In [3]:
import pandas
PDBE_SOLR_URL = "http://www.ebi.ac.uk/pdbe/search/pdb"
# or https://www.ebi.ac.uk/pdbe/search/pdb/select?rows=0&q=status:REL&wt=json
from mysolr import Solr
solr = Solr(PDBE_SOLR_URL, version=4)
response = solr.search(**{
"rows" : 0, # "fl" : "deposition_year",
"q" : 'status:REL',
"facet" : "true", "facet.limit" : 1000000, "facet.mincount" : 1,
"facet.field" : "deposition_year",
}
)
from matplotlib import pyplot as plt
# skip current year, which is not yet complete
years = sorted( response.facets['facet_fields']['deposition_year'] )[0:-1]
ticks = [1980, 1990, 2000, 2010]
fig = plt.figure()
plt.yscale('log')
plt.ylabel('PDB entries released')
plt.plot(years, [ response.facets['facet_fields']['deposition_year'][year] for year in years])
plt.xticks(ticks, [str(t) for t in ticks])
fig.autofmt_xdate()
plt.savefig('../../images/PDB.eps', format='eps', dpi=1000)
plt.show()
If an entry referes to more than one macromolecule (i.e. is heteromeric) then the work is more challenging that research of a single protein.
There is another case. A project might target a homodimer, or more generally a homo-oligomer. For a homomeric protein structure, the PDB does not record the submitter's judgement of whether the contacts between the molecules are complexation of biological relevance, or merely crystal contacts. So we cannot distinguish targeted work on homo-complexes from other work on single species. We therefore report simply the proportion of projects that aim at one rather than many macromolecular species.
In [22]:
import collections
# returns all values, in increasing order of frequency
def get_values(field):
response = solr.search(**{
"rows" : 0, "fl" : "deposition_year",
"q" : 'status:REL',
"facet" : "true", "facet.limit" : 10000000, "facet.mincount" : 1,
"facet.field" : field,
"group" : "true", "group.facet" : "true",
"group.field" : "pdb_id",
})
d = response.facets['facet_fields'][field]
return collections.OrderedDict(reversed(list(d.items())))
values = get_values('assembly_form')
values
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In [36]:
from string import Template
def get_timeline(field):
response = solr.search(**{
"rows" : 0, "fl" : "deposition_year",
"q" : 'status:REL',
'json.facet': Template("""{
deposition_year:{
type:range,start:1971,end:2018,gap:1,field:deposition_year,limit:20,
facet:{
facet1:{
type:terms, field: $field,
facet:{
grouped_facet_count:\"unique(pdb_id)\"
}
},
grouped_depositionyear_count:\"unique(pdb_id)\"
}
}
}""").substitute(field=field)
}
)
return response.raw_content['facets']['deposition_year']['buckets']
timeline = get_timeline('assembly_form')
# see an example of an annual report
timeline[-4]
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In [38]:
def get_df(synonyms, timeline):
counts0 = {'unknown':0}
for value in synonyms:
counts0[ synonyms[value] ] = 0
rows = []
for bucket in timeline:
year = int(bucket['val'])
counts = dict(counts0)
if bucket['count'] > 0:
total = bucket['grouped_depositionyear_count']
for bbucket in bucket['facet1']['buckets']:
synonym = synonyms[ bbucket['val'] ]
counts[ synonym ] = counts.get(synonym, 0)+bbucket['grouped_facet_count']
total = total - bbucket['grouped_facet_count']
counts['unknown'] = total
rows.append([year]+[ counts[label] for label in counts0])
df = pandas.DataFrame(rows, columns=['year']+ list(counts0.keys()) )
df = df[df.year>1977]
return df
df = get_df( {'homo':'homomeric', 'hetero':'heteromeric'}, timeline)
df.head()
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In [40]:
def plot_df(df, values):
df['total'] = pandas.Series([0]*len(df), index=df.index)
for value in values:
df['total'] = df['total'] + df[value]
running_total = pandas.Series([0]*len(df), index=df.index)
for value in values:
series = df[value] / df['total']
plt.fill_between(df.year, running_total, running_total+series, label=values[value])
running_total = running_total + series
plt.fill_between(df.year, running_total, [1]*len(df), color='black', label='unknown' )
plt.figure()
plot_df(df, {'heteromeric': 'heteromeric', 'homomeric': 'homomeric'})
plt.legend()
plt.title('Fraction of new structures that contain more than one macromolecular species')
plt.ylim([0, 1])
plt.savefig('../../images/complexes.eps', format='eps', dpi=1000)
plt.show()
In [41]:
values = get_values('superkingdom')
timeline = get_timeline('superkingdom')
df = get_df({value:value for value in values}, timeline)
plt.figure()
plot_df(df, {value: value for value in values})
plt.legend()
plt.show()
In [44]:
values = get_values("biological_cell_component")
d = {value : ('membrane' if 'membrane' in value else 'other') for value in values}
timeline = get_timeline("biological_cell_component")
df = get_df(d, timeline)
plt.figure()
plot_df(df, {'membrane':'membrane', 'other':'other'})
plt.legend()
plt.show()
In [63]:
values = get_values("experimental_method")
d = {}
for value in values:
d[value] = value if values[value]>120 else 'other'
timeline = get_timeline("experimental_method")
df = get_df(d, timeline)
plt.figure()
plot_df(df, {d[value]:d[value] for value in d})
plt.legend()
plt.show()