pycofitness 1.0

<i>In silico</i> mutagenesis of protein and RNA sequences using coevolution

About pycofitness

pycofitness is Python implemementation of in silico mutagenesis for multipe sequence alignments (MSA) of protein or RNA sequence families using pseudolikelihood maximization direct couplings analysis (DCA) algorithm. The input file for pycofitness must be an MSA of sequences in fasta format, with the first sequence representing the reference sequence. The software provides command line utilities or it can be used as a Python library.

Prerequisites

pycofitness is implemented mainly in Python with the pseudolikelihood maximization parameter inference part implemented using C++ backend to enable parallelization. It requires:

• Python 3
• C++ compiler that supports C++11, e.g., the GNU compiler collection (GCC)
• Optionally, OpenMP for multithreading support

Installing

To install the current version of pycofitness from PyPI, run on the command line

$ pip install pycofitness

Using pycofitness as a Python Library

After installation, pycofitness can be imported into other Python source codes and used. For example,

from pycofitness.mutation import PointMutation

point_mutation = PointMutation(msa_file, biomelcule)
deltas = point_mutation.delphi_epistatic()
#print the values of delta_dict by iteration over sites and bases/residues

for i in range(len(deltas)):
    for res in deltas[i]:
        print(i + 1, res, deltas[i][res])
#Note we added 1 to i to count sites starting from one as opposed to zero.

We can also pass parameters such as number of iterations, and number of threads for parallel execusion, and so on, as keyword arguments to PointMutation constructor:

point_mutation = PointMutation(msa_file, biomolecule,
    max_iterations = 1000,
    num_threads = 4,
    seqid = 0.9,
    lambda_J = 10.0,
    lambda_h = 5.0,
    verbose = True
)

where max_iterations is the number of maximum iterations for gradient decent, num_threads is the number of threads for parallel execution, seqid is sequence identity cut off value (if sequences have similarity more that this value, they are regarded as the same), lambda_J and lambda_h are penalizing constants for L2 regularization. If verbose is set to be True, logging information is enabled.

Running pycofitness From Command Line

When pycofitness is installed, it provides a command pycofitness that can be executed from the command line. For example:

$ pycofitness <biomolecule> <msa_file> --num_threads=4 --verbose

where <biomolecule> refers values "protein" or "RNA" (case insensitive) and <msa_file> is the MSA file.

Information about command line options can be obtained using:

$ pycofitness --help