pypdb advanced demos

In [1]:

    

%pylab inline
from IPython.display import HTML

# Import from local directory
import sys
sys.path.insert(0, '../pypdb')
from pypdb import *

## Import from installed package
# from pypdb import *

import pprint


    

    




Populating the interactive namespace from numpy and matplotlib

In [4]:

    

# Search for PDB IDs related to CRISPR
crispr_query = Query('crispr').search()

# Run BLAST on the top result
top_result = crispr_results[0]
blast_hits = get_blast2(top_result)


for item in blast_hits[0]:
    pdbdesc = describe_pdb(item)
    print(pdbdesc['title'])


    

    




Structure of Thermus Thermophilus Cse3 bound to an RNA representing a product complex
Structure of Thermus Thermophilus Cse3 bound to an RNA representing a pre-cleavage complex
Structure of Thermus Thermophilus Cse3 bound to an RNA representing a product mimic complex
Structure A of CRISPR endoribonuclease Cse3 bound to 19 nt RNA
Structure B of CRISPR endoribonuclease Cse3 bound to 19 nt RNA
Structure of CRISPR endoribonuclease Cse3 bound to 20 nt RNA
Crystal structure of a CRISPR-associated protein from thermus thermophilus
Crystal structure of E. coli Cascade bound to a PAM-containing dsDNA target at 2.45 angstrom resolution.
Crystal structure of E. coli Cascade bound to a PAM-containing dsDNA target (32-nt spacer) at 3.20 angstrom resolution.
The Type IE CRISPR Cascade complex from E. coli, with two assemblies in the asymmetric unit arranged back-to-back
Crystal structure of the E. coli CRISPR RNA-guided surveillance complex, Cascade
Crystal structure of a CRISPR RNA-guided surveillance complex, Cascade, bound to a ssDNA target
Crystal structure of RNA-guided immune Cascade complex from E.coli
Crystal structure of the CRISPR-associated protein Cas6e from Escherichia coli str. K-12
CRISPR RNA-guided surveillance complex, pre-nicking
CRISPR RNA-guided surveillance complex
CRISPR RNA-guided surveillance complex

In [4]:

    

# Choose a random sample because we don't want to call the database for every single entry
from random import choice

all_pdbs = get_all()

all_dates = list()

for ii in range(100):
    pdb_desc = describe_pdb( choice(all_pdbs) )
    depdate = (pdb_desc['deposition_date'])
    all_dates.append( int(depdate[:4]) )
    
all_dates = array(all_dates)

figure()
subs_v_time = hist(all_dates, max(all_dates)-min(all_dates))
show(subs_v_time)   

# Show power-law scaling
figure()
subs_v_time_loglog = loglog(subs_v_time[0],'.')
show(subs_v_time_loglog)


    

In [11]:

    

help(find_dates)


    

    




Help on function find_dates in module pypdb:

find_dates(search_term, **kwargs)
    Return an ordered list of the PDB submission dates returned by a
    keyword search of the RCSB PDB. This can be used to assess the
    popularity of a gievne keyword or topic
    
    Parameters
    ----------
    
    search_term : str
        The search keyword
    
    max_results : int
        The maximum number of results to return
    
    Returns
    -------
    
    all_dates : list of str
        A list of calendar strings associated with the search term, these can
        be converted directly into time or datetime objects

In [15]:

    

# Perform search
all_dates = find_dates('crispr', max_results=100)
all_dates = array(all_dates)
all_dates = array([int(depdate[:4]) for depdate in all_dates])
subs_v_time = histogram(all_dates, max(all_dates)-min(all_dates))
dates, num_entries = subs_v_time[1][1:], subs_v_time[0]
popgraph = fill_between(dates, 0, num_entries)

# Formatting the plots
xlim([dates[0], dates[-1]] )
gca().xaxis.set_major_formatter(FormatStrFormatter('%d'))
xticks(fontweight='bold')
yticks(fontweight='bold')
xlabel('Year',fontweight='bold')
ylabel('New PDB entries',fontweight='bold')
show(popgraph)


    

In [21]:

    

point_group = 'C1'
max_distance = 5.0
npts = 20

dist_vals = linspace(0.0, max_distance, npts)
dx = dist_vals[1]-dist_vals[0]

all_ids = []

for dist_val in dist_vals:
    idlist = do_protsym_search(point_group, min_rmsd=dist_val, max_rmsd=(dist_val+dx))
    all_ids.append(idlist)
counts = array([len(item) for item in all_ids])


fig = semilogy(dist_vals, counts)
title('Total results versus RMSD')
xlabel('Radius (A)')
ylabel('Number of results')
show(fig)


    

In [7]:

    

# Search for PDB IDs related to CRISPR
crispr_query = Query('crispr').search()

# Run BLAST on the top result
top_result = crispr_results[0]
blast_hits = get_blast2(top_result)

# Print list of associated taxa
pprint.pprint(list_taxa(blast_hits[0][:5]))
pprint.pprint(list_types(blast_hits[0][:5]))


    

    




['Thunnus thynnus',
 'Thunnus thynnus',
 'Thunnus thynnus',
 'Trematomus bernacchii',
 'Trematomus bernacchii']
['protein', 'protein', 'protein', 'protein', 'protein']

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