@article{MTMT:33536387, title = {Acetylation of nuclear receptors in health and disease: an update}, url = {https://m2.mtmt.hu/api/publication/33536387}, author = {Ashton, Anthony W. and Dhanjal, Harpreet K. and Rossner, Benjamin and Mahmood, Huma and Patel, Vivek I. and Nadim, Mohammad and Lota, Manpreet and Shahid, Farhan and Li, Zhiping and Joyce, David and Pajkos, Mátyás and Dosztányi, Zsuzsanna and Jiao, Xuanmao and Pestell, Richard G.}, doi = {10.1111/febs.16695}, journal-iso = {FEBS J}, journal = {FEBS JOURNAL}, volume = {291}, unique-id = {33536387}, issn = {1742-464X}, year = {2024}, eissn = {1742-4658}, pages = {217-236}, orcid-numbers = {Dosztányi, Zsuzsanna/0000-0002-3624-5937; Jiao, Xuanmao/0000-0003-4222-9172; Pestell, Richard G./0000-0003-3244-8777} } @article{MTMT:34239893, title = {The Origin of Discrepancies between Predictions and Annotations in Intrinsically Disordered Proteins}, url = {https://m2.mtmt.hu/api/publication/34239893}, author = {Pajkos, Mátyás and Erdős, Gábor and Dosztányi, Zsuzsanna}, doi = {10.3390/biom13101442}, journal-iso = {BIOMOLECULES}, journal = {BIOMOLECULES}, volume = {13}, unique-id = {34239893}, issn = {2218-273X}, abstract = {Disorder prediction methods that can discriminate between ordered and disordered regions have contributed fundamentally to our understanding of the properties and prevalence of intrinsically disordered proteins (IDPs) in proteomes as well as their functional roles. However, a recent large-scale assessment of the performance of these methods indicated that there is still room for further improvements, necessitating novel approaches to understand the strengths and weaknesses of individual methods. In this study, we compared two methods, IUPred and disorder prediction, based on the pLDDT scores derived from AlphaFold2 (AF2) models. We evaluated these methods using a dataset from the DisProt database, consisting of experimentally characterized disordered regions and subsets associated with diverse experimental methods and functions. IUPred and AF2 provided consistent predictions in 79% of cases for long disordered regions; however, for 15% of these cases, they both suggested order in disagreement with annotations. These discrepancies arose primarily due to weak experimental support, the presence of intermediate states, or context-dependent behavior, such as binding-induced transitions. Furthermore, AF2 tended to predict helical regions with high pLDDT scores within disordered segments, while IUPred had limitations in identifying linker regions. These results provide valuable insights into the inherent limitations and potential biases of disorder prediction methods.}, year = {2023}, eissn = {2218-273X}, orcid-numbers = {Erdős, Gábor/0000-0001-6218-5192; Dosztányi, Zsuzsanna/0000-0002-3624-5937} } @article{MTMT:34162712, title = {Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins}, url = {https://m2.mtmt.hu/api/publication/34162712}, author = {Kurgan, Lukasz and Hu, Gang and Wang, Kui and Ghadermarzi, Sina and Zhao, Bi and Malhis, Nawar and Erdős, Gábor and Gsponer, Jörg and Uversky, Vladimir N. and Dosztányi, Zsuzsanna}, doi = {10.1038/s41596-023-00876-x}, journal-iso = {NAT PROTOC}, journal = {NATURE PROTOCOLS}, volume = {18}, unique-id = {34162712}, issn = {1754-2189}, abstract = {Intrinsic disorder is instrumental for a wide range of protein functions, and its analysis, using computational predictions from primary structures, complements secondary and tertiary structure-based approaches. In this Tutorial, we provide an overview and comparison of 23 publicly available computational tools with complementary parameters useful for intrinsic disorder prediction, partly relying on results from the Critical Assessment of protein Intrinsic Disorder prediction experiment. We consider factors such as accuracy, runtime, availability and the need for functional insights. The selected tools are available as web servers and downloadable programs, offer state-of-the-art predictions and can be used in a high-throughput manner. We provide examples and instructions for the selected tools to illustrate practical aspects related to the submission, collection and interpretation of predictions, as well as the timing and their limitations. We highlight two predictors for intrinsically disordered proteins, flDPnn as accurate and fast and IUPred as very fast and moderately accurate, while suggesting ANCHOR2 and MoRFchibi as two of the best-performing predictors for intrinsically disordered region binding. We link these tools to additional resources, including databases of predictions and web servers that integrate multiple predictive methods. Altogether, this Tutorial provides a hands-on guide to comparatively evaluating multiple predictors, submitting and collecting their own predictions, and reading and interpreting results. It is suitable for experimentalists and computational biologists interested in accurately and conveniently identifying intrinsic disorder, facilitating the functional characterization of the rapidly growing collections of protein sequences. © 2023, Springer Nature Limited.}, year = {2023}, eissn = {1750-2799}, pages = {3157-3172}, orcid-numbers = {Kurgan, Lukasz/0000-0002-7749-0314; Malhis, Nawar/0000-0002-1317-833X; Erdős, Gábor/0000-0001-6218-5192; Dosztányi, Zsuzsanna/0000-0002-3624-5937} } @article{MTMT:34066608, title = {Minimum information guidelines for experiments structurally characterizing intrinsically disordered protein regions}, url = {https://m2.mtmt.hu/api/publication/34066608}, author = {Mészáros, Bálint and Hatos, András and Palopoli, Nicolas and Quaglia, Federica and Salladini, Edoardo and Van Roey, Kim and Arthanari, Haribabu and Dosztányi, Zsuzsanna and Felli, Isabella C. and Fischer, Patrick D. and Hoch, Jeffrey C. and Jeffries, Cy M. and Longhi, Sonia and Maiani, Emiliano and Orchard, Sandra and Pancsa, Rita and Papaleo, Elena and Pierattelli, Roberta and Piovesan, Damiano and Pritisanac, Iva and Tenorio, Luiggi and Viennet, Thibault and Tompa, Péter and Vranken, Wim and Tosatto, Silvio C. E. and Davey, Norman E.}, doi = {10.1038/s41592-023-01915-x}, journal-iso = {NAT METHODS}, journal = {NATURE METHODS}, volume = {20}, unique-id = {34066608}, issn = {1548-7091}, year = {2023}, eissn = {1548-7105}, pages = {1291-1303}, orcid-numbers = {Mészáros, Bálint/0000-0003-0919-4449; Hatos, András/0000-0001-9224-9820; Salladini, Edoardo/0000-0002-5152-5953; Arthanari, Haribabu/0000-0002-7281-1289; Dosztányi, Zsuzsanna/0000-0002-3624-5937; Jeffries, Cy M./0000-0002-8718-7343; Longhi, Sonia/0000-0002-6829-6771; Maiani, Emiliano/0000-0003-1432-5394; Orchard, Sandra/0000-0002-8878-3972; Pancsa, Rita/0000-0003-0849-9312; Pierattelli, Roberta/0000-0001-7755-0885; Viennet, Thibault/0000-0001-5349-0179; Vranken, Wim/0000-0001-7470-4324; Tosatto, Silvio C. E./0000-0003-4525-7793; Davey, Norman E./0000-0001-6988-4850} } @article{MTMT:33832027, title = {Pipeline for transferring annotations between proteins beyond globular domains.}, url = {https://m2.mtmt.hu/api/publication/33832027}, author = {Martínez-Pérez, Elizabeth and Pajkos, Mátyás and Tosatto, Silvio C E and Gibson, Toby J and Dosztányi, Zsuzsanna and Marino-Buslje, Cristina}, doi = {10.1002/pro.4655}, journal-iso = {PROTEIN SCI}, journal = {PROTEIN SCIENCE}, volume = {32}, unique-id = {33832027}, issn = {0961-8368}, abstract = {DisProt is the primary repository of Intrinsically Disordered Proteins (IDPs). This database is manually curated and the annotations there have strong experimental support. Currently, DisProt contains a relatively small number of proteins highlighting the importance of transferring annotations regarding verified disorder state and corresponding functions to homologous proteins in other species. In such a way, providing them with highly valuable information to better understand their biological roles. While the principles and practicalities of homology transfer are well-established for globular proteins, these are largely lacking for disordered proteins.We used DisProt to evaluate the transferability of the annotation terms to orthologous proteins. For each protein, we looked for their orthologs, with the assumption that they will have a similar function. Then, for each protein and their orthologs we made multiple sequence alignments (MSAs). Disordered sequences are fast evolving and can be hard to align: Therefore we implemented alignment quality control steps ensuring robust alignments before mapping the annotations.We have designed a pipeline to obtain good quality MSAs and to transfer annotations from any protein to their orthologs. Applying the pipeline to DisProt proteins, from the 1,731 entries with 5,623 annotations we can reach 97,555 orthologs and transfer a total of 301,190 terms by homology. We also provide a web server for consulting the results of DisProt proteins and execute the pipeline for any other protein. The server Homology Transfer IDP (HoTIDP) is accessible at http://hotidp.leloir.org.ar. This article is protected by copyright. All rights reserved.}, keywords = {ANNOTATION; intrinsically disordered proteins; MULTIPLE SEQUENCE ALIGNMENT; orthologous proteins; DisProt; homology transfer; ontology terms}, year = {2023}, eissn = {1469-896X}, orcid-numbers = {Dosztányi, Zsuzsanna/0000-0002-3624-5937} } @article{MTMT:33548424, title = {DisCanVis: Visualizing integrated structural and functional annotations to better understand the effect of cancer mutations located within disordered proteins}, url = {https://m2.mtmt.hu/api/publication/33548424}, author = {Deutsch, Norbert and Pajkos, Mátyás and Erdős, Gábor and Dosztányi, Zsuzsanna}, doi = {10.1002/pro.4522}, journal-iso = {PROTEIN SCI}, journal = {PROTEIN SCIENCE}, volume = {32}, unique-id = {33548424}, issn = {0961-8368}, year = {2023}, eissn = {1469-896X}, orcid-numbers = {Erdős, Gábor/0000-0001-6218-5192; Dosztányi, Zsuzsanna/0000-0002-3624-5937} } @{MTMT:33286659, title = {Prediction of protein structure and intrinsic disorder in the era of deep learning}, url = {https://m2.mtmt.hu/api/publication/33286659}, author = {Erdős, Gábor and Dosztányi, Zsuzsanna}, booktitle = {Structure and Intrinsic Disorder in Enzymology}, doi = {10.1016/B978-0-323-99533-7.00007-8}, unique-id = {33286659}, year = {2023}, pages = {199-224}, orcid-numbers = {Erdős, Gábor/0000-0001-6218-5192; Dosztányi, Zsuzsanna/0000-0002-3624-5937} } @article{MTMT:33202617, title = {Functional Tuning of Intrinsically Disordered Regions in Human Proteins by Composition Bias}, url = {https://m2.mtmt.hu/api/publication/33202617}, author = {Kastano, Kristina and Mier, Pablo and Dosztányi, Zsuzsanna and Promponas, Vasilis J. and Andrade-Navarro, Miguel A.}, doi = {10.3390/biom12101486}, journal-iso = {BIOMOLECULES}, journal = {BIOMOLECULES}, volume = {12}, unique-id = {33202617}, issn = {2218-273X}, abstract = {Intrinsically disordered regions (IDRs) in protein sequences are flexible, have low structural constraints and as a result have faster rates of evolution. This lack of evolutionary conservation greatly limits the use of sequence homology for the classification and functional assessment of IDRs, as opposed to globular domains. The study of IDRs requires other properties for their classification and functional prediction. While composition bias is not a necessary property of IDRs, compositionally biased regions (CBRs) have been noted as frequent part of IDRs. We hypothesized that to characterize IDRs, it could be helpful to study their overlap with particular types of CBRs. Here, we evaluate this overlap in the human proteome. A total of 2/3 of residues in IDRs overlap CBRs. Considering CBRs enriched in one type of amino acid, we can distinguish CBRs that tend to be fully included within long IDRs (R, H, N, D, P, G), from those that partially overlap shorter IDRs (S, E, K, T), and others that tend to overlap IDR terminals (Q, A). CBRs overlap more often IDRs in nuclear proteins and in proteins involved in liquid-liquid phase separation (LLPS). Study of protein interaction networks reveals the enrichment of CBRs in IDRs by tandem repetition of short linear motifs (rich in S or P), and the existence of E-rich polar regions that could support specific protein interactions with non-specific interactions. Our results open ways to pin down the function of IDRs from their partial compositional biases.}, year = {2022}, eissn = {2218-273X}, orcid-numbers = {Mier, Pablo/0000-0003-3663-2352; Dosztányi, Zsuzsanna/0000-0002-3624-5937; Promponas, Vasilis J./0000-0003-3352-4831} } @article{MTMT:33099749, title = {The interaction between LC8 and LCA5 reveals a novel oligomerization function of LC8 in the ciliary-centrosome system}, url = {https://m2.mtmt.hu/api/publication/33099749}, author = {Szaniszló, Tamás and Fülöp, Máté and Pajkos, Mátyás and Erdős, Gábor and Kovács, Réka Ágnes and Vadászi, Henrietta and Kardos, József and Dosztányi, Zsuzsanna}, doi = {10.1038/s41598-022-19454-4}, journal-iso = {SCI REP}, journal = {SCIENTIFIC REPORTS}, volume = {12}, unique-id = {33099749}, issn = {2045-2322}, abstract = {Dynein light chain LC8 is a small dimeric hub protein that recognizes its partners through short linear motifs and is commonly assumed to drive their dimerization. It has more than 100 known binding partners involved in a wide range of cellular processes. Recent large-scale interaction studies suggested that LC8 could also play a role in the ciliary/centrosome system. However, the cellular function of LC8 in this system remains elusive. In this work, we characterized the interaction of LC8 with the centrosomal protein lebercilin (LCA5), which is associated with a specific form of ciliopathy. We showed that LCA5 binds LC8 through two linear motifs. In contrast to the commonly accepted model, LCA5 forms dimers through extensive coiled coil formation in a LC8-independent manner. However, LC8 enhances the oligomerization ability of LCA5 that requires a finely balanced interplay of coiled coil segments and both binding motifs. Based on our results, we propose that LC8 acts as an oligomerization engine that is responsible for the higher order oligomer formation of LCA5. As LCA5 shares several common features with other centrosomal proteins, the presented LC8 driven oligomerization could be widespread among centrosomal proteins, highlighting an important novel cellular function of LC8.}, year = {2022}, eissn = {2045-2322}, orcid-numbers = {Szaniszló, Tamás/0000-0002-3130-9284; Erdős, Gábor/0000-0001-6218-5192; Kardos, József/0000-0002-2135-2932; Dosztányi, Zsuzsanna/0000-0002-3624-5937} } @article{MTMT:32587946, title = {DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation}, url = {https://m2.mtmt.hu/api/publication/32587946}, author = {Quaglia, Federica and Mészáros, Bálint and Salladini, Edoardo and Hatos, András and Pancsa, Rita and Chemes, Lucía B and Pajkos, Mátyás and Lazar, Tamas and Peña-Díaz, Samuel and Santos, Jaime and Ács, Veronika and Farahi, Nazanin and Fichó, Erzsébet and Aspromonte, Maria Cristina and Bassot, Claudio and Chasapi, Anastasia and Davey, Norman E and Davidović, Radoslav and Dobson, László and Elofsson, Arne and Erdős, Gábor and Gaudet, Pascale and Giglio, Michelle and Glavina, Juliana and Iserte, Javier and Iglesias, Valentín and Kálmán, Zsófia Etelka and Lambrughi, Matteo and Leonardi, Emanuela and Longhi, Sonia and Macedo-Ribeiro, Sandra and Maiani, Emiliano and Marchetti, Julia and Marino-Buslje, Cristina and Mészáros, Attila and Monzon, Alexander Miguel and Minervini, Giovanni and Nadendla, Suvarna and Nilsson, Juliet F and Novotný, Marian and Ouzounis, Christos A and Palopoli, Nicolás and Papaleo, Elena and Pereira, Pedro José Barbosa and Pozzati, Gabriele and Promponas, Vasilis J and Pujols, Jordi and Rocha, Alma Carolina Sanchez and Salas, Martin and Sawicki, Luciana Rodriguez and Schád, Éva and Shenoy, Aditi and Szaniszló, Tamás and Tsirigos, Konstantinos D and Veljkovic, Nevena and Parisi, Gustavo and Ventura, Salvador and Dosztányi, Zsuzsanna and Tompa, Péter and Tosatto, Silvio C E and Piovesan, Damiano}, doi = {10.1093/nar/gkab1082}, journal-iso = {NUCLEIC ACIDS RES}, journal = {NUCLEIC ACIDS RESEARCH}, volume = {50}, unique-id = {32587946}, issn = {0305-1048}, year = {2022}, eissn = {1362-4962}, pages = {D480-D487}, orcid-numbers = {Quaglia, Federica/0000-0002-0341-4888; Mészáros, Bálint/0000-0003-0919-4449; Salladini, Edoardo/0000-0002-5152-5953; Hatos, András/0000-0001-9224-9820; Pancsa, Rita/0000-0003-0849-9312; Chemes, Lucía B/0000-0003-0192-9906; Lazar, Tamas/0000-0001-7496-6711; Peña-Díaz, Samuel/0000-0002-2902-823X; Santos, Jaime/0000-0001-9045-7765; Farahi, Nazanin/0000-0002-6834-8578; Fichó, Erzsébet/0000-0002-3965-8438; Aspromonte, Maria Cristina/0000-0002-4937-6952; Bassot, Claudio/0000-0001-7161-9028; Chasapi, Anastasia/0000-0003-1986-5007; Davey, Norman E/0000-0001-6988-4850; Davidović, Radoslav/0000-0002-6097-6203; Elofsson, Arne/0000-0002-7115-9751; Erdős, Gábor/0000-0001-6218-5192; Gaudet, Pascale/0000-0003-1813-6857; Giglio, Michelle/0000-0001-7628-5565; Glavina, Juliana/0000-0001-6336-1290; Iserte, Javier/0000-0003-0056-1177; Iglesias, Valentín/0000-0002-6133-0869; Kálmán, Zsófia Etelka/0000-0003-4634-0433; Lambrughi, Matteo/0000-0002-0894-8627; Leonardi, Emanuela/0000-0001-8486-8461; Longhi, Sonia/0000-0002-6829-6771; Macedo-Ribeiro, Sandra/0000-0002-7698-1170; Maiani, Emiliano/0000-0003-1432-5394; Marchetti, Julia/0000-0002-2886-3647; Marino-Buslje, Cristina/0000-0002-6564-1920; Mészáros, Attila/0000-0002-4578-4879; Monzon, Alexander Miguel/0000-0003-0362-8218; Minervini, Giovanni/0000-0001-7013-5785; Nadendla, Suvarna/0000-0003-3643-281X; Nilsson, Juliet F/0000-0003-4203-5263; Novotný, Marian/0000-0001-8788-3202; Ouzounis, Christos A/0000-0002-0086-8657; Palopoli, Nicolás/0000-0001-7925-6436; Papaleo, Elena/0000-0002-7376-5894; Pereira, Pedro José Barbosa/0000-0003-0969-5438; Pozzati, Gabriele/0000-0002-4303-9939; Promponas, Vasilis J/0000-0003-3352-4831; Pujols, Jordi/0000-0001-9424-5866; Rocha, Alma Carolina Sanchez/0000-0001-7395-9173; Salas, Martin/0000-0001-6352-1282; Sawicki, Luciana Rodriguez/0000-0001-5782-6573; Shenoy, Aditi/0000-0001-7748-2501; Szaniszló, Tamás/0000-0002-3130-9284; Tsirigos, Konstantinos D/0000-0001-5280-1107; Veljkovic, Nevena/0000-0001-6562-5800; Parisi, Gustavo/0000-0001-7444-1624; Ventura, Salvador/0000-0002-9652-6351; Dosztányi, Zsuzsanna/0000-0002-3624-5937; Tosatto, Silvio C E/0000-0003-4525-7793; Piovesan, Damiano/0000-0001-8210-2390} }