+import pandas as pd
+import numpy as np
+import re
+import os
+
+from ptm_pose import pose_config, helpers
+
+
+#dictionaries for converting modification codes to modification names in PhosphoSitePlus data
+mod_shorthand_dict = {'p': 'Phosphorylation', 'ca':'Caspase Cleavage', 'hy':'Hydroxylation', 'sn':'S-Nitrosylation', 'ng':'Glycosylation', 'ub': 'Ubiquitination', 'pa': "Palmitoylation",'ne':'Neddylation','sc':'Succinylation', 'sm': 'Sumoylation', 'ga': 'Glycosylation', 'gl': 'Glycosylation', 'ac': 'Acetylation', 'me':'Methylation', 'm1':'Methylation', 'm2': 'Dimethylation', 'm3':'Trimethylation'}
+residue_dict = {'P': 'proline', 'Y':'tyrosine', 'S':'serine', 'T':'threonine', 'H':'histidine', 'D':'aspartic acid', 'I':'isoleucine', 'K':'lysine', 'R':'arginine', 'G':'glycine', 'N':'asparagine', 'M':'methionine'}
+annotation_col_dict = {'PhosphoSitePlus':{'Function':'PSP:ON_FUNCTION', 'Process':'PSP:ON_PROCESS', 'Interactions':'PSP:ON_PROT_INTERACT', 'Disease':'PSP:Disease_Association', 'Kinase':'PSP:Kinase','Perturbation':'PTMsigDB:PSP-PERT'},
+ 'ELM':{'Interactions':'ELM:Interactions', 'Motif Match':'ELM:Motif Matches'},
+ 'PTMcode':{'Intraprotein':'PTMcode:Intraprotein_Interactions', 'Interactions':'PTMcode:Interprotein_Interactions'},
+ 'PTMInt':{'Interactions':'PTMInt:Interactions'},
+ 'RegPhos':{'Kinase':'RegPhos:Kinase'},
+ 'DEPOD':{'Phosphatase':'DEPOD:Phosphatase'},
+ 'PTMsigDB': {'WikiPathway':'PTMsigDB:PATH-WP', 'NetPath':'PTMsigDB:PATH-NP','mSigDB':'PTMsigDB:PATH-BI', 'Pertubation (DIA2)':'PTMsigDB:PERT-P100-DIA2', 'Perturbation (DIA)': 'PTMsigDB:PERT-P100-DIA', 'Perturbation (PRM)':'PTMsigDB:PERT-P100-PRM', 'Kinase':'PTMsigDB:Kinase-iKiP'}}
+
+
+
+[docs]def add_custom_annotation(spliced_ptms, annotation_data, source_name, annotation_type, annotation_col, accession_col = 'UniProtKB Accession', residue_col = 'Residue', position_col = 'PTM Position in Canonical Isoform'):
+
"""
+
Add custom annotation data to spliced_ptms or altered flanking sequence dataframes
+
+
Parameters
+
----------
+
annotation_data: pandas.DataFrame
+
Dataframe containing the annotation data to be added to the spliced_ptms dataframe. Must contain columns for UniProtKB Accession, Residue, PTM Position in Canonical Isoform, and the annotation data to be added
+
source_name: str
+
Name of the source of the annotation data, will be used to label the columns in the spliced_ptms dataframe
+
annotation_type: str
+
Type of annotation data being added, will be used to label the columns in the spliced_ptms dataframe
+
annotation_col: str
+
Column name in the annotation data that contains the annotation data to be added to the spliced_ptms dataframe
+
+
+
Returns
+
-------
+
spliced_ptms: pandas.DataFrame
+
Contains the PTMs identified across the different splice events with an additional column for the custom annotation data
+
"""
+
#check if annotation data contains the annotation col
+
if isinstance(annotation_col, str):
+
if annotation_col not in annotation_data.columns:
+
raise ValueError(f'Could not find column indicated to contain {annotation_col} in annotation data. Please either change the name of your annotation data column with this information or indicate the correct column name with the annotation_col parameter')
+
else:
+
#make annotation col name based on source and annotation type
+
annotation_col_name = source_name + ':' + annotation_type
+
annotation_data = annotation_data.rename(columns = {annotation_col: annotation_col_name})
+
else:
+
raise ValueError('annotation_col must be a string indicating column with annotation data to be added to the spliced_ptms dataframe')
+
+
#check to make sure annotation data has the necessary columns
+
if not all([x in annotation_data.columns for x in [accession_col, residue_col, position_col]]):
+
raise ValueError(f'Could not find columns containing ptm information: {accession_col}, {residue_col}, and {position_col}. Please either change the name of your annotation data columns containing this information or indicate the correct column names with the accession_col, residue_col, and position_col parameters')
+
+
#if splice data already has the annotation columns, remove them
+
if annotation_col_name in spliced_ptms.columns:
+
spliced_ptms = spliced_ptms.drop(columns = [annotation_col_name])
+
+
#add to splice data
+
original_data_size = spliced_ptms.shape[0]
+
spliced_ptms = spliced_ptms.merge(annotation_data, how = 'left', left_on = ['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform'], right_on = [accession_col, residue_col, position_col])
+
if spliced_ptms.shape[0] != original_data_size:
+
raise RuntimeError('Dataframe size has changed, check for duplicates in spliced ptms or annotation dataframe')
+
+
#report the number of PTMs identified
+
num_ptms_with_custom_data = spliced_ptms.dropna(subset = annotation_col).groupby(['UniProtKB Accession', 'Residue']).size().shape[0]
+
print(f"{source_name} {annotation_type} data added: {num_ptms_with_custom_data} PTMs in dataset found with {source_name} {annotation_type} information")
+
+
return spliced_ptms
+
+[docs]def add_PSP_regulatory_site_data(spliced_ptms, file = 'Regulatory_sites.gz', report_success = True):
+
"""
+
Add functional information from PhosphoSitePlus (Regulatory_sites.gz) to spliced_ptms dataframe from project_ptms_onto_splice_events() function
+
+
Parameters
+
----------
+
file: str
+
Path to the PhosphoSitePlus Regulatory_sites.gz file. Should be downloaded from PhosphoSitePlus in the zipped format
+
+
Returns
+
-------
+
spliced_ptms: pandas.DataFrame
+
Contains the PTMs identified across the different splice events with additional columns for regulatory site information, including domains, biological process, functions, and protein interactions associated with the PTMs
+
"""
+
#check to make sure file exists
+
check_file(file, expected_extension='.gz')
+
+
#read in the kinase substrate data and add to spliced ptm info
+
regulatory_site_data = pd.read_csv(file, sep = '\t', header = 2, on_bad_lines='skip',compression = 'gzip')
+
regulatory_site_data = regulatory_site_data.rename(columns = {'ACC_ID':'UniProtKB Accession'})
+
#drop extra modification information that is not needed
+
regulatory_site_data['Residue'] = regulatory_site_data['MOD_RSD'].apply(lambda x: x.split('-')[0][0])
+
regulatory_site_data['PTM Position in Canonical Isoform'] = regulatory_site_data['MOD_RSD'].apply(lambda x: int(x.split('-')[0][1:]))
+
#add modification type
+
regulatory_site_data['Modification Class'] = regulatory_site_data['MOD_RSD'].apply(lambda x: mod_shorthand_dict[x.split('-')[1]])
+
+
#restrict to human data
+
regulatory_site_data = regulatory_site_data[regulatory_site_data['ORGANISM'] == 'human']
+
regulatory_site_data = regulatory_site_data[['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class', 'ON_PROCESS', 'ON_PROT_INTERACT', 'ON_OTHER_INTERACT', 'ON_FUNCTION']].drop_duplicates()
+
+
#group like modifications into a single column
+
regulatory_site_data = regulatory_site_data.groupby(['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class']).agg(lambda x: '; '.join([y for y in x if y == y])).reset_index()
+
regulatory_site_data = regulatory_site_data.replace('', np.nan)
+
+
#add 'PSP:' in front of each column
+
regulatory_site_data.columns = ['PSP:' + x if x not in ['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class'] else x for x in regulatory_site_data.columns]
+
+
#if splice data already has the annotation columns, remove them
+
if 'PSP:ON_FUNCTION' in spliced_ptms.columns:
+
spliced_ptms = spliced_ptms.drop(columns = ['PSP:ON_FUNCTION', 'PSP:ON_PROCESS', 'PSP:ON_PROT_INTERACT', 'PSP:ON_OTHER_INTERACT'])
+
+
#explode dataframe on modifications
+
if spliced_ptms['Modification Class'].str.contains(';').any():
+
spliced_ptms['Modification Class'] = spliced_ptms['Modification Class'].str.split(';')
+
spliced_ptms = spliced_ptms.explode('Modification Class').reset_index(drop = True)
+
+
#merge with spliced_ptm info
+
original_data_size = spliced_ptms.shape[0]
+
spliced_ptms = spliced_ptms.merge(regulatory_site_data, how = 'left', on = ['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class'])
+
if spliced_ptms.shape[0] != original_data_size:
+
raise RuntimeError('Dataset size changed upon merge, please make sure there are no duplicates in spliced ptms data')
+
+
+
#report the number of ptms with motif data
+
if report_success:
+
num_ptms_with_known_function = spliced_ptms.dropna(subset = 'PSP:ON_FUNCTION').groupby(['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class']).size().shape[0]
+
num_ptms_with_known_process = spliced_ptms.dropna(subset = 'PSP:ON_PROCESS').groupby(['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class']).size().shape[0]
+
num_ptms_with_known_interaction = spliced_ptms.dropna(subset = 'PSP:ON_PROT_INTERACT').groupby(['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class']).size().shape[0]
+
print(f"PhosphoSitePlus regulatory_site information added:\n\t ->{num_ptms_with_known_function} PTMs in dataset found associated with a molecular function \n\t ->{num_ptms_with_known_process} PTMs in dataset found associated with a biological process\n\t ->{num_ptms_with_known_interaction} PTMs in dataset found associated with a protein interaction")
+
return spliced_ptms
+
+[docs]def add_PSP_kinase_substrate_data(spliced_ptms, file = 'Kinase_Substrate_Dataset.gz', report_success = True):
+
"""
+
Add kinase substrate data from PhosphoSitePlus (Kinase_Substrate_Dataset.gz) to spliced_ptms dataframe from project_ptms_onto_splice_events() function
+
+
Parameters
+
----------
+
file: str
+
Path to the PhosphoSitePlus Kinase_Substrate_Dataset.gz file. Should be downloaded from PhosphoSitePlus in the zipped format
+
+
Returns
+
-------
+
spliced_ptms: pandas.DataFrame
+
Contains the PTMs identified across the different splice events with an additional column indicating the kinases known to phosphorylate that site (not relevant to non-phosphorylation PTMs)
+
+
"""
+
#check to make sure provided file exists
+
check_file(file, expected_extension='.gz')
+
+
#load data
+
ks_dataset = pd.read_csv(file, sep = '\t', header = 2, on_bad_lines='skip',compression = 'gzip', encoding = "cp1252")
+
#restrict to human data
+
ks_dataset = ks_dataset[ks_dataset['KIN_ORGANISM'] == 'human']
+
ks_dataset = ks_dataset[ks_dataset['SUB_ORGANISM'] == 'human']
+
+
ks_dataset = ks_dataset[['GENE', 'SUB_ACC_ID', 'SUB_MOD_RSD']].groupby(['SUB_ACC_ID', 'SUB_MOD_RSD']).agg(';'.join).reset_index()
+
ks_dataset.columns = ['UniProtKB Accession', 'Residue', 'PSP:Kinase']
+
+
#separate residue and position
+
ks_dataset['PTM Position in Canonical Isoform'] = ks_dataset['Residue'].apply(lambda x: int(x[1:]))
+
ks_dataset['Residue'] = ks_dataset['Residue'].apply(lambda x: x[0])
+
+
+
#if splice data already has the annotation columns, remove them
+
if 'PSP:Kinase' in spliced_ptms.columns:
+
spliced_ptms = spliced_ptms.drop(columns = ['PSP:Kinase'])
+
+
original_data_size = spliced_ptms.shape[0]
+
spliced_ptms = spliced_ptms.merge(ks_dataset, how = 'left', on = ['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform'])
+
if spliced_ptms.shape[0] != original_data_size:
+
raise RuntimeError('Dataset size changed upon merge, please make sure there are no duplicates in spliced ptms data')
+
+
+
#report the number of ptms with kinase substrate information
+
if report_success:
+
num_ptms_with_KS = spliced_ptms.dropna(subset = 'PSP:Kinase').groupby(['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class']).size().shape[0]
+
print(f"PhosphoSitePlus kinase-substrate interactions added: {num_ptms_with_KS} phosphorylation sites in dataset found associated with a kinase in PhosphoSitePlus")
+
return spliced_ptms
+
+[docs]def add_PSP_disease_association(spliced_ptms, file = 'Disease-associated_sites.gz', report_success = True):
+
"""
+
Process disease asociation data from PhosphoSitePlus (Disease-associated_sites.gz), and add to spliced_ptms dataframe from project_ptms_onto_splice_events() function
+
+
Parameters
+
----------
+
file: str
+
Path to the PhosphoSitePlus Kinase_Substrate_Dataset.gz file. Should be downloaded from PhosphoSitePlus in the zipped format
+
+
Returns
+
-------
+
spliced_ptms: pandas.DataFrame
+
Contains the PTMs identified across the different splice events with an additional column indicating the kinases known to phosphorylate that site (not relevant to non-phosphorylation PTMs)
+
+
"""
+
#check to make sure provided file exists
+
check_file(file, expected_extension='.gz')
+
+
#load data
+
disease_associated_sites = pd.read_csv(file, sep = '\t', header = 2, on_bad_lines='skip',compression = 'gzip')
+
disease_associated_sites = disease_associated_sites[disease_associated_sites['ORGANISM'] == 'human']
+
+
#removes sites without a specific disease annotation
+
disease_associated_sites = disease_associated_sites.dropna(subset = ['DISEASE'])
+
+
#drop extra modification information that is not needed
+
#drop extra modification information that is not needed
+
disease_associated_sites['Residue'] = disease_associated_sites['MOD_RSD'].apply(lambda x: x.split('-')[0][0])
+
disease_associated_sites['PTM Position in Canonical Isoform'] = disease_associated_sites['MOD_RSD'].apply(lambda x: int(x.split('-')[0][1:]))
+
#add modification type
+
disease_associated_sites['Modification Class'] = disease_associated_sites['MOD_RSD'].apply(lambda x: mod_shorthand_dict[x.split('-')[1]])
+
#if phosphorylation, add specific residue
+
disease_associated_sites['Modification Class'] = disease_associated_sites.apply(lambda x: x['Modification Class'] + residue_dict[x['Residue'][0]] if x['Modification Class'] == 'Phospho' else x['Modification Class'], axis = 1)
+
#change O-GalNac occurring on N to N-glycosylation
+
disease_associated_sites['Modification Class'] = disease_associated_sites.apply(lambda x: 'N-Glycosylation' if x['Modification Class'] == 'O-Glycosylation' and x['Residue'][0] == 'N' else x['Modification Class'], axis = 1)
+
+
+
#combine disease and alteration
+
disease_associated_sites['ALTERATION'] = disease_associated_sites.apply(lambda x: x['DISEASE']+'->'+x['ALTERATION'] if x['ALTERATION'] == x['ALTERATION'] else x['DISEASE'], axis = 1)
+
#grab only necessary columns and rename
+
disease_associated_sites = disease_associated_sites[['ACC_ID', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class', 'ALTERATION']]
+
disease_associated_sites.columns = ['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class', 'PSP:Disease_Association']
+
+
#aggregate multiple disease associations
+
disease_associated_sites = disease_associated_sites.groupby(['UniProtKB Accession', 'Residue','PTM Position in Canonical Isoform', 'Modification Class']).agg(';'.join).reset_index()
+
+
#if splice data already has the annotation columns, remove them
+
if 'PSP:Disease_Association' in spliced_ptms.columns:
+
spliced_ptms = spliced_ptms.drop(columns = ['PSP:Disease_Association'])
+
+
#explode dataframe on modifications
+
if spliced_ptms['Modification Class'].str.contains(';').any():
+
spliced_ptms['Modification Class'] = spliced_ptms['Modification Class'].str.split(';')
+
spliced_ptms = spliced_ptms.explode('Modification Class').reset_index(drop = True)
+
+
+
#merge with spliced_ptm info
+
original_data_size = spliced_ptms.shape[0]
+
spliced_ptms = spliced_ptms.merge(disease_associated_sites, how = 'left', on = ['UniProtKB Accession', 'Residue','PTM Position in Canonical Isoform', 'Modification Class'])
+
if spliced_ptms.shape[0] != original_data_size:
+
raise RuntimeError('Dataset size changed upon merge, please make sure there are no duplicates in spliced ptms data')
+
+
#
+
#report the number of ptms with motif data
+
if report_success:
+
num_ptms_with_disease = spliced_ptms.dropna(subset = 'PSP:Disease_Association').groupby(['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class']).size().shape[0]
+
print(f"PhosphoSitePlus disease associations added: {num_ptms_with_disease} PTM sites in dataset found associated with a disease in PhosphoSitePlus")
+
+
+
return spliced_ptms
+
+
+[docs]def add_ELM_interactions(spliced_ptms, file = None, report_success =True):
+
"""
+
Given a spliced ptms dataframe from the project module, add ELM interaction data to the dataframe
+
"""
+
#load data
+
if file is None:
+
elm_interactions = pd.read_csv('http://elm.eu.org/interactions/as_tsv', sep = '\t', header = 0)
+
else:
+
check_file(file, expected_extension='.tsv')
+
elm_interactions = pd.read_csv(file, sep = '\t', header = 0)
+
+
elm_interactions = elm_interactions[(elm_interactions['taxonomyElm'] == '9606(Homo sapiens)') & (elm_interactions['taxonomyDomain'] == '9606(Homo sapiens)')]
+
+
elm_list = []
+
elm_type = []
+
elm_interactor = []
+
for i, row in spliced_ptms.iterrows():
+
#grab ptm location from residue column (gives residue and position (S981), so need to remove residue and convert to int)
+
ptm_loc = int(row['PTM Position in Canonical Isoform']) if row['PTM Position in Canonical Isoform'] == row['PTM Position in Canonical Isoform'] and row['PTM Position in Canonical Isoform'] != 'none' else None
+
+
#if data does not have position information, move to the next
+
if ptm_loc is None:
+
elm_list.append(np.nan)
+
elm_type.append(np.nan)
+
elm_interactor.append(np.nan)
+
continue
+
+
#find if any of the linear motifs match ptm loc
+
protein_match = row['UniProtKB Accession'] == elm_interactions['interactorElm']
+
region_match = (ptm_loc >= elm_interactions['StartElm']) & (ptm_loc <=elm_interactions['StopElm'])
+
elm_subset_motif = elm_interactions[protein_match & region_match]
+
#if any interactions were found, record and continue to the next (assumes a single ptm won't be found as both a SLiM and domain)
+
if elm_subset_motif.shape[0] > 0:
+
elm_list.append(';'.join(elm_subset_motif['Elm'].values))
+
elm_type.append('SLiM')
+
elm_interactor.append(';'.join(elm_subset_motif['interactorDomain'].values))
+
continue
+
+
+
#domain
+
protein_match = row['UniProtKB Accession'] == elm_interactions['interactorDomain']
+
region_match = (ptm_loc >= elm_interactions['StartDomain']) & (ptm_loc <=elm_interactions['StopDomain'])
+
elm_subset_domain = elm_interactions[protein_match & region_match]
+
#if any interactions were found, record and continue to the next (assumes a single ptm won't be found as both a SLiM and domain)
+
if elm_subset_domain.shape[0] > 0:
+
elm_list.append(';'.join(elm_subset_domain['Elm'].values))
+
elm_type.append('Domain')
+
elm_interactor.append(';'.join(elm_subset_domain['interactorElm'].values))
+
continue
+
+
#if no interactions wer found, record as np.nan
+
elm_list.append(np.nan)
+
elm_type.append(np.nan)
+
elm_interactor.append(np.nan)
+
+
spliced_ptms['ELM:Interactions'] = elm_interactor
+
spliced_ptms['ELM:Location of PTM for Interaction'] = elm_type
+
spliced_ptms['ELM:Motifs Associated with Interactions'] = elm_list
+
+
#report the number of ptms with motif data
+
if report_success:
+
num_ptms_with_ELM_instance = spliced_ptms.dropna(subset = 'ELM:Interactions').groupby(['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform']).size().shape[0]
+
print(f"ELM interaction instances added: {num_ptms_with_ELM_instance} PTMs in dataset found associated with at least one known ELM instance")
+
return spliced_ptms
+
+
+def add_ELM_matched_motifs(spliced_ptms, flank_size = 7, file = None, report_success = True):
+ if file is None:
+ elm_classes = pd.read_csv('http://elm.eu.org/elms/elms_index.tsv', sep = '\t', header = 5)
+ else:
+ check_file(file, expected_extension='.tsv')
+ elm_classes = pd.read_csv(file, sep = '\t', header = 5)
+
+ ptm_coordinates = pose_config.ptm_coordinates.copy()
+ #create corresponding label for ptm_coordinate data
+ ptm_coordinates['PTM Label'] = ptm_coordinates['UniProtKB Accession'] + '_' + ptm_coordinates['Residue'] + ptm_coordinates['PTM Position in Canonical Isoform'].apply(lambda x: int(float(x)) if x == x else np.nan).astype(str)
+
+ match_list = []
+ for i, row in spliced_ptms.iterrows():
+ matches = []
+ #grab ptm information
+ #grab flanking sequence for the ptm
+ loc = int(row["PTM Position in Canonical Isoform"]) if row['PTM Position in Canonical Isoform'] == row['PTM Position in Canonical Isoform'] else np.nan
+ ptm = row['UniProtKB Accession'] + '_' + row['Residue'] + str(loc)
+
+
+ if ptm in ptm_coordinates['PTM Label'].values:
+ ptm_flanking_seq = ptm_coordinates.loc[ptm_coordinates['PTM Label'] == ptm, 'Expected Flanking Sequence'].values[0]
+ #make sure flanking sequence is present
+ if isinstance(ptm_flanking_seq, str):
+
+ #default flanking sequence is 10, if requested flanking sequence is different, then adjust
+ if flank_size > 10:
+ raise ValueError('Flanking size must be equal to or less than 10')
+ elif flank_size < 10:
+ ptm_flanking_seq = ptm_flanking_seq[10-flank_size:10+flank_size]
+
+ for j, elm_row in elm_classes.iterrows():
+ reg_ex = elm_row['Regex']
+ if re.search(reg_ex, ptm_flanking_seq) is not None:
+ matches.append(elm_row['ELMIdentifier'])
+
+ match_list.append(';'.join(matches))
+ else:
+ match_list.append(np.nan)
+ else:
+ #print(f'PTM {ptm} not found in PTM info file')
+ match_list.append(np.nan)
+
+ spliced_ptms['ELM:Motif Matches'] = match_list
+
+ #report the number of ptms with motif data
+ if report_success:
+ num_ptms_with_matched_motif = spliced_ptms.dropna(subset = 'ELM:Motif Matches').groupby(['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform']).size().shape[0]
+ print(f"ELM Class motif matches found: {num_ptms_with_matched_motif} PTMs in dataset found with at least one matched motif")
+ return spliced_ptms
+
+[docs]def add_PTMInt_data(spliced_ptms, file = None, report_success = True):
+
"""
+
Given spliced_ptms data from project module, add PTMInt interaction data, which will include the protein that is being interacted with, whether it enchances or inhibits binding, and the localization of the interaction. This will be added as a new column labeled PTMInt:Interactions and each entry will be formatted like 'Protein->Effect|Localization'. If multiple interactions, they will be separated by a semicolon
+
"""
+
#load file
+
if file is None:
+
PTMint = pd.read_csv('https://ptmint.sjtu.edu.cn/data/PTM%20experimental%20evidence.csv')
+
else:
+
check_file(file, expected_extension='.csv')
+
PTMint = pd.read_csv(file)
+
+
PTMint = PTMint.rename(columns={'Uniprot':'UniProtKB Accession', 'AA':'Residue', 'Site':'PTM Position in Canonical Isoform'})
+
#PTMint['Site'] = PTMint['AA'] + PTMint['Site'].astype(str)
+
PTMint['PTMInt:Interaction'] = PTMint['Int_gene']+'->'+PTMint['Effect']
+
PTMint = PTMint[['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'PTMInt:Interaction']]
+
#PTMint['PTM Position in Canonical Isoform'] = PTMint['PTM Position in Canonical Isoform'].astype(str)
+
+
#aggregate PTMint data on the same PTMs
+
PTMint = PTMint.groupby(['UniProtKB Accession','Residue','PTM Position in Canonical Isoform'], as_index = False).agg(';'.join)
+
+
#if splice data already has the annotation columns, remove them
+
if 'PTMInt:Interaction' in spliced_ptms.columns:
+
spliced_ptms = spliced_ptms.drop(columns = ['PTMInt:Interaction'])
+
+
#add to splice data
+
original_data_size = spliced_ptms.shape[0]
+
spliced_ptms = spliced_ptms.merge(PTMint[['UniProtKB Accession','Residue','PTM Position in Canonical Isoform', 'PTMInt:Interaction']], on = ['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform'], how = 'left')
+
if spliced_ptms.shape[0] != original_data_size:
+
raise RuntimeError('Dataframe size has changed, check for duplicates in spliced ptms dataframe')
+
+
#report the number of PTMs identified
+
if report_success:
+
num_ptms_with_PTMInt_data = spliced_ptms.dropna(subset = 'PTMInt:Interaction').groupby(['UniProtKB Accession', 'Residue']).size().shape[0]
+
print(f"PTMInt data added: {num_ptms_with_PTMInt_data} PTMs in dataset found with PTMInt interaction information")
+
+
return spliced_ptms
+ #delete source PTMint data
+ #os.remove(pdir + './Data/PTM_experimental_evidence.csv')
+
+#def add_PTMcode_intraprotein(spliced_ptms, fname = None, report_success = True):
+# #load ptmcode info
+# if fname is None:
+# ptmcode = pd.read_csv('https://ptmcode.embl.de/data/PTMcode2_associations_within_proteins.txt.gz', sep = '\t', header = 2, compression='gzip')
+# else:
+# check_file(fname, expected_extension = '.gz')
+# ptmcode = pd.read_csv(fname, sep = '\t', header = 2, compression = 'gzip')
+#
+# #grab humn data
+# ptmcode = ptmcode[ptmcode['Species'] == 'Homo sapiens']
+#
+# #add gene name to data
+# translator = pd.DataFrame(pose_config.uniprot_to_genename, index = ['Gene']).T
+# translator['Gene'] = translator['Gene'].apply(lambda x: x.split(' '))
+# translator = translator.explode('Gene')
+# translator = translator.reset_index()
+# translator.columns = ['UniProtKB/Swiss-Prot ID', 'Gene name']
+#
+# #add uniprot ID information
+# ptmcode = ptmcode.merge(translator.dropna().drop_duplicates(), left_on = '## Protein', right_on = 'Gene name', how = 'left')
+#
+# #convert modification names to match annotation data
+# convert_dict = {'Adp ribosylation': 'ADP Ribosylation', 'Glutamine deamidation':'Deamidation'}
+# new_mod_names = []
+# failed_mod = []
+# mod_list = ptmcode['PTM1'].unique()
+# for mod in mod_list:
+# mod = mod.capitalize()
+# if 'glycosylation' in mod: #if glycosylation, group into one gorup
+# new_mod_names.append('Glycosylation')
+# elif mod in pose_config.modification_conversion['Modification Class'].values: #if already in modification class data, keep
+# new_mod_names.append(mod)
+# elif mod in convert_dict.keys():
+# new_mod_names.append(convert_dict[mod])
+# else:
+# try:
+# new_mod = pose_config.modification_conversion[pose_config.modification_conversion['Modification'] == mod].values[0][0]
+# new_mod_names.append(new_mod)
+# except:
+# failed_mod.append(mod)
+# new_mod_names.append(mod)
+# conversion_df = pd.DataFrame({'PTM1':mod_list, 'Modification Class':new_mod_names})
+#
+# #add new modification labels to data
+# ptmcode = ptmcode.merge(conversion_df, on = 'PTM1', how = 'left')
+#
+# #groupby by PTM1 and rename to match column names in annotation data
+# ptmcode = ptmcode[['UniProtKB/Swiss-Prot ID', 'Modification Class', 'Residue1', 'Residue2']].dropna(subset = 'UniProtKB/Swiss-Prot ID')
+# ptmcode = ptmcode.groupby(['UniProtKB/Swiss-Prot ID', 'Modification Class', 'Residue1'])['Residue2'].agg(';'.join).reset_index()
+# ptmcode = ptmcode.rename(columns = {'UniProtKB/Swiss-Prot ID':'UniProtKB Accession', 'Residue1':'Residue', 'Residue2':'PTMcode:Intraprotein_Interactions'})
+#
+# #separate residue information into separate columns, one for amino acid and one for position
+# ptmcode['PTM Position in Canonical Isoform'] = ptmcode['Residue'].apply(lambda x: int(x[1:]))
+# ptmcode['Residue'] = ptmcode['Residue'].apply(lambda x: x[0])
+#
+# #if splice data already has the annotation columns, remove them
+# if 'PTMcode:Intraprotein_Interactions' in spliced_ptms.columns:
+# spliced_ptms = spliced_ptms.drop(columns = ['PTMcode:Intraprotein_Interactions'])
+#
+# #explode dataframe on modifications
+# if spliced_ptms['Modification Class'].str.contains(';').any():
+# spliced_ptms['Modification Class'] = spliced_ptms['Modification Class'].str.split(';')
+# spliced_ptms = spliced_ptms.explode('Modification Class').reset_index(drop = True)
+#
+# #add to splice data
+# original_data_size = spliced_ptms.shape[0]
+# spliced_ptms = spliced_ptms.merge(ptmcode, how = 'left', on = ['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class'])
+# if spliced_ptms.shape[0] != original_data_size:
+# raise RuntimeError('Dataframe size has changed, check for duplicates in spliced ptms dataframe')
+#
+# #report the number of PTMs identified
+# if report_success:
+# num_ptms_with_PTMcode_data = spliced_ptms.dropna(subset = 'PTMcode:Intraprotein_Interactions').groupby(['UniProtKB Accession', 'Residue']).size().shape[0]
+# print(f"PTMcode intraprotein interactions added: {num_ptms_with_PTMcode_data} PTMs in dataset found with PTMcode intraprotein interaction information")
+#
+# return spliced_ptms
+
+def extract_ids_PTMcode(df, col = '## Protein1'):
+
+ #add gene name to data
+ name_to_uniprot = pd.DataFrame(pose_config.uniprot_to_genename, index = ['Gene']).T
+ name_to_uniprot['Gene'] = name_to_uniprot['Gene'].apply(lambda x: x.split(' ') if x == x else np.nan)
+ name_to_uniprot = name_to_uniprot.explode('Gene')
+ name_to_uniprot = name_to_uniprot.reset_index()
+ name_to_uniprot.columns = ['UniProtKB/Swiss-Prot ID', 'Gene name']
+ name_to_uniprot = name_to_uniprot.drop_duplicates(subset = 'Gene name', keep = False)
+
+ #protein name is provided as either ensemble gene id or gene name check for both
+ df = df.merge(pose_config.translator[['Gene stable ID']].reset_index().dropna().drop_duplicates(), left_on = col, right_on = 'Gene stable ID', how = 'left')
+ df = df.rename(columns = {'index': 'From_ID'})
+ df = df.merge(name_to_uniprot, left_on = col, right_on = 'Gene name', how = 'left')
+ df = df.rename(columns = {'UniProtKB/Swiss-Prot ID': 'From_Name'})
+
+ #grab unique id from 'From_ID' and 'From_Name' column, if available
+ uniprot_ids = df['From_Name'].combine_first(df['From_ID'])
+ return uniprot_ids.values
+
+def add_PTMcode_interprotein(spliced_ptms, fname = None, report_success = True):
+ if fname is None:
+ ptmcode = pd.read_csv('https://ptmcode.embl.de/data/PTMcode2_associations_between_proteins.txt.gz', sep = '\t', header = 2, compression = 'gzip')
+ else:
+ check_file(fname, expected_extension = '.gz')
+ ptmcode = pd.read_csv(fname, sep = '\t', header = 2, compression='gzip')
+
+ #grab human interactions
+ ptmcode = ptmcode[ptmcode['Species'] == 'Homo sapiens']
+ #ignore intraprotein interactions
+ ptmcode = ptmcode[ptmcode['## Protein1'] != ptmcode['Protein2']]
+
+ #get uniprot id for primary protein and interacting protein
+ ptmcode['UniProtKB Accession'] = extract_ids_PTMcode(ptmcode, '## Protein1')
+ ptmcode['Interacting Protein'] = extract_ids_PTMcode(ptmcode, 'Protein2')
+
+ ptmcode = ptmcode.dropna(subset = ['UniProtKB Accession', 'Interacting Protein'])
+ #remove duplicate proteins (some entries have different ids but are actually the same protein)
+ ptmcode = ptmcode[ptmcode['UniProtKB Accession'] != ptmcode['Interacting Protein']]
+
+ #aggregate interactions
+ ptmcode['Interacting Residue'] = ptmcode['Interacting Protein'] + '_' + ptmcode['Residue2']
+
+
+ #convert modification names
+ convert_dict = {'Adp ribosylation': 'ADP Ribosylation', 'Glutamine deamidation':'Deamidation'}
+ new_mod_names = []
+ failed_mod = []
+ mod_list = ptmcode['PTM1'].unique()
+ for mod in mod_list:
+ mod = mod.capitalize()
+ if 'glycosylation' in mod:
+ new_mod_names.append('Glycosylation')
+ elif mod in pose_config.modification_conversion['Modification Class'].values:
+ new_mod_names.append(mod)
+ elif mod in convert_dict.keys():
+ new_mod_names.append(convert_dict[mod])
+ else:
+ try:
+ new_mod = pose_config.modification_conversion[pose_config.modification_conversion['Modification'] == mod].values[0][0]
+ new_mod_names.append(new_mod)
+ except:
+ failed_mod.append(mod)
+ new_mod_names.append(mod)
+ conversion_df = pd.DataFrame({'PTM1':mod_list, 'Modification Class':new_mod_names})
+
+ ptmcode = ptmcode.merge(conversion_df, on = 'PTM1', how = 'left')
+
+
+ ptmcode = ptmcode.rename(columns = {'Residue1':'Residue'})
+ ptmcode = ptmcode.groupby(['UniProtKB Accession', 'Residue', 'Modification Class'])['Interacting Residue'].agg(';'.join).reset_index()
+ ptmcode = ptmcode.rename(columns = {'UniProtKB/Swiss-Prot ID':'UniProtKB Accession', 'Residue1':'Residue', 'Interacting Residue':'PTMcode:Interprotein_Interactions'})
+
+ #separate residue information into separate columns, one for amino acid and one for position
+ ptmcode['PTM Position in Canonical Isoform'] = ptmcode['Residue'].apply(lambda x: float(x[1:]))
+ ptmcode['Residue'] = ptmcode['Residue'].apply(lambda x: x[0])
+
+ #if splice data already has the annotation columns, remove them
+ if 'PTMcode:Interprotein_Interactions' in spliced_ptms.columns:
+ spliced_ptms = spliced_ptms.drop(columns = ['PTMcode:Interprotein_Interactions'])
+
+ #explode dataframe on modifications
+ if spliced_ptms['Modification Class'].str.contains(';').any():
+ spliced_ptms['Modification Class'] = spliced_ptms['Modification Class'].str.split(';')
+ spliced_ptms = spliced_ptms.explode('Modification Class').reset_index(drop = True)
+
+ #add to splice data
+ original_data_size = spliced_ptms.shape[0]
+ spliced_ptms = spliced_ptms.merge(ptmcode, how = 'left', on = ['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class'])
+ if spliced_ptms.shape[0] != original_data_size:
+ raise RuntimeError('Dataframe size has changed, check for duplicates in spliced ptms dataframe')
+
+ #report the number of PTMs identified
+ if report_success:
+ num_ptms_with_PTMcode_data = spliced_ptms.dropna(subset = 'PTMcode:Interprotein_Interactions').groupby(['UniProtKB Accession', 'Residue']).size().shape[0]
+ print(f"PTMcode interprotein interactions added: {num_ptms_with_PTMcode_data} PTMs in dataset found with PTMcode interprotein interaction information")
+
+ return spliced_ptms
+
+
+
+def add_DEPOD_phosphatase_data(spliced_ptms, report_success = True):
+
+ #download data
+ depod1 = pd.read_excel('https://depod.bioss.uni-freiburg.de/download/PPase_protSubtrates_201903.xls', sheet_name='PSprots')
+ depod2 = pd.read_excel('https://depod.bioss.uni-freiburg.de/download/PPase_protSubtrates_newPairs_201903.xls', sheet_name = 'newPSprots')
+ depod = pd.concat([depod1, depod2])
+
+ #remove any rows with missing sit information
+ depod = depod.dropna(subset = 'Dephosphosites')
+
+ #remove excess annotations that make parsing difficult
+ depod['Dephosphosites'] = depod['Dephosphosites'].apply(lambda x: x.split('[')[0])
+ depod['Dephosphosites'] = depod['Dephosphosites'].apply(lambda x: x.split('(')[0])
+ depod['Dephosphosites'] = depod['Dephosphosites'].apply(lambda x: x.split(';')[0])
+ depod['Dephosphosites'] = depod['Dephosphosites'].apply(lambda x: x.split('in')[0])
+ depod['Dephosphosites'] = depod['Dephosphosites'].str.replace('in ref.', '')
+
+ #separate individual sites
+ depod['Dephosphosites'] = depod['Dephosphosites'].str.split(',')
+ depod = depod.explode('Dephosphosites')
+ depod = depod[(~depod['Dephosphosites'].str.contains('Isoform')) & (~depod['Dephosphosites'].str.contains('isoform'))]
+
+ #process dephosphosite strings to extract residue and position and explode so that each phosphosite is its own row
+ depod['Dephosphosites'] = depod['Dephosphosites'].apply(extract_positions_from_DEPOD)
+ depod = depod.explode('Dephosphosites')
+
+ #separate multiple substrate accessions into their own rows (many of these link back to the same ID, but will keep just in case)
+ depod['Substrate accession numbers'] = depod['Substrate accession numbers'].str.split(' ')
+ depod = depod.explode('Substrate accession numbers')
+ depod = depod.dropna(subset = ['Substrate accession numbers'])
+
+ #extract only needed information and add phosphorylation as modification type
+ #extract only needed information and add phosphorylation as modification type
+ depod['Residue'] = depod['Dephosphosites'].apply(lambda x: x[0] if x == x else np.nan)
+ depod['PTM Position in Canonical Isoform'] = depod['Dephosphosites'].apply(lambda x: int(x[1:]) if x == x else np.nan)
+ depod = depod.rename({'Substrate accession numbers': 'UniProtKB Accession', 'Phosphatase entry names':'DEPOD:Phosphatase'}, axis = 1)
+ depod = depod[['DEPOD:Phosphatase', 'UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform']]
+ depod['Modification Class'] = 'Phosphorylation'
+
+ #combine on the same PTM
+ depod = depod.drop_duplicates()
+ depod = depod.groupby(['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class'], as_index = False)['DEPOD:Phosphatase'].agg(';'.join)
+
+ #if splice data already has the annotation columns, remove them
+ if 'DEPOD:Phosphatase' in spliced_ptms.columns:
+ spliced_ptms = spliced_ptms.drop(columns = ['DEPOD:Phosphatase'])
+
+ #explode dataframe on modifications
+ if spliced_ptms['Modification Class'].str.contains(';').any():
+ spliced_ptms['Modification Class'] = spliced_ptms['Modification Class'].str.split(';')
+ spliced_ptms = spliced_ptms.explode('Modification Class').reset_index(drop = True)
+
+ #add to splice data
+ original_data_size = spliced_ptms.shape[0]
+ spliced_ptms = spliced_ptms.merge(depod, how = 'left', on = ['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class'])
+ if spliced_ptms.shape[0] != original_data_size:
+ raise RuntimeError('Dataframe size has changed, check for duplicates in spliced ptms dataframe')
+
+ #report the number of PTMs identified
+ if report_success:
+ num_ptms_with_PTMcode_data = spliced_ptms.dropna(subset = 'DEPOD:Phosphatase').groupby(['UniProtKB Accession', 'Residue']).size().shape[0]
+ print(f"DEPOD Phosphatase substrates added: {num_ptms_with_PTMcode_data} PTMs in dataset found with Phosphatase substrate information")
+
+ return spliced_ptms
+
+def add_RegPhos_data(spliced_ptms, file = None, report_success = True):
+ if file is None:
+ regphos = pd.read_csv('http://140.138.144.141/~RegPhos/download/RegPhos_Phos_human.txt', sep = '\t', dtype = {'position':int, 'description':str,'catalytic kinase':str, 'reference':'str'})
+ else:
+ check_file(file, expected_extension = '.txt')
+ regphos = pd.read_csv(file, sep = '\t')
+
+ regphos = regphos.dropna(subset = 'catalytic kinase')
+ #regphos['Residue'] = regphos['code'] + regphos['position'].astype(str)
+ regphos = regphos.rename(columns = {'code': 'Residue', 'position':'PTM Position in Canonical Isoform', 'AC': 'UniProtKB Accession', 'catalytic kinase': 'RegPhos:Kinase'})
+ regphos['Modification Class'] = 'Phosphorylation'
+ regphos = regphos[['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class', 'RegPhos:Kinase']].dropna()
+ regphos = regphos.groupby(['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class']).agg(';'.join).reset_index()
+
+ #if splice data already has the annotation columns, remove them
+ if 'RegPhos:Kinase' in spliced_ptms.columns:
+ spliced_ptms = spliced_ptms.drop(columns = ['RegPhos:Kinase'])
+
+ #explode dataframe on modifications
+ if spliced_ptms['Modification Class'].str.contains(';').any():
+ spliced_ptms['Modification Class'] = spliced_ptms['Modification Class'].str.split(';')
+ spliced_ptms = spliced_ptms.explode('Modification Class').reset_index(drop = True)
+
+ #add to splice data
+ original_data_size = spliced_ptms.shape[0]
+ spliced_ptms = spliced_ptms.merge(regphos, how = 'left', on = ['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class'])
+ if spliced_ptms.shape[0] != original_data_size:
+ raise RuntimeError('Dataframe size has changed, check for duplicates in spliced ptms dataframe')
+
+ #report the number of PTMs identified
+ if report_success:
+ num_ptms_with_regphos_data = spliced_ptms.dropna(subset = 'RegPhos:Kinase').groupby(['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform']).size().shape[0]
+ print(f"RegPhos kinase-substrate data added: {num_ptms_with_regphos_data} PTMs in dataset found with kinase-substrate information")
+
+ return spliced_ptms
+
+
+def add_PTMsigDB_data(spliced_ptms, file = None, report_success = True):
+ #if file is None:
+ # ptmsigdb = pd.read_excel('https://proteomics.broadapps.org/ptmsigdb/_w_8b062d9e/appff37efd164a676afcc8e6e42e6058e01/session/a2b28c4ed29deadd6779fdd26aec33c1/download/download.xlsx?w=8b062d9e', sheet_name = 'human')
+ #else:
+ check_file(file, expected_extension = '.xlsx')
+ ptmsigdb = pd.read_excel(file, sheet_name = 'human')
+
+
+ ptmsigdb['UniProtKB Accession'] = ptmsigdb['site.uniprot'].str.split(';').str[0]
+ ptmsigdb['Residue'] = ptmsigdb['site.uniprot'].str.split(';').str[1].str[0]
+ ptmsigdb['PTM Position in Canonical Isoform'] = ptmsigdb['site.uniprot'].apply(lambda x: int(x.split(';')[1].split('-')[0][1:]))
+
+ #filter out excess information in some of the site.ptm column, then convert to modification class details
+ ptmsigdb['site.ptm'] = ptmsigdb['site.ptm'].apply(lambda x: x.split(';')[1].split('-')[1] if ';' in x else x)
+ ptmsigdb['Modification Class'] = ptmsigdb['site.ptm'].map(mod_shorthand_dict)
+
+ #combine signature and direction for annotation column
+ ptmsigdb['Signature'] = ptmsigdb['signature'] +'->'+ ptmsigdb['site.direction']
+
+ #drop unneeded columns
+ ptmsigdb = ptmsigdb[['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class', 'Signature', 'category']]
+ ptmsigdb['Signature'] = ptmsigdb.apply(lambda x: x['Signature'].replace(x['category'] + '_', ''), axis = 1)
+ ptmsigdb['category'] = 'PTMsigDB:' + ptmsigdb['category']
+ ptmsigdb = ptmsigdb.drop_duplicates()
+
+ #convert to pivot table with each category being a separate column
+ ptmsigdb = ptmsigdb.pivot_table(index = ['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class'], columns = 'category', values = 'Signature', aggfunc=';'.join).reset_index()
+
+ #remove psp data if it is already in spliced ptms
+ if 'PSP:Kinase' in spliced_ptms.columns:
+ ptmsigdb = ptmsigdb.drop(columns = 'PTMsigDB:KINASE-PSP')
+
+ if 'PSP:Disease_Association' in spliced_ptms.columns:
+ ptmsigdb = ptmsigdb.drop(columns = 'PTMsigDB:DISEASE-PSP')
+
+
+ #if splice data already has the annotation columns, remove them
+ if 'PTMsigDB:PATH-BI' in spliced_ptms.columns:
+ cols_in_data = [col for col in spliced_ptms.columns if 'PTMsigDB' in col]
+ spliced_ptms = spliced_ptms.drop(columns = cols_in_data)
+
+
+ #explode dataframe on modifications
+ if spliced_ptms['Modification Class'].str.contains(';').any():
+ spliced_ptms['Modification Class'] = spliced_ptms['Modification Class'].str.split(';')
+ spliced_ptms = spliced_ptms.explode('Modification Class').reset_index(drop = True)
+
+ #merge with spliced_ptm info
+ original_data_size = spliced_ptms.shape[0]
+ spliced_ptms = spliced_ptms.merge(ptmsigdb, how = 'left', on = ['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class'])
+ if spliced_ptms.shape[0] != original_data_size:
+ raise RuntimeError('Dataset size changed upon merge, please make sure there are no duplicates in spliced ptms data')
+
+
+ #report the number of ptms with motif data
+ if report_success:
+ num_ptms_with_ikip = spliced_ptms.dropna(subset = 'PTMsigDB:KINASE-iKiP').groupby(['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class']).size().shape[0]
+ num_ptms_with_path_bi = spliced_ptms.dropna(subset = 'PTMsigDB:PATH-BI').groupby(['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class']).size().shape[0]
+ num_ptms_with_path_np= spliced_ptms.dropna(subset = 'PTMsigDB:PATH-NP').groupby(['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class']).size().shape[0]
+ num_ptms_with_path_wp = spliced_ptms.dropna(subset = 'PTMsigDB:PATH-WP').groupby(['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class']).size().shape[0]
+ num_ptms_with_dia_pert = spliced_ptms.dropna(subset = 'PTMsigDB:PERT-P100-DIA').groupby(['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class']).size().shape[0]
+ num_ptms_with_dia2_pert = spliced_ptms.dropna(subset = 'PTMsigDB:PERT-P100-DIA2').groupby(['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class']).size().shape[0]
+ num_ptms_with_prm_pert = spliced_ptms.dropna(subset = 'PTMsigDB:PERT-P100-PRM').groupby(['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class']).size().shape[0]
+ num_ptms_with_psp_pert = spliced_ptms.dropna(subset = 'PTMsigDB:PERT-PSP').groupby(['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class']).size().shape[0]
+ print(f"PTMsigDB added:\n\t ->{num_ptms_with_ikip} PTMs associated with kinases in iKiP\n\t ->{num_ptms_with_path_wp} PTMs associated with molecular pathway signatures from WikiPathways\n\t ->{num_ptms_with_path_np} PTMs associated with molecular pathway signatures from NetPath\n\t ->{num_ptms_with_psp_pert} PTMs with PhosphoSitePlus perturbations\n\t ->{num_ptms_with_dia_pert} with perturbations in LINCS P1000 DIA dataset \n\t ->{num_ptms_with_dia2_pert} with perturbations in LINCS P1000 DIA2 dataset\n\t ->{num_ptms_with_prm_pert} with perturbations in LINCS P1000 PRM dataset")
+ return spliced_ptms
+
+
+
+######### Functions for combining annotations from multiple sources ########
+
+[docs]def convert_PSP_label_to_UniProt(label):
+
"""
+
Given a label for an interacting protein from PhosphoSitePlus, convert to UniProtKB accession. Required as PhosphoSitePlus interactions are recorded in various ways that aren't necessarily consistent with other databases (i.e. not always gene name)
+
+
Parameters
+
----------
+
label: str
+
Label for interacting protein from PhosphoSitePlus
+
"""
+
if not hasattr(pose_config, 'genename_to_uniprot'):
+
#using uniprot to gene name dict, construct dict to go the other direction (gene name to uniprot id)
+
pose_config.genename_to_uniprot = pose_config.flip_uniprot_dict(pose_config.uniprot_to_genename)
+
+
+
#remove isoform label if present
+
if label in pose_config.genename_to_uniprot: #if PSP name is gene name found in uniprot
+
return pose_config.genename_to_uniprot[label]
+
elif label.upper() in pose_config.genename_to_uniprot:
+
return pose_config.genename_to_uniprot[label.upper()]
+
elif label.split(' ')[0].upper() in pose_config.genename_to_uniprot:
+
return pose_config.genename_to_uniprot[label.split(' ')[0].upper()]
+
elif label.replace('-', '').upper() in pose_config.genename_to_uniprot:
+
return pose_config.genename_to_uniprot[label.replace('-', '').upper()]
+
elif label in pose_config.psp_name_dict: # if PSP name is not gene name, but is in conversion dictionary
+
return pose_config.psp_name_dict[label]
+
else: #otherwise note that gene was missed
+
return np.nan
+ #missed_genes.append(gene)
+
+def extract_interaction_details(interaction, column = "PSP:ON_PROT_INTERACT"):
+
+ interaction_types = {'PTMcode:Interprotein_Interactions':'INDUCES', 'PSP:Kinase':'REGULATES', 'DEPOD:Phosphatase':'REGULATES', 'RegPhos:Kinase':'REGULATES', 'Combined:Kinase':'REGULATES', 'ELM:Interactions':'UNCLEAR'}
+ if column == 'PSP:ON_PROT_INTERACT':
+ type = interaction.split('(')[1].split(')')[0]
+ protein = interaction.split('(')[0].strip(' ')
+ elif column == 'PTMInt:Interaction':
+ ptmint_type_conversion = {'Inhibit':'DISRUPTS', 'Enhance':"INDUCES"}
+ type = ptmint_type_conversion[interaction.split('->')[1]]
+ protein = interaction.split('->')[0]
+ elif column == 'PTMcode:Interprotein_Interactions':
+ type = 'INDUCES'
+ protein = interaction.split('_')[0]
+ else:
+ type = interaction_types[column]
+ protein = interaction
+
+ return type, protein
+
+[docs]def unify_interaction_data(spliced_ptms, interaction_col, name_dict = {}):
+
"""
+
Given spliced ptm data and a column containing interaction data, extract the interacting protein, type of interaction, and convert to UniProtKB accession. This will be added as a new column labeled 'Interacting ID'
+
+
Parameters
+
----------
+
spliced_ptms: pd.DataFrame
+
Dataframe containing PTM data
+
interaction_col: str
+
column containing interaction information from a specific database
+
name_dict: dict
+
dictionary to convert names within given database to UniProt IDs. For cases when name is not necessarily one of the gene names listed in UniProt
+
+
Returns
+
-------
+
interact: pd.DataFrame
+
Contains PTMs and their interacting proteins, the type of influence the PTM has on the interaction (DISRUPTS, INDUCES, or REGULATES)
+
"""
+
if not hasattr(pose_config, 'genename_to_uniprot'):
+
#using uniprot to gene name dict, construct dict to go the other direction (gene name to uniprot id)
+
pose_config.genename_to_uniprot = pose_config.flip_uniprot_dict(pose_config.uniprot_to_genename)
+
+
#extract PSP data from annotated PTMs, separate cases in which single PTM has multipe interactions
+
data_cols = [col for col in spliced_ptms.columns if col in ['Significance', 'dPSI']]
+
interact = spliced_ptms.dropna(subset = interaction_col)[['Gene', 'UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Modification Class',interaction_col] + data_cols]
+
if interact.empty:
+
print(f"No PTMs associated with {interaction_col}")
+
return interact
+
+
interact[interaction_col] = interact[interaction_col].apply(lambda x: x.split(';'))
+
interact = interact.explode(interaction_col)
+
+
#extract protein and type of interaction (currently for phosphosite plus)
+
type = []
+
protein = []
+
for i, row in interact.iterrows():
+
processed = extract_interaction_details(row[interaction_col], interaction_col)
+
type.append(processed[0])
+
protein.append(processed[1])
+
interact['Type'] = type
+
interact['Interacting Protein'] = protein
+
+
+
#convert interacting protein to uniprot id for databases that are not reported in uniprot ids
+
if interaction_col not in ['PTMcode:Interprotein_Interactions', 'ELM:Interactions']:
+
interacting_id = []
+
missed_genes = []
+
for gene in interact['Interacting Protein']:
+
#remove isoform label if present
+
if gene in pose_config.genename_to_uniprot: #if PSP name is gene name found in uniprot
+
interacting_id.append(pose_config.genename_to_uniprot[gene])
+
elif gene.upper() in pose_config.genename_to_uniprot:
+
interacting_id.append(pose_config.genename_to_uniprot[gene.upper()])
+
elif gene.split(' ')[0].upper() in pose_config.genename_to_uniprot:
+
interacting_id.append(pose_config.genename_to_uniprot[gene.split(' ')[0].upper()])
+
elif gene.replace('-', '').upper() in pose_config.genename_to_uniprot:
+
interacting_id.append(pose_config.genename_to_uniprot[gene.replace('-', '').upper()])
+
elif gene in name_dict: # if PSP name is not gene name, but is in conversion dictionary
+
interacting_id.append(name_dict[gene])
+
else: #otherwise note that gene was missed
+
interacting_id.append(np.nan)
+
missed_genes.append(gene)
+
+
#save information
+
interact['Interacting ID'] = interacting_id
+
interact = interact.dropna(subset = 'Interacting ID')
+
+
+
#check if there multiple in one row
+
if interact['Interacting ID'].str.contains(';').any():
+
interact['Interacting ID'] = interact['Interacting ID'].apply(lambda x: x.split(';'))
+
interact = interact.explode('Interacting ID')
+
else:
+
interact['Interacting ID'] = interact['Interacting Protein']
+
+
+
interact['Interacting ID'] = interact['Interacting ID'].apply(lambda x: x.split(' ')[0] if x == x else np.nan)
+
interact = interact.explode('Interacting ID')
+
interact = interact.dropna(subset = 'Interacting ID')
+
interact = interact.drop(columns = interaction_col).drop_duplicates()
+
+
return interact
+
+[docs]def add_annotation(spliced_ptms, database = 'PhosphoSitePlus', annotation_type = 'Function', file = None, check_existing = False):
+
"""
+
Given a desired database and annotation type, add the corresponding annotation data to the spliced ptm dataframe
+
+
Parameters
+
----------
+
spliced_ptms: pd.DataFrame
+
Dataframe containing PTM data
+
database: str
+
Database to extract annotation data from. Options include 'PhosphoSitePlus', 'PTMcode', 'PTMInt', 'RegPhos', 'DEPOD'
+
annotation_type: str
+
Type of annotation to extract. Options include 'Function', 'Process', 'Interactions', 'Disease', 'Kinase', 'Phosphatase', but depend on the specific database (run analyze.get_annotation_categories())
+
file: str
+
File path to annotation data. If None, will download from online source, except for PhosphoSitePlus (due to licensing restrictions)
+
"""
+
if check_existing:
+
annot_col = annotation_col_dict[database][annotation_type]
+
if annot_col in spliced_ptms.columns:
+
print(f"Annotation data for {database} {annotation_type} already present in provided dataframe, skipping. If you would like to update annotation data, set check_existing = False")
+
return spliced_ptms
+
+
if database == "PhosphoSitePlus":
+
if annotation_type in ['Function', 'Process', 'Interactions']:
+
check_file(file, expected_extension='.gz')
+
spliced_ptms = add_PSP_regulatory_site_data(spliced_ptms, file = file)
+
elif annotation_type == 'Disease':
+
check_file(file, expected_extension='.gz')
+
spliced_ptms = add_PSP_disease_association(spliced_ptms, file = file)
+
elif annotation_type == 'Kinase':
+
check_file(file, expected_extension='.gz')
+
spliced_ptms = add_PSP_kinase_substrate_data(spliced_ptms, file = file)
+
else:
+
raise ValueError(f"Annotation type {annotation_type} not recognized for PhosphoSitePlus")
+
elif database == 'PTMcode':
+
#if annotation_type == 'Intraprotein':
+
# if file is not None:
+
# check_file(file, expected_extension='.gz')
+
# spliced_ptms = add_PTMcode_intraprotein(spliced_ptms, file = file)
+
# else:
+
# spliced_ptms = add_PTMcode_intraprotein(spliced_ptms)
+
if annotation_type == 'Interactions':
+
if file is not None:
+
check_file(file, expected_extension='.gz')
+
spliced_ptms = add_PTMcode_interprotein(spliced_ptms, file = file)
+
else:
+
spliced_ptms = add_PTMcode_interprotein(spliced_ptms)
+
else:
+
raise ValueError(f"Annotation type {annotation_type} not recognized for PTMcode")
+
elif database == 'PTMInt':
+
if annotation_type == 'Interactions':
+
if file is not None:
+
check_file(file, expected_extension='.csv')
+
spliced_ptms = add_PTMInt_data(spliced_ptms, file = file)
+
else:
+
spliced_ptms = add_PTMInt_data(spliced_ptms)
+
else:
+
raise ValueError(f"Annotation type {annotation_type} not recognized for PTMInt")
+
elif database == 'RegPhos':
+
if annotation_type == 'Kinase':
+
if file is not None:
+
check_file(file, expected_extension='.txt')
+
spliced_ptms = add_RegPhos_data(spliced_ptms, file = file)
+
else:
+
spliced_ptms = add_RegPhos_data(spliced_ptms)
+
else:
+
raise ValueError(f"Annotation type {annotation_type} not recognized for RegPhos")
+
elif database == 'DEPOD':
+
if annotation_type == 'Phosphatase':
+
spliced_ptms = add_DEPOD_phosphatase_data(spliced_ptms, file = file)
+
else:
+
raise ValueError(f"Annotation type {annotation_type} not recognized for RegPhos")
+
elif database == 'Combined':
+
if annotation_type == 'Kinase':
+
if 'PSP:Kinase' not in spliced_ptms.columns:
+
raise ValueError("PhosphoSitePlus kinase data not found in spliced PTM dataframe, please annotate with this first")
+
if 'RegPhos:Kinase' not in spliced_ptms.columns:
+
spliced_ptms = add_RegPhos_data(spliced_ptms)
+
spliced_ptms = combine_KS_data(spliced_ptms)
+
elif annotation_type == 'Interactions':
+
spliced_ptms = combine_interaction_data(spliced_ptms)
+
else:
+
raise ValueError(f"Database {database} not recognized")
+
+
return spliced_ptms
+
+
+[docs]def combine_interaction_data(spliced_ptms, interaction_databases = ['PhosphoSitePlus', 'PTMcode', 'PTMInt', 'RegPhos', 'DEPOD', 'ELM'], include_enzyme_interactions = True):
+
"""
+
Given annotated spliced ptm data, extract interaction data from various databases and combine into a single dataframe. This will include the interacting protein, the type of interaction, and the source of the interaction data
+
+
Parameters
+
----------
+
spliced_ptms: pd.DataFrame
+
Dataframe containing PTM data and associated interaction annotations from various databases
+
interaction_databases: list
+
List of databases to extract interaction data from. Options include 'PhosphoSitePlus', 'PTMcode', 'PTMInt', 'RegPhos', 'DEPOD'. These should already have annotation columns in the spliced_ptms dataframe, otherwise they will be ignored. For kinase-substrate interactions, if combined column is present, will use that instead of individual databases
+
include_enzyme_interactions: bool
+
If True, will include kinase-substrate and phosphatase interactions in the output dataframe
+
+
Returns
+
-------
+
interact_data: list
+
List of dataframes containing PTMs and their interacting proteins, the type of influence the PTM has on the interaction (DISRUPTS, INDUCES, or REGULATES), and the source of the interaction data
+
+
"""
+
interact_data = []
+
combined_added = False
+
for database in interaction_databases:
+
if database == 'PhosphoSitePlus' and 'PSP:ON_PROT_INTERACT' in spliced_ptms.columns:
+
if not spliced_ptms['PSP:ON_PROT_INTERACT'].isna().all():
+
print('PhosphoSitePlus regulatory site data found and added')
+
interact = unify_interaction_data(spliced_ptms, 'PSP:ON_PROT_INTERACT', pose_config.psp_name_dict)
+
interact['Source'] = database
+
interact_data.append(interact)
+
+
+
if database == 'PTMcode' and 'PTMcode:Interprotein_Interactions' in spliced_ptms.columns:
+
if not spliced_ptms['PTMcode:Interprotein_Interactions'].isna().all():
+
print('PTMcode data found and added')
+
interact = unify_interaction_data(spliced_ptms, 'PTMcode:Interprotein_Interactions')
+
interact['Source'] = database
+
interact_data.append(interact)
+
if database == 'PTMInt' and 'PTMInt:Interaction' in spliced_ptms.columns:
+
if not spliced_ptms['PTMInt:Interaction'].isna().all():
+
print('PTMInt data found and added')
+
interact = unify_interaction_data(spliced_ptms, 'PTMInt:Interaction')
+
interact['Source'] = database
+
interact_data.append(interact)
+
if database == 'ELM' and 'ELM:Interactions' in spliced_ptms.columns:
+
if not spliced_ptms['ELM:Interactions'].isna().all():
+
print('ELM data found and added')
+
interact = unify_interaction_data(spliced_ptms, 'ELM:Interactions')
+
interact['Source'] = database
+
interact_data.append(interact)
+
+
if include_enzyme_interactions:
+
#dictionary to convert kinase names to gene names
+
ks_genes_to_uniprot = {'ABL1(ABL)':'P00519', 'ACK':'Q07912', 'AURC':'Q9UQB9', 'ERK1(MAPK3)':'P27361','ERK2(MAPK1)':'P28482', 'ERK5(MAPK7)':'Q13164','JNK1(MAPK8)':'P45983', 'CK1A':'P48729', 'JNK2(MAPK9)':'P45984', 'JNK3(MAPK10)':'P53779', 'P38A(MAPK14)':'Q16539','P38B(MAPK11)':'Q15759', 'P38G(MAPK12)':'P53778','P70S6K' :'Q9UBS0', 'PAK':'Q13153', 'PKCZ':'Q05513', 'CK2A':'P19784', 'ABL2':'P42684', 'AMPKA1':'Q13131', 'AMPKA2':'Q13131', 'AURB':'Q96GD4', 'CAMK1A':'Q14012', 'CDC42BP':'Q9Y5S2','CK1D':'P48730','CK1E':'P49674','CK2B':'P67870','DMPK1':'Q09013', 'DNAPK':'P78527','DSDNA KINASE':'P78527', 'EG3 KINASE':'P49840','ERK3(MAPK6)':'Q16659','GSK3':'P49840', 'MRCKA':'Q5VT25', 'P38D(MAPK13)':'O15264','P70S6KB':'Q9UBS0','PDKC':'P78527','PKCH':'P24723','PKCI':'P41743','PKCT':'Q04759','PKD3':'O94806','PKG1':'Q13976','PKG2':'Q13237','SMMLCK':'Q15746'}
+
if 'Combined:Kinase' in spliced_ptms.columns and not combined_added:
+
if not spliced_ptms['Combined:Kinase'].isna().all():
+
print('Combined kinase-substrate data found and added')
+
interact = unify_interaction_data(spliced_ptms, 'Combined:Kinase', ks_genes_to_uniprot)
+
interact['Source'] = 'PSP/RegPhos'
+
interact_data.append(interact)
+
combined_added = True
+
elif 'Combined:Kinase' not in spliced_ptms.columns:
+
if 'RegPhos:Kinase' in spliced_ptms.columns and database == 'RegPhos':
+
if not spliced_ptms['RegPhos:Kinase'].isna().all():
+
print('RegPhos kinase-substrate data found and added')
+
interact = unify_interaction_data(spliced_ptms, 'RegPhos:Kinase', ks_genes_to_uniprot)
+
interact['Source'] = database
+
interact_data.append(interact)
+
if 'PSP:Kinase' in spliced_ptms.columns and database == 'PhosphoSitePlus':
+
if not spliced_ptms['PSP:Kinase'].isna().all():
+
print('PhosphoSitePlus kinase-substrate data found and added')
+
interact = unify_interaction_data(spliced_ptms, 'PSP:Kinase', ks_genes_to_uniprot)
+
interact['Source'] = database
+
interact_data.append(interact)
+
+
if database == 'DEPOD' and 'DEPOD:Phosphatase' in spliced_ptms.columns:
+
if not spliced_ptms['DEPOD:Phosphatase'].isna().all():
+
print('DEPOD phosphatase-substrate data found and added')
+
interact = unify_interaction_data(spliced_ptms, 'DEPOD:Phosphatase')
+
interact['Source'] = database
+
interact_data.append(interact)
+
+
if len(interact_data) > 0:
+
interact_data = pd.concat(interact_data)
+
extra_cols = [col for col in interact_data.columns if col in ['dPSI', 'Significance']]
+
interact_data = interact_data.groupby(['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform', 'Interacting ID', 'Type']+extra_cols, dropna = False, as_index = False)['Source'].apply(helpers.join_unique_entries)
+
+
#convert uniprot ids back to gene names for interpretability
+
ptm_gene = []
+
interacting_gene = []
+
for i, row in interact_data.iterrows():
+
ptm_gene.append(pose_config.uniprot_to_genename[row['UniProtKB Accession'].split('-')[0]].split(' ')[0]) if row['UniProtKB Accession'].split('-')[0] in pose_config.uniprot_to_genename else ptm_gene.append(row['UniProtKB Accession'])
+
interacting_gene.append(pose_config.uniprot_to_genename[row['Interacting ID'].split('-')[0]].split(' ')[0]) if row['Interacting ID'].split('-')[0] in pose_config.uniprot_to_genename else interacting_gene.append(row['Interacting ID'])
+
interact_data['Modified Gene'] = ptm_gene
+
interact_data["Interacting Gene"] = interacting_gene
+
+
+
return interact_data.drop_duplicates()
+
else:
+
return pd.DataFrame()
+
+
+
+[docs]def combine_KS_data(spliced_ptms, ks_databases = ['PhosphoSitePlus', 'RegPhos'], regphos_conversion = {'ERK1(MAPK3)':'MAPK3', 'ERK2(MAPK1)':'MAPK1', 'JNK2(MAPK9)':'MAPK9','CDC2':'CDK1', 'CK2A1':'CSNK2A1', 'PKACA':'PRKACA', 'ABL1(ABL)':'ABL1'}):
+
"""
+
Given spliced ptm information, combine kinase-substrate data from multiple databases (currently support PhosphoSitePlus and RegPhos), assuming that the kinase data from these resources has already been added to the spliced ptm data. The combined kinase data will be added as a new column labeled 'Combined:Kinase'
+
+
Parameters
+
----------
+
spliced_ptms: pd.DataFrame
+
Spliced PTM data from project module
+
ks_databases: list
+
List of databases to combine kinase data from. Currently support PhosphoSitePlus and RegPhos
+
regphos_conversion: dict
+
Allows conversion of RegPhos names to matching names in PhosphoSitePlus.
+
+
Returns
+
-------
+
splicde_ptms: pd.DataFrame
+
Spliced PTM data with combined kinase data added
+
+
"""
+
if not hasattr(pose_config, 'genename_to_uniprot'):
+
pose_config.genename_to_uniprot = pose_config.flip_uniprot_dict(pose_config.uniprot_to_genename)
+
+
ks_data = []
+
for i, row in spliced_ptms.iterrows():
+
combined = []
+
for db in ks_databases:
+
if db == 'PhosphoSitePlus':
+
psp = row['PSP:Kinase'].split(';') if row['PSP:Kinase'] == row['PSP:Kinase'] else []
+
#convert PSP names to a common name (first gene name provided by uniprot)
+
psp = [pose_config.uniprot_to_genename[pose_config.genename_to_uniprot[kin]].split(' ')[0] if kin in pose_config.genename_to_uniprot else kin for kin in psp]
+
combined += psp
+
elif db == 'RegPhos':
+
regphos = row['RegPhos:Kinase'].split(';') if row['RegPhos:Kinase'] == row['RegPhos:Kinase'] else []
+
for i, rp in enumerate(regphos):
+
if rp in pose_config.genename_to_uniprot:
+
regphos[i] = pose_config.uniprot_to_genename[pose_config.genename_to_uniprot[rp]].split(' ')[0]
+
elif rp.split('(')[0] in pose_config.genename_to_uniprot:
+
regphos[i] = pose_config.uniprot_to_genename[pose_config.genename_to_uniprot[rp.split('(')[0]]].split(' ')[0]
+
elif rp.upper() in regphos_conversion:
+
regphos[i] = regphos_conversion[rp.upper()]
+
else:
+
regphos[i] = rp.upper()
+
combined += regphos
+
+
+
if len(combined) > 0:
+
ks_data.append(';'.join(set(combined)))
+
else:
+
ks_data.append(np.nan)
+
+
spliced_ptms['Combined:Kinase'] = ks_data
+
return spliced_ptms
+
+
+[docs]def check_file(fname, expected_extension = '.tsv'):
+
"""
+
Given a file name, check if the file exists and has the expected extension. If the file does not exist or has the wrong extension, raise an error.
+
+
Parameters
+
----------
+
fname: str
+
File name to check
+
expected_extension: str
+
Expected file extension. Default is '.tsv'
+
"""
+
if fname is None:
+
raise ValueError('Annotation file path must be provided')
+
if not os.path.exists(fname):
+
raise ValueError(f'File {fname} not found')
+
+
if not fname.endswith(expected_extension):
+
raise ValueError(f'File {fname} does not have the expected extension ({expected_extension})')
+
+
+
+
+
+[docs]def annotate_ptms(spliced_ptms, psp_regulatory_site_file = None, psp_ks_file = None, psp_disease_file = None, elm_interactions = False, elm_motifs = False, PTMint = False, PTMcode_interprotein = False, DEPOD = False, RegPhos = False, ptmsigdb_file = None, interactions_to_combine = ['PTMcode', 'PhosphoSitePlus', 'RegPhos', 'PTMInt'], kinases_to_combine = ['PhosphoSitePlus', 'RegPhos'], combine_similar = True):
+
"""
+
Given spliced ptm data, add annotations from various databases. The annotations that can be added are the following:
+
- PhosphoSitePlus
+
- regulatory site data (file must be provided)
+
- kinase-substrate data (file must be provided)
+
- disease association data (file must be provided)
+
- ELM
+
- interaction data (can be downloaded automatically or provided as a file)
+
- motif matches (elm class data can be downloaded automatically or provided as a file)
+
- PTMInt
+
- interaction data (will be downloaded automatically)
+
- PTMcode
+
- intraprotein interactions (can be downloaded automatically or provided as a file)
+
- interprotein interactions (can be downloaded automatically or provided as a file)
+
- DEPOD
+
- phosphatase-substrate data (will be downloaded automatically)
+
- RegPhos
+
- kinase-substrate data (will be downloaded automatically)
+
+
Parameters
+
----------
+
spliced_ptms: pd.DataFrame
+
Spliced PTM data from project module
+
psp_regulatory_site_file: str
+
File path to PhosphoSitePlus regulatory site data
+
psp_ks_file: str
+
File path to PhosphoSitePlus kinase-substrate data
+
psp_disease_file: str
+
File path to PhosphoSitePlus disease association data
+
elm_interactions: bool or str
+
If True, download ELM interaction data automatically. If str, provide file path to ELM interaction data
+
elm_motifs: bool or str
+
If True, download ELM motif data automatically. If str, provide file path to ELM motif data
+
PTMint: bool
+
If True, download PTMInt data automatically
+
PTMcode_intraprotein: bool or str
+
If True, download PTMcode intraprotein data automatically. If str, provide file path to PTMcode intraprotein data
+
PTMcode_interprotein: bool or str
+
If True, download PTMcode interprotein data automatically. If str, provide file path to PTMcode interprotein data
+
DEPOD: bool
+
If True, download DEPOD data automatically
+
RegPhos: bool
+
If True, download RegPhos data automatically
+
ptmsigdb_file: str
+
File path to PTMsigDB data
+
interactions_to_combine: list
+
List of databases to combine interaction data from. Default is ['PTMcode', 'PhosphoSitePlus', 'RegPhos', 'PTMInt']
+
kinases_to_combine: list
+
List of databases to combine kinase-substrate data from. Default is ['PhosphoSitePlus', 'RegPhos']
+
combine_similar: bool
+
Whether to combine annotations of similar information (kinase, interactions, etc) from multiple databases into another column labeled as 'Combined'. Default is True
+
"""
+
if psp_regulatory_site_file is not None:
+
try:
+
check_file(psp_regulatory_site_file, expected_extension='.gz')
+
spliced_ptms = add_PSP_regulatory_site_data(spliced_ptms, file = psp_regulatory_site_file)
+
except Exception as e:
+
raise RuntimeError(f'Error adding PhosphoSitePlus regulatory site data. Error message: {e}')
+
if psp_ks_file is not None:
+
try:
+
check_file(psp_ks_file, expected_extension='.gz')
+
spliced_ptms = add_PSP_kinase_substrate_data(spliced_ptms, file = psp_ks_file)
+
except Exception as e:
+
raise RuntimeError(f'Error adding PhosphoSitePlus kinase-substrate data. Error message: {e}')
+
if psp_disease_file is not None:
+
try:
+
check_file(psp_disease_file, expected_extension='.gz')
+
spliced_ptms = add_PSP_disease_association(spliced_ptms, file = psp_disease_file)
+
except Exception as e:
+
raise RuntimeError(f'Error adding PhosphoSitePlus disease association data. Error message: {e}')
+
if elm_interactions:
+
try:
+
if isinstance(elm_interactions, bool):
+
spliced_ptms = add_ELM_interactions(spliced_ptms)
+
elif isinstance(elm_interactions, str):
+
check_file(elm_interactions, expected_extension='.tsv')
+
spliced_ptms = add_ELM_interactions(spliced_ptms, file = elm_interactions)
+
else:
+
raise ValueError('elm_interactions must be either a boolean (download elm data automatically, slower) or a string (path to elm data tsv file, faster)')
+
except Exception as e:
+
raise RuntimeError(f'Error adding ELM interaction data. Error message: {e}')
+
if elm_motifs:
+
try:
+
if isinstance(elm_motifs, bool):
+
spliced_ptms = add_ELM_matched_motifs(spliced_ptms)
+
elif isinstance(elm_motifs, str):
+
check_file(elm_motifs, expected_extension='.tsv')
+
spliced_ptms = add_ELM_matched_motifs(spliced_ptms, file = elm_motifs)
+
else:
+
raise ValueError('elm_interactions must be either a boolean (download elm data automatically, slower) or a string (path to elm data tsv file, faster)')
+
except Exception as e:
+
raise RuntimeError(f'Error adding ELM motif matches. Error message: {e}')
+
if PTMint:
+
try:
+
if isinstance(PTMint, bool):
+
spliced_ptms = add_PTMInt_data(spliced_ptms)
+
elif isinstance(PTMint, str):
+
check_file(PTMint, expected_extension='.csv')
+
spliced_ptms = add_PTMInt_data(spliced_ptms, file = PTMint)
+
else:
+
raise ValueError('PTMint must be either a boolean (download PTMInt data automatically, slower) or a string (path to PTMInt data csv file, faster)')
+
except Exception as e:
+
raise RuntimeError(f'Error adding PTMInt interaction data. Error message: {e}')
+
#if PTMcode_intraprotein:
+
# try:
+
# if isinstance(PTMcode_intraprotein, bool):
+
# spliced_ptms = add_PTMcode_intraprotein(spliced_ptms)
+
# elif isinstance(PTMcode_intraprotein, str):
+
# check_file(PTMcode_intraprotein, expected_extension='.gz')
+
# spliced_ptms = add_PTMcode_intraprotein(spliced_ptms, fname = PTMcode_intraprotein)
+
# else:
+
# raise ValueError('PTMcode_intraprotein must be either a boolean (download PTMcode data automatically, slower) or a string (path to PTMcode data file, faster)')
+
# except Exception as e:
+
# print(f'Error adding PTMcode intraprotein interaction data. Error message: {e}')
+
if PTMcode_interprotein:
+
try:
+
if isinstance(PTMcode_interprotein, bool):
+
spliced_ptms = add_PTMcode_interprotein(spliced_ptms)
+
elif isinstance(PTMcode_interprotein, str):
+
check_file(PTMcode_interprotein, expected_extension='.gz')
+
spliced_ptms = add_PTMcode_interprotein(spliced_ptms, fname = PTMcode_interprotein)
+
else:
+
raise ValueError('PTMcode_interprotein must be either a boolean (download PTMcode data automatically, slower) or a string (path to PTMcode data file, faster)')
+
except Exception as e:
+
raise RuntimeError(f'Error adding PTMcode interprotein interaction data. Error message: {e}')
+
if DEPOD:
+
try:
+
spliced_ptms = add_DEPOD_phosphatase_data(spliced_ptms)
+
except Exception as e:
+
raise RuntimeError(f'Error adding DEPOD phosphatase data. Error message: {e}')
+
if RegPhos:
+
try:
+
if isinstance(RegPhos, str):
+
check_file(RegPhos, expected_extension='.txt')
+
spliced_ptms = add_RegPhos_data(spliced_ptms, file = RegPhos)
+
else:
+
spliced_ptms = add_RegPhos_data(spliced_ptms)
+
except Exception as e:
+
raise RuntimeError(f'Error adding RegPhos kinase substrate data data. Error message: {e}')
+
if ptmsigdb_file is not None:
+
try:
+
spliced_ptms = add_PTMsigDB_data(spliced_ptms, file = ptmsigdb_file)
+
except Exception as e:
+
raise RuntimeError(f'Error adding PTMsigDB data. Error message: {e}')
+
+
if combine_similar:
+
interaction_cols = ['PTMcode:Interprotein_Interactions', 'PSP:ON_PROT_INTERACT', 'PSP:Kinase', 'PTMInt:Interaction', 'RegPhos:Kinase', 'DEPOD:Phosphatase']
+
if set(interaction_cols).intersection(spliced_ptms.columns) != 0:
+
print('\nCombining interaction data from multiple databases')
+
interact = combine_interaction_data(spliced_ptms, interaction_databases = interactions_to_combine)
+
if not interact.empty:
+
interact['Combined:Interactions'] = interact['Interacting Gene']+'->'+interact['Type']
+
interact = interact.groupby(['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform'], dropna = False, as_index = False)['Combined:Interactions'].apply(lambda x: ';'.join(np.unique(x)))
+
if 'Combined:Interactions' in spliced_ptms.columns:
+
spliced_ptms = spliced_ptms.drop(columns = ['Combined:Interactions'])
+
+
spliced_ptms = spliced_ptms.merge(interact, how = 'left', on = ['UniProtKB Accession', 'Residue', 'PTM Position in Canonical Isoform'])
+
else:
+
spliced_ptms['Combined:Interactions'] = np.nan
+
+
#check for what kinase data is available
+
spliced_ptms = combine_KS_data(spliced_ptms, ks_databases=kinases_to_combine) #add combined kinase column
+
+
+
return spliced_ptms
+
+
+
+
+
+
+
+
+
+
+
+
+