Similarity calculation

calculate_similarity(df_query, df_target=None, data_type='protein', similarity_metric='mmseqs+prefilter', field_name='sequence', threshold=0.3, threads=cpu_count(), verbose=0, save_alignment=False, filename=None, distance='tanimoto', bits=1024, radius=2, denominator='shortest', representation='3di+aa', config=None)

Calculate similarity between entities in df_query and df_target. Entities can be biological sequences (nucleic acids or proteins), protein structures or small molecules (in SMILES format).

Parameters:

Name	Type	Description	Default
df_query	DataFrame	DataFrame with query entities to calculate similarities	required
df_target	DataFrame	DataFrame with target entities to calculate similarities. If not specified, the df_query will be used as df_target as well, defaults to None	None
data_type	str	Biochemical data_type to which the data belongs. Options: protein, DNA, RNA, or small_molecule; defaults to 'protein'	'protein'
similarity_metric	str	Similarity function to use. Options: - protein: mmseqs (local alignment), mmseqs+prefilter (fast local alignment), needle (global alignment), or foldseek (structural alignment). - DNA or RNA: mmseqs (local alignment), mmseqs+prefilter (fast local alignment), or needle (global alignment). - small molecule: scaffold (boolean comparison of Bemis-Murcko scaffolds: either identical or not) or fingerprint (Tanimoto distance between ECFP (extended connectivity fingerprints)) Defaults to mmseqs+prefilter.	'mmseqs+prefilter'
field_name	str	Name of the field with the entity information (e.g., protein_sequence or structure_path), defaults to 'sequence'.	'sequence'
threshold	float	Similarity value above which entities will be considered similar, defaults to 0.3	0.3
threads	int	Number of threads available for parallalelization, defaults to cpu_count()	cpu_count()
verbose	int	How much information will be displayed. Options: - 0: Errors, - 1: Warnings, - 2: All Defaults to 0	0
save_alignment	bool	Save file with similarity calculations, defaults to False	False
filename	str	Filename where to save the similarity calculations requires save_alignment set to True, defaults to None	None
distance	str	Distance metrics for small molecule comparison. Currently, it is restricted to Tanimoto distance will be extended in future patches; if interested in a specific metric please let us know. Options: - tanimoto: Calculates the Tanimoto distance Defaults to 'tanimoto'.	'tanimoto'
bits	int	Number of bits for ECFP, defaults to 1024	1024
radius	int	Radius for ECFP calculation, defaults to 2	2
denominator	str	Denominator for sequence alignments, refers to which lenght to be used as denominator for calculating the sequence identity. Options: - shortest: The shortest sequence of the pair - longest: The longest sequence of the pair (recomended only for peptides) - n_aligned: Full alignment length (recomended with global alignment) Defaults to 'shortest'	'shortest'
representation	str	Representation for protein structures as interpreted by Foldseek. Options: - 3di: 3D interactions vocabulary. - 3di+aa: 3D interactions vocabulary and amino acid sequence. - TM: global structural alignment (slow) Defaults to '3di+aa'	'3di+aa'
config	dict	Dictionary with options for EMBOSS needle module Default values: - "gapopen": 10, - "gapextend": 0.5, - "endweight": True, - "endopen": 10, - "endextend": 0.5, - "matrix": "EBLOSUM62"	None

Returns:

Type	Description
pd.DataFrame	DataFrame with similarities (metric) between query and target. query and target are named as the indexes obtained from the pd.unique function on the corresponding input DataFrames.

Raises:

Type	Description
NotImplementedError	Biochemical data_type is not supported see data_type.
NotImplementedError	Similarity metric is not supported see similarity_algorithm

sim_df2mtx(sim_df, threshold=0.05)

Generates a similarity matrix from a DataFrame with the results from similarity calculations in the form of query, target, and metric.

Parameters:

Name	Type	Description	Default
sim_df	DataFrame	DataFrame with similarity calculations with the columns query, target, and metric.	required
threshold	float	Similarity threshold below which elements are considered dissimilar, defaults to 0.05	0.05

Returns:

Type	Description
spr.bsr_matrix	Sparse similarity matrix with shape nxn where n are the unique elements in the query column.