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PPI-networks

The objective of this workflow is to capture relevant information about an indication of interest using different databases such as CHEMBL, TTD, DRUGBANK, STRING and OPEN TARGETS to obtain a completelist of targets, drugs and their phase of study, as well as the most important protein-protein interactions in the characterization of the modularity of the biological process associated with the disease. The information obtained through this workflow serves as a basis for building a functional association network between: protein-protein, protein-drug and as a support for the design of multitarget drugs. This workflowwas standardized using Alzheimer's disease as an example. 2. Enrichment of the small database of the indication of interest from otherdatabases 1. Inputs - Acquisition of datafrom different databases Target ID and group classification(Table 1) Protein-Protein Interaction(Table 2) Select disease Specify the path where thefile is located 4. Outputs - Results Target ID and group classification(Table 1) Protein-Protein Interaction(Table 2) We post-process the files from the different databases to filter the targets and drugs related to the indication of interest. In addition, the targets are internally categorized as T1, T2 and T3,according to the phase of study their drugs are in. T1: Approved or phase 4T2: Clinical trials, phase 1, 2 or 3 or patented T3: Investigational phase or terminated or preclinical or phase 0To complement the information on the pathology of interest, a query is made to the Open Targets database through the EFO (Experimetal Factor Ontology), which serves as a disease identifier.Finally, a small database of the pathology of interest is obtained, enriched by the four databases, with the respective IDS, used by each database to identify the targets and drugs. This smalldatabase can be accessed through the output table 3. On the other hand, the node entitled "T1/T2/T3 classification" lists the targets classified as T1, T2 and T3 and their Uniprot ID. The nodesand metanodes used in this workflow are duly documented to describe the process being performed. Bibliography1. Ochoa, D. et al. Open Targets Platform: Supporting systematic drug-target identification and prioritisation. Nucleic Acids Res. 49, D1302–D1310 (2021). This workflow has three output files:The first gives us a detailed list of the targets associated with an indication of interest alongwith their Uniprot ID, their classification (T1,T2,T3 or T4), the target score and thenormalized score. .The second provides information on the protein-protein interaction, in this case, the targetswe have classified as T1, T2 or T3 with which the target (T4) interacts and the Stringdatabase score, which shows the strength of that interaction.The third file presents a small database of the indication of interest, where the targets (T1/T2/T3) are associated with their respective drug, the study phase andthe IDs of both targets and drugs, given for identification in the different databases (TTD,DRUGBAK and CHEMBL). It is recommended to set the output path only for file 1, since by default the other files will besaved in the same path. Protein-Protein Interaction networks Proteín - compound interactions(Table 4) 3. Protein protein interactions (PPI)usign String To integrate the protein-protein association network for targets classified as T1, T2 and T3, theSTRING database was used. Targets interacting with T1 or T2 or T3 (neighbors), according tothe STRING database were named T4 targets, and were filtered taking into account the targetsreported by the Open targets database for the indication of interest. Then, a list and an internalscore were established, with their respective normalization according to the internalclassification of each target, either as T1,T2, T3 or T4. Finally, a list of targets (T1/T2/T3) andtheir interaction with T4, together with the total score, was obtained from the String database.Each metanode is properly documented for a better clarity of the processes. Bibliography1. Szklarczyk, D. et al. The STRING database in 2021: Customizable protein-proteinnetworks, and functional characterization of user-uploaded gene/measurement sets. NucleicAcids Res. 49, D605–D612 (2021). Information on active compounds (drugs), their phase and targets associatedwith a specific pathology are captured from three databases DRUGBANK, TTDand CHEMBL through this workflow. The following steps show how to proceedto load the inputs:1. Indicate the path where the file 1_Drugs_TTD.txt is located (red box), whenexecuting the workflow, the other input files will be loaded automatically as longas they are in the same folder as file 1.2 In the red box of the Select Disease node, interactively select the pathology ofinterest by right clicking and selecting the Interactive View: single selectionwidget option.Bibliography1. Gaulton, A. et al. The ChEMBL database in 2017. Nucleic Acids Res. 45,D945–D954 (2017).2. Wishart, D. S. et al. DrugBank 5.0: A major update to the DrugBankdatabase for 2018. Nucleic Acids Res. 46, D1074–D1082 (2018).3. Wang, Y. et al. Therapeutic target database 2020: Enriched resource forfacilitating research and early development of targeted therapeutics. NucleicAcids Res. 48, D1031–D1041 (2020). 01_output-Target-Groups.xlsx02_output_Protein-ProteinInteraction network1_Drugs_TTD.txt2_Target_TTD.txtNode 1247Node 125503_mini-database TTD, CHEMBL and DRUGBANKNode 1299 Select the disease of interestinteractive Node 1319Node 1346Node 1354Node 1400Node 14243_Target_disease_TTD.txtNode 1446Node 1449Node 14504_all_efos_info_CHEMBL28_feb2021.csvNode 1457Node 1458Node 1459Node 14605_database_drugbank_2021.xlsxNode 1462Execution Time Excel Writer (XLS)(deprecated) Disease targetsfrom Opentarget Excel Writer (XLS)(deprecated) Simple File Reader Simple File Reader dataset_paths PPI Excel Writer (XLS)(deprecated) Target results filtered byindication of interest. TTD database Single SelectionWidget Data sortedby indication Drug and target linkage by indicationof interest from TTD database. Comparing data from STRING withOpen Targets Plataform info Drugs results filtered by indicationof interest. DRUGBANK database Target and drugs results filtered byindication of interest. CHEMBL database T1/T2/T3 associatedproteins by STRING Simple File Reader Classification of targetsby indication from TTD Filtering of targets and drugs associatedwith the indication of interest General organizationof input data CSV Reader Complementaryinformation on the IDS Mini-database of the indicationof interest: T1/T2/T3 Classification of theTargets as: T1/T2/T3 Metanode Excel Reader TARGET SCORENORMALIZE The objective of this workflow is to capture relevant information about an indication of interest using different databases such as CHEMBL, TTD, DRUGBANK, STRING and OPEN TARGETS to obtain a completelist of targets, drugs and their phase of study, as well as the most important protein-protein interactions in the characterization of the modularity of the biological process associated with the disease. The information obtained through this workflow serves as a basis for building a functional association network between: protein-protein, protein-drug and as a support for the design of multitarget drugs. This workflowwas standardized using Alzheimer's disease as an example. 2. Enrichment of the small database of the indication of interest from otherdatabases 1. Inputs - Acquisition of datafrom different databases Target ID and group classification(Table 1) Protein-Protein Interaction(Table 2) Select disease Specify the path where thefile is located 4. Outputs - Results Target ID and group classification(Table 1) Protein-Protein Interaction(Table 2) We post-process the files from the different databases to filter the targets and drugs related to the indication of interest. In addition, the targets are internally categorized as T1, T2 and T3,according to the phase of study their drugs are in. T1: Approved or phase 4T2: Clinical trials, phase 1, 2 or 3 or patented T3: Investigational phase or terminated or preclinical or phase 0To complement the information on the pathology of interest, a query is made to the Open Targets database through the EFO (Experimetal Factor Ontology), which serves as a disease identifier.Finally, a small database of the pathology of interest is obtained, enriched by the four databases, with the respective IDS, used by each database to identify the targets and drugs. This smalldatabase can be accessed through the output table 3. On the other hand, the node entitled "T1/T2/T3 classification" lists the targets classified as T1, T2 and T3 and their Uniprot ID. The nodesand metanodes used in this workflow are duly documented to describe the process being performed. Bibliography1. Ochoa, D. et al. Open Targets Platform: Supporting systematic drug-target identification and prioritisation. Nucleic Acids Res. 49, D1302–D1310 (2021). This workflow has three output files:The first gives us a detailed list of the targets associated with an indication of interest alongwith their Uniprot ID, their classification (T1,T2,T3 or T4), the target score and thenormalized score. .The second provides information on the protein-protein interaction, in this case, the targetswe have classified as T1, T2 or T3 with which the target (T4) interacts and the Stringdatabase score, which shows the strength of that interaction.The third file presents a small database of the indication of interest, where the targets (T1/T2/T3) are associated with their respective drug, the study phase andthe IDs of both targets and drugs, given for identification in the different databases (TTD,DRUGBAK and CHEMBL). It is recommended to set the output path only for file 1, since by default the other files will besaved in the same path. Protein-Protein Interaction networks Proteín - compound interactions(Table 4) 3. Protein protein interactions (PPI)usign String To integrate the protein-protein association network for targets classified as T1, T2 and T3, theSTRING database was used. Targets interacting with T1 or T2 or T3 (neighbors), according tothe STRING database were named T4 targets, and were filtered taking into account the targetsreported by the Open targets database for the indication of interest. Then, a list and an internalscore were established, with their respective normalization according to the internalclassification of each target, either as T1,T2, T3 or T4. Finally, a list of targets (T1/T2/T3) andtheir interaction with T4, together with the total score, was obtained from the String database.Each metanode is properly documented for a better clarity of the processes. Bibliography1. Szklarczyk, D. et al. The STRING database in 2021: Customizable protein-proteinnetworks, and functional characterization of user-uploaded gene/measurement sets. NucleicAcids Res. 49, D605–D612 (2021). Information on active compounds (drugs), their phase and targets associatedwith a specific pathology are captured from three databases DRUGBANK, TTDand CHEMBL through this workflow. The following steps show how to proceedto load the inputs:1. Indicate the path where the file 1_Drugs_TTD.txt is located (red box), whenexecuting the workflow, the other input files will be loaded automatically as longas they are in the same folder as file 1.2 In the red box of the Select Disease node, interactively select the pathology ofinterest by right clicking and selecting the Interactive View: single selectionwidget option.Bibliography1. Gaulton, A. et al. The ChEMBL database in 2017. Nucleic Acids Res. 45,D945–D954 (2017).2. Wishart, D. S. et al. DrugBank 5.0: A major update to the DrugBankdatabase for 2018. Nucleic Acids Res. 46, D1074–D1082 (2018).3. Wang, Y. et al. Therapeutic target database 2020: Enriched resource forfacilitating research and early development of targeted therapeutics. NucleicAcids Res. 48, D1031–D1041 (2020). 01_output-Target-Groups.xlsx02_output_Protein-ProteinInteraction network1_Drugs_TTD.txt2_Target_TTD.txtNode 1247Node 125503_mini-database TTD, CHEMBL and DRUGBANKNode 1299 Select the disease of interestinteractive Node 1319Node 1346Node 1354Node 1400Node 14243_Target_disease_TTD.txtNode 1446Node 1449Node 14504_all_efos_info_CHEMBL28_feb2021.csvNode 1457Node 1458Node 1459Node 14605_database_drugbank_2021.xlsxNode 1462Execution Time Excel Writer (XLS)(deprecated) Disease targetsfrom Opentarget Excel Writer (XLS)(deprecated) Simple File Reader Simple File Reader dataset_paths PPI Excel Writer (XLS)(deprecated) Target results filtered byindication of interest. TTD database Single SelectionWidget Data sortedby indication Drug and target linkage by indicationof interest from TTD database. Comparing data from STRING withOpen Targets Plataform info Drugs results filtered by indicationof interest. DRUGBANK database Target and drugs results filtered byindication of interest. CHEMBL database T1/T2/T3 associatedproteins by STRING Simple File Reader Classification of targetsby indication from TTD Filtering of targets and drugs associatedwith the indication of interest General organizationof input data CSV Reader Complementaryinformation on the IDS Mini-database of the indicationof interest: T1/T2/T3 Classification of theTargets as: T1/T2/T3 Metanode Excel Reader TARGET SCORENORMALIZE

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Created by: apadi
Created at: 2021-02-02
On NodePit since: 2021-06-04
Last update: 2024-11-21
Created with KNIME version: v4.3.2

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