3. Protein test case study

Application: retrieving information about a given protein

This section uses BioServices to demonstrate the interest of combining several services together within a single framework using the Python language as a glue language.

In this tutorial we are interested in using BioServices to obtain information about a specific protein. Let us focus on ZAP70 protein (homo sapiens).

3.1. Get a unique identifier and gene names from a name

From Uniprot, we can obtain the unique accession number of ZAP70, which may be useful later on. Let us try to use the search() method:

>>> from bioservices import *
>>> u = UniProt(verbose=False)
>>> u.search("ZAP70_HUMAN") # could be lower case

The default format of the returned answer is “tabulated”:

>>> res = u.search("ZAP70_HUMAN", frmt="tab")
>>> print(res)
Entry   Entry name  Status  Protein names   Gene names  Organism    Length
P43403  ZAP70_HUMAN reviewed    Tyrosine-protein kinase ZAP-70 (EC (70 kDa zeta-chain associated protein) (Syk-related tyrosine kinase)    ZAP70 SRK Homo sapiens (Human)    619

It is better, but let us simplify even further. In BioServices, the output of the tabulated format contains several columns but we can select only a subset such as the Entry (accession number) and the gene names, which are coded as “id” and “genes” in uniprot database:

>>> res = u.search("ZAP70_HUMAN", frmt="tab", columns="id,genes")
>>> print(res)
Entry   Gene names
P43403  ZAP70 SRK

So here we got the Entry P43403. Entry and Gene names can be saved in two variables as follows:

>>> res = u.search("ZAP70_HUMAN", frmt="tab", columns="id,genes")
>>> entry, gene_names = res.split("\n")[1].split("\t")

3.2. Getting the fasta sequence

It is then straightforward to obtain the FASTA sequence of ZAP70 using another method from the UniProt class called retrieve():

>>> sequence = u.retrieve("P43403", "fasta")
>>> print(sequence)
>sp|P43403|ZAP70_HUMAN Tyrosine-protein kinase ZAP-70 OS=Homo sapiens GN=ZAP70 PE=1 SV=1


There are many services that provides access to the FASTA sequence. We chose uniprot but you could use the Entrez utilities as well as other services.

3.3. Using BLAST on the sequence

You can then analyse this sequence with your favorite tool. As an example, within BioServices you can use NCIBlast but first let us extract the sequence itself (without the header):

sequence = sequence.split("\n", 1)[1].strip("\n")


>>> s = NCBIblast(verbose=False)
>>> jobid = s.run(program="blastp", sequence=sequence, stype="protein", \
...    database="uniprotkb", email="cokelaer@ebi.ac.uk")
>>> print(s.getResult(jobid, "out")[0:1000])
BLASTP 2.2.26 [Sep-21-2011]

Reference: Altschul, Stephen F., Thomas L. Madden, Alejandro A. Schaffer,
Jinghui Zhang, Zheng Zhang, Webb Miller, and David J. Lipman (1997),
"Gapped BLAST and PSI-BLAST: a new generation of protein database search
programs",  Nucleic Acids Res. 25:3389-3402.

Query= EMBOSS_001
         (619 letters)

Database: uniprotkb
           32,727,302 sequences; 10,543,978,207 total letters


                                                                 Score    E
Sequences producing significant alignments:                      (bits) Value

SP:ZAP70_HUMAN P43403 Tyrosine-protein kinase ZAP-70 OS=Homo sap...  1279   0.0
TR:H2QIE3_PANTR H2QIE3 Tyrosine-protein kinase OS=Pan troglodyte...  1278   0.0
TR:G3QGN8_GORGO G3QGN8 Tyrosine-protein kinase OS=Gorilla gorill...  1278   0.0
TR:G1QLX3_NOMLE G1QLX3 Tyrosine-protein kinase OS=Nomascus leuco...  1249   0.0

The last command waits for the job to be finised before printing the results, which may be quite long. We could look at the beginnin of the reported results and select only HUMAN sequences to see that the best sequence found is indeed ZAP70_HUMAN as expected:

>>> [x for x in s.getResult(jobid, "out").split("\n") if "HUMAN" in x]
['SP:ZAP70_HUMAN P43403 Tyrosine-protein kinase ZAP-70 OS=Homo sap...  1279 0.0  ',
 'SP:KSYK_HUMAN P43405 Tyrosine-protein kinase SYK OS=Homo sapiens...   691 0.0  ',
 'TR:A8K4G2_HUMAN A8K4G2 Tyrosine-protein kinase OS=Homo sapiens P...   691 0.0  ',

3.4. Searching for relevant pathways

The KEGG services provides pathways, so let try to find pathways that contains our targetted protein. First we need to know the KEGG Id that corresponds to ZAP70. We can use the find method form KEGG service:

>>> from bioservices import KEGG
>>> k = KEGG(verbose=False)
>>> k.find("hsa", "zap70")  # "hsa" stands for homo sapiens
hsa:7535 ZAP70, SRK, STCD, STD, TZK, ZAP-70; zeta-chain (TCR) associated protein kinase 70kDa (EC:; K07360 tyrosine-protein kinase ZAP-70 [EC:

There are other ways to perform this conversion using the bioservices.uniprot.UniProt.mapping() or bioeservices.KEGG.conv() methods (e.g., textit{k.conv(“hsa”, “up:P43403”)}).

Now, let us get the pathways that contains this ID:

>>> k.get_pathway_by_gene("7535", "hsa")
{'hsa04064': ' NF-kappa B signaling pathway',
'hsa04650': 'Natural killer cell mediated cytotoxicity',
 'hsa04660': 'T cell receptor signaling pathway',
 'hsa05340': 'Primary immunodeficiency'}

We can look at the first pathway in a browser (highlighting the ZAP70 node):

>>> k.show_pathway("hsa04064", keggid={"7535": "red"})

3.5. Searching for binary Interactions

For this purpose, we could use PSICQUIC services to find the interactions that involve ZAP70 in the mint database:

>>> from bioservices import PSICQUIC
>>> s = PSICQUIC(verbose=False)
>>> data = s.query("mint", "ZAP70 AND species:9606")

where 9606 is the taxonomy Id for homo sapiens. We could also figure out how many interactions could be found in each dabase for this particular query:

>>> s.getInteractionCounter("zap70 AND species:9606")
{'apid': 82,
 'bar': 0,
 'bind': 4,
 'bindingdb': 29,
 'biogrid': 73,
 'chembl': 161,
 'dip': 0,
 'i2d-imex': 0,
 'innatedb': 13,
 'innatedb-imex': 0,
 'intact': 11,
 'interoporc': 0,
 'irefindex': 273,
 'matrixdb': 0,
 'mbinfo': 0,
 'mint': 34,
 'molcon': 0,
 'mpidb': 0,
 'reactome': 0,
 'reactome-fis': 134,
 'spike': 47,
 'string': 319,
 'topfind': 0,
 'uniprot': 0}

We see for instance that the mint database has 34 interactions. Coming back to the interactions returned by s.query, we find indeed 34 intercations between ZAP70 and another component:

>>> len(data)

Let us look at the first one:

>>> for x in data[0]: print(x)
uniprotkb:VAV1(gene name)|uniprotkb:VAV(gene name synonym)
uniprotkb:ZAP70(gene name)|uniprotkb:SRK(gene name synonym)|uniprotkb:70 kDa
zeta-associated protein(gene name synonym)|uniprotkb:Syk-related tyrosine
kinase(gene name synonym)
taxid:9606(Homo sapiens)
taxid:9606(Homo sapiens)

The First two elements are the entries for specy A and B. The last element is the score. The 11th element is the type of interaction and so on.

What could be useful is to convert these elements into uniprot ID only. With mint database it is irrelevant for this particular entry but with other DBs or entries, it may be useful (e.g., biogrid).

BioServices provides such a function called convert():

>>> data = s.query("biogrid", "ZAP70 AND species:9606")
>>> data2 = s.convert(data, "biogrid")


some databases may be offline. If so, try we another database. Type “s.activeDBs”.

convert method converts all entries from data into uniprot ID. If this is not possible, the entry is removed. The query and convert works on a single database but you we could query all or a subset of all databases using the queryAll and convertAll functions:

>>> data = s.queryAll("ZAP70 AND species:9606", databases=["mint", "biogrid"])
>>> data2 = s.convertAll(data)

However, extra cleaning is required to remove entries that are not relevant (no match to uniprot ID, redundant, not a protein, self interactions, ...). In order to ease this tast, the psicquic.AppsPPI class is very useful.

from bioservices import psicquic
s = psicquic.AppsPPI()
s.queryAll("ZAP70 AND species:9606", databases=["mint", "biogrid", "intact", "reactome-fis"])

(Source code, png, hires.png, pdf)


The summary function print a useful summary about the number of found interactions and overlap between databases:

 >>> s.summary()
 Found 8 interactions within intact database
 Found 124 interactions within reactome-fis database
 Found 19 interactions within mint database
 Found 67 interactions within biogrid database
 Found 152 interactions in 1 common databases
 Found 14 interactions in 2 common databases
 Found 0 interactions in 3 common databases
 Found 1 interactions in 4 common databases

This may be different depending on the available databases. Finally, you can obtain the relation that was found in the 4 databases:

 >>> s.relevant_interactions[4]

3.6. What’s next ?

There are lots of other services that could be usefule. An example is the wikipathway (see Wikipathway) to retrieve even more pathways that include the ZAP70 protein. Another example is the BioMart portal. You could use it to retrieve pathways from REACTOME (see BioMart). You can also retrieve target from ChEMBL given the uniprot ID ( get_target_by_uniprotId(“P43403”) ) and so on.