NOTICE: This package is going to be deprecated in favour of a peer-reviewed package, get-contacts. Please use that package going forward.
Computes a molecular graph for protein structures.
Proteins fold into 3D structures, and have a natural graph representation: amino acids are nodes, and biochemical interactions are edges.
I wrote this package as part of a larger effort to do graph convolutional neural networks on protein structures (represented as graphs). However, that's not the only thing I can foresee doing with this.
One may be interested in the topology of proteins across species and over evolutionary time. This package can aid in answering this question.
Currently only pip-installable:
$ pip install proteingraphThis package assumes that you have a standard protein structure file (e.g. a PDB file). This may be a file generated after solving the NMR or crystal structure of a protein, or it may be generated from homology modelling.
Once that has been generated, the molecular graph can be generated using Python code.
from proteingraph import ProteinInteractionNetwork
p = ProteinInteractionNetwork('my_model.pdb')Because the ProteinInteractionNetwork class inherits from NetworkX's Graph class, all methods that Graph has are inherited by ProteinInteractionNetwork, and it behaves just as a NetworkX graph does.
What this means is that all graph-theoretic metrics (e.g. degree centrality, betweenness centrality etc.) can be computed on the ProteinInteractionNetwork object.
See the HIV1 homology model example in the examples/ directory for a minimal example.