catGRANULE 2.0 : accurate predictions of liquid-liquid phase separating proteins at single amino acid resolution
Monti, Michele (Fondazione Istituto Italiano di Tecnologia. Center for Human Technologies)
Fiorentino, Jonathan (Fondazione Istituto Italiano di Tecnologia. Center for Human Technologies)
Miltiadis-Vrachnos, Dimitrios (University of Rome Sapienza. Department of Biology and Biotechnologies)
Bini, Giorgio (University of Genoa. Physics Department)
Cotrufo, Tiziana (Universitat de Barcelona. Departament de Biologia Cel·lular, Fisiologia i Immunologia)
Sánchez de Groot, Natalia (Universitat Autònoma de Barcelona. Departament de Bioquímica i de Biologia Molecular)
Armaos, Alexandros (Fondazione Istituto Italiano di Tecnologia. Center for Human Technologies)
Tartaglia, Gian Gaetano (Fondazione Istituto Italiano di Tecnologia. Center for Human Technologies)

Data:	2025
Resum:	Liquid-liquid phase separation (LLPS) enables the formation of membraneless organelles, essential for cellular organization and implicated in diseases. We introduce catGRANULE 2. 0 ROBOT, an algorithm integrating physicochemical properties and AlphaFold-derived structural features to predict LLPS at single-amino-acid resolution. The method achieves high performance and reliably evaluates mutation effects on LLPS propensity, providing detailed predictions of how specific mutations enhance or inhibit phase separation. Supported by experimental validations, including microscopy data, it predicts LLPS across diverse organisms and cellular compartments, offering valuable insights into LLPS mechanisms and mutational impacts.
Ajuts:	European Commission 855923 European Commission 727658 European Commission 825080 European Commission 101098989
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Liquid-liquid phase separation ; Machine learning ; Subcellular compartmentalization ; Protein features ; Mutations
Publicat a:	Genome biology, Vol. 26 (February 2025) , art. 33, ISSN 1474-760X

DOI: 10.1186/s13059-025-03497-7
PMID: 39979996

25 p, 2.5 MB

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