Comprehensive protein datasets and benchmarking for liquid-liquid phase separation studies

Proteins self-organize in dynamic cellular environments by assembling into reversible biomolecular condensates through liquid-liquid phase separation (LLPS). These condensates can comprise single or multiple proteins, with different roles in the ensemble's structural and functional integrity. D...

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Autores: Pintado-Grima, Carlos|||0000-0002-8544-959X, Bárcenas, Oriol|||0000-0002-8439-4005, Arribas-Ruiz, Eva, Iglesias, Valentin|||0000-0002-6133-0869, Burdukiewicz, Michał|||0000-0001-8926-582X, Ventura, Salvador|||0000-0002-9652-6351
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:dnet:uabarcelona_::622843f2779a441fa4a2b1a79a8c82e6
Acceso en línea:https://ddd.uab.cat/record/327734
https://dx.doi.org/urn:doi:10.1186/s13059-025-03668-6
Access Level:acceso abierto
Palabra clave:Liquid-liquid phase separation
Datasets
Integration
Driver
Client
Negative
Proteins
Disorder
Machine learning
Benchmark
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spelling Comprehensive protein datasets and benchmarking for liquid-liquid phase separation studiesPintado-Grima, Carlos|||0000-0002-8544-959XBárcenas, Oriol|||0000-0002-8439-4005Arribas-Ruiz, EvaIglesias, Valentin|||0000-0002-6133-0869Burdukiewicz, Michał|||0000-0001-8926-582XVentura, Salvador|||0000-0002-9652-6351Liquid-liquid phase separationDatasetsIntegrationDriverClientNegativeProteinsDisorderMachine learningBenchmarkProteins self-organize in dynamic cellular environments by assembling into reversible biomolecular condensates through liquid-liquid phase separation (LLPS). These condensates can comprise single or multiple proteins, with different roles in the ensemble's structural and functional integrity. Driver proteins form condensates autonomously, while client proteins just localize within them. Although several databases exist to catalog proteins undergoing LLPS, they often contain divergent data that impedes interoperability between these resources. Additionally, there is a lack of consensus on selecting proteins without explicit experimental association with condensates under physiological conditions (non-LLPS proteins or negative proteins). These two aspects have prevented the generation of reliable predictive models and fair benchmarks. In this work, we use an integrated biocuration protocol to analyze information from all relevant LLPS databases and generate confident datasets of client and driver proteins. We introduce standardized negative datasets, encompassing both globular and disordered proteins. To validate our datasets, we investigate specific physicochemical traits related to LLPS across different subsets of protein sequences and benchmark them against 16 predictive algorithms. We observe significant differences not only between positive and negative instances but also among LLPS proteins themselves. The datasets from this study are available as a website at and as a data repository at . Our datasets offer a reliable means for confidently assessing the specific roles of proteins in LLPS and identifying key differences in physicochemical properties underlying this process. Moreover, we describe limitations in classical and state-of-the-art predictive algorithms by providing the most comprehensive benchmark to date. {'@id': 'Par4', 'graphic': {'@position': 'anchor', '@id': 'MO1', '@orientation': 'portrait', '@xlink:href': '13059_2025_3668_Figa_HTML.jpg'}} The online version contains supplementary material available at 10.1186/s13059-025-03668-6. 22025-01-0120252025-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/327734https://dx.doi.org/urn:doi:10.1186/s13059-025-03668-6reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengAgencia Estatal de Investigación https://doi.org/10.13039/501100011033 PID2022-137963OB-I00Generalitat de Catalunya https://doi.org/10.13039/501100002809 2021-SGR-00635open accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:dnet:uabarcelona_::622843f2779a441fa4a2b1a79a8c82e62026-06-06T12:50:31Z
dc.title.none.fl_str_mv Comprehensive protein datasets and benchmarking for liquid-liquid phase separation studies
title Comprehensive protein datasets and benchmarking for liquid-liquid phase separation studies
spellingShingle Comprehensive protein datasets and benchmarking for liquid-liquid phase separation studies
Pintado-Grima, Carlos|||0000-0002-8544-959X
Liquid-liquid phase separation
Datasets
Integration
Driver
Client
Negative
Proteins
Disorder
Machine learning
Benchmark
title_short Comprehensive protein datasets and benchmarking for liquid-liquid phase separation studies
title_full Comprehensive protein datasets and benchmarking for liquid-liquid phase separation studies
title_fullStr Comprehensive protein datasets and benchmarking for liquid-liquid phase separation studies
title_full_unstemmed Comprehensive protein datasets and benchmarking for liquid-liquid phase separation studies
title_sort Comprehensive protein datasets and benchmarking for liquid-liquid phase separation studies
dc.creator.none.fl_str_mv Pintado-Grima, Carlos|||0000-0002-8544-959X
Bárcenas, Oriol|||0000-0002-8439-4005
Arribas-Ruiz, Eva
Iglesias, Valentin|||0000-0002-6133-0869
Burdukiewicz, Michał|||0000-0001-8926-582X
Ventura, Salvador|||0000-0002-9652-6351
author Pintado-Grima, Carlos|||0000-0002-8544-959X
author_facet Pintado-Grima, Carlos|||0000-0002-8544-959X
Bárcenas, Oriol|||0000-0002-8439-4005
Arribas-Ruiz, Eva
Iglesias, Valentin|||0000-0002-6133-0869
Burdukiewicz, Michał|||0000-0001-8926-582X
Ventura, Salvador|||0000-0002-9652-6351
author_role author
author2 Bárcenas, Oriol|||0000-0002-8439-4005
Arribas-Ruiz, Eva
Iglesias, Valentin|||0000-0002-6133-0869
Burdukiewicz, Michał|||0000-0001-8926-582X
Ventura, Salvador|||0000-0002-9652-6351
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Liquid-liquid phase separation
Datasets
Integration
Driver
Client
Negative
Proteins
Disorder
Machine learning
Benchmark
topic Liquid-liquid phase separation
Datasets
Integration
Driver
Client
Negative
Proteins
Disorder
Machine learning
Benchmark
description Proteins self-organize in dynamic cellular environments by assembling into reversible biomolecular condensates through liquid-liquid phase separation (LLPS). These condensates can comprise single or multiple proteins, with different roles in the ensemble's structural and functional integrity. Driver proteins form condensates autonomously, while client proteins just localize within them. Although several databases exist to catalog proteins undergoing LLPS, they often contain divergent data that impedes interoperability between these resources. Additionally, there is a lack of consensus on selecting proteins without explicit experimental association with condensates under physiological conditions (non-LLPS proteins or negative proteins). These two aspects have prevented the generation of reliable predictive models and fair benchmarks. In this work, we use an integrated biocuration protocol to analyze information from all relevant LLPS databases and generate confident datasets of client and driver proteins. We introduce standardized negative datasets, encompassing both globular and disordered proteins. To validate our datasets, we investigate specific physicochemical traits related to LLPS across different subsets of protein sequences and benchmark them against 16 predictive algorithms. We observe significant differences not only between positive and negative instances but also among LLPS proteins themselves. The datasets from this study are available as a website at and as a data repository at . Our datasets offer a reliable means for confidently assessing the specific roles of proteins in LLPS and identifying key differences in physicochemical properties underlying this process. Moreover, we describe limitations in classical and state-of-the-art predictive algorithms by providing the most comprehensive benchmark to date. {'@id': 'Par4', 'graphic': {'@position': 'anchor', '@id': 'MO1', '@orientation': 'portrait', '@xlink:href': '13059_2025_3668_Figa_HTML.jpg'}} The online version contains supplementary material available at 10.1186/s13059-025-03668-6.
publishDate 2025
dc.date.none.fl_str_mv 2
2025-01-01
2025
2025-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/327734
https://dx.doi.org/urn:doi:10.1186/s13059-025-03668-6
url https://ddd.uab.cat/record/327734
https://dx.doi.org/urn:doi:10.1186/s13059-025-03668-6
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 PID2022-137963OB-I00
Generalitat de Catalunya https://doi.org/10.13039/501100002809 2021-SGR-00635
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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