In this tutorial we will cover:

• which file formats are supported

• how to convert between them

• what external integrations are available

Supported formats

biobuild Molecules can be loaded from a number of different input types and exported to them as well. Here is an overview of supported formats:

flowchart TB

  node_1("Molecule")
  node_2(["PDB"])
  node_3(["mmCIF"])
  node_9(["Molfile"])
  node_11(("SMILES"))
  node_4(["JSON\n(biobuild)"])
  node_5(["pickle\n(biobuild)"])
  node_6{{"rdkit\n(Chem.rdchem.Mol)"}}
  node_7{{"openbabel\n(pybel.Molecule)"}}
  node_8{{"biopython\n(PDB.Structure)"}}
  node_10{{"openmm\n(openmm.PDBFile)"}}
  node_2 <--> node_1
  node_3 <--> node_1
  node_9 <-.-> node_1
  node_11 <-.-> node_1
  node_4 <--> node_1
  node_5 <--> node_1
  node_6 <--> node_1
  node_7 <--> node_1
  node_8 <--> node_1
  node_10 <--> node_1

Here are is a table with the corresponding methods available in the Molecule class:

Format	As input	As output
PDB	from_pdb	to_pdb
mmCIF	from_cif	to_cif
Molfile	from_molfile	to_molfile
SMILES	from_smiles	to_smiles
JSON	from_json	to_json
pickle	load	save
rdkit	from_rdkit	to_rdkit
openbabel	from_pybel	to_pybel
openmm	from_openmm	to_openmm
biopython	<normal init>	to_biopython

In fact, using the toplevel molecule function will automatically try to discern which input type is given and perform the necessary file-reading, parsing, and/or format conversions. For some of the methods there are also functional equivalents available such as the read_pdb and write_pdb functions as equivalents to the from_pbd and to_pdb methods.

Examples

Assuming we want to read a structure from a PDB file, relabel the atoms and save the structure to a CIF file, we could do:

import plotly
plotly.offline.init_notebook_mode()

import biobuild as bb

man = bb.Molecule.from_pdb("files/man.pdb")

# relabel the atoms to match the CHARMM force field nomenclature
man.autolabel()

# save as a CIF file
man.to_cif("files/man.cif")

External integrations

Biobuild is built on top of biopython so naturally all biobuild molecules can function with biopython out of the box. In case any functionality is not working, the extra biobuild-overhead can be removed using the to_biopython method. However, biobuild is not limited to biopython. It also supports direct conversions with RDKit and openbabel.

So maybe we want to perform some more intricate chemical analyses using rdkit but first we want to make use of biobuild’s easy methods for creating larger molecules. Maybe we want to generate a tri-peptide HIS-GLU-HIS which we can then analyse further.

from rdkit.Chem.Draw import SimilarityMaps
from rdkit import Chem
from rdkit.Chem import AllChem

# first we need to load the amino acids data
bb.load_amino_acids()

# now we get the amino acids we want to connect
his = bb.Molecule.from_compound("HIS")
glu = bb.Molecule.from_compound("GLU")

# connect the amino acids using the peptide linkage (called "LINK")
peptide = his % "LINK" + glu + his

# convert to an RDKit molecule
peptide = peptide.to_rdkit()

# remove the 3D coordinates
peptide.RemoveAllConformers()

# now we can use RDKit to perform further analysis
AllChem.ComputeGasteigerCharges(peptide)
fig = SimilarityMaps.GetSimilarityMapFromWeights(peptide, [x.GetDoubleProp("_GasteigerCharge") for x in peptide.GetAtoms()], colorMap='jet', contourLines=10)