Data Reference
Graph Format
Each graph contains structure information for one model of a PDB entry containing at least one RNA chain.
Graphs are stored in JSON node-link format which can be loaded by [networkx](https://networkx.org/documentation/stable/reference/readwrite/generated/networkx.readwrite.json_graph.node_link_data.html#networkx.readwrite.json_graph.node_link_data). All data comes from the output of x3dna-dssr which can be downloaded [here](https://x3dna.org/) and our custom interface extraction tools.
Graphs are dumped as JSONs in the node-link format.
import json
from networkx.readwrite.json_graph import node_link_graph
G = node_link_graph(open('path/to/graph', 'r'))
Nodes
Node IDs
Node IDs are strings in the form [pdb id].[chain name].[residue number].
Node Data
To access node data dictionary:
G.nodes[<node_id>]
These are the keys in the node data dictionary:
‘index’: (int), relative index along chain starting at 1 (e.g. 1)
‘index_chain’: (int) 26 (e.g. 26)
‘chain_name’: (str), name of chain. (e.g. A)
‘nt_resnum’: (int), residue number according to PDB. (e.g. 101)
‘nt_name’: (str), nucleotide name (e.g. G)
‘nt_code’: (str), (e.g. ‘U’)
‘nt_id’: (str) unique nucleotide ID generated by DSSR. <chain name>.<nt name><nt_resnum> (e.g. ‘A.U42’),
‘nt_type’: (str) molecule type of residue (e.g. ‘RNA’)
‘dbn’: (str) dot-bracket notation for the residue (e.g. ‘)’)
‘summary’: (str) additional residue info (e.g. “anti,~C3’-endo,BI,canonical,non-pair-contact,helix,stem,coaxial-stack”)
‘alpha’: (float) base angle in degrees [-180, 180].
‘beta’: (float) base angle
‘gamma’: (float) base angle
‘delta’: (float) base angle
‘epsilon’: (float)
‘zeta’: (float)
‘epsilon_zeta’: (float)
‘bb_type’: (str) Backbone type ‘BI’,
‘chi’: (float)
‘glyco_bond’: str (e.g. ‘anti’
‘C5prime_xyz’: (list), 5’ Carbon xyz coordinates (e.g. `[-1.343, 8.453, 1.288])
‘P_xyz’: (list) Phosphate coordinates.
‘form’: (str) (e.g. ‘A’) classification of a dinucleotide step comprising the bp above the given designation and the bp that follows it. Types include ‘A’, ‘B’ or ‘Z’ for the common A-, B- and Z- form helices, ‘.’ for an unclassified step , and ‘x’ for a step without a continuous backbone.
‘ssZp’: (float) (e.g. 4.41),
‘Dp’: (float) (e.g. 4.404)
‘splay_angle’: (float) (e.g. 21.6),
‘splay_distance’: (float) (e.g. 3.612)
‘splay_ratio’: (float) (e.g. 0.199)
‘eta’: (float) (e.g. 169.652),
‘theta’: -167.457,
‘eta_prime’: (float) (e.g. -176.189)
‘theta_prime’: (float) (e.g. -167.27)
‘eta_base’: (float) (e.g. -135.681)
‘theta_base’: (float) (e.g. -141.003)
‘v0’: (float) (e.g 8.194)
‘v1’: (float) (e.g. -28.393),
‘v2’: (float)
‘v3’: (float)
‘v4’: (float)
‘amplitude’: (float)
‘phase_angle’: (float)
‘puckering’: (str) (e.g. “C3’-endo”)
‘sugar_class’: (str) (e.g. “~C3’-endo”)
‘bin’: (str) (e.g. ‘33t’) ( name of the 12 bins based on [ delta (i -1) , delta , gamma ], where delta (i -1) and delta can be either 3 ( for C3 ‘- endo sugar ) or 2 ( for C2 ‘- endo ) and gamma can be p/t/ m ( for gauche +/ trans / gauche - conformations , respectively ) (2 x2x3 =12 combinations : 33p , 33t , … 22m); ‘inc’ refers to incomplete cases (i .e., with missing torsions ) , and ‘trig’ to triages ( i.e., with torsion angle outliers ),[1]
‘cluster’: (str) (e.g. ‘1c’) (2-char suite name, for one of 53 reported clusters (46 certain and 7 wannabes ) , ‘__’ for incomplete cases , and ‘!!’ for outliers),[1]
‘suiteness’: (float) (measure of conformer - match quality ( low to high in range 0 to 1) ) [1]
‘filter_rmsd’: (float)
‘frame’: (dict) e.g. ({‘rmsd’: 0.006, ‘origin’: [-4.856, 8.564, -1.171], ‘x_axis’: [0.922, 0.386, -0.006], ‘y_axis’: [0.098, -0.25, -0.963], ‘z_axis’: [-0.374, 0.888, -0.269], ‘quaternion’: [0.592, -0.781, -0.155, 0.122]}
‘sse’: (dict) Secondary structure info (e.g. residue inside third hairpin {‘sse’: ‘hairpin_3’})
‘binding_protein’: (dict) RNA-Protein interface. If no interface found, None. Else, dictionary (e.g. {‘nt-aa’: ‘C-arg’, ‘nt’: ‘A.C37’, ‘aa’: ‘A.ARG47’, ‘Tdst’: ‘6.62’, ‘Rdst’: ‘-114.00’, ‘Tx’: ‘-1.15’, ‘Ty’: ‘1.89’, ‘Tz’: ‘6.23’, ‘Rx’: ‘-53.57’, ‘Ry’: ‘19.41’, ‘Rz’: ‘-103.42’, ‘sse’: ‘a-helix’})
‘binding_ion’: (string) molecule ID of ion if residue is at a binding site (otherwise None) (e.g. ‘Ca’)
‘binding_small-molecule’: (string) molecule ID of small molecule if residue is at a binding site (otherwise None) (e.g. ‘SAM’)
Edge data
Each edge also has an attribute dictionary:
G.edges[(<node_1>, <node_2>)]
‘index’: (int) Index of edge in DSSR ordering.
‘nt1’: (str) DSSR nucleotide ID of first base (e.g. ‘A.G17’)
‘nt2’: (str) DSSR nucleotide ID of second base (e.g. ‘A.G29’)
‘bp’ (str): Nucleotide identity of paired residues (e.g. ‘G-C’)
‘name’: (str) (e.g. ‘WC’)
‘Saenger’: (str) Saenger base pairing category (e.g. ‘19-XIX’),
‘LW’: (str) Leontis-Westhof base pair geometry category (e.g. ‘cWW’)
‘DSSR’: (str) Custom DSSR base pair geometry category (e.g. ‘cW-W’)
Graph-level data
Each graph also has an attribute dictionary:
G.graph
‘dbn’: a dict containing information on the chains contained in the graph, such as the sequences or their length
‘resolution_{low,high}’: bounds on the resolution, present in most (~80%) of the graphs
‘proteins’: A list of the residues in contact with a protein
‘ligands’: A list of the ligands interacting with the graph nucleotides. Each ligand is a dict with the ligand Biopython id, its name, and the rna residues it is bound to
‘ions’: same thing with ions
## Graph creation pipeline * dssr_to_graphs.py : runs dssr on the cif file to get the first networkx graph object. It moreover computes the RNA/protein interfaces (since it uses dssr) and annotates at the level of the node. * annotations.py’: Completes the graph using the mmcif file to include resolution and interaction with small molecules and ions * main.py : The script to call to build or update the data releases
References
[1] [Richardson et al. (2008): “RNA backbone: consensus all-angle conformers and modular string nomenclature (an RNA Ontology Consortium contribution). RNA, 14(3):465-481](https://rnajournal.cshlp.org/content/14/3/465.short)