MAGPIE: A Machine Learning Approach to Decipher Protein–Protein Interactions in Human Plasma
Immunoprecipitation coupled to tandem mass spectrometry (IP-MS/MS) methods are often used to identify protein–protein interactions (PPIs). While these approaches are prone to false positive identifications through contamination and antibody nonspecific binding, their results can be filtered using ne...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2025
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| الموضوعات: | |
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| _version_ | 1852023603521912832 |
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| author | Emily Hashimoto-Roth (9564009) |
| author2 | Diane Forget (2007433) Vanessa P. Gaspar (16511018) Steffany A. L. Bennett (9564015) Marie-Soleil Gauthier (6863540) Benoit Coulombe (8211) Mathieu Lavallée-Adam (1343481) |
| author2_role | author author author author author author |
| author_facet | Emily Hashimoto-Roth (9564009) Diane Forget (2007433) Vanessa P. Gaspar (16511018) Steffany A. L. Bennett (9564015) Marie-Soleil Gauthier (6863540) Benoit Coulombe (8211) Mathieu Lavallée-Adam (1343481) |
| author_role | author |
| dc.creator.none.fl_str_mv | Emily Hashimoto-Roth (9564009) Diane Forget (2007433) Vanessa P. Gaspar (16511018) Steffany A. L. Bennett (9564015) Marie-Soleil Gauthier (6863540) Benoit Coulombe (8211) Mathieu Lavallée-Adam (1343481) |
| dc.date.none.fl_str_mv | 2025-01-14T20:19:21Z |
| dc.identifier.none.fl_str_mv | 10.1021/acs.jproteome.4c00160.s003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/MAGPIE_A_Machine_Learning_Approach_to_Decipher_Protein_Protein_Interactions_in_Human_Plasma/28152901 |
| dc.rights.none.fl_str_mv | CC BY-NC 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biophysics Biochemistry Medicine Molecular Biology Physiology Immunology Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified tandem mass spectrometry novel machine learning effectively detect false antibody nonspecific binding false positive identifications existing modeling algorithms machine learning approach leverages negative controls magpie significantly outperformed negative controls used human plasma samples positive interactions often used human plasma computational modeling based approach approach provides unprecedented ability proteins cannot predicted ppis ppis detected ppis ). introduce magpie identifying ppis identified known first constructed execution without biological processes better understanding 77 %. |
| dc.title.none.fl_str_mv | MAGPIE: A Machine Learning Approach to Decipher Protein–Protein Interactions in Human Plasma |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | Immunoprecipitation coupled to tandem mass spectrometry (IP-MS/MS) methods are often used to identify protein–protein interactions (PPIs). While these approaches are prone to false positive identifications through contamination and antibody nonspecific binding, their results can be filtered using negative controls and computational modeling. However, such filtering does not effectively detect false-positive interactions when IP-MS/MS is performed on human plasma samples. Therein, proteins cannot be overexpressed or inhibited, and existing modeling algorithms are not adapted for execution without such controls. Hence, we introduce MAGPIE, a novel machine learning-based approach for identifying PPIs in human plasma using IP-MS/MS, which leverages negative controls that include antibodies targeting proteins not expected to be present in human plasma. A set of negative controls used for false positive interaction modeling is first constructed. MAGPIE then assesses the reliability of PPIs detected in IP-MS/MS experiments using antibodies that target known plasma proteins. When applied to five IP-MS/MS experiments as a proof of concept, our algorithm identified 68 PPIs with an FDR of 20.77%. MAGPIE significantly outperformed a state-of-the-art PPI discovery tool and identified known and predicted PPIs. Our approach provides an unprecedented ability to detect human plasma PPIs, which enables a better understanding of biological processes in plasma. |
| eu_rights_str_mv | openAccess |
| id | Manara_e2d4105b3e93c1e7bb658ff5d036df8d |
| identifier_str_mv | 10.1021/acs.jproteome.4c00160.s003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28152901 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY-NC 4.0 |
| spelling | MAGPIE: A Machine Learning Approach to Decipher Protein–Protein Interactions in Human PlasmaEmily Hashimoto-Roth (9564009)Diane Forget (2007433)Vanessa P. Gaspar (16511018)Steffany A. L. Bennett (9564015)Marie-Soleil Gauthier (6863540)Benoit Coulombe (8211)Mathieu Lavallée-Adam (1343481)BiophysicsBiochemistryMedicineMolecular BiologyPhysiologyImmunologyBiological Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedtandem mass spectrometrynovel machine learningeffectively detect falseantibody nonspecific bindingfalse positive identificationsexisting modeling algorithmsmachine learning approachleverages negative controlsmagpie significantly outperformednegative controls usedhuman plasma samplespositive interactionsoften usedhuman plasmacomputational modelingbased approachapproach providesunprecedented abilityproteins cannotpredicted ppisppis detectedppis ).introduce magpieidentifying ppisidentified knownfirst constructedexecution withoutbiological processesbetter understanding77 %.Immunoprecipitation coupled to tandem mass spectrometry (IP-MS/MS) methods are often used to identify protein–protein interactions (PPIs). While these approaches are prone to false positive identifications through contamination and antibody nonspecific binding, their results can be filtered using negative controls and computational modeling. However, such filtering does not effectively detect false-positive interactions when IP-MS/MS is performed on human plasma samples. Therein, proteins cannot be overexpressed or inhibited, and existing modeling algorithms are not adapted for execution without such controls. Hence, we introduce MAGPIE, a novel machine learning-based approach for identifying PPIs in human plasma using IP-MS/MS, which leverages negative controls that include antibodies targeting proteins not expected to be present in human plasma. A set of negative controls used for false positive interaction modeling is first constructed. MAGPIE then assesses the reliability of PPIs detected in IP-MS/MS experiments using antibodies that target known plasma proteins. When applied to five IP-MS/MS experiments as a proof of concept, our algorithm identified 68 PPIs with an FDR of 20.77%. MAGPIE significantly outperformed a state-of-the-art PPI discovery tool and identified known and predicted PPIs. Our approach provides an unprecedented ability to detect human plasma PPIs, which enables a better understanding of biological processes in plasma.2025-01-14T20:19:21ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1021/acs.jproteome.4c00160.s003https://figshare.com/articles/dataset/MAGPIE_A_Machine_Learning_Approach_to_Decipher_Protein_Protein_Interactions_in_Human_Plasma/28152901CC BY-NC 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/281529012025-01-14T20:19:21Z |
| spellingShingle | MAGPIE: A Machine Learning Approach to Decipher Protein–Protein Interactions in Human Plasma Emily Hashimoto-Roth (9564009) Biophysics Biochemistry Medicine Molecular Biology Physiology Immunology Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified tandem mass spectrometry novel machine learning effectively detect false antibody nonspecific binding false positive identifications existing modeling algorithms machine learning approach leverages negative controls magpie significantly outperformed negative controls used human plasma samples positive interactions often used human plasma computational modeling based approach approach provides unprecedented ability proteins cannot predicted ppis ppis detected ppis ). introduce magpie identifying ppis identified known first constructed execution without biological processes better understanding 77 %. |
| status_str | publishedVersion |
| title | MAGPIE: A Machine Learning Approach to Decipher Protein–Protein Interactions in Human Plasma |
| title_full | MAGPIE: A Machine Learning Approach to Decipher Protein–Protein Interactions in Human Plasma |
| title_fullStr | MAGPIE: A Machine Learning Approach to Decipher Protein–Protein Interactions in Human Plasma |
| title_full_unstemmed | MAGPIE: A Machine Learning Approach to Decipher Protein–Protein Interactions in Human Plasma |
| title_short | MAGPIE: A Machine Learning Approach to Decipher Protein–Protein Interactions in Human Plasma |
| title_sort | MAGPIE: A Machine Learning Approach to Decipher Protein–Protein Interactions in Human Plasma |
| topic | Biophysics Biochemistry Medicine Molecular Biology Physiology Immunology Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified tandem mass spectrometry novel machine learning effectively detect false antibody nonspecific binding false positive identifications existing modeling algorithms machine learning approach leverages negative controls magpie significantly outperformed negative controls used human plasma samples positive interactions often used human plasma computational modeling based approach approach provides unprecedented ability proteins cannot predicted ppis ppis detected ppis ). introduce magpie identifying ppis identified known first constructed execution without biological processes better understanding 77 %. |