Encode protein sequence as a distribution of its physicochemical properties
Encode protein sequence as a distribution of its physicochemical properties.
Protein is as sequence of amino acid residues connected by peptide bonds. Each
amino acid residue is characterized by a unique combination of its physical and chemical
proteinko takes advantage of this to represent protein sequence as
a spatial distribution of the physicochemical properties of its amino acid
residues, capturing the complementing or cancelling effect of neighbouring amino acid
proteinko enables numerical representation of a protein sequence
while preserving the information of its underlying physicochemical properties. This
allows the investigation of relationships and interactions between proteins as well as
potential discovery of underlying physicochemical properties which facilitate those interactions.
proteinko implements a fairly simple algorithm. The protein sequence is mapped to a
V representing a distribution of a certain physicochemical property of the entire protein.
Each amino acid residue
Ai is modeled independently as a Gaussian curve
scaled by the corresponding value from the encoding scheme.
Gi is mapped to
the slice of
V which is centered at a position correspondig to the position of
Ai in the sequence and
L neighbouring slices on each side.
The overlap allows to sum the complementing or cancelling effects
that the neighbouring amino acid residues may exert on the local physicochemical
property of the protein. The extent of overlap is determined by two factors:
overlap distance (
L) and sigma factor. Overlap distance determines how many
Gi spans on each side. Sigma determines the shape of the Gaussian curve
of each of the amino acid residues (see example). Both of these parameters
proteinko accepts as
function arguments allowing users to modify the shape of final distribution as needed.
pip install proteinko
proteinko implements two functions:
Both functions have
encoding_scheme parameter which accepts a python dictionary with
amino acid one-letter codes as keys.
from proteinko import model_distribution, encode_sequence import matplotlib.pyplot as plt from pyaaisc import Aaindex sequence = 'MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGP' encoding_scheme = Aaindex().get('ARGP820101', dbkey='aaindex1').index_data dist_1 = model_distribution(sequence, encoding_scheme, overlap_distance=2, sigma=0.4) dist_2 = model_distribution(sequence, encoding_scheme, overlap_distance=3, sigma=0.8) encoded = encode_sequence(sequence, encoding_scheme) fig, ax = plt.subplots(3, 1, sharey=True, figsize=(12,5)) ax.plot(dist_1) ax.grid() ax.set_xticklabels() ax.set_title('Modeled distribution, overlap_distance=2, sigma=0.4') ax.plot(dist_2) ax.grid() ax.set_xticklabels() ax.set_title('Modeled distribution, overlap_distance=3, sigma=0.8') ax.set_ylabel('Hydrophobicity index - ARGP820101') ax.bar(range(len(encoded)), encoded) ax.grid() ax.set_xticks(range(len(sequence))) ax.set_xticklabels([x for x in sequence]) ax.set_title('Sequence') plt.show()
from proteinko import model_distribution import matplotlib.pyplot as plt from pyaaisc import Aaindex sequence = 'MEEPQSDPSVE' encoding_scheme = Aaindex().get('ARGP820101', dbkey='aaindex1').index_data dist = model_distribution(sequence, encoding_scheme, overlap_distance=2, sigma=0.4) sampled_dist = model_distribution(sequence, encoding_scheme, overlap_distance=2, sigma=0.4, sampling_points=16) fig, ax = plt.subplots(2, 1, figsize=(6,4)) ax.plot(dist) ax.grid() ax.set_xticklabels() ax.set_title('Modeled distribution') ax.set_ylabel('Hydrophobicity index') ax.bar(range(16), sampled_dist) ax.grid() ax.set_xticklabels() ax.set_title('Sampled distribution') ax.set_ylabel('Hydrophobicity index') plt.show()
- Number of overlaping neigbouring amino acid residues has been added as function argument
and default value set to
sigmavalue has been changed from
- Normalization and standardization of modeled distribution are deprecated. No pre or post processing is applied.
- Scaling factor has been decreased from
40, reducing the number of computations and increasing the performance of algorithm.
Major code changes:
Proteinkoclass has been removed and algorithm is implemented under
- New function
encode_sequencehas been introduced which simply encodes sequence with values provided in the encoding table.
- Encoding tables are now passed as python dictionaries instead of
- Use of
scipypackages has been replaced with python functions making the code more lightweight and increasing the performance of algorithm.
Minor code changes:
vlenparameter has been renamed to
sampling_pointsbecause it is the number of points to sample from final distribution.
schemaparameter has been renamed to
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