An AlphaFold2 map of the 53BP1 pathway identifies a direct SHLD3–RIF1 interaction critical for shieldin activity

Abstract

53BP1 is a chromatin‐binding protein that promotes DNA double‐strand break repair through the recruitment of downstream effectors including RIF1, shieldin, and CST. The structural basis of the protein–protein interactions within the 53BP1‐RIF1‐shieldin‐CST pathway that are essential for its DNA repair activity is largely unknown. Here, we used AlphaFold2‐Multimer (AF2) to predict all possible pairwise combinations of proteins within this pathway and provide structural models of seven previously characterized interactions. This analysis also predicted an entirely novel binding interface between the HEAT‐repeat domain of RIF1 and the eIF4E‐like domain of SHLD3. Extensive interrogation of this interface through both in vitro pulldown analysis and cellular assays supports the AF2‐predicted model and demonstrates that RIF1‐SHLD3 binding is essential for shieldin recruitment to sites of DNA damage, and for its role in antibody class switch recombination and PARP inhibitor sensitivity. Direct physical interaction between RIF1 and SHLD3 is therefore essential for 53BP1‐RIF1‐shieldin‐CST pathway activity.

Interrogation and validation of the interactome of neuronal Munc18-interacting Mint proteins with AlphaFold2

“…These predictions provide insights into those interactions that are likely to be directly mediated by the Mint proteins, as validated in one instance with the direct binding of TJAP1 confirmed. Our results thus provide an example of how AlphaFold2 and similar algorithms can be used as a type of triage of large proteomic datasets, providing additional confidence in the plausibility of direct interactions (Burke et al, 2023;Gao et al, 2022a;Gao et al, 2022b;Humphreys et al, 2021;Sifri et al, 2023;Yu et al, 2023). Such an approach has the potential to inform and accelerate subsequent experimental validation of molecular complexes detected in high-throughput screens, by providing greater assurance as to which hits represent specific interactors.…”

“…Putative Mint1 and Mint2 interactors from the BioGRID repository (Oughtred et al, 2021) were screened for direct complex formation with Mint1 and Mint2 respectively using the ColabFold Batch implementation of AlphaFold2 (Mirdita et al, 2022) (Table S1; Table S2). To assign a direct 'interactor' from these in silico analyses, we used an approach similar to recent work by Sifri and colleagues (Sifri et al, 2023). We initially assessed both the AlphaFold2-derived interfacial PTM (iPTM) score and the resultant PAE graphs which provide confidence metrics for the interactions between the proteins.…”

“…For interactome-wide analysis of other protein-protein interactors of Mint1 and Mint2 we obtained a list of putative interactors from the BioGRID repository (Oughtred et al, 2021), and predicted whether they formed direct complexes using the ColabFold Batch implementation of AlphaFold2 (Mirdita et al, 2022) (Table S1; Table S2). To assign a direct 'interactor' from these in silico analyses we used an approach similar to recent work by Sifri and colleagues (Sifri et al, 2023).…”

NFATC2IP is a mediator of SUMO-dependent genome integrity

“…To do so, we first surveyed the potential for protein–protein interactions between NFATC2IP and SMC5/6 complex members using AlphaFold-Multimer ( Evans et al 2021 ; Jumper et al 2021 ; Mirdita et al 2022 ) by testing pairwise combinations among NFATC2IP and SMC5/6 complex members, known SMC5/6 regulators that included SLF1, SLF2, and RAD18, as previously described ( Supplemental Table S5 ; Sifri et al 2023 ). This analysis recapitulated known protein–protein interactions in this complex, including the interaction between the BRCT domains of SLF1 and phosphorylatable residues in RAD18 ( Räschle et al 2015 ) or between the NSMCE3 subunit and NSMCE1 and NSMCE4 ( Fig.…”

Predictomes: A classifier-curated database of AlphaFold-modeled protein-protein interactions

“…AF-M was trained using five distinct regimens, yielding five models, each of which makes a structure prediction. Many groups are now using AF-M to uncover novel PPIs on the scale of pathways and organisms [12][13][14][15][16][17][18][19] . While many studies have examined AF-M's ability to correctly model individual protein complexes and predict structures for complexes in curated PPI databases [20][21][22] , there has been considerably less focus on finding systematic ways to separate true from false interactions in large-scale unbiased screens.…”