TY - JOUR AU - Ashton, Anthony W. AU - Dhanjal, Harpreet K. AU - Rossner, Benjamin AU - Mahmood, Huma AU - Patel, Vivek I. AU - Nadim, Mohammad AU - Lota, Manpreet AU - Shahid, Farhan AU - Li, Zhiping AU - Joyce, David AU - Pajkos, Mátyás AU - Dosztányi, Zsuzsanna AU - Jiao, Xuanmao AU - Pestell, Richard G. TI - Acetylation of nuclear receptors in health and disease: an update JF - FEBS JOURNAL J2 - FEBS J VL - 291 PY - 2024 IS - 2 SP - 217 EP - 236 PG - 20 SN - 1742-464X DO - 10.1111/febs.16695 UR - https://m2.mtmt.hu/api/publication/33536387 ID - 33536387 N1 - Xavier University School of Medicine at Aruba, Oranjestad, Aruba Lankenau Institute for Medical Research, Wynnewood, PA, United States Pennsylvania Cancer and Regenerative Medicine Research Center, Baruch S. Blumberg Institute, Wynnewood, PA, United States Medical School, Faculty of Health and Medical Sciences, The University of Western Australia, Crawley, WA, Australia Department of Biochemistry, ELTE Eötvös Loránd University, Budapest, Hungary The Wistar Cancer Center, Philadelphia, PA, United States Export Date: 5 January 2023 CODEN: FJEOA Correspondence Address: Jiao, X.; Xavier University School of Medicine at ArubaAruba; email: xuanmao.jiao@bblumberg.org Correspondence Address: Pestell, R.G.; Pennsylvania Cancer and Regenerative Medicine Research Center, United States; email: richard.pestell@bblumberg.org LA - English DB - MTMT ER - TY - JOUR AU - Pajkos, Mátyás AU - Erdős, Gábor AU - Dosztányi, Zsuzsanna TI - The Origin of Discrepancies between Predictions and Annotations in Intrinsically Disordered Proteins JF - BIOMOLECULES J2 - BIOMOLECULES VL - 13 PY - 2023 IS - 10 SN - 2218-273X DO - 10.3390/biom13101442 UR - https://m2.mtmt.hu/api/publication/34239893 ID - 34239893 N1 - Export Date: 17 November 2023 Correspondence Address: Dosztányi, Z.; Department of Biochemistry, Pázmány Péter Stny 1/c, Hungary; email: zsuzsanna.dosztanyi@ttk.elte.hu AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Kurgan, Lukasz AU - Hu, Gang AU - Wang, Kui AU - Ghadermarzi, Sina AU - Zhao, Bi AU - Malhis, Nawar AU - Erdős, Gábor AU - Gsponer, Jörg AU - Uversky, Vladimir N. AU - Dosztányi, Zsuzsanna TI - Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins JF - NATURE PROTOCOLS J2 - NAT PROTOC VL - 18 PY - 2023 IS - 11 SP - 3157 EP - 3172 PG - 16 SN - 1754-2189 DO - 10.1038/s41596-023-00876-x UR - https://m2.mtmt.hu/api/publication/34162712 ID - 34162712 N1 - Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, China Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada MTA-ELTE Momentum Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest, Hungary Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, United States Byrd Alzheimer’s Center and Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, United States Export Date: 28 September 2023 Correspondence Address: Kurgan, L.; Department of Computer Science, United States; email: lkurgan@vcu.edu Correspondence Address: Gsponer, J.; Michael Smith Laboratories, Canada; email: gsponer@msl.ubc.ca Correspondence Address: Dosztányi, Z.; MTA-ELTE Momentum Bioinformatics Research Group, Hungary; email: zsuzsanna.dosztanyi@ttk.elte.hu Correspondence Address: Uversky, V.N.; Department of Molecular Medicine, United States; email: vuversky@usf.edu WoS:hiba:001069972100001 2023-12-31 21:57 DOI azonosító nem egyezik AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Mészáros, Bálint AU - Hatos, András AU - Palopoli, Nicolas AU - Quaglia, Federica AU - Salladini, Edoardo AU - Van Roey, Kim AU - Arthanari, Haribabu AU - Dosztányi, Zsuzsanna AU - Felli, Isabella C. AU - Fischer, Patrick D. AU - Hoch, Jeffrey C. AU - Jeffries, Cy M. AU - Longhi, Sonia AU - Maiani, Emiliano AU - Orchard, Sandra AU - Pancsa, Rita AU - Papaleo, Elena AU - Pierattelli, Roberta AU - Piovesan, Damiano AU - Pritisanac, Iva AU - Tenorio, Luiggi AU - Viennet, Thibault AU - Tompa, Péter AU - Vranken, Wim AU - Tosatto, Silvio C. E. AU - Davey, Norman E. TI - Minimum information guidelines for experiments structurally characterizing intrinsically disordered protein regions JF - NATURE METHODS J2 - NAT METHODS VL - 20 PY - 2023 SP - 1291 EP - 1303 PG - 13 SN - 1548-7091 DO - 10.1038/s41592-023-01915-x UR - https://m2.mtmt.hu/api/publication/34066608 ID - 34066608 N1 - Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Department of Biomedical Sciences, University of Padova, Padova, Italy Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes - CONICET, Bernal, Buenos Aires, Argentina Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR-IBIOM), Bari, Italy Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium Harvard Medical School (HMS), Boston, MA, United States Department of Cancer Biology, Dana-Farber Cancer Institute (DFCI), Boston, MA, United States Department of Biochemistry, Eötvös Loránd University, Budapest, Hungary Department of Chemistry ‘Ugo Schiff’ and Magnetic Resonance Center, University of Florence, Sesto Fiorentino (Florence), Italy Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT, United States European Molecular Biology Laboratory (EMBL), Hamburg Unit, c/o Deutsches Elektronen-Synchrotron, Hamburg, Germany Laboratory Architecture et Fonction des Macromolécules Biologiques (AFMB), UMR 7257, Aix Marseille University and Centre National de la Recherche Scientifique (CNRS), Marseille, France Cancer Structural Biology, Danish Cancer Society Research Center, Copenhagen, Denmark UniCamillus - Saint Camillus International University of Health and Medical Sciences, Rome, Italy European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, United Kingdom Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Hungary Cancer Systems Biology, Section for Bioinformatics, Department of Health and Technology, Technical University of Denmark, Lyngby, Denmark Hospital for Sick Children, Toronto, ON, Canada Medical University of Graz, Graz, Austria VIB-VUB Center for Structural Biology, Brussels, Belgium Structural Biology Brussels, Department of Bioengineering Sciences, Vrije Universiteit Brussel, Brussels, Belgium Division Of Cancer Biology, Institute of Cancer Research, Chester Beatty Laboratories, Chelsea, London, United Kingdom Department of Structural Biology and Center for Data Driven Discovery, St Jude Children’s Research Hospital, Memphis, TN, United States 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 Export Date: 01 August 2023; Cited By: 0; Correspondence Address: N.E. Davey; Division Of Cancer Biology, Institute of Cancer Research, Chester Beatty Laboratories, Chelsea, London, United Kingdom; email: norman.davey@icr.ac.uk LA - English DB - MTMT ER - TY - JOUR AU - Martínez-Pérez, Elizabeth AU - Pajkos, Mátyás AU - Tosatto, Silvio C E AU - Gibson, Toby J AU - Dosztányi, Zsuzsanna AU - Marino-Buslje, Cristina TI - Pipeline for transferring annotations between proteins beyond globular domains. JF - PROTEIN SCIENCE J2 - PROTEIN SCI VL - 32 PY - 2023 IS - 7 SN - 0961-8368 DO - 10.1002/pro.4655 UR - https://m2.mtmt.hu/api/publication/33832027 ID - 33832027 N1 - Bioinformatics Unit, Fundación Instituto Leloir/IIBBA, Buenos Aires, Argentina Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Department of Biochemistry, Eötvös Loránd University, Budapest, Hungary Department of Biomedical Sciences, University of Padua, Padua, Italy Export Date: 28 September 2023 CODEN: PRCIE Correspondence Address: Marino-Buslje, C.; Bioinformatics Unit, Argentina; email: cmb@leloir.org.ar AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Deutsch, Norbert AU - Pajkos, Mátyás AU - Erdős, Gábor AU - Dosztányi, Zsuzsanna TI - DisCanVis: Visualizing integrated structural and functional annotations to better understand the effect of cancer mutations located within disordered proteins JF - PROTEIN SCIENCE J2 - PROTEIN SCI VL - 32 PY - 2023 IS - 1 SN - 0961-8368 DO - 10.1002/pro.4522 UR - https://m2.mtmt.hu/api/publication/33548424 ID - 33548424 N1 - Export Date: 17 January 2023 CODEN: PRCIE Correspondence Address: Dosztányi, Z.; Department of Biochemistry, Hungary; email: zsuzsanna.dosztanyi@ttk.elte.hu LA - English DB - MTMT ER - TY - CHAP AU - Erdős, Gábor AU - Dosztányi, Zsuzsanna ED - Munishwar, Nath Gupta ED - Vladimir, N. Uversky TI - Prediction of protein structure and intrinsic disorder in the era of deep learning T2 - Structure and Intrinsic Disorder in Enzymology PB - Elsevier CY - London CY - San Diego (CA) SN - 9780323995337 PY - 2023 SP - 199 EP - 224 PG - 26 DO - 10.1016/B978-0-323-99533-7.00007-8 UR - https://m2.mtmt.hu/api/publication/33286659 ID - 33286659 LA - English DB - MTMT ER - TY - JOUR AU - Kastano, Kristina AU - Mier, Pablo AU - Dosztányi, Zsuzsanna AU - Promponas, Vasilis J. AU - Andrade-Navarro, Miguel A. TI - Functional Tuning of Intrinsically Disordered Regions in Human Proteins by Composition Bias JF - BIOMOLECULES J2 - BIOMOLECULES VL - 12 PY - 2022 IS - 10 SN - 2218-273X DO - 10.3390/biom12101486 UR - https://m2.mtmt.hu/api/publication/33202617 ID - 33202617 N1 - Institute of Organismic and Molecular Evolution, Faculty of Biology, Johannes Gutenberg University, Biozentrum I, Hans-Dieter-Hüsch-Weg 15, Mainz, 55128, Germany Department of Biochemistry, ELTE Eötvös Loránd University, Pázmány Péter stny 1/c, Budapest, H-1117, Hungary Bioinformatics Research Laboratory, Department of Biological Sciences, University of Cyprus, Nicosia, 1678, Cyprus Export Date: 7 November 2022 Correspondence Address: Andrade-Navarro, M.A.; Institute of Organismic and Molecular Evolution, Biozentrum I, Hans-Dieter-Hüsch-Weg 15, Germany; email: andrade@uni-mainz.de AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Szaniszló, Tamás AU - Fülöp, Máté AU - Pajkos, Mátyás AU - Erdős, Gábor AU - Kovács, Réka Ágnes AU - Vadászi, Henrietta AU - Kardos, József AU - Dosztányi, Zsuzsanna TI - The interaction between LC8 and LCA5 reveals a novel oligomerization function of LC8 in the ciliary-centrosome system JF - SCIENTIFIC REPORTS J2 - SCI REP VL - 12 PY - 2022 IS - 1 SN - 2045-2322 DO - 10.1038/s41598-022-19454-4 UR - https://m2.mtmt.hu/api/publication/33099749 ID - 33099749 N1 - Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary ELTE NAP Neuroimmunology Research Group, Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary Export Date: 28 November 2022 Correspondence Address: Dosztányi, Z.; Department of Biochemistry, Hungary; email: zsuzsanna.dosztanyi@ttk.elte.hu AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Quaglia, Federica AU - Mészáros, Bálint AU - Salladini, Edoardo AU - Hatos, András AU - Pancsa, Rita AU - Chemes, Lucía B AU - Pajkos, Mátyás AU - Lazar, Tamas AU - Peña-Díaz, Samuel AU - Santos, Jaime AU - Ács, Veronika AU - Farahi, Nazanin AU - Fichó, Erzsébet AU - Aspromonte, Maria Cristina AU - Bassot, Claudio AU - Chasapi, Anastasia AU - Davey, Norman E AU - Davidović, Radoslav AU - Dobson, László AU - Elofsson, Arne AU - Erdős, Gábor AU - Gaudet, Pascale AU - Giglio, Michelle AU - Glavina, Juliana AU - Iserte, Javier AU - Iglesias, Valentín AU - Kálmán, Zsófia Etelka AU - Lambrughi, Matteo AU - Leonardi, Emanuela AU - Longhi, Sonia AU - Macedo-Ribeiro, Sandra AU - Maiani, Emiliano AU - Marchetti, Julia AU - Marino-Buslje, Cristina AU - Mészáros, Attila AU - Monzon, Alexander Miguel AU - Minervini, Giovanni AU - Nadendla, Suvarna AU - Nilsson, Juliet F AU - Novotný, Marian AU - Ouzounis, Christos A AU - Palopoli, Nicolás AU - Papaleo, Elena AU - Pereira, Pedro José Barbosa AU - Pozzati, Gabriele AU - Promponas, Vasilis J AU - Pujols, Jordi AU - Rocha, Alma Carolina Sanchez AU - Salas, Martin AU - Sawicki, Luciana Rodriguez AU - Schád, Éva AU - Shenoy, Aditi AU - Szaniszló, Tamás AU - Tsirigos, Konstantinos D AU - Veljkovic, Nevena AU - Parisi, Gustavo AU - Ventura, Salvador AU - Dosztányi, Zsuzsanna AU - Tompa, Péter AU - Tosatto, Silvio C E AU - Piovesan, Damiano TI - DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation JF - NUCLEIC ACIDS RESEARCH J2 - NUCLEIC ACIDS RES VL - 50 PY - 2022 IS - D1 SP - D480 EP - D487 SN - 0305-1048 DO - 10.1093/nar/gkab1082 UR - https://m2.mtmt.hu/api/publication/32587946 ID - 32587946 N1 - Funding Agency and Grant Number: Italian Ministry of University and Research (MIUR)Ministry of Education, Universities and Research (MIUR) [2017483NH8]; European UnionEuropean Commission [778247, 842490 MIMIC, 101028908, 952334 PhasAGE]; Tempus Public Foundation [158534]; NRDI Office of the Hungarian government [FK128133]; National Agency for the Promotion of Science and Technology (ANPCyT)ANPCyT [PICT-2017-1924, PICT-2018-3457]; Spanish Ministry of Science and InnovationSpanish Government [FPU17/01157]; Swedish Research Council for Natural Science [VR-2016-06301]; Swedish E-science Research Center; National Human Genome Research InstituteUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Human Genome Research Institute (NHGRI) [U41 HG02273]; CNRSCentre National de la Recherche Scientifique (CNRS)European Commission; Infectiopole Sud post-doctoral fellowship; ELIXIR CZ Research Infrastructure [MEYS CR] [LM2018131]; Ministry of Education, Science, and Technological Development of the Republic of SerbiaMinistry of Education, Science & Technological Development, Serbia; Universidad Nacional de Quilmes [PUNQ-2019-1309/19]; Hungarian Scientific Research Fund (OTKA)Orszagos Tudomanyos Kutatasi Alapprogramok (OTKA) [K124670, K131702, K129164, K139284]; VUB Spearhead grant [2019-24] [SRP51]; ElixirGR [MIS 5002780]; Cancer Research UK Senior Cancer Research FellowshipCancer Research UK [C68484/A28159]; University of Cyprus Funding text: Italian Ministry of University and Research (MIUR) [2017483NH8]; European Union's Horizon 2020 [778247 to S.C.E.T.]; European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie [842490 MIMIC) to B.M.]; Tempus Public Foundation [158534]; NRDI Office [FK128133] of the Hungarian government (to R.P.); National Agency for the Promotion of Science and Technology (ANPCyT) [PICT-2017-1924 to L.B.C.]; Spanish Ministry of Science and Innovation via a doctoral grant [FPU17/01157 to J.S.]; European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie [101028908 (SMILE) to L.D.]; Swedish Research Council for Natural Science [VR-2016-06301]; Swedish E-science Research Center (to A.E.); National Human Genome Research Institute grant [U41 HG02273 to P.G.]; CNRS (to S.L.); Infectiop <^>ole Sud post-doctoral fellowship (to J.F.N.); ELIXIR CZ Research Infrastructure [ID LM2018131, MEYS CR]; Ministry of Education, Science, and Technological Development of the Republic of Serbia (to R.D., N.V.); Universidad Nacional de Quilmes [PUNQ-2019-1309/19]; National Agency for the Promotion of Science and Technology (ANPCyT) [PICT-2018-3457 to G.P.]; Hungarian Scientific Research Fund (OTKA) [K124670, K131702]; VUB Spearhead grant [SRP51, 2019-24 to P.T.]; ElixirGR [MIS 5002780 to A.C., C.A.O.]; J.M. is a PhD student; J.G. is a postdoc and GP, C.M.-B., J.I., L.B.C. are researchers of the National Research Council (CONICET) of Argentina; Cancer Research UK Senior Cancer Research Fellowship [C68484/A28159 to N.E.D.]; University of Cyprus [Computational approaches towardsmechanistic insights and improved detection of functional LIR-motifs in selective autophagy receptors and adaptors. (idLIR)] internal grant (to V.J.P.); Hungarian Scientific Research Fund (OTKA) [K129164, K139284 to Z.D.]; European Union's Horizon 2020 research and innovation programme [952334 PhasAGE to S.M.-R.]. Funding for open access charge: European Union's Horizon 2020 research and innovation programme [778247]. LA - English DB - MTMT ER -