Pipeline Design¶

0. Required dependencies¶

Before we are getting started, please ensure that you have the following dependencies installed:

• HH-suite Repo
• HHblits database
• CCMpred Repo

1. Importing ConKit modules¶

At the top of our pipeline script, we need to import ConKit Python modules to be able to run a contact prediction

To handle file conversions, we need the ConKit I/O package.

>>> from conkit import io

We also need to import the command line application wrappers so we can execute our applications from within Python.

>>> from conkit import applications

2. Creating I/O filenames¶

The next step is to define some filenames and variables so we do not get confused as we go along.

>>> name = 'conkit_example'
>>> a3m_fname = name + '.a3m'
>>> a3m_filtered_fname = name + '.filtered.a3m'
>>> jones_filtered_fname = name + '.filtered.jones'
>>> matrix_fname = name + '.mat'
>>> casprr_fname = name + '.rr'
>>> hhblits_db = 'path_to_hhblits_db'

The files defined are all the files we need in a very simplified contact prediction pipeline. I opted for starting all my filenames with conkit_example so we could identify them in a single directory.

Now, we also need to provide a FASTA file. Here, my arbitrary FASTA file is called conkit.fasta. Let’s define it

>>> fasta = 'conkit.fasta'

3. Generating a Multiple Sequence Alignment¶

Above, we defined all filenames required in this pipeline, so let us continue and generate our first Multiple Sequence Alignment.

>>> hhblits_cline = HHblitsCommandLine(
...     input=seq_fname, database=hhblits_db, niterations=2, evalue=0.001, oa3m=a3m_fname
... )

The above code looks more complex than the previous ones. However, this design gives us great flexibility when it comes to trialing different options.

First, we create a new conkit.applications.HHblitsCommandLine instance and provide certain options, which instruct HHblits on which parameters to use during execution. Most of them are pretty self-explanatory, but as you can see the filenames we created earlier are now provided to this instance.

At this point we have not actually generated a Multiple Sequence Alignment. All we have done is instructed HHblits on how to run our query. To validate the command that will be executed, you can print the full statement.

>>> print(hhblits_cline)
hhblits -n 2 -evalue 0.001 -oa3m conkit_example.a3m -i conkit.fasta -d path_to_hhblits_db

Finally, to invoke HHblits, run the following command.

>>> hhblits_cline()

Great, now we have generated a sequence alignment, which is stored in the filename defined in the a3m_fname variable.

4. Filtering the MSA to remove redundant sequences¶

Next, we would like to remove some of the redundancy in the sequence alignment file to reduce the prediction bias. Luckily, the HH-suite provides us with a filtering algorithm that does exactly that. Let us create a command line instance just like before, only using the conkit.applications.HHfilterCommandLine class this time.

>>> hhfilter_cline = HHfilterCommandLine(
...     input=a3m_fname, output=a3m_filtered_fname, pairwise_identity=90
... )

The above command follows the identical style as the conkit.applications.HHblitsCommandLine wrapper. Note, we have defined our pairwise sequence identity to be 90%, i.e. all sequences with a higher sequence identity will be removed. Finally, let us invoke this command and filter our alignment.

>>> hhfilter_cline()

5. Converting the sequence alignment¶

This command results in a final, filtered alignment which is nearly ready to be subjected to CCMpred for contact prediction. However, CCMpred does not like the A3M format the HHblits and HHfilter produced. Thus, we need to convert it into a format that CCMpred recognises. This is the point where ConKit main functionality comes in, the conversion of files.

First, we need to create file handlers for the input and output files:

>>> f_in = open(a3m_filtered_fname, 'r')
>>> f_out = open(jones_filtered_fname, 'w')

Once these files are open, we can parse them to the ConKit I/O package for conversion, whereby we need to specify the input format, here a3m and output format jones. For a full list of file formats available, head over to the file_formats.

>>> io.convert(f_in, 'a3m', f_out, 'jones')

6. Predicting contacts¶

Finally, we can predict contacts using our generated alignment file. To do this, we use CCMpred [note: the syntax is always the same for command line applications].

>>> ccmpred_cline = CCMpredCommandLine(
...     alnfile=jones_filtered_fname, matfile=matrix_fname, renormalize=True
... )
>>> ccmpred_cline()

Our final contact prediction matrix is now stored in the file with the name conkit_example.mat. Again, this format is not really human-readable and you might want to convert it to a more standardised format, e.g.

>>> with open(matrix_fname, 'r') as f_in, open(casprr_fname, 'w') as f_out:
...    io.convert(f_in, 'ccmpred', f_out, 'casprr')

This will produce your final contact prediction in Casp RR format in the file conkit_example.rr