the PDF version to know more.
Motors that move DNA, or that move along DNA, play essential roles in DNA replication, transcription, recombination, and chromosome segregation. The mechanisms by which these DNA translocases operate remain largely unknown. Some double-stranded DNA (dsDNA) viruses use an ATP-dependent motor to drive DNA into preformed capsids. These include several human pathogens, as well as dsDNA bacteriophages (viruses that infect bacteria). We previously proposed that DNA is not a passive substrate of bacteriophage packaging motors but is, instead, an active component of the machinery. Computational studies on dsDNA in the channel of viral portal proteins reported here reveal DNA conformational changes consistent with that hypothesis. dsDNA becomes longer (“stretched”) in regions of high negative electrostatic potential, and shorter (“scrunched”) in regions of high positive potential. These results suggest a mechanism that couples the energy released by ATP hydrolysis to DNA translocation: The chemical cycle of ATP binding, hydrolysis and product release drives a cycle of protein conformational changes. This produces changes in the electrostatic potential in the channel through the portal, and these drive cyclic changes in the length of dsDNA. The DNA motions are captured by a coordinated protein-DNA grip-and-release cycle to produce DNA translocation. In short, the ATPase, portal and dsDNA work synergistically to promote genome packaging.
An abasic site is a location in DNA or RNA where a purine or pyrimidine base is missing. It is also termed an AP site (i.e., apurinic/apyrimidinic site) in biochemistry and molecular genetics. The abasic site can be formed either spontaneously (e.g., depurination) or due to DNA damage (occurring as intermediates in base excision repair). According to Wikipedia, “It has been estimated that under physiological conditions 10,000 apurinic sites and 500 apyrimidinic may be generated in a cell daily.”
In DSSR and 3DNA v2.x, nucleotides are recognized using standard atom names and base planarity. Thus, abasic sites are not taken as nucleotides (by default), simply because they do not have base atoms. DSSR introduced the --abasic option to account for abasic sites, a feature useful for detecting loops with backbone connectivity.
For example, by default, DSSR identifies one internal loop (no. 1 in the list below) in PDB entry 1l2c. With the --abasic option, two internal loops (including the one with the abasic site C.HPD18, no. 2) are detected.
List of 2 internal loops
1 symmetric internal loop: nts=6; [1,1]; linked by [#-1,#1]
summary: [2] 1 1 [B.1 C.24 B.3 C.22] 1 4
nts=6 GTATAC B.DG1,B.DT2,B.DA3,C.DT22,C.DA23,C.DC24
nts=1 T B.DT2
nts=1 A C.DA23
2 symmetric internal loop: nts=6; [1,1]; linked by [#1,#2]
summary: [2] 1 1 [B.6 C.19 B.8 C.17] 4 5
nts=6 CTTA?G B.DC6,B.DT7,B.DT8,C.DA17,C.HPD18,C.DG19
nts=1 T B.DT7
nts=1 ? C.HPD18
Note that C.HPD18 in 1l2c is a non-standard residue, as shown in the HETATM records below. Since the identity of C.HPD18 cannot be deduced from the atomic records, its one-letter code is designated as ?.
HETATM 346 P HPD C 18 -14.637 52.299 29.949 1.00 49.12 P
HETATM 347 O5' HPD C 18 -14.658 52.173 28.359 1.00 48.28 O
HETATM 348 O1P HPD C 18 -15.167 51.040 30.537 1.00 49.35 O
HETATM 349 O2P HPD C 18 -13.303 52.798 30.369 1.00 46.43 O
HETATM 350 C5' HPD C 18 -15.703 51.469 27.687 1.00 45.70 C
HETATM 351 O4' HPD C 18 -16.364 50.501 25.561 1.00 44.15 O
HETATM 352 O3' HPD C 18 -13.990 51.738 24.335 1.00 45.75 O
HETATM 353 C1' HPD C 18 -16.105 54.187 25.684 1.00 52.47 C
HETATM 354 O1' HPD C 18 -17.309 54.085 26.496 1.00 56.16 O
HETATM 355 C3' HPD C 18 -14.756 52.250 25.426 1.00 46.23 C
HETATM 356 C4' HPD C 18 -15.263 51.093 26.291 1.00 45.72 C
HETATM 357 C2' HPD C 18 -16.030 52.889 24.898 1.00 49.05 C
In contrast, the R.U-8 in PDB entry 4ifd is a standard U, and is properly labeled by DSSR.
ATOM 26418 P U R -8 139.362 21.962 129.430 1.00208.29 P
ATOM 26419 OP1 U R -8 140.062 20.821 130.074 1.00207.30 O
ATOM 26420 OP2 U R -8 140.113 23.208 129.129 1.00208.44 O1+
ATOM 26421 O5' U R -8 138.712 21.439 128.071 1.00157.60 O
ATOM 26422 C5' U R -8 139.507 20.790 127.087 1.00155.47 C
ATOM 26423 C4' U R -8 138.843 20.804 125.731 1.00152.27 C
ATOM 26424 O4' U R -8 138.538 22.172 125.352 1.00149.29 O
ATOM 26425 C3' U R -8 139.677 20.275 124.572 1.00152.70 C
ATOM 26426 O3' U R -8 139.670 18.859 124.478 1.00155.04 O
ATOM 26427 C2' U R -8 139.053 20.969 123.369 1.00150.26 C
ATOM 26428 O2' U R -8 137.849 20.322 122.984 1.00146.83 O
ATOM 26429 C1' U R -8 138.700 22.334 123.958 1.00147.35 C
This is yet another little detail that DSSR takes care of. It is the close consideration to many such subtle points that makes DSSR different. Overall, DSSR represents my view of what a scientific software program could be (or should be).
Recently, while analyzing a representative set of RNA structures from the PDB, I came across three weird entries. They are documented below, primarily for my own record.
- 5els — “Structure of the KH domain of T-STAR in complex with AAAUAA RNA”. There are two alternative conformations for the six-nt
AAAUAA RNA component, labeled A and B, respectively. Normally, the A/B alternative coordinates for each atom are put directly next to each other, and assigned the same chain id, as in 1msy for the phosphate group of G2669 on chain A. In 5els, however, the two alternative conformations (A/B) are separated into two chains: chain H for A, and chain I for B.
- 1vql — “The structure of the transition state analogue ‘DCSN’ bound to the large ribosomal subunit of Haloarcula marismortui”. The three-nt fragment DA179—C180—C181 on chain 4 is in the 3’—>5’ direction.
- 4r3i — “The crystal structure of m(6)A RNA with the YTHDC1 YTH domain”. The mmCIF file has a model number of 0, instead of 1 (as in other cases I am aware of).
3DNA contains 55 fiber models compiled from literature, plus a derived RNA model (as of v2.1). To the best of my knowledge, this is the most comprehensive collection of regular DNA/RNA models. Please see Table 4 of the 2003 3DNA NAR paper for detailed structural features of these models and references.
The 55 models are based on the following works:
- Chandrasekaran & Arnott (from #1 to #43) — the most well-known set of fiber models
- Alexeev et al. (#44-#45)
- van Dam & Levitt (#46-#47)
- Premilat & Albiser (#48-#55)
The utility program fiber makes the generation of all these fiber models in a simple, consistent interface, and produces coordinate files in either PDB or PDBML format. Of those models, some can be built with an arbitrary sequence of A, C, G and T (e.g., A-/B-/C-DNA from calf thymus), while others are of fixed sequences (e.g., Z-DNA with GC repeats). The sequence can be specified either from command-line or a plain text file, in either lower, UPPER, or MixED cases.
Once 3DNA in properly installed, the command-line interface is the most versatile and convenient way to generate, e.g., a regular double-stranded DNA (mostly, B-DNA) of arbitrary sequence. The command-help message (generated with fiber -h) is as below:
NAME
fiber - generate 55 fiber models based on Arnott and other's work
SYNOPSIS
fiber [OPTION] PDBFILE
DESCRIPTION
generate 55 fiber models based on the repeating unit from Arnott's
work, including the canonical A-, B-, C- and Z-DNA, triplex, etc
-xml output structure coordinates in PDBML format
-num a structure identification number in the range (1-55)
-m, -l brief description of the 55 fiber structures
-a, -1 A-DNA model (calf thymus)
-b, -4 B-DNA (calf thymus, default)
-c, -47 C-DNA (BII-type nucleotides)
-d, -48 D(A)-DNA ploy d(AT) : ploy d(AT) (right-handed)
-z, -15 Z-DNA poly d(GC) : poly d(GC)
-rna for RNA with arbitrary base sequence
-seq=string specifying an arbitrary base sequence
-single output a single-stranded structure
-h this help message (any non-recognized options will do)
INPUT
An structural identification number (symbol)
EXAMPLES
fiber fiber-BDNA.pdb
# fiber -4 fiber-BDNA.pdb
# fiber -b fiber-BDNA.pdb
fiber -a fiber-ADNA.pdb
fiber -seq=AAAGGUUU -rna fiber-RNA.pdb
fiber -seq=AAAGGUUU -rna -single fiber-ssRNA.pdb
OUTPUT
PDB file
SEE ALSO
analyze, anyhelix, find_pair
AUTHOR
3DNA v2.3-2016sept06, created and maintained by Xiang-Jun Lu (PhD)
Please post questions/comments on the 3DNA Forum: http://forum.x3dna.org/
Moreover, the w3DNA, 3D-DART web-interfaces, and the PyMOL wrapper make it easy to generate a regular DNA (or RNA) model, especially for occasional users or for educational purposes.
In principle, nothing is worth showing off with regard to 3DNA’s fiber model generation functionality. Nevertheless, this handy tool serves as a clear example of the differences between a “proof of concept” and a pragmatic software application. I initially decided to work on this tool simply for my own convenience. At that time, I had access to A-DNA and B-DNA fiber model generators, each as a separate program. Moreover, the constructed models did not comply to the PDB format in atom naming, among other subtitles.
I started with the Chandrasekaran & Arnott fiber models which I had a copy of data files. However, there were many details to work out, typos to correct, etc. to put them in a consistent framework. For other models, I had to read each original publication, and to type raw atomic cylindrical coordinates into computer. Again, quite a few inconsistencies popped up between the different publications with a time span over decades.
Overall, it was a quite tedious undertaking, requiring great attention to details. I am glad that I did that: I learned so much from the process, and more importantly, others can benefit from my effort. As I put in the 3DNA Nature Protocol paper (BOX 6 | FIBER-DIFFRACTION MODELS),
In preparing this set of fiber models, we have taken great care to ensure the accuracy and consistency of the models. For completeness and user verification, 3DNA includes, in addition to 3DNA-processed files, the original coordinates collected from the literature.
For those who want to understand what’s going on under the hood, there is no better way than to try to reproduce the process using, e.g., fiber B-DNA as an example.
From the very beginning, I had expected the 3DNA fiber functionality to serve as a handy tool for building a regular DNA duplex of chosen sequence. Over the years, the fiber program has gradually attracted attention from the community. The recent PyMOL wrapper by Thomas Holder is a clear sign of its increased popularity, and has prompted me to write this post, adapted largely from the one titled Fiber models in 3DNA make it easy to build regular DNA helices (dated Friday, October 9, 2009).
See also PyMOL wrapper to 3DNA fiber models
Given below is the content of the README file for fiber models in 3DNA:
1. The repeating units of each fiber structure are mostly based on the
work of Chandrasekaran & Arnott (from #1 to #43). More recent fiber
models are based on Alexeev et al. (#44-#45), van Dam & Levitt (#46
-#47) and Premilat & Albiser (#48-#55).
2. Clean up of each residue
a. currently ignore hydrogen atoms [can be easily added]
b. change ME/C7 group of thymine to C5M
c. re-assign O3' atom to be attached with C3'
d. change distance unit from nm to A [most of the entries]
e. re-ordering atoms according to the NDB convention
3. Fix up of problem structures.
a. str#8 has no N9 atom for guanine
b. str#10 is not available from the disk, manually input
c. str#14 C5M atom was named C5 for Thymine, resulting two C5 atoms
d. str#17 has wrong assignment of O3' atom on Guanine
e. str#33 has wrong C6 position in U3
f. str#37 to #str41 were typed in manually following Arnott's
new list as given in "Oxford Handbook of Nucleic Acid Structure"
edited by S. Neidle (Oxford Press, 1999)
g. str#38 coordinates for N6(A) and N3(T) are WRONG as given in the
original literature
h. str#39 and #40 have the same O3' coordinates for the 2nd strand
4. str#44 & 45 have fixed strand II residues (T)
5. str#46 & 47 have +z-axis upwards (based on BI.pdb & BII.pdb)
6. str#48 to 55 have +z-axis upwards
List of 55 fiber structures
id# Twist Rise Structure description
(dgrees) (A)
-------------------------------------------------------------------------------
1 32.7 2.548 A-DNA (calf thymus; generic sequence: A, C, G and T)
2 65.5 5.095 A-DNA poly d(ABr5U) : poly d(ABr5U)
3 0.0 28.030 A-DNA (calf thymus) poly d(A1T2C3G4G5A6A7T8G9G10T11) :
poly d(A1C2C3A4T5T6C7C8G9A10T11)
4 36.0 3.375 B-DNA (calf thymus; generic sequence: A, C, G and T)
5 72.0 6.720 B-DNA poly d(CG) : poly d(CG)
6 180.0 16.864 B-DNA (calf thymus) poly d(C1C2C3C4C5) : poly d(G6G7G8G9G10)
7 38.6 3.310 C-DNA (calf thymus; generic sequence: A, C, G and T)
8 40.0 3.312 C-DNA poly d(GGT) : poly d(ACC)
9 120.0 9.937 C-DNA poly d(G1G2T3) : poly d(A4C5C6)
10 80.0 6.467 C-DNA poly d(AG) : poly d(CT)
11 80.0 6.467 C-DNA poly d(A1G2) : poly d(C3T4)
12 45.0 3.013 D-DNA poly d(AAT) : poly d(ATT)
13 90.0 6.125 D-DNA poly d(CI) : poly d(CI)
14 -90.0 18.500 D-DNA poly d(A1T2A3T4A5T6) : poly d(A1T2A3T4A5T6)
15 -60.0 7.250 Z-DNA poly d(GC) : poly d(GC)
16 -51.4 7.571 Z-DNA poly d(As4T) : poly d(As4T)
17 0.0 10.200 L-DNA (calf thymus) poly d(GC) : poly d(GC)
18 36.0 3.230 B'-DNA alpha poly d(A) : poly d(T) (H-DNA)
19 36.0 3.233 B'-DNA beta2 poly d(A) : poly d(T) (H-DNA beta)
20 32.7 2.812 A-RNA poly (A) : poly (U)
21 30.0 3.000 A'-RNA poly (I) : poly (C)
22 32.7 2.560 Hybrid poly (A) : poly d(T)
23 32.0 2.780 Hybrid poly d(G) : poly (C)
24 36.0 3.130 Hybrid poly d(I) : poly (C)
25 32.7 3.060 Hybrid poly d(A) : poly (U)
26 36.0 3.010 10-fold poly (X) : poly (X)
27 32.7 2.518 11-fold poly (X) : poly (X)
28 32.7 2.596 Poly (s2U) : poly (s2U) (symmetric base-pair)
29 32.7 2.596 Poly (s2U) : poly (s2U) (asymmetric base-pair)
30 32.7 3.160 Poly d(C) : poly d(I) : poly d(C)
31 30.0 3.260 Poly d(T) : poly d(A) : poly d(T)
32 32.7 3.040 Poly (U) : poly (A) : poly(U) (11-fold)
33 30.0 3.040 Poly (U) : poly (A) : poly(U) (12-fold)
34 30.0 3.290 Poly (I) : poly (A) : poly(I)
35 31.3 3.410 Poly (I) : poly (I) : poly(I) : poly(I)
36 60.0 3.155 Poly (C) or poly (mC) or poly (eC)
37 36.0 3.200 B'-DNA beta2 Poly d(A) : poly d(U)
38 36.0 3.240 B'-DNA beta1 Poly d(A) : poly d(T)
39 72.0 6.480 B'-DNA beta2 Poly d(AI) : poly d(CT)
40 72.0 6.460 B'-DNA beta1 Poly d(AI) : poly d(CT)
41 144.0 13.540 B'-DNA Poly d(AATT) : poly d(AATT)
42 32.7 3.040 Poly(U) : poly d(A) : poly(U) [cf. #32]
43 36.0 3.200 Beta Poly d(A) : Poly d(U) [cf. #37]
44 36.0 3.233 Poly d(A) : poly d(T) (Ca salt)
45 36.0 3.233 Poly d(A) : poly d(T) (Na salt)
46 36.0 3.38 B-DNA (BI-type nucleotides; generic sequence: A, C, G and T)
47 40.0 3.32 C-DNA (BII-type nucleotides; generic sequence: A, C, G and T)
48 87.8 6.02 D(A)-DNA ploy d(AT) : ploy d(AT) (right-handed)
49 60.0 7.20 S-DNA ploy d(CG) : poly d(CG) (C_BG_A, right-handed)
50 60.0 7.20 S-DNA ploy d(GC) : poly d(GC) (C_AG_B, right-handed)
51 31.6 3.22 B*-DNA poly d(A) : poly d(T)
52 90.0 6.06 D(B)-DNA poly d(AT) : poly d(AT) [cf. #48]
53 -38.7 3.29 C-DNA (generic sequence: A, C, G and T) (depreciated)
54 32.73 2.56 A-DNA (generic sequence: A, C, G and T) [cf. #1]
55 36.0 3.39 B-DNA (generic sequence: A, C, G and T) [cf. #4]
-------------------------------------------------------------------------------
List 1-41 based on Struther Arnott: ``Polynucleotide secondary structures:
an historical perspective'', pp. 1-38 in ``Oxford Handbook of Nucleic
Acid Structure'' edited by Stephen Neidle (Oxford Press, 1999).
#42 and #43 are from Chandrasekaran & Arnott: "The Structures of DNA
and RNA Helices in Oriented Fibers", pp 31-170 in "Landolt-Bornstein
Numerical Data and Functional Relationships in Science and Technology"
edited by W. Saenger (Springer-Verlag, 1990).
#44-#45 based on Alexeev et al., ``The structure of poly(dA) . poly(dT)
as revealed by an X-ray fiber diffraction''. J. Biomol. Str. Dyn, 4,
pp. 989-1011, 1987.
#46-#47 based on van Dam & Levitt, ``BII nucleotides in the B and C forms
of natural-sequence polymeric DNA: a new model for the C form of DNA''.
J. Mol. Biol., 304, pp. 541-561, 2000.
#48-#55 based on Premilat & Albiser, ``A new D-DNA form of poly(dA-dT) .
poly(dA-dT): an A-DNA type structure with reversed Hoogsteen Pairing''.
Eur. Biophys. J., 30, pp. 404-410, 2001 (and several other publications).
Recently, I heard from Thomas Holder, the PyMOL Principal Developer (Schrödinger, Inc.), that he had written a wrapper to the 3DNA fiber command. This PyMOL wrapper is implemented as part of his versatile PSICO library (see the PyMOL Wiki page Psico for details), and exposes the 55 fiber models based on Arnott and other’s work to the wide PyMOL user community. Moreover, the wrapper can be accessed directly from PyMOL (without installing PSICO), as shown below with an example:
PyMOL> run https://raw.githubusercontent.com/speleo3/pymol-psico/master/psico/creating.py
PyMOL> fiber CTAGCG
The resulting fiber model is the default B-form DNA of calf thymus, with twist of 36.0° and rise of 3.375 Å (see figure below). Note that cases in base sequence do not matter, so fiber ctagcg or fiber CTAgcg will give the same result.
Running PyMOL>help fiber gives the following detailed usages info, which should be sufficient to get one started with this fiber tool in PyMOL.
PyMOL> help fiber
DESCRIPTION
Run X3DNA's "fiber" tool.
For the list of structure identification numbers, see for example:
http://xiang-jun.blogspot.com/2009/10/fiber-models-in-3dna.html
USAGE
fiber seq [, num [, name [, rna [, single ]]]]
ARGUMENTS
seq = str: single letter code sequence or number of repeats for
repeat models.
num = int: structure identification number {default: 4}
name = str: name of object to create {default: random unused name}
rna = 0/1: 0=DNA, 1=RNA {default: 0}
single = 0/1: 0=double stranded, 1=single stranded {default: 0}
EXAMPLES
# environment (this could go into ~/.pymolrc or ~/.bashrc)
os.environ["X3DNA"] = "/opt/x3dna-v2.3"
# B or A DNA from sequence
fiber CTAGCG
fiber CTAGCG, 1, ADNA
# double or single stranded RNA from sequence
fiber AAAGGU, name=dsRNA, rna=1
fiber AAAGGU, name=ssRNA, rna=1, single=1
# poly-GC Z-DNA repeat model with 10 repeats
fiber 10, 15
Thanks to Thomas, for making another connection between PyMOL and 3DNA/DSSR. The other one is the DSSR-plugin for PyMOL to create “block” shaped cartoons for nucleic acid bases and base pairs.
See also 3DNA fiber models
