1
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Kumar M, Michael S, Alvarado-Valverde J, Zeke A, Lazar T, Glavina J, Nagy-Kanta E, Donagh J, Kalman Z, Pascarelli S, Palopoli N, Dobson L, Suarez C, Van Roey K, Krystkowiak I, Griffin J, Nagpal A, Bhardwaj R, Diella F, Mészáros B, Dean K, Davey N, Pancsa R, Chemes L, Gibson T. ELM-the Eukaryotic Linear Motif resource-2024 update. Nucleic Acids Res 2024; 52:D442-D455. [PMID: 37962385 PMCID: PMC10767929 DOI: 10.1093/nar/gkad1058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/22/2023] [Accepted: 10/24/2023] [Indexed: 11/15/2023] Open
Abstract
Short Linear Motifs (SLiMs) are the smallest structural and functional components of modular eukaryotic proteins. They are also the most abundant, especially when considering post-translational modifications. As well as being found throughout the cell as part of regulatory processes, SLiMs are extensively mimicked by intracellular pathogens. At the heart of the Eukaryotic Linear Motif (ELM) Resource is a representative (not comprehensive) database. The ELM entries are created by a growing community of skilled annotators and provide an introduction to linear motif functionality for biomedical researchers. The 2024 ELM update includes 346 novel motif instances in areas ranging from innate immunity to both protein and RNA degradation systems. In total, 39 classes of newly annotated motifs have been added, and another 17 existing entries have been updated in the database. The 2024 ELM release now includes 356 motif classes incorporating 4283 individual motif instances manually curated from 4274 scientific publications and including >700 links to experimentally determined 3D structures. In a recent development, the InterPro protein module resource now also includes ELM data. ELM is available at: http://elm.eu.org.
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Affiliation(s)
- Manjeet Kumar
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Sushama Michael
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Jesús Alvarado-Valverde
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
- Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences, Germany
| | - András Zeke
- Institute of Enzymology, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Tamas Lazar
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie, Pleinlaan 2, 1050 Brussels, Belgium
- Structural Biology Brussels, Department of Bioengineering, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
| | - Juliana Glavina
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), CP 1650, Buenos Aires, Argentina
- Escuela de Bio y Nanotecnologías (EByN), Universidad Nacional de San Martín, Av. 25 de Mayo y Francia, CP1650 San Martín, Buenos Aires, Argentina
| | - Eszter Nagy-Kanta
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, Budapest 1083, Hungary
| | - Juan Mac Donagh
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bernal, Buenos Aires, Argentina
| | - Zsofia E Kalman
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, Budapest 1083, Hungary
| | - Stefano Pascarelli
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Nicolas Palopoli
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bernal, Buenos Aires, Argentina
| | - László Dobson
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
- Department of Bioinformatics, Semmelweis University, Tűzoltó u. 7, Budapest 1094, Hungary
| | - Carmen Florencia Suarez
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), CP 1650, Buenos Aires, Argentina
- Escuela de Bio y Nanotecnologías (EByN), Universidad Nacional de San Martín, Av. 25 de Mayo y Francia, CP1650 San Martín, Buenos Aires, Argentina
| | - Kim Van Roey
- Health Services Research, Sciensano, Brussels, Belgium
| | - Izabella Krystkowiak
- Institute of Cancer Research, Chester Beatty Laboratories, 237 Fulham Rd, Chelsea, London SW3 6JB, UK
| | - Juan Esteban Griffin
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bernal, Buenos Aires, Argentina
| | - Anurag Nagpal
- Department of Biological Sciences, BITS Pilani, K. K. Birla Goa campus, Zuarinagar, Goa 403726, India
| | - Rajesh Bhardwaj
- Inselspital, University of Bern, Freiburgstrasse 15, CH-3010 Bern, Switzerland
| | - Francesca Diella
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Bálint Mészáros
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA
| | - Kellie Dean
- School of Biochemistry and Cell Biology, 3.91 Western Gateway Building, University College Cork, Cork, Ireland
| | - Norman E Davey
- Institute of Cancer Research, Chester Beatty Laboratories, 237 Fulham Rd, Chelsea, London SW3 6JB, UK
| | - Rita Pancsa
- Institute of Enzymology, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Lucía B Chemes
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), CP 1650, Buenos Aires, Argentina
- Escuela de Bio y Nanotecnologías (EByN), Universidad Nacional de San Martín, Av. 25 de Mayo y Francia, CP1650 San Martín, Buenos Aires, Argentina
| | - Toby J Gibson
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
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2
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Mészáros B, Hatos A, Palopoli N, Quaglia F, Salladini E, Van Roey K, Arthanari H, Dosztányi Z, Felli IC, Fischer PD, Hoch JC, Jeffries CM, Longhi S, Maiani E, Orchard S, Pancsa R, Papaleo E, Pierattelli R, Piovesan D, Pritisanac I, Tenorio L, Viennet T, Tompa P, Vranken W, Tosatto SCE, Davey NE. Minimum information guidelines for experiments structurally characterizing intrinsically disordered protein regions. Nat Methods 2023; 20:1291-1303. [PMID: 37400558 DOI: 10.1038/s41592-023-01915-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 05/18/2023] [Indexed: 07/05/2023]
Abstract
An unambiguous description of an experiment, and the subsequent biological observation, is vital for accurate data interpretation. Minimum information guidelines define the fundamental complement of data that can support an unambiguous conclusion based on experimental observations. We present the Minimum Information About Disorder Experiments (MIADE) guidelines to define the parameters required for the wider scientific community to understand the findings of an experiment studying the structural properties of intrinsically disordered regions (IDRs). MIADE guidelines provide recommendations for data producers to describe the results of their experiments at source, for curators to annotate experimental data to community resources and for database developers maintaining community resources to disseminate the data. The MIADE guidelines will improve the interpretability of experimental results for data consumers, facilitate direct data submission, simplify data curation, improve data exchange among repositories and standardize the dissemination of the key metadata on an IDR experiment by IDR data sources.
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Affiliation(s)
- Bálint Mészáros
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Department of Structural Biology and Center for Data Driven Discovery, St Jude Children's Research Hospital, Memphis, TN, USA
| | - András Hatos
- Department of Biomedical Sciences, University of Padova, Padova, Italy
- Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Swiss Cancer Center Leman, Lausanne, Switzerland
| | - Nicolas Palopoli
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - CONICET, Bernal, Buenos Aires, Argentina
| | - Federica Quaglia
- Department of Biomedical Sciences, University of Padova, Padova, Italy
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR-IBIOM), Bari, Italy
| | - Edoardo Salladini
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Kim Van Roey
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium
| | - Haribabu Arthanari
- Harvard Medical School (HMS), Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute (DFCI), Boston, MA, USA
| | | | - Isabella C Felli
- Department of Chemistry 'Ugo Schiff' and Magnetic Resonance Center, University of Florence, Sesto Fiorentino (Florence), Italy
| | - Patrick D Fischer
- Harvard Medical School (HMS), Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute (DFCI), Boston, MA, USA
| | - Jeffrey C Hoch
- Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT, USA
| | - Cy M Jeffries
- European Molecular Biology Laboratory (EMBL), Hamburg Unit, c/o Deutsches Elektronen-Synchrotron, Hamburg, Germany
| | - Sonia Longhi
- Laboratory Architecture et Fonction des Macromolécules Biologiques (AFMB), UMR 7257, Aix Marseille University and Centre National de la Recherche Scientifique (CNRS), Marseille, France
| | - Emiliano Maiani
- Cancer Structural Biology, Danish Cancer Society Research Center, Copenhagen, Denmark
- UniCamillus - Saint Camillus International University of Health and Medical Sciences, Rome, Italy
| | - Sandra Orchard
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, UK
| | - Rita Pancsa
- Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Hungary
| | - Elena Papaleo
- Cancer Structural Biology, Danish Cancer Society Research Center, Copenhagen, Denmark
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and Technology, Technical University of Denmark, Lyngby, Denmark
| | - Roberta Pierattelli
- Department of Chemistry 'Ugo Schiff' and Magnetic Resonance Center, University of Florence, Sesto Fiorentino (Florence), Italy
| | - Damiano Piovesan
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Iva Pritisanac
- Hospital for Sick Children, Toronto, Ontario, Canada
- Medical University of Graz, Graz, Austria
| | - Luiggi Tenorio
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Thibault Viennet
- Harvard Medical School (HMS), Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute (DFCI), Boston, MA, USA
| | - Peter Tompa
- Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Hungary
- VIB-VUB Center for Structural Biology, Brussels, Belgium
- Structural Biology Brussels, Department of Bioengineering Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Wim Vranken
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium
- Structural Biology Brussels, Department of Bioengineering Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | | | - Norman E Davey
- Division Of Cancer Biology, Institute of Cancer Research, Chester Beatty Laboratories, Chelsea, London, UK.
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3
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Mészáros B, Park E, Malinverni D, Sejdiu BI, Immadisetty K, Sandhu M, Lang B, Babu MM. Recent breakthroughs in computational structural biology harnessing the power of sequences and structures. Curr Opin Struct Biol 2023; 80:102608. [PMID: 37182396 DOI: 10.1016/j.sbi.2023.102608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/12/2023] [Accepted: 04/17/2023] [Indexed: 05/16/2023]
Abstract
Recent advances in computational approaches and their integration into structural biology enable tackling increasingly complex questions. Here, we discuss several key areas, highlighting breakthroughs and remaining challenges. Theoretical modeling has provided tools to accurately predict and design protein structures on a scale currently difficult to achieve using experimental approaches. Molecular Dynamics simulations have become faster and more precise, delivering actionable information inaccessible by current experimental methods. Virtual screening workflows allow a high-throughput approach to discover ligands that bind and modulate protein function, while Machine Learning methods enable the design of proteins with new functionalities. Integrative structural biology combines several of these approaches, pushing the frontiers of structural and functional characterization to ever larger systems, advancing towards a complete understanding of the living cell. These breakthroughs will accelerate and significantly impact diverse areas of science.
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Affiliation(s)
- Bálint Mészáros
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA.
| | - Electa Park
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA.
| | - Duccio Malinverni
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA. https://twitter.com/DucMalinverni
| | - Besian I Sejdiu
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA. https://twitter.com/bisejdiu
| | - Kalyan Immadisetty
- Department of Bone Marrow Transplantation & Cellular Therapy, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA. https://twitter.com/k_immadisetty
| | - Manbir Sandhu
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA. https://twitter.com/M5andhu
| | - Benjamin Lang
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA. https://twitter.com/langbnj
| | - M Madan Babu
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA.
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4
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Akdel M, Pires DEV, Pardo EP, Jänes J, Zalevsky AO, Mészáros B, Bryant P, Good LL, Laskowski RA, Pozzati G, Shenoy A, Zhu W, Kundrotas P, Serra VR, Rodrigues CHM, Dunham AS, Burke D, Borkakoti N, Velankar S, Frost A, Basquin J, Lindorff-Larsen K, Bateman A, Kajava AV, Valencia A, Ovchinnikov S, Durairaj J, Ascher DB, Thornton JM, Davey NE, Stein A, Elofsson A, Croll TI, Beltrao P. A structural biology community assessment of AlphaFold2 applications. Nat Struct Mol Biol 2022; 29:1056-1067. [PMID: 36344848 PMCID: PMC9663297 DOI: 10.1038/s41594-022-00849-w] [Citation(s) in RCA: 176] [Impact Index Per Article: 88.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 09/20/2022] [Indexed: 11/09/2022]
Abstract
Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods for protein structure predictions have reached the accuracy of experimentally determined models. Although this has been independently verified, the implementation of these methods across structural-biology applications remains to be tested. Here, we evaluate the use of AlphaFold2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modeling of interactions; and modeling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modeled when compared with homology modeling, identifying structural features rarely seen in the Protein Data Bank. AF2-based predictions of protein disorder and complexes surpass dedicated tools, and AF2 models can be used across diverse applications equally well compared with experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life-science research.
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Affiliation(s)
- Mehmet Akdel
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, Wageningen, the Netherlands
| | - Douglas E V Pires
- School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - Eduard Porta Pardo
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Jürgen Jänes
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Arthur O Zalevsky
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russian Federation
| | | | - Patrick Bryant
- Dep of Biochemistry and Biophysics and Science for Life Laboratory, Solna, Sweden
| | - Lydia L Good
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Roman A Laskowski
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Gabriele Pozzati
- Dep of Biochemistry and Biophysics and Science for Life Laboratory, Solna, Sweden
| | - Aditi Shenoy
- Dep of Biochemistry and Biophysics and Science for Life Laboratory, Solna, Sweden
| | - Wensi Zhu
- Dep of Biochemistry and Biophysics and Science for Life Laboratory, Solna, Sweden
| | - Petras Kundrotas
- Dep of Biochemistry and Biophysics and Science for Life Laboratory, Solna, Sweden
| | | | - Carlos H M Rodrigues
- School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - Alistair S Dunham
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - David Burke
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Neera Borkakoti
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Sameer Velankar
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Adam Frost
- Department of Biochemistry and Biophysics University of California, San Francisco, CA, USA
| | - Jérôme Basquin
- Department of Structural Cell Biology, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Alex Bateman
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Andrey V Kajava
- Université de Montpellier, Centre de Recherche en Biologie Cellulaire de Montpellier (CRBM) CNRS, Montpellier, France
| | | | - Sergey Ovchinnikov
- Faculty of Arts and Sciences, Division of Science, Harvard University, Cambridge, MA, USA.
| | | | - David B Ascher
- School of Chemistry and Molecular Biology, University of Queensland, Brisbane, Queensland, Australia.
| | - Janet M Thornton
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.
| | | | - Amelie Stein
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
| | - Arne Elofsson
- Dep of Biochemistry and Biophysics and Science for Life Laboratory, Solna, Sweden.
| | - Tristan I Croll
- Cambridge Institute for Medical Research, Department of Haematology, The University of Cambridge, Cambridge, UK.
| | - Pedro Beltrao
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland.
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5
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Kalman ZE, Dudola D, Mészáros B, Gáspári Z, Dobson L. PSINDB: the postsynaptic protein-protein interaction database. Database (Oxford) 2022; 2022:baac007. [PMID: 35234850 PMCID: PMC9216581 DOI: 10.1093/database/baac007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 01/21/2022] [Accepted: 02/07/2022] [Indexed: 06/14/2023]
Abstract
The postsynaptic region is the receiving part of the synapse comprising thousands of proteins forming an elaborate and dynamically changing network indispensable for the molecular mechanisms behind fundamental phenomena such as learning and memory. Despite the growing amount of information about individual protein-protein interactions (PPIs) in this network, these data are mostly scattered in the literature or stored in generic databases that are not designed to display aspects that are fundamental to the understanding of postsynaptic functions. To overcome these limitations, we collected postsynaptic PPIs complemented by a high amount of detailed structural and biological information and launched a freely available resource, the Postsynaptic Interaction Database (PSINDB), to make these data and annotations accessible. PSINDB includes tens of thousands of binding regions together with structural features, mediating and regulating the formation of PPIs, annotated with detailed experimental information about each interaction. PSINDB is expected to be useful for various aspects of molecular neurobiology research, from experimental design to network and systems biology-based modeling and analysis of changes in the protein network upon various stimuli. Database URL https://psindb.itk.ppke.hu/.
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Affiliation(s)
- Zsofia E Kalman
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, Budapest 1083, Hungary
| | - Dániel Dudola
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, Budapest 1083, Hungary
| | - Bálint Mészáros
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstraße 1, Heidelberg 69117, Germany
| | - Zoltán Gáspári
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, Budapest 1083, Hungary
| | - Laszlo Dobson
- *Corresponding author: Tel: +49 6221 387 8398; Fax: +49 6221 387 8530;
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6
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Quaglia F, Mészáros B, Salladini E, Hatos A, Pancsa R, Chemes LB, Pajkos M, Lazar T, Peña-Díaz S, Santos J, Ács V, Farahi N, Fichó E, Aspromonte M, Bassot C, Chasapi A, Davey N, Davidović R, Dobson L, Elofsson A, Erdős G, Gaudet P, Giglio M, Glavina J, Iserte J, Iglesias V, Kálmán Z, Lambrughi M, Leonardi E, Longhi S, Macedo-Ribeiro S, Maiani E, Marchetti J, Marino-Buslje C, Mészáros A, Monzon A, Minervini G, Nadendla S, Nilsson JF, Novotný M, Ouzounis C, Palopoli N, Papaleo E, Pereira P, Pozzati G, Promponas V, Pujols J, Rocha AS, Salas M, Sawicki LR, Schad E, Shenoy A, Szaniszló T, Tsirigos K, Veljkovic N, Parisi G, Ventura S, Dosztányi Z, Tompa P, Tosatto SCE, Piovesan D. DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation. Nucleic Acids Res 2022; 50:D480-D487. [PMID: 34850135 PMCID: PMC8728214 DOI: 10.1093/nar/gkab1082] [Citation(s) in RCA: 79] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/15/2021] [Accepted: 10/20/2021] [Indexed: 02/03/2023] Open
Abstract
The Database of Intrinsically Disordered Proteins (DisProt, URL: https://disprot.org) is the major repository of manually curated annotations of intrinsically disordered proteins and regions from the literature. We report here recent updates of DisProt version 9, including a restyled web interface, refactored Intrinsically Disordered Proteins Ontology (IDPO), improvements in the curation process and significant content growth of around 30%. Higher quality and consistency of annotations is provided by a newly implemented reviewing process and training of curators. The increased curation capacity is fostered by the integration of DisProt with APICURON, a dedicated resource for the proper attribution and recognition of biocuration efforts. Better interoperability is provided through the adoption of the Minimum Information About Disorder (MIADE) standard, an active collaboration with the Gene Ontology (GO) and Evidence and Conclusion Ontology (ECO) consortia and the support of the ELIXIR infrastructure.
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Affiliation(s)
- Federica Quaglia
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR-IBIOM), Bari, Italy
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Bálint Mészáros
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Edoardo Salladini
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - András Hatos
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Rita Pancsa
- Institute of Enzymology, Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Lucía B Chemes
- Instituto de Investigaciones Biotecnológicas (IIBiO-CONICET), Universidad Nacional de San Martín, Av. 25 de Mayo y Francia, CP1650 Buenos Aires, Argentina
| | - Mátyás Pajkos
- Department of Biochemistry, Eötvös Loránd University, Pázmány Péter stny 1/c, Budapest H-1117, Hungary
| | - Tamas Lazar
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnology, Brussels, Belgium
- Structural Biology Brussels (SBB), Bioengineering Sciences Department, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Samuel Peña-Díaz
- Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, Barcelona, Spain
- Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jaime Santos
- Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, Barcelona, Spain
- Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Veronika Ács
- Institute of Enzymology, Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Nazanin Farahi
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnology, Brussels, Belgium
- Structural Biology Brussels (SBB), Bioengineering Sciences Department, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Erzsébet Fichó
- Institute of Enzymology, Research Centre for Natural Sciences, Budapest 1117, Hungary
- Cytocast Kft., Vecsés, Hungary
| | - Maria Cristina Aspromonte
- Department of Woman and Child Health, University of Padova, Padova, Italy
- Pediatric Research Institute, Città della Speranza, Padova, Italy
| | - Claudio Bassot
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 171 21 Solna, Sweden
| | - Anastasia Chasapi
- Biological Computation & Process Laboratory, Chemical Process & Energy Resources Institute, Centre for Research & Technology Hellas, Thermi, Thessalonica 57001, Greece
| | - Norman E Davey
- Institute of Cancer Research, Chester Beatty Laboratories, 237 Fulham Rd, Chelsea, London, UK
| | - Radoslav Davidović
- Laboratory for Bioinformatics and Computational Chemistry, Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, 11000Belgrade, Serbia
| | - Laszlo Dobson
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
- Institute of Enzymology, Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Arne Elofsson
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 171 21 Solna, Sweden
| | - Gábor Erdős
- Department of Biochemistry, Eötvös Loránd University, Pázmány Péter stny 1/c, Budapest H-1117, Hungary
| | - Pascale Gaudet
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Michelle Giglio
- Institute for Genome Sciences, University of Maryland School of Medicine 670 W. Baltimore St., Baltimore, MD 21201, USA
| | - Juliana Glavina
- Instituto de Investigaciones Biotecnológicas (IIBiO-CONICET), Universidad Nacional de San Martín, Av. 25 de Mayo y Francia, CP1650 Buenos Aires, Argentina
| | - Javier Iserte
- Bioinformatics Unit, Fundación Instituto Leloir, Buenos Aires, C1405BWE, Argentina
| | - Valentín Iglesias
- Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, Barcelona, Spain
- Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Zsófia Kálmán
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, 1083 Budapest, Hungary
| | - Matteo Lambrughi
- Cancer Structural Biology, Danish Cancer Society Research Center, Strandboulevarden 49, 2100 Copenhagen, Denmark
| | - Emanuela Leonardi
- Department of Woman and Child Health, University of Padova, Padova, Italy
- Pediatric Research Institute, Città della Speranza, Padova, Italy
| | - Sonia Longhi
- Lab. Architecture et Fonction des Macromolécules Biologiques (AFMB), UMR 7257, Aix Marseille University and Centre National de la Recherche Scientifique (CNRS), 163 Avenue de Luminy, Case 932, 13288, Marseille, France
| | - Sandra Macedo-Ribeiro
- Instituto de Biologia Molecular e Celular (IBMC), Universidade do Porto, 4200-135 Porto, Portugal
- Instituto de Investigação e Inovação em Saúde (i3S), Universidade do Porto, 4200-135 Porto, Portugal
| | - Emiliano Maiani
- Cancer Structural Biology, Danish Cancer Society Research Center, Strandboulevarden 49, 2100 Copenhagen, Denmark
| | - Julia Marchetti
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - CONICET, Bernal, Buenos Aires B1876BXD, Argentina
| | | | - Attila Mészáros
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnology, Brussels, Belgium
- Structural Biology Brussels (SBB), Bioengineering Sciences Department, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | | | | | - Suvarna Nadendla
- Institute for Genome Sciences, University of Maryland School of Medicine 670 W. Baltimore St., Baltimore, MD 21201, USA
| | - Juliet F Nilsson
- Lab. Architecture et Fonction des Macromolécules Biologiques (AFMB), UMR 7257, Aix Marseille University and Centre National de la Recherche Scientifique (CNRS), 163 Avenue de Luminy, Case 932, 13288, Marseille, France
| | - Marian Novotný
- Dep. of Cell Biology, Faculty of Science, Vinicna 7, 128 43, Prague, Czech Republic
| | - Christos A Ouzounis
- Biological Computation & Process Laboratory, Chemical Process & Energy Resources Institute, Centre for Research & Technology Hellas, Thermi, Thessalonica 57001, Greece
- Biological Computation & Computational Biology Group, Artificial Intelligence & Information Analysis Lab, Department of Computer Science, Aristotle University of Thessalonica, Thessalonica 54124, Greece
| | - Nicolás Palopoli
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - CONICET, Bernal, Buenos Aires B1876BXD, Argentina
| | - Elena Papaleo
- Cancer Structural Biology, Danish Cancer Society Research Center, Strandboulevarden 49, 2100 Copenhagen, Denmark
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and Technology, Technical University of Denmark, Lyngby, Denmark
| | - Pedro José Barbosa Pereira
- Instituto de Biologia Molecular e Celular (IBMC), Universidade do Porto, 4200-135 Porto, Portugal
- Instituto de Investigação e Inovação em Saúde (i3S), Universidade do Porto, 4200-135 Porto, Portugal
| | - Gabriele Pozzati
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 171 21 Solna, Sweden
| | - Vasilis J Promponas
- Bioinformatics Research Laboratory, Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus
| | - Jordi Pujols
- Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, Barcelona, Spain
- Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
| | | | - Martin Salas
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - CONICET, Bernal, Buenos Aires B1876BXD, Argentina
| | - Luciana Rodriguez Sawicki
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - CONICET, Bernal, Buenos Aires B1876BXD, Argentina
| | - Eva Schad
- Institute of Enzymology, Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Aditi Shenoy
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 171 21 Solna, Sweden
| | - Tamás Szaniszló
- Department of Biochemistry, Eötvös Loránd University, Pázmány Péter stny 1/c, Budapest H-1117, Hungary
| | - Konstantinos D Tsirigos
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | - Nevena Veljkovic
- Laboratory for Bioinformatics and Computational Chemistry, Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, University of Belgrade, 11000Belgrade, Serbia
| | - Gustavo Parisi
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - CONICET, Bernal, Buenos Aires B1876BXD, Argentina
| | - Salvador Ventura
- Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, Barcelona, Spain
- Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
- ICREA, Barcelona, Spain
| | - Zsuzsanna Dosztányi
- Department of Biochemistry, Eötvös Loránd University, Pázmány Péter stny 1/c, Budapest H-1117, Hungary
| | - Peter Tompa
- Institute of Enzymology, Research Centre for Natural Sciences, Budapest 1117, Hungary
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnology, Brussels, Belgium
- Structural Biology Brussels (SBB), Bioengineering Sciences Department, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | | | - Damiano Piovesan
- Department of Biomedical Sciences, University of Padova, Padova, Italy
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7
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Nadendla S, Jackson R, Munro J, Quaglia F, Mészáros B, Olley D, Hobbs ET, Goralski SM, Chibucos M, Mungall CJ, Tosatto SCE, Erill I, Giglio MG. ECO: the Evidence and Conclusion Ontology, an update for 2022. Nucleic Acids Res 2022; 50:D1515-D1521. [PMID: 34986598 PMCID: PMC8728134 DOI: 10.1093/nar/gkab1025] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/12/2021] [Accepted: 10/18/2021] [Indexed: 11/12/2022] Open
Abstract
The Evidence and Conclusion Ontology (ECO) is a community resource that provides an ontology of terms used to capture the type of evidence that supports biomedical annotations and assertions. Consistent capture of evidence information with ECO allows tracking of annotation provenance, establishment of quality control measures, and evidence-based data mining. ECO is in use by dozens of data repositories and resources with both specific and general areas of focus. ECO is continually being expanded and enhanced in response to user requests as well as our aim to adhere to community best-practices for ontology development. The ECO support team engages in multiple collaborations with other ontologies and annotating groups. Here we report on recent updates to the ECO ontology itself as well as associated resources that are available through this project. ECO project products are freely available for download from the project website (https://evidenceontology.org/) and GitHub (https://github.com/evidenceontology/evidenceontology). ECO is released into the public domain under a CC0 1.0 Universal license.
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Affiliation(s)
- Suvarna Nadendla
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Rebecca Jackson
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - James Munro
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Federica Quaglia
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR-IBIOM), Bari, Italy.,Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Bálint Mészáros
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Dustin Olley
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Elizabeth T Hobbs
- Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, Maryland, United States
| | - Stephen M Goralski
- Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, Maryland, United States
| | - Marcus Chibucos
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Christopher John Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Lab, Berkeley, California, USA
| | | | - Ivan Erill
- Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, Maryland, United States
| | - Michelle G Giglio
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, USA
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8
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Kumar M, Michael S, Alvarado-Valverde J, Mészáros B, Sámano‐Sánchez H, Zeke A, Dobson L, Lazar T, Örd M, Nagpal A, Farahi N, Käser M, Kraleti R, Davey N, Pancsa R, Chemes L, Gibson T. The Eukaryotic Linear Motif resource: 2022 release. Nucleic Acids Res 2022; 50:D497-D508. [PMID: 34718738 PMCID: PMC8728146 DOI: 10.1093/nar/gkab975] [Citation(s) in RCA: 98] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 10/27/2021] [Indexed: 02/03/2023] Open
Abstract
Almost twenty years after its initial release, the Eukaryotic Linear Motif (ELM) resource remains an invaluable source of information for the study of motif-mediated protein-protein interactions. ELM provides a comprehensive, regularly updated and well-organised repository of manually curated, experimentally validated short linear motifs (SLiMs). An increasing number of SLiM-mediated interactions are discovered each year and keeping the resource up-to-date continues to be a great challenge. In the current update, 30 novel motif classes have been added and five existing classes have undergone major revisions. The update includes 411 new motif instances mostly focused on cell-cycle regulation, control of the actin cytoskeleton, membrane remodelling and vesicle trafficking pathways, liquid-liquid phase separation and integrin signalling. Many of the newly annotated motif-mediated interactions are targets of pathogenic motif mimicry by viral, bacterial or eukaryotic pathogens, providing invaluable insights into the molecular mechanisms underlying infectious diseases. The current ELM release includes 317 motif classes incorporating 3934 individual motif instances manually curated from 3867 scientific publications. ELM is available at: http://elm.eu.org.
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Affiliation(s)
- Manjeet Kumar
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Sushama Michael
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Jesús Alvarado-Valverde
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
- Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences
| | - Bálint Mészáros
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Hugo Sámano‐Sánchez
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
- Zhejiang University School of Medicine, International Campus, Zhejiang University, Haining, China
- Biomedical Sciences, Edinburgh Medical School, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - András Zeke
- Institute of Enzymology, Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Laszlo Dobson
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
- Institute of Enzymology, Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Tamas Lazar
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie, Pleinlaan 2, 1050 Brussels, Belgium
- Structural Biology Brussels, Department of Bioengineering, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
| | - Mihkel Örd
- Institute of Cancer Research, Chester Beatty Laboratories, 237 Fulham Rd, Chelsea, London SW3 6JB, UK
| | - Anurag Nagpal
- Department of Biological Sciences, BITS Pilani, K. K. Birla Goa campus, Zuarinagar, Goa 403726, India
| | - Nazanin Farahi
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie, Pleinlaan 2, 1050 Brussels, Belgium
- Structural Biology Brussels, Department of Bioengineering, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
| | - Melanie Käser
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
- Institute of Pharmacy and Molecular Biotechnology (IPMB), Heidelberg University, Heidelberg, Germany
| | - Ramya Kraleti
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
- Justus Liebig University Giessen, Ludwigstraße 23, 35390 Gießen, Germany
| | - Norman E Davey
- Institute of Cancer Research, Chester Beatty Laboratories, 237 Fulham Rd, Chelsea, London SW3 6JB, UK
| | - Rita Pancsa
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
- Institute of Enzymology, Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Lucía B Chemes
- Instituto de Investigaciones Biotecnológicas “Dr. Rodolfo A. Ugalde”, IIB-UNSAM, IIBIO-CONICET, Universidad Nacional de San Martín, Av. 25 de Mayo y Francia, CP1650 San Martín, Buenos Aires, Argentina
| | - Toby J Gibson
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
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9
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Del Toro N, Shrivastava A, Ragueneau E, Meldal B, Combe C, Barrera E, Perfetto L, How K, Ratan P, Shirodkar G, Lu O, Mészáros B, Watkins X, Pundir S, Licata L, Iannuccelli M, Pellegrini M, Martin MJ, Panni S, Duesbury M, Vallet SD, Rappsilber J, Ricard-Blum S, Cesareni G, Salwinski L, Orchard S, Porras P, Panneerselvam K, Hermjakob H. The IntAct database: efficient access to fine-grained molecular interaction data. Nucleic Acids Res 2021; 50:D648-D653. [PMID: 34761267 PMCID: PMC8728211 DOI: 10.1093/nar/gkab1006] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 10/06/2021] [Accepted: 10/21/2021] [Indexed: 01/18/2023] Open
Abstract
The IntAct molecular interaction database (https://www.ebi.ac.uk/intact) is a curated resource of molecular interactions, derived from the scientific literature and from direct data depositions. As of August 2021, IntAct provides more than one million binary interactions, curated by twelve global partners of the International Molecular Exchange consortium, for which the IntAct database provides a shared curation and dissemination platform. The IMEx curation policy has always emphasised a fine-grained data and curation model, aiming to capture the relevant experimental detail essential for the interpretation of the provided molecular interaction data. Here, we present recent curation focus and progress, as well as a completely redeveloped website which presents IntAct data in a much more user-friendly and detailed way.
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Affiliation(s)
- Noemi Del Toro
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Anjali Shrivastava
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Eliot Ragueneau
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Birgit Meldal
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Colin Combe
- Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3BF, UK
| | - Elisabet Barrera
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Livia Perfetto
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridgeshire CB10 1SD, UK.,Fondazione Human Technopole, Milan 20157, Italy
| | - Karyn How
- UCLA-DOE Institute for Genomics and Proteomics, University of California, Los Angeles, CA 90095, USA
| | - Prashansa Ratan
- UCLA-DOE Institute for Genomics and Proteomics, University of California, Los Angeles, CA 90095, USA
| | - Gautam Shirodkar
- UCLA-DOE Institute for Genomics and Proteomics, University of California, Los Angeles, CA 90095, USA
| | - Odilia Lu
- UCLA-DOE Institute for Genomics and Proteomics, University of California, Los Angeles, CA 90095, USA
| | - Bálint Mészáros
- Gibson Group, European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Xavier Watkins
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Sangya Pundir
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Luana Licata
- Bioinformatics and Computational Biology Unit, Dept. of Molecular Biology, University of Rome Tor Vergata, Rome, Italy
| | - Marta Iannuccelli
- Bioinformatics and Computational Biology Unit, Dept. of Molecular Biology, University of Rome Tor Vergata, Rome, Italy
| | - Matteo Pellegrini
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA 90095, USA
| | - Maria Jesus Martin
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Simona Panni
- Dipartimento di Biologia, Ecologia e Scienze della Terra, Università della Calabria, Rende, Italy
| | - Margaret Duesbury
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridgeshire CB10 1SD, UK.,UCLA-DOE Institute for Genomics and Proteomics, University of California, Los Angeles, CA 90095, USA
| | - Sylvain D Vallet
- ICBMS UMR CNRS 5246, University Lyon 1, Lyon, Villeurbanne 69622, France
| | - Juri Rappsilber
- Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3BF, UK.,Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, Berlin 13355, Germany
| | - Sylvie Ricard-Blum
- ICBMS UMR CNRS 5246, University Lyon 1, Lyon, Villeurbanne 69622, France
| | - Gianni Cesareni
- Bioinformatics and Computational Biology Unit, Dept. of Molecular Biology, University of Rome Tor Vergata, Rome, Italy
| | - Lukasz Salwinski
- UCLA-DOE Institute for Genomics and Proteomics, University of California, Los Angeles, CA 90095, USA
| | - Sandra Orchard
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Pablo Porras
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Kalpana Panneerselvam
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Henning Hermjakob
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridgeshire CB10 1SD, UK
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10
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Mészáros B, Hajdu-Soltész B, Zeke A, Dosztányi Z. Mutations of Intrinsically Disordered Protein Regions Can Drive Cancer but Lack Therapeutic Strategies. Biomolecules 2021; 11:biom11030381. [PMID: 33806614 PMCID: PMC8000335 DOI: 10.3390/biom11030381] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 02/22/2021] [Accepted: 02/24/2021] [Indexed: 12/22/2022] Open
Abstract
Many proteins contain intrinsically disordered regions (IDRs) which carry out important functions without relying on a single well-defined conformation. IDRs are increasingly recognized as critical elements of regulatory networks and have been also associated with cancer. However, it is unknown whether mutations targeting IDRs represent a distinct class of driver events associated with specific molecular and system-level properties, cancer types and treatment options. Here, we used an integrative computational approach to explore the direct role of intrinsically disordered protein regions driving cancer. We showed that around 20% of cancer drivers are primarily targeted through a disordered region. These IDRs can function in multiple ways which are distinct from the functional mechanisms of ordered drivers. Disordered drivers play a central role in context-dependent interaction networks and are enriched in specific biological processes such as transcription, gene expression regulation and protein degradation. Furthermore, their modulation represents an alternative mechanism for the emergence of all known cancer hallmarks. Importantly, in certain cancer patients, mutations of disordered drivers represent key driving events. However, treatment options for such patients are currently severely limited. The presented study highlights a largely overlooked class of cancer drivers associated with specific cancer types that need novel therapeutic options.
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Affiliation(s)
- Bálint Mészáros
- Department of Biochemistry, ELTE Eötvös Loránd University, H-1117 Budapest, Hungary; (B.M.); (B.H.-S.)
- EMBL Heidelberg, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Borbála Hajdu-Soltész
- Department of Biochemistry, ELTE Eötvös Loránd University, H-1117 Budapest, Hungary; (B.M.); (B.H.-S.)
| | - András Zeke
- Institute of Enzymology, RCNS, P.O. Box 7, H-1518 Budapest, Hungary;
| | - Zsuzsanna Dosztányi
- Department of Biochemistry, ELTE Eötvös Loránd University, H-1117 Budapest, Hungary; (B.M.); (B.H.-S.)
- Correspondence: ; Tel.: +36-1-372 2500/8537
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11
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Pancsa R, Vranken W, Mészáros B. Computational resources for identifying and describing proteins driving liquid-liquid phase separation. Brief Bioinform 2021; 22:6124912. [PMID: 33517364 PMCID: PMC8425267 DOI: 10.1093/bib/bbaa408] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 11/23/2020] [Accepted: 12/12/2020] [Indexed: 01/06/2023] Open
Abstract
One of the most intriguing fields emerging in current molecular biology is the study of membraneless organelles formed via liquid–liquid phase separation (LLPS). These organelles perform crucial functions in cell regulation and signalling, and recent years have also brought about the understanding of the molecular mechanism of their formation. The LLPS field is continuously developing and optimizing dedicated in vitro and in vivo methods to identify and characterize these non-stoichiometric molecular condensates and the proteins able to drive or contribute to LLPS. Building on these observations, several computational tools and resources have emerged in parallel to serve as platforms for the collection, annotation and prediction of membraneless organelle-linked proteins. In this survey, we showcase recent advancements in LLPS bioinformatics, focusing on (i) available databases and ontologies that are necessary to describe the studied phenomena and the experimental results in an unambiguous way and (ii) prediction methods to assess the potential LLPS involvement of proteins. Through hands-on application of these resources on example proteins and representative datasets, we give a practical guide to show how they can be used in conjunction to provide in silico information on LLPS.
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Affiliation(s)
- Rita Pancsa
- Enzymology Institute of the Research Centre for Natural Sciences, Budapest, Hungary
| | - Wim Vranken
- Computer Science, chemistry and biomedical sciences at the Vrije Universiteit Brussel
| | - Bálint Mészáros
- Structural and Computational Biology Unit at the European Molecular Biology Laboratory, Heidelberg 69117, Germany
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12
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Mészáros B, Sámano-Sánchez H, Alvarado-Valverde J, Čalyševa J, Martínez-Pérez E, Alves R, Shields DC, Kumar M, Rippmann F, Chemes LB, Gibson TJ. Short linear motif candidates in the cell entry system used by SARS-CoV-2 and their potential therapeutic implications. Sci Signal 2021; 14:eabd0334. [PMID: 33436497 PMCID: PMC7928535 DOI: 10.1126/scisignal.abd0334] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 12/10/2020] [Indexed: 12/12/2022]
Abstract
The first reported receptor for SARS-CoV-2 on host cells was the angiotensin-converting enzyme 2 (ACE2). However, the viral spike protein also has an RGD motif, suggesting that cell surface integrins may be co-receptors. We examined the sequences of ACE2 and integrins with the Eukaryotic Linear Motif (ELM) resource and identified candidate short linear motifs (SLiMs) in their short, unstructured, cytosolic tails with potential roles in endocytosis, membrane dynamics, autophagy, cytoskeleton, and cell signaling. These SLiM candidates are highly conserved in vertebrates and may interact with the μ2 subunit of the endocytosis-associated AP2 adaptor complex, as well as with various protein domains (namely, I-BAR, LC3, PDZ, PTB, and SH2) found in human signaling and regulatory proteins. Several motifs overlap in the tail sequences, suggesting that they may act as molecular switches, such as in response to tyrosine phosphorylation status. Candidate LC3-interacting region (LIR) motifs are present in the tails of integrin β3 and ACE2, suggesting that these proteins could directly recruit autophagy components. Our findings identify several molecular links and testable hypotheses that could uncover mechanisms of SARS-CoV-2 attachment, entry, and replication against which it may be possible to develop host-directed therapies that dampen viral infection and disease progression. Several of these SLiMs have now been validated to mediate the predicted peptide interactions.
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Affiliation(s)
- Bálint Mészáros
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany.
| | - Hugo Sámano-Sánchez
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Jesús Alvarado-Valverde
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
- Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences
| | - Jelena Čalyševa
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
- Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences
| | - Elizabeth Martínez-Pérez
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
- Laboratorio de bioinformática estructural, Fundación Instituto Leloir, C1405BWE Buenos Aires, Argentina
| | - Renato Alves
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Denis C Shields
- School of Medicine, University College Dublin, Dublin 4, Ireland
| | - Manjeet Kumar
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany.
| | - Friedrich Rippmann
- Computational Chemistry & Biology, Merck KGaA, Frankfurter Str. 250, 64293 Darmstadt, Germany
| | - Lucía B Chemes
- Instituto de Investigaciones Biotecnológicas "Dr. Rodolfo A. Ugalde", IIB-UNSAM, IIBIO-CONICET, Universidad Nacional de San Martín, CP1650 San Martín, Buenos Aires, Argentina.
| | - Toby J Gibson
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany.
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13
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Kalman ZE, Mészáros B, Gáspári Z, Dobson L. Distribution of disease-causing germline mutations in coiled-coils implies an important role of their N-terminal region. Sci Rep 2020; 10:17333. [PMID: 33060664 PMCID: PMC7562717 DOI: 10.1038/s41598-020-74354-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 09/21/2020] [Indexed: 11/08/2022] Open
Abstract
Next-generation sequencing resulted in the identification of a huge number of naturally occurring variations in human proteins. The correct interpretation of the functional effects of these variations necessitates the understanding of how they modulate protein structure. Coiled-coils are α-helical structures responsible for a diverse range of functions, but most importantly, they facilitate the structural organization of macromolecular scaffolds via oligomerization. In this study, we analyzed a comprehensive set of disease-associated germline mutations in coiled-coil structures. Our results suggest an important role of residues near the N-terminal part of coiled-coil regions, possibly critical for superhelix assembly and folding in some cases. We also show that coiled-coils of different oligomerization states exhibit characteristically distinct patterns of disease-causing mutations. Our study provides structural and functional explanations on how disease emerges through the mutation of these structural motifs.
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Affiliation(s)
- Zsofia E Kalman
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, 1083, Budapest, Hungary
- 3in-PPCU Research Group, 2500, Esztergom, Hungary
| | - Bálint Mészáros
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - Zoltán Gáspári
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, 1083, Budapest, Hungary.
| | - Laszlo Dobson
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, 1083, Budapest, Hungary.
- Research Centre for Natural Sciences, Magyar Tudósok Körútja 2, 1117, Budapest, Hungary.
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14
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Mészáros B, Erdős G, Szabó B, Schád É, Tantos Á, Abukhairan R, Horváth T, Murvai N, Kovács OP, Kovács M, Tosatto SCE, Tompa P, Dosztányi Z, Pancsa R. PhaSePro: the database of proteins driving liquid-liquid phase separation. Nucleic Acids Res 2020; 48:D360-D367. [PMID: 31612960 PMCID: PMC7145634 DOI: 10.1093/nar/gkz848] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 09/11/2019] [Accepted: 10/07/2019] [Indexed: 11/13/2022] Open
Abstract
Membraneless organelles (MOs) are dynamic liquid condensates that host a variety of specific cellular processes, such as ribosome biogenesis or RNA degradation. MOs form through liquid-liquid phase separation (LLPS), a process that relies on multivalent weak interactions of the constituent proteins and other macromolecules. Since the first discoveries of certain proteins being able to drive LLPS, it emerged as a general mechanism for the effective organization of cellular space that is exploited in all kingdoms of life. While numerous experimental studies report novel cases, the computational identification of LLPS drivers is lagging behind, and many open questions remain about the sequence determinants, composition, regulation and biological relevance of the resulting condensates. Our limited ability to overcome these issues is largely due to the lack of a dedicated LLPS database. Therefore, here we introduce PhaSePro (https://phasepro.elte.hu), an openly accessible, comprehensive, manually curated database of experimentally validated LLPS driver proteins/protein regions. It not only provides a wealth of information on such systems, but improves the standardization of data by introducing novel LLPS-specific controlled vocabularies. PhaSePro can be accessed through an appealing, user-friendly interface and thus has definite potential to become the central resource in this dynamically developing field.
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Affiliation(s)
- Bálint Mészáros
- MTA-ELTE Momentum Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest H-1117, Hungary
| | - Gábor Erdős
- MTA-ELTE Momentum Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest H-1117, Hungary
| | - Beáta Szabó
- Institute of Enzymology, Research Centre for Natural Sciences of the Hungarian Academy of Sciences, Budapest H-1117, Hungary
| | - Éva Schád
- Institute of Enzymology, Research Centre for Natural Sciences of the Hungarian Academy of Sciences, Budapest H-1117, Hungary
| | - Ágnes Tantos
- Institute of Enzymology, Research Centre for Natural Sciences of the Hungarian Academy of Sciences, Budapest H-1117, Hungary
| | - Rawan Abukhairan
- Institute of Enzymology, Research Centre for Natural Sciences of the Hungarian Academy of Sciences, Budapest H-1117, Hungary
| | - Tamás Horváth
- Institute of Enzymology, Research Centre for Natural Sciences of the Hungarian Academy of Sciences, Budapest H-1117, Hungary
| | - Nikoletta Murvai
- Institute of Enzymology, Research Centre for Natural Sciences of the Hungarian Academy of Sciences, Budapest H-1117, Hungary
| | - Orsolya P Kovács
- Institute of Enzymology, Research Centre for Natural Sciences of the Hungarian Academy of Sciences, Budapest H-1117, Hungary
| | - Márton Kovács
- Institute of Enzymology, Research Centre for Natural Sciences of the Hungarian Academy of Sciences, Budapest H-1117, Hungary
| | - Silvio C E Tosatto
- Department of Biomedical Sciences, University of Padova CNR Institute of Neuroscience, Padova, Italy
| | - Péter Tompa
- Institute of Enzymology, Research Centre for Natural Sciences of the Hungarian Academy of Sciences, Budapest H-1117, Hungary.,Structural Biology (CSB), Brussels, Belgium; Structural Biology Brussels (SBB), Vrije Universiteit Brussel (VUB), Brussels 1050, Belgium
| | - Zsuzsanna Dosztányi
- MTA-ELTE Momentum Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest H-1117, Hungary
| | - Rita Pancsa
- Institute of Enzymology, Research Centre for Natural Sciences of the Hungarian Academy of Sciences, Budapest H-1117, Hungary
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15
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Hatos A, Hajdu-Soltész B, Monzon AM, Palopoli N, Álvarez L, Aykac-Fas B, Bassot C, Benítez GI, Bevilacqua M, Chasapi A, Chemes L, Davey NE, Davidović R, Dunker AK, Elofsson A, Gobeill J, Foutel NSG, Sudha G, Guharoy M, Horvath T, Iglesias V, Kajava AV, Kovacs OP, Lamb J, Lambrughi M, Lazar T, Leclercq JY, Leonardi E, Macedo-Ribeiro S, Macossay-Castillo M, Maiani E, Manso JA, Marino-Buslje C, Martínez-Pérez E, Mészáros B, Mičetić I, Minervini G, Murvai N, Necci M, Ouzounis CA, Pajkos M, Paladin L, Pancsa R, Papaleo E, Parisi G, Pasche E, Barbosa Pereira PJ, Promponas VJ, Pujols J, Quaglia F, Ruch P, Salvatore M, Schad E, Szabo B, Szaniszló T, Tamana S, Tantos A, Veljkovic N, Ventura S, Vranken W, Dosztányi Z, Tompa P, Tosatto SCE, Piovesan D. DisProt: intrinsic protein disorder annotation in 2020. Nucleic Acids Res 2020; 48:D269-D276. [PMID: 31713636 PMCID: PMC7145575 DOI: 10.1093/nar/gkz975] [Citation(s) in RCA: 98] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 10/11/2019] [Accepted: 10/12/2019] [Indexed: 11/29/2022] Open
Abstract
The Database of Protein Disorder (DisProt, URL: https://disprot.org) provides manually curated annotations of intrinsically disordered proteins from the literature. Here we report recent developments with DisProt (version 8), including the doubling of protein entries, a new disorder ontology, improvements of the annotation format and a completely new website. The website includes a redesigned graphical interface, a better search engine, a clearer API for programmatic access and a new annotation interface that integrates text mining technologies. The new entry format provides a greater flexibility, simplifies maintenance and allows the capture of more information from the literature. The new disorder ontology has been formalized and made interoperable by adopting the OWL format, as well as its structure and term definitions have been improved. The new annotation interface has made the curation process faster and more effective. We recently showed that new DisProt annotations can be effectively used to train and validate disorder predictors. We believe the growth of DisProt will accelerate, contributing to the improvement of function and disorder predictors and therefore to illuminate the ‘dark’ proteome.
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Affiliation(s)
- András Hatos
- Department of Biomedical Sciences, University of Padova, Padova 35121, Italy
| | - Borbála Hajdu-Soltész
- MTA-ELTE Lendület Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest 1117, Hungary
| | - Alexander M Monzon
- Department of Biomedical Sciences, University of Padova, Padova 35121, Italy
| | - Nicolas Palopoli
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - CONICET, Bernal, Buenos Aires B1876BXD, Argentina
| | - Lucía Álvarez
- Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Biotecnológicas IIBIO, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina
| | - Burcu Aykac-Fas
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen DK-2100, Denmark
| | - Claudio Bassot
- Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Box 1031, Solna 17121, Sweden
| | - Guillermo I Benítez
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - CONICET, Bernal, Buenos Aires B1876BXD, Argentina
| | - Martina Bevilacqua
- Department of Biomedical Sciences, University of Padova, Padova 35121, Italy
| | - Anastasia Chasapi
- Biological Computation & Process Laboratory, Chemical Process & Energy Resources Institute, Centre for Research & Technology Hellas, Thessalonica GR-57500, Greece
| | - Lucia Chemes
- Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Biotecnológicas IIBIO, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina.,Departamento de Fisiología y Biología Molecular y Celular (DFBMC), Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Norman E Davey
- Division of Cancer Biology, The Institute of Cancer Research, Chelsea, London SW3 6BJ, UK
| | - Radoslav Davidović
- Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade 11001, Serbia
| | - A Keith Dunker
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, IN 46202, USA
| | - Arne Elofsson
- Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Box 1031, Solna 17121, Sweden
| | - Julien Gobeill
- Swiss Institute of Bioinformatics and HES-SO \ HEG, Geneva 1200, Switzerland
| | - Nicolás S González Foutel
- Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Biotecnológicas IIBIO, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina
| | - Govindarajan Sudha
- Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Box 1031, Solna 17121, Sweden
| | - Mainak Guharoy
- Structural Biology Brussels, Vrije Universiteit Brussel (VUB), Brussels 1050, Belgium.,VIB-VUB Center for Structural Biology, Flanders Institute for Biotechnology (VIB), Brussels 1050, Belgium
| | - Tamas Horvath
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest H-1117, Hungary
| | - Valentin Iglesias
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, Bellaterra 08193, Spain
| | - Andrey V Kajava
- Centre de Recherche en Biologie cellulaire de Montpellier (CRBM), UMR 5237 CNRS, Université Montpellier, Montpellier 34293, France.,Institut de Biologie Computationnelle(IBC), Montpellier 34095, France
| | - Orsolya P Kovacs
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest H-1117, Hungary
| | - John Lamb
- Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Box 1031, Solna 17121, Sweden
| | - Matteo Lambrughi
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen DK-2100, Denmark
| | - Tamas Lazar
- Structural Biology Brussels, Vrije Universiteit Brussel (VUB), Brussels 1050, Belgium.,VIB-VUB Center for Structural Biology, Flanders Institute for Biotechnology (VIB), Brussels 1050, Belgium
| | - Jeremy Y Leclercq
- Centre de Recherche en Biologie cellulaire de Montpellier (CRBM), UMR 5237 CNRS, Université Montpellier, Montpellier 34293, France
| | - Emanuela Leonardi
- Department of Woman and Child Health, University of Padova, Padova 35127, Italy.,Fondazione Istituto di Ricerca Pediatrica (IRP), Città della Speranza, Padova 35127, Italy
| | - Sandra Macedo-Ribeiro
- Instituto de Biologia Molecular e Celular (IBMC) and Instituto de Investigação e Inovação em Saúde (i3S), Universidade do Porto, Porto 4200-135, Portugal
| | - Mauricio Macossay-Castillo
- Structural Biology Brussels, Vrije Universiteit Brussel (VUB), Brussels 1050, Belgium.,VIB-VUB Center for Structural Biology, Flanders Institute for Biotechnology (VIB), Brussels 1050, Belgium
| | - Emiliano Maiani
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen DK-2100, Denmark
| | - José A Manso
- Instituto de Biologia Molecular e Celular (IBMC) and Instituto de Investigação e Inovação em Saúde (i3S), Universidade do Porto, Porto 4200-135, Portugal
| | - Cristina Marino-Buslje
- Bioinformatics Unit. Fundación Instituto Leloir, Ciudad de Buenos Aires C1405BWE, Argentina
| | | | - Bálint Mészáros
- MTA-ELTE Lendület Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest 1117, Hungary
| | - Ivan Mičetić
- Department of Biomedical Sciences, University of Padova, Padova 35121, Italy
| | - Giovanni Minervini
- Department of Biomedical Sciences, University of Padova, Padova 35121, Italy
| | - Nikoletta Murvai
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest H-1117, Hungary
| | - Marco Necci
- Department of Biomedical Sciences, University of Padova, Padova 35121, Italy
| | - Christos A Ouzounis
- Biological Computation & Process Laboratory, Chemical Process & Energy Resources Institute, Centre for Research & Technology Hellas, Thessalonica GR-57500, Greece
| | - Mátyás Pajkos
- MTA-ELTE Lendület Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest 1117, Hungary
| | - Lisanna Paladin
- Department of Biomedical Sciences, University of Padova, Padova 35121, Italy
| | - Rita Pancsa
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest H-1117, Hungary
| | - Elena Papaleo
- Computational Biology Laboratory, Danish Cancer Society Research Center, Copenhagen DK-2100, Denmark.,Translational Disease Systems Biology, Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research University of Copenhagen, Copenhagen DK-2200, Denmark
| | - Gustavo Parisi
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - CONICET, Bernal, Buenos Aires B1876BXD, Argentina
| | - Emilie Pasche
- Swiss Institute of Bioinformatics and HES-SO \ HEG, Geneva 1200, Switzerland
| | - Pedro J Barbosa Pereira
- Instituto de Biologia Molecular e Celular (IBMC) and Instituto de Investigação e Inovação em Saúde (i3S), Universidade do Porto, Porto 4200-135, Portugal
| | - Vasilis J Promponas
- Bioinformatics Research Laboratory, Department of Biological Sciences, University of Cyprus, Nicosia, CY 1678, Cyprus
| | - Jordi Pujols
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, Bellaterra 08193, Spain
| | - Federica Quaglia
- Department of Biomedical Sciences, University of Padova, Padova 35121, Italy
| | - Patrick Ruch
- Swiss Institute of Bioinformatics and HES-SO \ HEG, Geneva 1200, Switzerland
| | - Marco Salvatore
- Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Box 1031, Solna 17121, Sweden
| | - Eva Schad
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest H-1117, Hungary
| | - Beata Szabo
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest H-1117, Hungary
| | - Tamás Szaniszló
- MTA-ELTE Lendület Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest 1117, Hungary
| | - Stella Tamana
- Bioinformatics Research Laboratory, Department of Biological Sciences, University of Cyprus, Nicosia, CY 1678, Cyprus
| | - Agnes Tantos
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest H-1117, Hungary
| | - Nevena Veljkovic
- Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade 11001, Serbia
| | - Salvador Ventura
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, Bellaterra 08193, Spain
| | - Wim Vranken
- Structural Biology Brussels, Vrije Universiteit Brussel (VUB), Brussels 1050, Belgium.,VIB-VUB Center for Structural Biology, Flanders Institute for Biotechnology (VIB), Brussels 1050, Belgium.,Interuniversity Institute of Bioinformatics in Brussels (IB2), ULB-VUB, Brussels 1050, Belgium
| | - Zsuzsanna Dosztányi
- MTA-ELTE Lendület Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest 1117, Hungary
| | - Peter Tompa
- Structural Biology Brussels, Vrije Universiteit Brussel (VUB), Brussels 1050, Belgium.,VIB-VUB Center for Structural Biology, Flanders Institute for Biotechnology (VIB), Brussels 1050, Belgium.,Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest H-1117, Hungary
| | - Silvio C E Tosatto
- Department of Biomedical Sciences, University of Padova, Padova 35121, Italy.,CNR Institute of Neurosceince, Padova 35121, Italy
| | - Damiano Piovesan
- Department of Biomedical Sciences, University of Padova, Padova 35121, Italy
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16
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Mészáros B, Erdos G, Dosztányi Z. IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding. Nucleic Acids Res 2019; 46:W329-W337. [PMID: 29860432 PMCID: PMC6030935 DOI: 10.1093/nar/gky384] [Citation(s) in RCA: 814] [Impact Index Per Article: 162.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 05/11/2018] [Indexed: 01/31/2023] Open
Abstract
The structural states of proteins include ordered globular domains as well as intrinsically disordered protein regions that exist as highly flexible conformational ensembles in isolation. Various computational tools have been developed to discriminate ordered and disordered segments based on the amino acid sequence. However, properties of IDRs can also depend on various conditions, including binding to globular protein partners or environmental factors, such as redox potential. These cases provide further challenges for the computational characterization of disordered segments. In this work we present IUPred2A, a combined web interface that allows to generate energy estimation based predictions for ordered and disordered residues by IUPred2 and for disordered binding regions by ANCHOR2. The updated web server retains the robustness of the original programs but offers several new features. While only minor bug fixes are implemented for IUPred, the next version of ANCHOR is significantly improved through a new architecture and parameters optimized on novel datasets. In addition, redox-sensitive regions can also be highlighted through a novel experimental feature. The web server offers graphical and text outputs, a RESTful interface, access to software download and extensive help, and can be accessed at a new location: http://iupred2a.elte.hu.
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Affiliation(s)
- Bálint Mészáros
- MTA-ELTE Momentum Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest H-1117, Hungary
| | - Gábor Erdos
- MTA-ELTE Momentum Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest H-1117, Hungary
| | - Zsuzsanna Dosztányi
- MTA-ELTE Momentum Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest H-1117, Hungary
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17
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Piovesan D, Tabaro F, Paladin L, Necci M, Micetic I, Camilloni C, Davey N, Dosztányi Z, Mészáros B, Monzon AM, Parisi G, Schad E, Sormanni P, Tompa P, Vendruscolo M, Vranken WF, Tosatto SCE. MobiDB 3.0: more annotations for intrinsic disorder, conformational diversity and interactions in proteins. Nucleic Acids Res 2019; 46:D471-D476. [PMID: 29136219 PMCID: PMC5753340 DOI: 10.1093/nar/gkx1071] [Citation(s) in RCA: 156] [Impact Index Per Article: 31.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Accepted: 10/19/2017] [Indexed: 01/30/2023] Open
Abstract
The MobiDB (URL: mobidb.bio.unipd.it) database of protein disorder and mobility annotations has been significantly updated and upgraded since its last major renewal in 2014. Several curated datasets for intrinsic disorder and folding upon binding have been integrated from specialized databases. The indirect evidence has also been expanded to better capture information available in the PDB, such as high temperature residues in X-ray structures and overall conformational diversity. Novel nuclear magnetic resonance chemical shift data provides an additional experimental information layer on conformational dynamics. Predictions have been expanded to provide new types of annotation on backbone rigidity, secondary structure preference and disordered binding regions. MobiDB 3.0 contains information for the complete UniProt protein set and synchronization has been improved by covering all UniParc sequences. An advanced search function allows the creation of a wide array of custom-made datasets for download and further analysis. A large amount of information and cross-links to more specialized databases are intended to make MobiDB the central resource for the scientific community working on protein intrinsic disorder and mobility.
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Affiliation(s)
- Damiano Piovesan
- Department of Biomedical Sciences, University of Padua, via U. Bassi 58/b, 35131 Padua, Italy
| | - Francesco Tabaro
- Department of Biomedical Sciences, University of Padua, via U. Bassi 58/b, 35131 Padua, Italy.,Institute of Biosciences and Medical Technology, Arvo Ylpön katu 34, 33520 Tampere, Finland
| | - Lisanna Paladin
- Department of Biomedical Sciences, University of Padua, via U. Bassi 58/b, 35131 Padua, Italy
| | - Marco Necci
- Department of Biomedical Sciences, University of Padua, via U. Bassi 58/b, 35131 Padua, Italy.,Department of Agricultural Sciences, University of Udine, via Palladio 8, 33100 Udine, Italy.,Fondazione Edmund Mach, Via E. Mach 1, 38010 S. Michele all'Adige, Italy
| | - Ivan Micetic
- Department of Biomedical Sciences, University of Padua, via U. Bassi 58/b, 35131 Padua, Italy
| | - Carlo Camilloni
- Department of Biosciences, University of Milan, 20133 Milano, Italy
| | - Norman Davey
- Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland.,UCD School of Medicine & Medical Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - Zsuzsanna Dosztányi
- MTA-ELTE Lendület Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, 1/c Pázmány Péter sétány, H-1117, Budapest, Hungary
| | - Bálint Mészáros
- MTA-ELTE Lendület Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, 1/c Pázmány Péter sétány, H-1117, Budapest, Hungary.,Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, PO Box 7, H-1518 Budapest, Hungary
| | - Alexander M Monzon
- Structural Bioinformatics Group, Department of Science and Technology, National University of Quilmes, CONICET, Roque Saenz Pena 182, Bernal B1876BXD, Argentina
| | - Gustavo Parisi
- Structural Bioinformatics Group, Department of Science and Technology, National University of Quilmes, CONICET, Roque Saenz Pena 182, Bernal B1876BXD, Argentina
| | - Eva Schad
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, PO Box 7, H-1518 Budapest, Hungary
| | - Pietro Sormanni
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Peter Tompa
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, PO Box 7, H-1518 Budapest, Hungary.,Structural Biology Brussels, Vrije Universiteit Brussel (VUB), Brussels 1050, Belgium.,VIB-VUB Center for Structural Biology, Flanders Institute for Biotechnology (VIB), Brussels 1050, Belgium
| | | | - Wim F Vranken
- Structural Biology Brussels, Vrije Universiteit Brussel (VUB), Brussels 1050, Belgium.,VIB-VUB Center for Structural Biology, Flanders Institute for Biotechnology (VIB), Brussels 1050, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, ULB/VUB, 1050 Brussels, Belgium
| | - Silvio C E Tosatto
- Department of Biomedical Sciences, University of Padua, via U. Bassi 58/b, 35131 Padua, Italy.,CNR Institute of Neuroscience, via U. Bassi 58/b, 35131 Padua, Italy
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18
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Erdős G, Mészáros B, Reichmann D, Dosztányi Z. Large-Scale Analysis of Redox-Sensitive Conditionally Disordered Protein Regions Reveals Their Widespread Nature and Key Roles in High-Level Eukaryotic Processes. Proteomics 2019; 19:e1800070. [PMID: 30628183 DOI: 10.1002/pmic.201800070] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2018] [Revised: 12/13/2018] [Indexed: 12/17/2022]
Abstract
Recently developed quantitative redox proteomic studies enable the direct identification of redox-sensing cysteine residues that regulate the functional behavior of target proteins in response to changing levels of reactive oxygen species. At the molecular level, redox regulation can directly modify the active sites of enzymes, although a growing number of examples indicate the importance of an additional underlying mechanism that involves conditionally disordered proteins. These proteins alter their functional behavior by undergoing a disorder-to-order transition in response to changing redox conditions. However, the extent to which this mechanism is used in various proteomes is currently unknown. Here, a recently developed sequence-based prediction tool incorporated into the IUPred2A web server is used to estimate redox-sensitive conditionally disordered regions at a large scale. It is shown that redox-sensitive conditional disorder is fairly widespread in various proteomes and that its presence strongly correlates with the expansion of specific domains in multicellular organisms that largely rely on extra stability provided by disulfide bonds or zinc ion binding. The analyses of yeast redox proteomes and human disease data further underlie the significance of this phenomenon in the regulation of a wide range of biological processes, as well as its biomedical importance.
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Affiliation(s)
- Gábor Erdős
- MTA-ELTE Lendület Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest, H-1117, Hungary
| | - Bálint Mészáros
- MTA-ELTE Lendület Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest, H-1117, Hungary.,Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, 69117, Germany
| | - Dana Reichmann
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, Safra Campus Givat Ram, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel
| | - Zsuzsanna Dosztányi
- MTA-ELTE Lendület Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest, H-1117, Hungary
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Dobson L, Mészáros B, Tusnády GE. Structural Principles Governing Disease-Causing Germline Mutations. J Mol Biol 2018; 430:4955-4970. [DOI: 10.1016/j.jmb.2018.10.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Accepted: 10/11/2018] [Indexed: 01/03/2023]
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Schad E, Fichó E, Pancsa R, Simon I, Dosztányi Z, Mészáros B. DIBS: a repository of disordered binding sites mediating interactions with ordered proteins. Bioinformatics 2018; 34:535-537. [PMID: 29385418 PMCID: PMC5860366 DOI: 10.1093/bioinformatics/btx640] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Accepted: 10/06/2017] [Indexed: 12/14/2022] Open
Abstract
Motivation Intrinsically Disordered Proteins (IDPs) mediate crucial protein–protein interactions, most notably in signaling and regulation. As their importance is increasingly recognized, the detailed analyses of specific IDP interactions opened up new opportunities for therapeutic targeting. Yet, large scale information about IDP-mediated interactions in structural and functional details are lacking, hindering the understanding of the mechanisms underlying this distinct binding mode. Results Here, we present DIBS, the first comprehensive, curated collection of complexes between IDPs and ordered proteins. DIBS not only describes by far the highest number of cases, it also provides the dissociation constants of their interactions, as well as the description of potential post-translational modifications modulating the binding strength and linear motifs involved in the binding. Together with the wide range of structural and functional annotations, DIBS will provide the cornerstone for structural and functional studies of IDP complexes. Availability and implementation DIBS is freely accessible at http://dibs.enzim.ttk.mta.hu/. The DIBS application is hosted by Apache web server and was implemented in PHP. To enrich querying features and to enhance backend performance a MySQL database was also created. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Eva Schad
- Research Centre for Natural Sciences, Institute of Enzymology, Hungarian Academy of Sciences, Budapest H-1117, Hungary
| | - Erzsébet Fichó
- Research Centre for Natural Sciences, Institute of Enzymology, Hungarian Academy of Sciences, Budapest H-1117, Hungary
| | - Rita Pancsa
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge CB2 0QH, UK
| | - István Simon
- Research Centre for Natural Sciences, Institute of Enzymology, Hungarian Academy of Sciences, Budapest H-1117, Hungary
| | - Zsuzsanna Dosztányi
- MTA-ELTE Momentum Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest H-1117, Hungary
| | - Bálint Mészáros
- Research Centre for Natural Sciences, Institute of Enzymology, Hungarian Academy of Sciences, Budapest H-1117, Hungary.,MTA-ELTE Momentum Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest H-1117, Hungary
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Fichó E, Reményi I, Simon I, Mészáros B. MFIB: a repository of protein complexes with mutual folding induced by binding. Bioinformatics 2018; 33:3682-3684. [PMID: 29036655 PMCID: PMC5870711 DOI: 10.1093/bioinformatics/btx486] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2017] [Accepted: 08/02/2017] [Indexed: 12/02/2022] Open
Abstract
Motivation It is commonplace that intrinsically disordered proteins (IDPs) are involved in crucial interactions in the living cell. However, the study of protein complexes formed exclusively by IDPs is hindered by the lack of data and such analyses remain sporadic. Systematic studies benefited other types of protein–protein interactions paving a way from basic science to therapeutics; yet these efforts require reliable datasets that are currently lacking for synergistically folding complexes of IDPs. Results Here we present the Mutual Folding Induced by Binding (MFIB) database, the first systematic collection of complexes formed exclusively by IDPs. MFIB contains an order of magnitude more data than any dataset used in corresponding studies and offers a wide coverage of known IDP complexes in terms of flexibility, oligomeric composition and protein function from all domains of life. The included complexes are grouped using a hierarchical classification and are complemented with structural and functional annotations. MFIB is backed by a firm development team and infrastructure, and together with possible future community collaboration it will provide the cornerstone for structural and functional studies of IDP complexes. Availability and implementation MFIB is freely accessible at http://mfib.enzim.ttk.mta.hu/. The MFIB application is hosted by Apache web server and was implemented in PHP. To enrich querying features and to enhance backend performance a MySQL database was also created. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Erzsébet Fichó
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest H-1117, Hungary
| | - István Reményi
- Institute of Enzymology, RCNS, Hungarian Academy of Sciences, 'Momentum' Membrane Protein Bioinformatics Research Group, Budapest H-1117, Hungary
| | - István Simon
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest H-1117, Hungary
| | - Bálint Mészáros
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest H-1117, Hungary
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Mészáros B, Zeke A, Reményi A, Simon I, Dosztányi Z. Systematic analysis of somatic mutations driving cancer: uncovering functional protein regions in disease development. Biol Direct 2016; 11:23. [PMID: 27150584 PMCID: PMC4858844 DOI: 10.1186/s13062-016-0125-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2016] [Accepted: 04/20/2016] [Indexed: 11/16/2022] Open
Abstract
Background Recent advances in sequencing technologies enable the large-scale identification of genes that are affected by various genetic alterations in cancer. However, understanding tumor development requires insights into how these changes cause altered protein function and impaired network regulation in general and/or in specific cancer types. Results In this work we present a novel method called iSiMPRe that identifies regions that are significantly enriched in somatic mutations and short in-frame insertions or deletions (indels). Applying this unbiased method to the complete human proteome, by using data enriched through various cancer genome projects, we identified around 500 protein regions which could be linked to one or more of 27 distinct cancer types. These regions covered the majority of known cancer genes, surprisingly even tumor suppressors. Additionally, iSiMPRe also identified novel genes and regions that have not yet been associated with cancer. Conclusions While local somatic mutations correspond to only a subset of genetic variations that can lead to cancer, our systematic analyses revealed that they represent an accompanying feature of most cancer driver genes regardless of the primary mechanism by which they are perturbed during tumorigenesis. These results indicate that the accumulation of local somatic mutations can be used to pinpoint genes responsible for cancer formation and can also help to understand the effect of cancer mutations at the level of functional modules in a broad range of cancer driver genes. Reviewers This article was reviewed by Sándor Pongor, Michael Gromiha and Zoltán Gáspári. Electronic supplementary material The online version of this article (doi:10.1186/s13062-016-0125-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Bálint Mészáros
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, 2 Magyar Tudósok krt, Budapest, H-1117, Hungary.
| | - András Zeke
- Lendület Protein Interaction Group, Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, 2 Magyar Tudósok krt, Budapest, H-1117, Hungary
| | - Attila Reményi
- Lendület Protein Interaction Group, Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, 2 Magyar Tudósok krt, Budapest, H-1117, Hungary
| | - István Simon
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, 2 Magyar Tudósok krt, Budapest, H-1117, Hungary
| | - Zsuzsanna Dosztányi
- MTA-ELTE Lendület Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, 11/c Pázmány Péter stny, Budapest, H-1117, Hungary.
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Zeke A, Bastys T, Alexa A, Garai Á, Mészáros B, Kirsch K, Dosztányi Z, Kalinina OV, Reményi A. Systematic discovery of linear binding motifs targeting an ancient protein interaction surface on MAP kinases. Mol Syst Biol 2015; 11:837. [PMID: 26538579 PMCID: PMC4670726 DOI: 10.15252/msb.20156269] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Mitogen‐activated protein kinases (MAPK) are broadly used regulators of cellular signaling. However, how these enzymes can be involved in such a broad spectrum of physiological functions is not understood. Systematic discovery of MAPK networks both experimentally and in silico has been hindered because MAPKs bind to other proteins with low affinity and mostly in less‐characterized disordered regions. We used a structurally consistent model on kinase‐docking motif interactions to facilitate the discovery of short functional sites in the structurally flexible and functionally under‐explored part of the human proteome and applied experimental tools specifically tailored to detect low‐affinity protein–protein interactions for their validation in vitro and in cell‐based assays. The combined computational and experimental approach enabled the identification of many novel MAPK‐docking motifs that were elusive for other large‐scale protein–protein interaction screens. The analysis produced an extensive list of independently evolved linear binding motifs from a functionally diverse set of proteins. These all target, with characteristic binding specificity, an ancient protein interaction surface on evolutionarily related but physiologically clearly distinct three MAPKs (JNK, ERK, and p38). This inventory of human protein kinase binding sites was compared with that of other organisms to examine how kinase‐mediated partnerships evolved over time. The analysis suggests that most human MAPK‐binding motifs are surprisingly new evolutionarily inventions and newly found links highlight (previously hidden) roles of MAPKs. We propose that short MAPK‐binding stretches are created in disordered protein segments through a variety of ways and they represent a major resource for ancient signaling enzymes to acquire new regulatory roles.
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Affiliation(s)
- András Zeke
- Lendület Protein Interaction Group, Institute of Enzymology Research Center for Natural Sciences Hungarian Academy of Sciences, Budapest, Hungary
| | - Tomas Bastys
- Max Planck Institute for Informatics, Saarbrücken, Germany Graduate School of Computer Science, Saarland University, Saarbrücken, Germany
| | - Anita Alexa
- Lendület Protein Interaction Group, Institute of Enzymology Research Center for Natural Sciences Hungarian Academy of Sciences, Budapest, Hungary
| | - Ágnes Garai
- Lendület Protein Interaction Group, Institute of Enzymology Research Center for Natural Sciences Hungarian Academy of Sciences, Budapest, Hungary
| | - Bálint Mészáros
- Institute of Enzymology Research Center for Natural Sciences Hungarian Academy of Sciences, Budapest, Hungary
| | - Klára Kirsch
- Lendület Protein Interaction Group, Institute of Enzymology Research Center for Natural Sciences Hungarian Academy of Sciences, Budapest, Hungary
| | - Zsuzsanna Dosztányi
- MTA-ELTE Lendület Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest, Hungary
| | | | - Attila Reményi
- Lendület Protein Interaction Group, Institute of Enzymology Research Center for Natural Sciences Hungarian Academy of Sciences, Budapest, Hungary
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Field AR, Dunai D, Gaffka R, Ghim YC, Kiss I, Mészáros B, Krizsanóczi T, Shibaev S, Zoletnik S. Beam emission spectroscopy turbulence imaging system for the MAST spherical tokamak. Rev Sci Instrum 2012; 83:013508. [PMID: 22299952 DOI: 10.1063/1.3669756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
A new beam emission spectroscopy turbulence imaging system has recently been installed onto the MAST spherical tokamak. The system utilises a high-throughput, direct coupled imaging optics, and a single large interference filter for collection of the Doppler shifted D(α) emission from the ~2 MW heating beam of ~70 keV injection energy. The collected light is imaged onto a 2D array detector with 8 × 4 avalanche photodiode sensors which is incorporated into a custom camera unit to perform simultaneous 14-bit digitization at 2 MHz of all 32 channels. The array is imaged at the beam to achieve a spatial resolution of ~2 cm in the radial (horizontal) and poloidal (vertical) directions, which is sufficient for detection of the ion-scale plasma turbulence. At the typical photon fluxes of ~10(11) s(-1) the achieved signal-to-noise ratio of ~300 at the 0.5 MHz analogue bandwidth is sufficient for detection of relative density fluctuations at the level of a few 0.1%. The system is to be utilised for the study of the characteristics of the broadband, ion-scale turbulence, in particular its interaction with flow shear, as well as coherent fluctuations due to various types of MHD activity.
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Affiliation(s)
- A R Field
- EURATOM/UKAEA Fusion Association, Culham Science Centre, Abingdon, Oxon, United Kingdom
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26
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Ilkei T, Bede O, Madeleine S, Aiello A, Baross T, Ghidersa BE, Grunda G, Hajek P, Keller D, Kosek L, Mészáros B, Nagy D, Németh J, Nitti F, Tulipán S, Wagrez J. European test blanket ancillary equipment unit development. Fusion Engineering and Design 2011. [DOI: 10.1016/j.fusengdes.2010.12.058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Pajkos M, Mészáros B, Simon I, Dosztányi Z. Is there a biological cost of protein disorder? Analysis of cancer-associated mutations. Mol Biosyst 2011; 8:296-307. [PMID: 21918772 DOI: 10.1039/c1mb05246b] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
As many diseases can be traced back to altered protein function, studying the effect of genetic variations at the level of proteins can provide a clue to understand how changes at the DNA level lead to various diseases. Cellular processes rely not only on proteins with well-defined structure but can also involve intrinsically disordered proteins (IDPs) that exist as highly flexible ensembles of conformations. Disordered proteins are mostly involved in signaling and regulatory processes, and their functional repertoire largely complements that of globular proteins. However, it was also suggested that protein disorder entails an increased biological cost. This notion was supported by a set of individual IDPs involved in various diseases, especially in cancer, and the increased amount of disorder observed among disease-associated proteins. In this work, we tested if there is any biological risk associated with protein disorder at the level of single nucleotide mutations. Specifically, we analyzed the distribution of mutations within ordered and disordered segments. Our results demonstrated that while neutral polymorphisms were more likely to occur within disordered segments, cancer-associated mutations had a preference for ordered regions. Additionally, we proposed an alternative explanation for the association of protein disorder and the involvement in cancer with the consideration of functional annotations. Individual examples also suggested that although disordered segments are fundamental functional elements, their presence is not necessarily accompanied with an increased mutation rate in cancer. The presented study can help to understand how the different structural properties of proteins influence the consequences of genetic mutations.
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Affiliation(s)
- Mátyás Pajkos
- Institute of Enzymology, Hungarian Academy of Sciences, Budapest, Hungary
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Mészáros B, Tóth J, Vértessy BG, Dosztányi Z, Simon I. Proteins with complex architecture as potential targets for drug design: a case study of Mycobacterium tuberculosis. PLoS Comput Biol 2011; 7:e1002118. [PMID: 21814507 PMCID: PMC3140968 DOI: 10.1371/journal.pcbi.1002118] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2011] [Accepted: 05/24/2011] [Indexed: 02/04/2023] Open
Abstract
Lengthy co-evolution of Homo sapiens and Mycobacterium tuberculosis, the main causative agent of tuberculosis, resulted in a dramatically successful pathogen species that presents considerable challenge for modern medicine. The continuous and ever increasing appearance of multi-drug resistant mycobacteria necessitates the identification of novel drug targets and drugs with new mechanisms of action. However, further insights are needed to establish automated protocols for target selection based on the available complete genome sequences. In the present study, we perform complete proteome level comparisons between M. tuberculosis, mycobacteria, other prokaryotes and available eukaryotes based on protein domains, local sequence similarities and protein disorder. We show that the enrichment of certain domains in the genome can indicate an important function specific to M. tuberculosis. We identified two families, termed pkn and PE/PPE that stand out in this respect. The common property of these two protein families is a complex domain organization that combines species-specific regions, commonly occurring domains and disordered segments. Besides highlighting promising novel drug target candidates in M. tuberculosis, the presented analysis can also be viewed as a general protocol to identify proteins involved in species-specific functions in a given organism. We conclude that target selection protocols should be extended to include proteins with complex domain architectures instead of focusing on sequentially unique and essential proteins only.
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Affiliation(s)
- Bálint Mészáros
- Institute of Enzymology, Hungarian Academy of Sciences, Budapest, Hungary
| | - Judit Tóth
- Institute of Enzymology, Hungarian Academy of Sciences, Budapest, Hungary
| | - Beáta G. Vértessy
- Institute of Enzymology, Hungarian Academy of Sciences, Budapest, Hungary
- Department of Applied Biotechnology, Budapest University of Technology and Economics, Budapest, Hungary
| | - Zsuzsanna Dosztányi
- Institute of Enzymology, Hungarian Academy of Sciences, Budapest, Hungary
- * E-mail: (ZD); (IS)
| | - István Simon
- Institute of Enzymology, Hungarian Academy of Sciences, Budapest, Hungary
- * E-mail: (ZD); (IS)
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Abstract
A frequently neglected aspect of protein-protein interactions is flexibility. Small-scale fluctuations are present even in globular proteins, and alternative conformations can have a significant influence on the binding process. However, flexibility becomes highly prominent in complexes involving intrinsically disordered proteins. The importance of disordered regions in protein interactions has been recognized only relatively recently. In this survey we examine the basic properties of the complexes of disordered and ordered proteins from three different directions. The comparison of the interface properties shows that although disordered proteins can also adopt well-defined conformations in their bound form, their inherently dynamic nature is cast into their complexes. Furthermore, an overview of prediction methods indicates that disordered proteins as well as their binding regions can be recognized from the amino acid sequence by capturing the basic biophysical properties of these segments. Finally, we propose the generalization of the 'energy landscape model' for the description of complex formation that can help to put the various types of protein associations on a common ground.
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Affiliation(s)
- Bálint Mészáros
- Institute of Enzymology, Hungarian Academy of Sciences, PO Box 7, H-1518 Budapest, Hungary
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Abstract
Summary: ANCHOR is a web-based implementation of an original method that takes a single amino acid sequence as an input and predicts protein binding regions that are disordered in isolation but can undergo disorder-to-order transition upon binding. The server incorporates the result of a general disorder prediction method, IUPred and can carry out simple motif searches as well. Availability: The web server is available at http://anchor.enzim.hu. The program package is freely available for academic users. Contact:zsuzsa@enzim.hu
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Affiliation(s)
- Zsuzsanna Dosztányi
- Institute of Enzymology, Biological Research Center, Hungarian Academy of Sciences, PO Box 7, H-1518 Budapest, Hungary.
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Abstract
Many disordered proteins function via binding to a structured partner and undergo
a disorder-to-order transition. The coupled folding and binding can confer
several functional advantages such as the precise control of binding specificity
without increased affinity. Additionally, the inherent flexibility allows the
binding site to adopt various conformations and to bind to multiple partners.
These features explain the prevalence of such binding elements in signaling and
regulatory processes. In this work, we report ANCHOR, a method for the
prediction of disordered binding regions. ANCHOR relies on the pairwise energy
estimation approach that is the basis of IUPred, a previous general disorder
prediction method. In order to predict disordered binding regions, we seek to
identify segments that are in disordered regions, cannot form enough favorable
intrachain interactions to fold on their own, and are likely to gain stabilizing
energy by interacting with a globular protein partner. The performance of ANCHOR
was found to be largely independent from the amino acid composition and adopted
secondary structure. Longer binding sites generally were predicted to be
segmented, in agreement with available experimentally characterized examples.
Scanning several hundred proteomes showed that the occurrence of disordered
binding sites increased with the complexity of the organisms even compared to
disordered regions in general. Furthermore, the length distribution of binding
sites was different from disordered protein regions in general and was dominated
by shorter segments. These results underline the importance of disordered
proteins and protein segments in establishing new binding regions. Due to their
specific biophysical properties, disordered binding sites generally carry a
robust sequence signal, and this signal is efficiently captured by our method.
Through its generality, ANCHOR opens new ways to study the essential functional
sites of disordered proteins. Intrinsically unstructured/disordered proteins (IUPs/IDPs) do not adopt a stable
structure in isolation but exist as a highly flexible ensemble of conformations.
Despite the lack of a well-defined structure these proteins carry out important
functions. Many IUPs/IDPs function via binding specifically to other
macromolecules that involves a disorder-to-order transition. The molecular
recognition functions of IUPs/IDPs include regulatory and signaling interactions
where binding to multiple partners and high-specificity/low-affinity
interactions play a crucial role. Due to their specific functional and
structural properties, these binding regions have distinct properties compared
to both globular proteins and disordered regions in general. Here, we present a
general method to identify disordered binding regions from the amino acid
sequence. Our method targets the essential feature of these regions: they behave
in a characteristically different manner in isolation than bound to their
partner protein. This prediction method allows us to compare the binding
properties of short and long binding sites. The evolutionary relationship
between the amount of disordered binding regions and general disordered regions
in various organisms was also analyzed. Our results suggest that disordered
binding regions can be recognized even without taking into account their adopted
secondary structure or their specific binding partner.
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Affiliation(s)
- Bálint Mészáros
- Institute of Enzymology, Biological Research Center, Hungarian Academy of
Sciences, Budapest, Hungary
| | - István Simon
- Institute of Enzymology, Biological Research Center, Hungarian Academy of
Sciences, Budapest, Hungary
| | - Zsuzsanna Dosztányi
- Institute of Enzymology, Biological Research Center, Hungarian Academy of
Sciences, Budapest, Hungary
- * E-mail:
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Mészáros B, Tompa P, Simon I, Dosztányi Z. Molecular principles of the interactions of disordered proteins. J Mol Biol 2007; 372:549-61. [PMID: 17681540 DOI: 10.1016/j.jmb.2007.07.004] [Citation(s) in RCA: 214] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2007] [Revised: 06/29/2007] [Accepted: 07/02/2007] [Indexed: 11/18/2022]
Abstract
Thorough knowledge of the molecular principles of protein-protein recognition is essential to our understanding of protein function at the cellular level. Whereas interactions of ordered proteins have been analyzed in great detail, complexes of intrinsically unstructured/disordered proteins (IUPs) have hardly been addressed so far. Here, we have collected a database of 39 complexes of experimentally verified IUPs, and compared their interfaces with those of 72 complexes of ordered, globular proteins. The characteristic differences found between the two types of complexes suggest that IUPs represent a distinct molecular implementation of the principles of protein-protein recognition. The interfaces do not differ in size, but those of IUPs cover a much larger part of the surface of the protein than for their ordered counterparts. Moreover, IUP interfaces are significantly more hydrophobic relative to their overall amino acid composition, but also in absolute terms. They rely more on hydrophobic-hydrophobic than on polar-polar interactions. Their amino acids in the interface realize more intermolecular contacts, which suggests a better fit with the partner due to induced folding upon binding that results in a better adaptation to the partner. The two modes of interaction also differ in that IUPs usually use only a single continuous segment for partner binding, whereas the binding sites of ordered proteins are more segmented. Probably, all these features contribute to the increased evolutionary conservation of IUP interface residues. These noted molecular differences are also manifested in the interaction energies of IUPs. Our approximation of these by low-resolution force-fields shows that IUPs gain much more stabilization energy from intermolecular contacts, than from folding, i.e. they use their binding energy for folding. Overall, our findings provide a structural rationale to the prior suggestions that many IUPs are specialized for functions realized by protein-protein interactions.
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Affiliation(s)
- Bálint Mészáros
- Institute of Enzymology, Biological Research Center, Hungarian Academy of Sciences, 1518, Budapest, Hungary
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Daood H, Illés V, Gnayfeed M, Mészáros B, Horváth G, Biacs P. Extraction of pungent spice paprika by supercritical carbon dioxide and subcritical propane. J Supercrit Fluids 2002. [DOI: 10.1016/s0896-8446(02)00022-0] [Citation(s) in RCA: 80] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Mészáros B. [Treatment of mandibular fractures by extra-oral pin-fixation]. Fogorv Sz 1966; 59:11-5. [PMID: 5217031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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