Integrated mass spectrometry strategy for functional protein complex discovery and structural characterization
The discovery of functional protein complex and the interrogation of the complex structure-function relationship (SFR) play crucial roles in the understanding and intervention of biological processes. Affinity purification-mass spectrometry (AP-MS) has been proved as a powerful tool in the discovery...
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Veröffentlicht in: | Current opinion in chemical biology 2023-06, Vol.74, p.102305-102305, Article 102305 |
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creator | Liu, Zheyi Chen, Xiong Yang, Shirui Tian, Ruijun Wang, Fangjun |
description | The discovery of functional protein complex and the interrogation of the complex structure-function relationship (SFR) play crucial roles in the understanding and intervention of biological processes. Affinity purification-mass spectrometry (AP-MS) has been proved as a powerful tool in the discovery of protein complexes. However, validation of these novel protein complexes as well as elucidation of their molecular interaction mechanisms are still challenging. Recently, native top-down MS (nTDMS) is rapidly developed for the structural analysis of protein complexes. In this review, we discuss the integration of AP-MS and nTDMS in the discovery and structural characterization of functional protein complexes. Further, we think the emerging artificial intelligence (AI)-based protein structure prediction is highly complementary to nTDMS and can promote each other. We expect the hybridization of integrated structural MS with AI prediction to be a powerful workflow in the discovery and SFR investigation of functional protein complexes. |
doi_str_mv | 10.1016/j.cbpa.2023.102305 |
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Affinity purification-mass spectrometry (AP-MS) has been proved as a powerful tool in the discovery of protein complexes. However, validation of these novel protein complexes as well as elucidation of their molecular interaction mechanisms are still challenging. Recently, native top-down MS (nTDMS) is rapidly developed for the structural analysis of protein complexes. In this review, we discuss the integration of AP-MS and nTDMS in the discovery and structural characterization of functional protein complexes. Further, we think the emerging artificial intelligence (AI)-based protein structure prediction is highly complementary to nTDMS and can promote each other. We expect the hybridization of integrated structural MS with AI prediction to be a powerful workflow in the discovery and SFR investigation of functional protein complexes.</description><identifier>ISSN: 1367-5931</identifier><identifier>EISSN: 1879-0402</identifier><identifier>DOI: 10.1016/j.cbpa.2023.102305</identifier><identifier>PMID: 37071953</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Affinity purification-mass spectrometry ; Artificial Intelligence ; Artificial intelligence prediction ; Mass Spectrometry - methods ; Native top-down mass spectrometry ; Protein complex ; Proteins - chemistry ; Structure-function relationship</subject><ispartof>Current opinion in chemical biology, 2023-06, Vol.74, p.102305-102305, Article 102305</ispartof><rights>2023 Elsevier Ltd</rights><rights>Copyright © 2023 Elsevier Ltd. 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We expect the hybridization of integrated structural MS with AI prediction to be a powerful workflow in the discovery and SFR investigation of functional protein complexes.</description><subject>Affinity purification-mass spectrometry</subject><subject>Artificial Intelligence</subject><subject>Artificial intelligence prediction</subject><subject>Mass Spectrometry - methods</subject><subject>Native top-down mass spectrometry</subject><subject>Protein complex</subject><subject>Proteins - chemistry</subject><subject>Structure-function relationship</subject><issn>1367-5931</issn><issn>1879-0402</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kEtPwzAQhC0E4lH4AxxQjlxS1nbzsMQFVbykSlzgbDnOprhK4mA7FeXX46jAkZNX3pnRzkfIJYU5BZrfbOa6GtScAePxg3HIDsgpLQuRwgLYYZx5XqSZ4PSEnHm_AYCcldkxOeEFFFRk_JT0z33AtVMB66RT3id-QB2c7TC4XeLDtFnvksa6pBl7HYztVZsMzgY0faJtN7T4mdTGa7vF6FB9PblGHUYXhfpdOaUDOvOlJu85OWpU6_Hi552Rt4f71-VTunp5fF7erVLNszykRV4AraESouSoGWNCNwwy1lRlFUvoWK9hvALB67ikRSNUJTDjgi9KURYln5HrfW689GNEH2QXT8S2VT3a0UtWAuccFoxFKdtLtbPeO2zk4Eyn3E5SkBNnuZETZzlxlnvO0XT1kz9WHdZ_ll-wUXC7F2BsuTXopNcGe421cRGwrK35L_8bBLKQKA</recordid><startdate>202306</startdate><enddate>202306</enddate><creator>Liu, Zheyi</creator><creator>Chen, Xiong</creator><creator>Yang, Shirui</creator><creator>Tian, Ruijun</creator><creator>Wang, Fangjun</creator><general>Elsevier Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-0608-5482</orcidid></search><sort><creationdate>202306</creationdate><title>Integrated mass spectrometry strategy for functional protein complex discovery and structural characterization</title><author>Liu, Zheyi ; Chen, Xiong ; Yang, Shirui ; Tian, Ruijun ; Wang, Fangjun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c356t-76701d0b9983ec2229cf2052fb8b006c402f23b093d22217f9ab9e53934898783</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Affinity purification-mass spectrometry</topic><topic>Artificial Intelligence</topic><topic>Artificial intelligence prediction</topic><topic>Mass Spectrometry - methods</topic><topic>Native top-down mass spectrometry</topic><topic>Protein complex</topic><topic>Proteins - chemistry</topic><topic>Structure-function relationship</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Zheyi</creatorcontrib><creatorcontrib>Chen, Xiong</creatorcontrib><creatorcontrib>Yang, Shirui</creatorcontrib><creatorcontrib>Tian, Ruijun</creatorcontrib><creatorcontrib>Wang, Fangjun</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Current opinion in chemical biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Zheyi</au><au>Chen, Xiong</au><au>Yang, Shirui</au><au>Tian, Ruijun</au><au>Wang, Fangjun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Integrated mass spectrometry strategy for functional protein complex discovery and structural characterization</atitle><jtitle>Current opinion in chemical biology</jtitle><addtitle>Curr Opin Chem Biol</addtitle><date>2023-06</date><risdate>2023</risdate><volume>74</volume><spage>102305</spage><epage>102305</epage><pages>102305-102305</pages><artnum>102305</artnum><issn>1367-5931</issn><eissn>1879-0402</eissn><abstract>The discovery of functional protein complex and the interrogation of the complex structure-function relationship (SFR) play crucial roles in the understanding and intervention of biological processes. 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We expect the hybridization of integrated structural MS with AI prediction to be a powerful workflow in the discovery and SFR investigation of functional protein complexes.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>37071953</pmid><doi>10.1016/j.cbpa.2023.102305</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0003-0608-5482</orcidid></addata></record> |
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source | MEDLINE; ScienceDirect Journals (5 years ago - present) |
subjects | Affinity purification-mass spectrometry Artificial Intelligence Artificial intelligence prediction Mass Spectrometry - methods Native top-down mass spectrometry Protein complex Proteins - chemistry Structure-function relationship |
title | Integrated mass spectrometry strategy for functional protein complex discovery and structural characterization |
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