Identification of proximal SUMO-dependent interactors using SUMO-ID

[EN]The fast dynamics and reversibility of posttranslational modifications by the ubiquitin family pose significant challenges for research. Here we present SUMO-ID, a technology that merges proximity biotinylation by TurboID and protein-fragment complementation to find SUMO-dependent interactors of proteins of interest. We develop an optimized split-TurboID version and show SUMO interaction-dependent labelling of proteins proximal to PML and RANGAP1. SUMO-dependent interactors of PML are involved in transcription, DNA damage, stress response and SUMO modification and are highly enriched in SUMO Interacting Motifs, but may only represent a subset of the total PML proximal proteome. Likewise, SUMO-ID also allow us to identify interactors of SUMOylated SALL1, a less characterized SUMO substrate. Furthermore, using TP53 as a substrate, we identify SUMO1, SUMO2 and Ubiquitin preferential interactors. Thus, SUMO-ID is a powerful tool that allows to study the consequences of SUMO-dependent interactions, and may further unravel the complexity of the ubiquitin code.We are thankful to Iraide Escobes for her work in the Proteomics Platform at CIC bioGUNE and Arnoud de Ru in the Center for Proteomics and Metabolomics at the LUMC. O.B.-G., F.T., R.B. and A.C.O.V. acknowledge funding by the grant 765445-EU (UbiCODE Program). R.B. acknowledges funding by grants BFU2017-84653-P and PID2020-114178GB-I00 (MINECO/FEDER, EU), SEV-2016-0644 (Severo Ochoa Excellence Program), SAF2017-90900-REDT (UBIRed Program) and IT1165-19 (Basque Country Government). Additional support was provided by the Department of Industry, Tourism, and Trade of the Basque Country Government (Elkartek Research Programs) and by the Innovation Technology Department of the Bizkaia County. VM acknowledges FPI grant PRE2018-086230 (MINECO/FEDER, EU). F.E. is at Proteomics Platform, member of ProteoRed-ISCIII (PT13/0001/0027). F.E. and A.M.A. acknowledge CIBERehd. A.C. acknowledges the Basque Department of education (IKERTALDE IT1106-16), the MCIU (PID2019-108787RB-I00 (FEDER/EU), Severo Ochoa Excellence Accreditation SEV-2016-0644, Excellence Networks RED2018-102769-T), the AECC (GCTRA18006CARR), La Caixa Foundation (ID 100010434), under the agreement LCF/PR/HR17, and the European Research Council (Starting Grant 336343, PoC 754627, Consolidator grant 819242). CIBERONC was co-funded with FEDER funds. U.M. acknowledges the Basque Government Department of Education (IT1165-19) and the Spanish MCIU (SAF2016-76898-P (FEDER/EU))