Coevolution Matrix Analysis

Warning

Documentation sites are Work in progress

Quick Summary

pycom.analysis.CoMAnalysis.add_contact_predictions(df)	Takes in a datafrmae containing coevolution matrices ('matrix' column) and adds a column with contact predictions ('contact_matrix' column).
pycom.analysis.CoMAnalysis.scaled_matrix_to_contact_predictions(...)	Takes in a scaled coevolution matrix and returns a contact prediction matrix
pycom.analysis.CoMAnalysis.get_top_contacts_from_coevolution(...)	returns 'N' top scoring residues as a dataframe
pycom.analysis.CoMAnalysis.get_residue_frequencies(...)	Calculate the residue frequencies count from the top_scoring_residue pairs list
pycom.analysis.CoMAnalysis.calculate_scaled_coevolution_matrix(matrix)	Scales coevolution matrix by average :param matrix: :return: scaled_matrix
pycom.analysis.CoMAnalysis.get_top_scoring_residues(matrix)	returns top scoring residues using percentile cut-off
pycom.analysis.CoMAnalysis.scale_and_normalise_coevolution_matrices(df)	scales coevolution matrix by average, set all values <average to 0 --> S scale S by max of all S in the data frame and add additional column of normalised matrices :param df: :param normalise_matrix: :return: data frame with normalised and scaled coevolution matrix columns
pycom.analysis.CoMAnalysis.save_top_scoring_residue_pairs(df)	Extract top percentile (default: 90) pairs from the chosen matrix and write them in sorted order to an ASCII txt file

Documentation

class pycom.analysis.CoMAnalysis

CoMAnalysis provides functions to manipulate coevolution matrices from the pandas dataframe and save the interesting residue pairs into an ASCII file

add_contact_predictions(df: DataFrame, contact_factor: int = 1.5)

Takes in a datafrmae containing coevolution matrices (‘matrix’ column) and adds a column with contact predictions (‘contact_matrix’ column).

By default, the top 1.5xL pairs are returned as contact predictions, L being the length of the protein This can be modified by changing the contact_factor parameter.

Parameters:

• df – dataframe with coevolution matrices (matrix column)

• contact_factor – 1.5 (default)

Returns:

dataframe with contact predictions (contact_matrix column)

static calculate_scaled_coevolution_matrix(matrix) → ndarray

Scales coevolution matrix by average :param matrix: :return: scaled_matrix

get_residue_frequencies(top_residue_pairs)

Calculate the residue frequencies count from the top_scoring_residue pairs list

Parameters:

top_residue_pairs –

Returns:

dataframe with residueID and count df_res_count

get_top_contacts_from_coevolution(scaled_matrix, num_contact_factor=1.5)

returns ‘N’ top scoring residues as a dataframe by default, consistent with GREMLIN, top 1.5xL pairs are returned, L being the length of the protein

Parameters:

• scaled_matrix –

• num_contact_factor –

Returns:

data frame with top residue pairs

get_top_scoring_residues(matrix, res_gap=5, percentile=90)

returns top scoring residues using percentile cut-off

Parameters:

• matrix –

• res_gap –

• percentile –

Returns:

data frame with top residue pairs

save_top_scoring_residue_pairs(df: DataFrame, data_folder='output', matrix_type='matrix_S', res_gap=5, percentile=90)

Extract top percentile (default: 90) pairs from the chosen matrix and write them in sorted order to an ASCII txt file

Parameters:

• df – data frame with coevolution matrices

• data_folder – output folder for all files

• matrix_type – matrix or matrix_S or matrix_N

• res_gap – 5 (default)

• percentile – 90

Returns:

None

static scale_and_normalise_coevolution_matrices(df: DataFrame, normalise_matrix=True) → DataFrame

scales coevolution matrix by average, set all values <average to 0 –> S scale S by max of all S in the data frame and add additional column of normalised matrices :param df: :param normalise_matrix: :return: data frame with normalised and scaled coevolution matrix columns

scaled_matrix_to_contact_predictions(scaled_matrix, num_contact_factor=1.5)

Takes in a scaled coevolution matrix and returns a contact prediction matrix

By default, the leading diagonal (self contacts) are not set to 1.

Parameters:

• scaled_matrix – scaled coevolution matrix

• num_contact_factor – 1.5 (default)

Returns:

contact prediction matrix