Multi-scale classification decodes the complexity of the human E3 ligome

Domain: physics
Published: 2025-12-25
Editorial outcome: major_revision

Abstract
E3 ubiquitin ligases are vital enzymes that define the ubiquitin code in cells. Beyond promoting protein degradation to maintain cellular health, they also mediate non-degradative processes like DNA repair, signaling, and immunity. Despite their therapeutic potential, a comprehensive framework for understanding the relationships among diverse E3 ligases is lacking. Here, we classify the “human E3 ligome”—an extensive set of catalytic human E3s—by integrating multi-layered data, including protein sequences, domain architectures, 3D structures, functions, and expression patterns. Our classification is based on a metric-learning paradigm and uses a weakly supervised hierarchical framework to capture authentic relationships across E3 families and subfamilies. It extends the categorization of E3s into RING, HECT, and RBR classes, including non-canonical mechanisms, successfully explains their functional segregation, distinguishes between multi-subunit complexes and standalone enzymes, and maps E3s to substrates and potential drug interactions. Our analysis provides a global view of E3 biology, opening strategies for drugging E3-substrate networks, including drug repurposing and designing specific E3 handles.

Introduction
Cells constantly modulate their proteomes in response to physiological and environmental changes. The timely removal and turnover of cellular proteins is integral to protein homeostasis1. In eukaryotes, individual proteins, complexes, and large assemblies are degraded via either autophagy or the ubiquitin-proteasome system (UPS)2. In mammalian cells, ~80% of the cellular proteome is degraded through the UPS1. In this pathway, the designated protein cargo is tagged with ubiquitin (Ub) molecules through a series of enzymatic reactions, marking them for degradation by the proteasome3. Following the action of E1 and E2 enzymes, the E3 ligase brings both the E2–ubiquitin complex and the substrate protein in proximity, allowing the transfer of Ub from the E2 enzyme to a lysine residue on the target protein4,5. This process is often repeated (poly-ubiquitination), resulting in substrates with distinct types of Ub-chains. In UPS, for instance, K48-linked Ub-chains are recognized by Ub-binding domains (UBDs) on 19S proteasomal particles, initiating the degradation of substrates1. In autophagy, ubiquitination often serves as a necessary condition for identifying substrates, conferring specificity6. Cargo components, damaged organelles, and intracellular pathogens targeted for degradation are often ubiquitinated. Further, autophagy receptors are enriched in UBDs to recognize modified cargo components7 or themselves strongly ubiquitinated to trigger aggregation of protein assemblies in the cytosol and organellar membranes8,9, thus enhancing autophagic flux.

E3 ubiquitin ligases confer substrate specificity for ubiquitination. They recognize distinct targets, operate in diverse cellular locations, and exert spatial control of protein turnover10,11. In addition to controlling homeostatic processes, E3 ligases regulate immunity and inflammation pathways12,13. Given their tissue-specific expressions and association with developmental and metabolic syndromes, including cancer progression, E3 ligases have emerged as promising candidates, particularly for drugging previously undruggable targets14. In stark contrast to E1 (~10) and E2 enzymes (~50), a substantial number of E3 ligases (~600) have been recognized in humans15,16. This count of putative E3s stems from various investigations: Li et al.17 identified ~617 potential human E3-encoding genes by conducting a genome-wide search to detect RING (Really Interesting New Gene) finger catalytic domains using hidden Markov models. Subsequently, Deshaies and Joazeiro18 characterized ~300 RING and U-box E3 ligases, while Medvar et al.19 documented ~377 E3 ligases, with a primary focus on confirmed catalytic activity. Despite these efforts, many human E3 ligases have been only partially characterized. A significant fraction remains unexplored and hypothetical or unknown20. To date, those studied exhibit extensive heterogeneity in their sequence, domain composition, 3D structure, subcellular localization, and tissue expression, establishing them as one of the most diverse classes of enzymes. Furthermore, several E3 ligases function as multi-subunit complexes with varied substrate specificities modulated by specific receptors, adaptors, and scaffold proteins21. The extensive variety and large numbers of E3 ubiquitin ligases create a bottleneck for pattern recognition and large-scale study. Therefore, detailed characterization and analysis of the human E3 ligome—the complete set of E3 ubiquitin ligases encoded by the human genome—is essential for a comprehensive understanding.

The current classification of the E3 ligases—based on the ubiquitin-transfer mechanism—categorizes them into three main classes: RING (Really Interesting New Gene), HECT (Homologous to the E6AP Carboxyl Terminus), and RBR (RING-Between-RING) classes15. This classification drastically oversimplifies the mechanistic diversity of E3 ligases, compels the grouping of enzymes with hybrid characteristics, and fails to accommodate emerging information on recent and atypical ligases, limiting its overall utility18. A multi-scale classification of the human E3 ligome offers a unique solution to tackle the complexity and remarkable diversity inherent in these enzymes at various scales. This organized approach can provide more accurate and functional groupings, crucial for a nuanced understanding of different E3 ligase families. Further, emerging patterns detected help trace evolutionary relationships more effectively, revealing conserved elements and adaptive changes that are not evident. Furthermore, mapping essential information such as functional diversity, substrate-specificities, and druggability onto the classification provides a global view, guiding specific and directed investigations to fill in the missing information.

Here, we systematically catalog all E3 ubiquitin ligases to build a comprehensive and manually curated human E3 ligome. We then encode the relationships between high-confidence E3 ligases using multiple distance measures at various granular layers spanning the molecular- and the systems-level organization. By amalgamating selected distance measures from multiple layers into an optimized emergent distance metric, we group all human E3 ligases into distinct families and subfamilies. Our classification delineates features and patterns specific to E3 ligase families, providing insights into their organization. By combining CRISPR-Cas9 dropout screens and proteomic analysis with functional enrichment analysis, we identify essential E3s differentially regulated under stress conditions. We demonstrate the utility of this unbiased classification by mapping the existing state of knowledge on E3 ligase domain architecture, 3D structure, function, substrate networks, and small molecule interactions to gain generic and family-specific insights. The multiscale classification framework developed here embodies canonical and atypical E3 mechanisms largely reflecting the ubiquitin code, offering a comprehensive roadmap to navigate the vast landscape of E3 ligase biology, laying the groundwork for future therapeutic applications.

Results
Assembly of the human E3 ligome
To comprehensively identify all E3 ligases in the human genome, we conducted a census using datasets from previously published studies and public repositories. By visualizing their overlaps, we found that all existing datasets were largely inconsistent (Fig. 1a and Supplementary Fig. 1a). Most strikingly, only 99 proteins were consistently categorized as human E3 ligases from all eight datasets. The low overlap in these datasets reflects the diverse approaches and often variable definitions used to collate E3 systems (Supplementary Table 1). We resolved these conflicts by explicitly defining the catalytic components of E3 systems, i.e., polypeptide sequences containing one or more catalytic domains (C ={d c}, see “Methods”). Using this objective criterion (fX i 2 8 n=1 j9d i 2Cg; Supplementary Table 2) facilitated proper annotation and targeted analysis of E3s. We found that 462 polypeptide sequences, across all datasets ( 8 n=1 A n=1448), contain at least one catalytic domain constituting the curated E3 ligome (Fig. 1b and Supplementary Fig. 1b).

To substantiate our curation process, we defined a consensus score for each protein based on its presence in various source datasets (Fig. 1c). We found that the HECT and RBR classes of E3 ligases showed high agreement across datasets (confidence score ≥ 0.6; orange and purple bars). The RING class (green bars) had a broad distribution of consensus scores indicative of annotation challenges. However, the most significant discrepancy among the datasets (confidence score ≤ 0.25) was due to misannotated proteins. E1, E2, and other non-catalytic components of E3 systems, such as receptors, scaffolds, and adaptor proteins, were often merged with E3 ligases, accounting for an additional 298 proteins, leaving 514 unclassified and 174 with no domain annotations (Fig. 1b). Furthermore, several proteins obtained from UniProt and BioGRID using keyword-based searches (Supplementary Fig. 1c) were not included by others, have low consensus scores, and remain unclassified and unannotated, excluding 688 proteins from the curated E3 ligome (Fig. 1c, black bars). Our approach thus minimized false positives and true negatives, includes high-confidence catalytically active E3s and accounts for pseudo-E3 ligases and other E3s with untested catalytic activities, providing a detailed assessment of the completeness of the human E3 ligome (see Supplementary Note 1).

To quantify the diversity of the human E3 ligome, we mapped the sequence, structure, and functional features of individual E3s corresponding to well-known E3 classes (RING, HECT, and RBR). We found that the length distribution of the E3s is broad, ranging from 100 to 5000 residues (mean size = 635 residues; Fig. 1d). The average fractional coverage of E3s annotated with unique domains is 37%, 42%, and 53% for RING, HECT, and RBR classes, respectively (Fig. 1e). Furthermore, on average, the RING, HECT, and RBR domains span 23%, 31%, and 39% of their total lengths, respectively (Fig. 1f). By mapping information from the Protein Data Bank (PDB), we found 2259 distinct structures representing RING, HECT, and RBR-containing proteins (2001+168+90), providing partial structural information for 51% (208+21+8 unique UniProt records) of the E3 ligome (Fig. 1g). Analysis of AlphaFold models revealed that for most E3s, the coverage of structured domains is high, and the amount of intrinsic disorder is generally low (pLDDT ≤ 50 covering only ≤ 10% E3 length; Supplementary Fig. 1d). We quantified the functional diversity of the E3 ligome by retrieving the unique Gene Ontology (GO) annotations corresponding to Biological Processes (BP), Cellular Component (CC), and Molecular Function (MF). We annotated 96−100% of the E3s with unique GO terms (Fig. 1h). The number of distinct GO terms captured the diversity of functional assignments attributed to the three E3 classes.

Metric learning for classification of the human E3 ligome
To study the organization and relationships of proteins within the human E3 ligome, we attempted to classify these enzymes using multiple sequence alignment (MSA) followed by phylogenetic tree construction. However, we obtained a low-quality MSA with numerous gaps (Supplementary Fig. 2a), primarily due to (i) high sequence divergence, (ii) numerous proteins with uneven length distributions, (iii) inadequate alignment of conserved, catalytic domains, and (iv) an extensive repertoire of domain architectures (Supplementary Fig. 2b).

To capture the complex relationships within the human E3 ligome, we used a machine-learning approach to learn an emergent distance measure. Using a linear sum model, we combined multiple distance measures with optimal weights to reproduce class-level organization (partial ground truth) in hierarchical clustering (Fig. 2a). We first computed twelve pairwise distance matrices for all E3 ligase pairs (d i PQ) which represent the distance between proteins P and Q across all granular layers: primary sequence; domain composition; 3D structure; and functional level organization, with weights (w i ∈ {0.05, ⋯ , 0.95} in 0.1 intervals). These distances capture intrinsic molecular attributes and their relationships spanning the molecular scale. By uniformly sampling the weights, we constructed 10^5 combination measures as a function of the hyperparameter (fractional tree cutoff, h, between 0.05 and 0.95). By simultaneously maximizing element-centric similarity22 of the emergent hierarchical clusters resulting from combined measures, with partial ground truth (weakly-supervised scheme, Fig. 2d), we optimized an emergent distance measure (D PQ) with appropriate weights (wb i). We found that the linear combination of distances provided clusters with high element-centric similarity S EC compared to clusters obtained from individual distances (Fig. 2e, black curve vs. colored).

Normalized Mutual Information (NMI) and Fowlkes–Mallows Index (FMI) compare clustering assignments (various distance-based vs. ground truth), but they are sensitive to cluster count (determined by tree cutoff, h; Supplementary Fig. 3a). Therefore, optimized weights wb i were obtained by averaging one hundred realizations of hierarchical clustering with maximum S EC22. The weights corresponding to maximum S EC initially varied and then plateaued (at h ≥ 0.75; Fig. 2f), resulting in the construction of an optimized emergent distance measure, D PQ (Eq. (1)). We found that the relative influence of 3D structure, domain composition, and sequence alignment (wb i ≥ 0.5) was more significant on the final learned metric and its ability to reproduce class labels accurately. Compared to the emergent distance measure, we found variable tree topologies with poor overlap and highly entangled trees for all four individual distances (Supplementary Figs. 3b–e).

D PQ = 0.43d MF PQ + 0.55d γ PQ + 0.60d Jac PQ + 0.70d Str PQ (1)

Organization of the human E3 ligome
Using the optimized emergent distance metric, D PQ (Eq. (1)), we constructed a scaled hierarchical tree classifying the human E3 ligome (Fig. 3 and Supplementary Fig. 4a). To assess the validity of nodes, branch stability, and the robustness of our classification, we sampled the emergent distance matrix (n = 500) and assigned bootstrap support at each branch point (Fig. 3, grey circles). The bootstrap support for all nodes beyond tree cutoff, h > 0.15, is 95−100%, indicating a stable branch pattern (Supplementary Fig. 4b) with a fixed tree topology. At h ≤ 0.15, the bootstrap support for the nodes dropped drastically. This allowed us to use a tree cutoff threshold, h = 0.25, to parse the dendrogram and obtain robust and stable clusters with clear family and subfamily patterns while preserving RING-, HECT-, and RBR-class segregation.

We identified thirteen distinct clusters or E3 families (h = 0.25). At the class level, the E3 ligome is well segregated into ten RING families (Fig. 3, blue to green colors; clockwise arrangement from RING1 to RING10), two HECT (Fig. 3, top-branch; orange), and one RBR family (Fig. 3, bottom-branch; purple). Each E3 family is subdivided into one or more subfamilies (Fig. 3, boxes) with distinct patterns. Mapping domain architecture information onto the individual leaves aids recognition of well-preserved sequence and domain features, consistent with family and subfamily grouping, a pattern more evident in the unscaled circular dendrogram of the E3 ligome (Supplementary Fig. 4a). Further, few heterogeneous families are grouped more closely and emerge from single branches (bootstrap support ≈ 90−95%; Supplementary Fig. 4b) hinting at divergence of plausible superfamilies: (i) RBR and RING1–3 branch (small E3s), (ii) RING7–9 branch (medium E3s), and (iii) HECT2–RING10 branch (large E3s). This organization stems from the central node that bifurcates the E3 ligome into two groups characterized by average protein size (Fig. 3). The bottom branch displays six families with smaller E3s, while the top branch groups seven larger E3 families.

The E3 family organization reflects mechanistic differences (Supplementary Fig. 4c and Supplementary Table 3). The RING E3s mediate the direct transfer of Ub to the substrate, while the RBR and HECT E3s enable ubiquitin transfer via a two-step mechanism. The RBR-containing E3s form a homogeneous cluster, highlighting their conserved sequence and the TRIAD supra domain. Similarly, HECT-domain-containing E3s are organized into two clusters/families, HECT1 and HECT2. The HECT1 family is homogeneous and includes three subfamilies: NEDD4-like, HERC, and other HECT E3s. The HECT2 family contains a pure HECT E3 subfamily and an outlier subfamily containing large multi-domain RING-type E3s (>2000 residues) often with atypical mechanisms (e.g., MYCBP2, RNF213, see Supplementary Note 2).

The most abundant RING-domain-containing E3s are organized into 10 families, each characterized by further grouping related proteins into distinct subfamilies with shared sequence elements, domain architectures, and structural features. For instance, the RING2 family comprises membrane-associated RING-CH-type domain (MARCH) E3 ligases (Fig. 3, bottom-right). This family includes all small MARCH E3 ligases characterized by their transmembrane domains and sequence lengths below 500 amino acids. TRIM E3 ligases are exclusively limited to two distinct families, RING5 and RING8, and feature the SPRY domain (Fig. 3, bottom-left). E3 ligases containing BTB/POZ and Zn-finger domain repeats are grouped into the RING6 family (Fig. 3, upper-left).

Although our emergent metric largely maximizes pure and homogeneous clusters (e.g., RBR, RING2, RING5, RING6, RING8, and HECT1), heterogeneity often arises at the subfamily level, resulting in sub-groupings of E3s with varied and unique domain architectures. Isolated proteins (singletons) in the RING1, RING7, RING8, and RING9 families form distinct subfamily groupings, complicating pattern detection. Only RING1, RING7, and HECT2 families display occasional class-level outliers (Supplementary Table 3). Supplementary Notes 3 to 15 describe each family structure in detail with information on subfamily branching, characteristic features, and distinct patterns, along with outliers providing a nuanced description (Supplementary Figs. 5–17 and Supplementary Notes 3–15).

Functional segregation of the human E3 ligome
To evaluate the human E3 ligome, we conducted a CRISPR-Cas9 dropout screen of UPS genes, using cellular fitness as the main phenotype. We identified 53 catalytic and 32 non-catalytic E3 components to be essential for cell fitness (FDR ≤ 0.05 and |log 2 (FC)| ≥ 1.0; Fig. 4a, b). Notably, these essential E3s were predominantly enriched in RING1/4/7, and RING9 families, suggesting critical biological roles (Fig. 4a). Several E2 enzymes and adaptors were also essential, reinforcing the importance of the ubiquitin conjugation and multi-subunit E3s (Fig. 4b). Overall, our CRISPR screen replicates correlated well with DepMap data (Pearson r ≥ 0.5; Supplementary Fig. 18a). GO analysis of 53 essential E3s showed significant enrichment for nuclear components and DNA damage, replication, and repair processes (Fig. 4c), indicating their roles in genome integrity and nuclear regulation fundamental to cell survival. These findings point to essential E3 components crucial for cell viability.

To understand the functional diversity of the human E3 ligome, we filtered high-confidence GO terms and mapped them onto our classification, enabling us to draw functional clusters and visualize their networks across all three ontologies. This allowed recognition of generic and family-specific functions (enriched, -log 10 (p) ≥ 2). At the BP level, as expected, the network analysis revealed prominent core functional sub-clusters associated with all terms containing “ubiquitination (Ub)” (Fig. 4e, Top). These BPs are shared across all families, indicating their generality. Additional core clusters relate to innate immunity, host-driven viral restriction, NF-κB regulation, and IL-17 signaling (Fig. 4d). Further, cooperative diverse non-degradative functions such as regulation of gene expression, protein stability, cell growth, and ERAD pathway are enriched within the E3 ligome (see Supplementary Note 16).

Examining family-specific GO enrichment uncovered functional specialization supported by experimental evidence (Fig. 4e, colored triangles; Supplementary Table 4). For instance, the RBR family members, RNF14, RNF144A, and PRKN, demonstrated specificity for K6-linked-Ub (Fig. 4f, left). K6-linked chains flag stalled RNA-protein cross-linked complexes (RNF14), DNA-sensing adaptor STING for activation of interferon signaling (RNF144), and damaged mitochondria for clearance (PRKN)23–25. Similarly, TRIM E3s (RING5) were significantly enriched in antiviral innate immune response (Fig. 4f, right). They regulate pattern recognition receptor activity in cells, such as RIG-1 and MDA5-mediated responses26,27.

To test family-specific functions, we performed whole-cell proteomics in response to cellular perturbations (EBSS and CPT treatment) and monitored the differential expression of E3 ligases. The cellular responses to EBSS and CPT treatments are broad and multi-faceted, impinging on a wide range of BP functional clusters (Fig. 4e, blue and orange triangles). 18/34 implicated E3s have direct evidence linking them to starvation response pathways (Supplementary Table 4). Notably, MGRN9 and BRCA1 were also essential in our CRISPR screens (Fig. 4g, left). Our analysis revealed a differential expression (p value ≤ 0.05) of the E3 ligases TRIM27/32, and UBR1 in addition to key autophagy regulators SQSTM1, CALCOCO2, GABARAPL2, and MAP1LC3B2, highlighting a coordinated modulation of the autophagic machinery during starvation (Fig. 4g, right colored vs. blue). In contrast, the observed up-regulation of EIF4EBP1, a translational repressor regulated by mTOR signaling28, and GDF15, a stress-responsive cytokine involved in metabolic adaptation29, indicates activation of complementary stress response pathways that may support cellular survival. TRIM27/32 regulate autophagy initiation and selective degradation pathways by ubiquitinating essential autophagy proteins such as ULK1 and p62, thereby promoting autophagosome formation and cargo recognition30–32.

Similarly, experimental evidence links 29/87 implicated E3s directly to DNA damage response (DDR) (Supplementary Table 4), of which 12 E3s turned out to be essential (e.g., TRAF3/7, MDM2; Fig. 4h, left). We found that the E3 ligases TRIM27, UHRF1, TNFAIP3, and RNF168 were significantly down-regulated in response to CPT, while UBR5 was up-regulated in addition to the control proteins (e.g., TOP1, XRCC6, DDB1; Fig. 4h, right, colored vs. blue). TOP1, targeted by CPT, forms covalent DNA-protein cross-links that cause replication-associated double-strand breaks and trigger DDR33. In response, ubiquitin signaling, via RNF8/168-mediated K63-linked histone-Ub, recruits DDR mediators like 53BP1, BRCA1, and RAD1834,35. UBR5 limits Ub-signaling by degrading RNF168, ensuring DNA repair fidelity. Further, UHRF1 promotes DDR by driving chromatin remodeling and BRCA1 recruitment36. Additionally, UHRF1, RNF168, and RNF8 function in DNA replication37. Together, these mechanisms underscore Ub-signaling to maintain cellular homeostasis and genome integrity.

At the MF level, all E3s have “ubiquitin-protein ligase” activity (Generic; Supplementary Fig. 18b). More than 20 MFs could be attributed to family-specific domain architectures (Supplementary Fig. 18d). The Zn-finger domains are also common to transcription factors. For e.g., they equip E3s for p53 binding (RING3), histone-Ub (RING9), and unmethylated CpG binding (RING7). Other non-catalytic E3 domains mediate PPI interactions with actin, SH3-domains, Kinases, and proteases. Distinct subcellular localization of E3s exerts spatial control of protein-Ub (Supplementary Fig. 18c). Most E3 ligases are cytosolic, which form an essential part of the “ubiquitin ligase complexes” (CC: Generic). We find enriched E3 families for distinct CCs: CD40 receptor, GID, and nBAF complexes (RING1); early endosomes and lytic vacuoles (RING2). In total, we identified 17 unique cellular components with distinct E3-specific enrichment patterns (Supplementary Figs. 18e, 19a).

GO annotations from author/curator statements and electronic methods—despite varying confidence levels—provide a rich, abundant dataset of key testable hypotheses on E3 systems (Supplementary Figs. 18f, 19a, and Supplementary Note 17). Publication counts for each protein-specific annotation highlight knowledge gaps and pinpoint well-studied and underexplored E3 systems (Supplementary Fig. 19b).

Interaction landscape of the human E3 ligome
E3 ligases can operate as standalone or multi-subunit enzymes. Complex E3s consist of scaffolds, adaptors, and substrate receptors that determine specificity, stability, and regulation21. For instance, RBX1 binds scaffolds (CUL1–CUL5) and anchors the E2 enzyme to form the catalytic core for Ub transfer (Fig. 5a). Its interactions with various cullins, adaptors, and receptors enable ~250 CRL configurations, providing modular regulation and substrate specificity. By contrast, standalone E3 ligases, like MDM2, c-CBL, PARKIN, or SMURF1/2, either have specialized domains or undergo specific PTMs that recognize substrates and facilitate E2 binding and ubiquitin transfer. For example, HECTD3 operates via a two-step ubiquitin transfer mechanism (Fig. 5b). However, substrate binding occurs through specific motifs within the non-HECT regions that presumably recognize particular degrons (sequence motifs, distinct PTMs, or unique structural elements).

Previous annotations38,39 report 6 complex, 329 standalone, and several unclassified E3s. We extended this annotation by curating non-catalytic components and cataloging their direct interactions with E3s (Fig. 5c). Multi-subunit complex structures are only resolved for four E3s (RBX1/2, ARI1, and APC11). While partial complexes are resolved for 12 E3s (e.g., APC11, ARI1/2), we found several binary direct interactions between E3s and non-catalytic subunits, re-annotating 75 E3s operating as complexes (Fig. 5d, black), leaving 277 standalone38 and 110 unclassified E3s (Fig. 5d, red). RING8 family displayed many complex E3s (50%), followed by RING1 (26%), while RING2 and HECT2 families displayed entirely standalone E3s (Fig. 5e, Supplementary Table 5). Consistent with our findings, we observe that MARCH-type E3s (RING2) operate in the membrane environment primarily as standalone enzymes. HECT2 proteins (e.g., HECD3) possess multiple domains for adaptor, receptor, and scaffolding, explaining their standalone function.

Next, we assembled the E3–substrate interaction (ESI) network by integrating data from known ESIs (n = 2012), direct PPIs (n = 5844), indirect PPIs (n = 6530), and predicted ESIs (n = 64802; Fig. 5f, see Methods). Integrating these data and verifying their ubiquitination status resulted in excluding false positives (E3-associated) and improving the annotation of likely substrates (Supplementary Figs. 20a–c). This enabled mapping ≈ 62% (n = 7691 substrates) of the ubiquitinated human proteome (Fig. 5g).

Analysis of the E3–substrate network revealed distinct specificity patterns. Using well-known ESIs, we observed that most E3 ligases have a single substrate (~10 2), fewer target multiple (~10 1), and only a handful possess extensive substrate sets (Supplementary Fig. 20d). Most substrates are targeted by E3s belonging to two or more families (n = 5520 Promiscuous substrates, n = 72749 interactions; Fig. 5h, bottom; Supplementary Table 6). We also identified substrates that are potentially ubiquitinated by two or more E3s belonging to the same E3 family (n = 804 Family-specific substrates, n = 3292 interactions; Fig. 5h, middle) and substrates uniquely targeted by specific E3 ligases (n = 1367 E3-specific substrates, n = 1367 interactions; Fig. 5h, top). For instance, the E3 ligase SMUF1 specifically targets TBX6 for degradation during cell differentiation40. Similarly, MARCH5 specifically targets FIS1 for ubiquitination (Fig. 5i) to regulate mitochondrial fission41. Both NEDD4 and ITCH belong to the HECT family and ubiquitinate MART1 to exert complementary functions for the sorting and degradation42, and PACS2 is ubiquitinated by BIRC2 and BIRC3, members of the RING3 family (Fig. 5i), conferring TRAIL resistance to hepatobiliary cancer cell lines43. CDN1A, a central regulator of cell cycle and DNA damage response, is ubiquitously targeted by diverse E3 ligases, linking multiple signaling pathways to replication checkpoints (Fig. 5i). Overall, integrating the ESI network obtained here with ubiquitination and degron mapping efforts provides a powerful framework to uncover additional E3-substrate relationships, validate candidate ligases, and reveal functional redundancies or specificity (Supplementary Note 17).

Druggability map of the human E3 ligome
To learn likely avenues of proximity-based therapeutics and leverage the relationships within the human E3 ligome, we mapped existing E3 handles derived from known Proteolysis Targeting Chimeras (PROTACs) and E3 binders to individual E3s and their families (Supplementary Fig. 21a, Supplementary Table 7). Only 16 proteins (9 catalytic E3s and 7 adaptors) are directly targeted by existing E3 handles (Fig. 6a, top). A large fraction of the designed E3 handles are specific to adaptor proteins (e.g., VHL, CRBN), and only a very select few directly target the catalytic E3s (e.g., XIAP, MDM2/4/7, BIRC2/3/7; Supplementary Fig. 21b). Nearest neighbor analysis using our E3 ligome identified five closely related proteins (BIRC8, RN166/181/141, and UBR2; Fig. 6a, top; grey boxes). Given their high structural similarity (often paralogs), the same E3 handles could be repurposed to target them. Mapping small-molecule E3 binders gave us a potential set of compounds targeting 25 additional E3s and 15 non-catalytic components, thus identifying unexplored targets and avenues for lead development for the rational design of E3 handles (Fig. 6a, bottom; red labeled).

Next, we mapped the chemical landscape of E3 handles and E3 binders using the Uniform Manifold Approximation and Projection (UMAP) embedding of high-dimensional 2048-bit Morgan fingerprints. By clustering them, we visualized their molecular similarities. Comparison with other dimensionality reduction methods, such as PCA and t-SNE, did not provide optimal clusters (Supplementary Figs. 21c–d). We detected 20 chemically distinct clusters within the UMAP space (Fig. 6b, indexed, distinct colors) by fast search and identification of density peaks44. 12 compound clusters contain only E3 binders, while the remaining 8 also contain E3 handles targeting specific proteins (Supplementary Fig. 22). The top 6 clusters occupy a large central subspace, indicating cluster heterogeneity (relatively large chemical space). These compound clusters predominantly target proteins from RING3 and RING7 and adaptors (9123 molecules, 67%). E.g., the cluster #2 contains 1222 ligands forming 5 dense sub-clusters representing distinct chemo-types that selectively target MDM2/4, IRAK4, XIAP, HDAC6, and BIRC2, respectively (Fig. 6c). Further, the compounds from clusters #1–6 target a large number of proteins and display less specificity (Fig. 6d; LP ij ≤ 3, low-binding likelihood). The clusters #9–20 are relatively more homogeneous, occupy small peripheral regions of the UMAP-space, and display high selectivity (1–3 targets/cluster with LP ij ≥ 3, cyan squares).

3D structures of compound–E3 interactions are limited. Our mapping showed that only 231/14615 pair-wise interactions are resolved in the PDB (Fig. 6e, covering 212/13620 compounds). These structures mainly feature well-studied E3 handles and binders, highlighting clear examples for drug repurposing. In cluster #13, the MDM2/4-specific E3 handles could be repurposed to target SMUF1 (HECT) or BCL6 (RING6). The presence of SMUF1 binders or BCL6 binders, forming dense sub-clusters nearby, reflects their chemical similarity (Fig. 6f). Further, analysis of surface pockets of E3s could provide structural context and reveal similarity patterns for the development of lead compounds into E3 handles. To this end, we integrated the ELIOT data45, which includes detailed pocket analysis for 73 compounds bound to 8 E3 ligases from our small-molecule interaction dataset (n = 74, Fig. 6e). Interestingly, in cluster #4, we found a compound, 4HQ, bound to the bromodomain of TIF1A (Fig. 6g). Given its moderate affinity and favorable ELIOT pocket assessment, it could be developed into a specific E3 handle for either TIF1A or homologous TRI33 (85% pocket similarity). Moreover, 4HQ is similar to JQ1, a well-known ligand targeting the bromodomain of human BRD4 (PDB code: 3mxf). Further, integration and comparison of sub-cellular location, cell- and tissue-specific expression patterns of E3s will be essential for effective drugging of the human E3 ligome (Supplementary Fig. 23). In summary, this approach enables rational and family-informed PROTAC design by linking chemical tractability with context-aware E3-substrate relationships (Supplementary Note 17).

Methods
Assembly of the human E3 ligome
We collected eight individual human E3 ligase datasets (A, ⋯ , A 8) including previously published reports17–19 and public repositories: E3Net39, UbiHub38, UbiNet 2.057, UniProt (retrieved on 2023-02-13 with keyword “e3 ubiquitin-protein ligase”)58, and BioGRID (retrieved on 2022-01-26)59 compiled using multiple distinct criteria. We merged all of them to form an initial dataset (j 8 n=1 A n j=1448), visualized the overlap of individual resources using UpSet plot60, and assigned a consensus score to each entry based on its presence/absence among the source datasets. We then compiled a list of distinct, well-studied E3 catalytic domains from InterPro61 corresponding to RING, HECT, and RBR classes from all published sources (C={d C}). Using the presence of characteristic catalytic domain(s) d i within each polypeptide, we identified and filtered 1448 proteins corresponding to all catalytic subunits of E3 ligases, fX i 2 8 n=1 j9d i 2Cg. This was followed by manual curation based on InterPro domain descriptions of possible catalytic activity (E2-binding and Ub transfer) to obtain the final refined set of 462 E3 ligases (E3 ligome).

Cullin-RING ubiquitin ligases (CRL) are multi-subunit E3s that operate in cells as the most prominent class of ubiquitin-protein ligases. They use multiple subunits to cooperate to bring an E2~Ub in proximity to a specific substrate. Large, rigid, central scaffold proteins (e.g., the Cullin family, Cul1–Cul5) organize the ligase complex by simultaneously binding the catalytic RING-finger subunit and the docking sites for adaptors or receptors. Adaptor proteins bridge modules that link a scaffold’s N-terminal docking surface to a separate substrate-receptor. They often recognize a conserved motif on the receptor subunit (e.g., F-box, SOCS-box, WD40 proteins). The receptor proteins are specificity determinants that directly recognize and bind degron motifs on substrates. They define which substrates get ubiquitinated (e.g., Skp2, Keap1, VHL). We independently annotate and classify each of the three classes: 151 adaptors, 106 receptors, and 8 scaffold proteins, and use their PPIs to map distinct substrates for multi-subunit E3s.

Multi-scale distance measures
We computed twelve pairwise distance measures for all E3 ligase pairs (d i PQ) spanning the molecular- and systems-level organization: sequence, domain architecture, 3D structure, function, subcellular location, and cell line/tissue expression. All the distance measures were scaled between [0, 1] for comparison and even combination.

At the sequence level, we used an alignment-free local matching score-based (LMS) distance and an alignment-based γ distance between protein pairs using the canonical isoform sequences. The LMS distance dLMS PQ between two proteins P and Q is given by

dLMS PQ = 1 - 2LMS(P,Q) / (LMS(P,P)+LMS(Q,Q)), (2)

where LMS(P,Q) = i2fP0,Q0g M[i,i] captures the extent of local similarity by summing BLOSSUM62 substitution scores for overlapping 5-residue fragment pairs fP0,Q0g from proteins P and Q62,63. The pairwise γ distance measures the evolutionary distance between the globally aligned sequences of two proteins, P and Q, where p PQ is the fraction of alignment positions with residue substitutions and indels, and a=264.

dγ PQ = a[(1 - p PQ) - 1/a - 1], (3)

To quantify the preservation of domain architectures among all protein pairs, we computed three distances: Jaccard, Goodman–Kruskal γ, and domain duplication distances, using domain annotations obtained from the InterPro database61 (Nov 2022). The Jaccard distance65,66 represents the compositional similarity of protein domains. It is the ratio of the number of shared (N0 PQ) and unique domains (N0 P, N0 Q) between proteins P and Q,

dJac PQ = 1 - N0 PQ / (N0 P + N0 Q - N0 PQ). (4)

The Goodman-Kruskal γ distance compares the order of domain arrangements between two proteins, P and Q, and is computed as

dGKγ PQ = 1 - 1 + γ PQ / 2, (5)

where γ PQ = (NS PQ - NR PQ) / (NS PQ + NR PQ) with NS PQ and NR PQ denoting the same- and reversed-ordered pairs of proteins P and Q, respectively66,67. Finally, the domain duplication distance66 compares the overlap of tandem domain repeats and is given by

dDup PQ = 1 - exp [ - (N0 P + N0 Q) / S ] * |NP i - NQ i | / (N0 P + N0 Q), (6)

where S = max(NP i, NQ i); N0 P and N0 Q are unique domains in proteins P and Q with NP i and NQ i repeats, respectively.

To compute distances between structures of pairs of ligases, we used AlphaFold2 models (version 4)68. We restricted comparisons to contiguous protein segments containing all catalytic domains for each protein to avoid comparing flexible regions of the full-length structures. We computed the TM-score as implemented in US-align69. The TM-score between the 3D structures of proteins P and Q is given by,

TM-score(P,Q) = max [ 1/L P * i 1 (1 + (d i / d 0 (L P)) 2 ) - 1 ], (7)

where L P is the length of protein P, L ali is the number of common residues between aligned proteins P and Q, and d 0 (L P) = 1.243 L P - 15 - 1.869. To account for the inherent asymmetry in the TM similarity scores due to normalization by reference protein length L P, we computed the structural distance between protein structures P and Q by averaging their TM similarities as

dStr PQ = 1 - (TM-score(P,Q) + TM-score(Q,P)) / 2. (8)

Functional distances among the protein pairs P and Q were captured using semantic similarities of annotated GO terms corresponding to the three GO ontologies—molecular functions, biological processes, and cellular components—using the package GOGO70. The protein to GO term mappings were retrieved (in Feb. 2023) from the Open Biological and Biomedical Ontology Foundry and the Gene Ontology resource48,71. For each annotated GO term x, we defined the semantic distance to the target term x as

S x (t) = 1 if t=x, max w S x (t0) j t0 2 children(t) if t≠x:

Further, the semantic similarity between two GO terms x and y, represented by two graphs DAG x and DAG y, is defined as

Sim Wang (x,y) = P t2Tx\Ty S x (t) + S y (t) / P t2Tx S x (t) + P t2Ty S y (t)

By extension, the semantic similarity between a single GO term x and a set of GO terms GO Y = {y 1 , y 2 , ⋯ , yk} is defined as the maximum semantic similarity between x and any of the terms in Y:

Sim(x,GO Y ) = max 1≤i≤k Sim Wang (x,y i ):

Finally, the semantic distance between proteins P and Q, annotated with sets of GO terms GO P ={p 1 , p 2 , ⋯ , pm} and GO Q ={q 1 , q 2 , ⋯ , qn}, respectively, is calculated as

dSem PQ = 1 - Sim(GO P, GO Q) = 1 - ( P 1≤i≤m Sim(pi,GO Q) + P 1≤j≤n Sim(q j,GO P) ) / (m+n) (9)

Using Eq. (9), we computed three semantic distances dBP PQ, dCC PQ, and dMF PQ for the three different GO ontologies. To compute the subcellular localization distance dScL PQ, each protein’s main and auxiliary subcellular locations were mapped from the Human Protein Atlas73 and used to construct a location vector with weights 1 and 0.3, respectively. We then computed dScL PQ using the cosine similarity between the location vectors of proteins P and Q as

dScL PQ = 1 - (P * Q) / (||P|| ||Q||) (10)

Finally, we computed the tissue (dTE PQ) and cell line co-expression (dClE PQ) distances from the tissue and cell line expression profiles of the proteins P and Q. We retrieved expression data from the Human Protein Atlas73, transcripts per millions of mRNA levels from the 253 human tissues of RNA HPA tissue gene dataset and 1055 cell lines of RNA HPA cell line gene dataset, respectively. Both distances were calculated using Spearman’s rank correlation coefficient r S,PQ as

dTE PQ = 1 - (1 + r S, E PQ) / 2 and dClE PQ = 1 - (1 + r S, lE PQ) / 2 (11)

Metric optimization, clustering, bootstrapping, and classification
We combined the pairwise gamma (dγ PQ), Jaccard (dJac PQ), structural (dStr PQ), and semantic molecular function (dMF PQ) distances to capture all orthogonal information from the four significant hierarchies—sequence, domain architecture, 3D structure, and molecular function—into a single metric spanning the entire molecular scale. We used a weighted-sum model of these four distances, D PQ = P 4 i=1 w i d i PQ , by uniformly sampling the weights as a function of tree cutoff, h, a hyperparameter. Optimized weights, wb i were obtained by maximizing the element-centric similarity index22, which represents the similarity between clusters derived from parsing the emergent dendrogram, at evenly spaced cutoffs, h ∈ (0,1) derived from the combined distance and the class-level grouping of E3s into RING, HECT, and RBR classes (partial ground truth). At each cutoff h, we sampled ~10 4 emergent distance matrices (∑ i w i d i), obtained their emergent hierarchical clusters, and computed S EC for each one of them. We chose 100 emergent metrics with the highest S EC for each h and computed the averages and standard deviations of their corresponding weights. The stabilized weights wb i at h ≥ 0.9 corresponding to the maximum S EC were chosen to construct the optimized distance measure. Dendrograms were computed from hierarchical clustering of individual and combined distance matrices using Ward’s minimum variance method74 as implemented in SciPy. The emergent metric was resampled 500 times by swapping protein labels to compute bootstrap support at each bifurcation node. Unrooted trees with scaled distances were drawn and annotated with domain architectures of individual E3 leaves using iToL75. The final tree was parsed at tree cutoff h = 0.25 to produce optimal emergent clusters (E3 families). Each family was manually analyzed for shared sequence and domain-architectural features to identify subfamilies and outliers.

Gene essentiality and CRISPR-Cas9 dropout screens
A pooled multiplexed CRISPR-Cas9 library targeting 822 genes of the human ubiquitin-proteasome system (UPS) was generated according to previously reported methods76,77. Each UPS gene was targeted by 2 gRNAs, each expressed from a human 7SK promoter of a dual gRNA-containing lentiviral plasmid, co-expressing puromycin. The second gRNA cassette expressed an AAVS1-targeting gRNA as a neutral control. NGS verified successful cloning, and lentiviral particles were obtained from VectorBuilder.

Library preparation was done using RPE1 cells obtained from ATCC stably expressing Cas9. Cell lines were routinely tested for Mycoplasma contamination. They were transduced with infectious lentiviral particles of the UPS library at 50X coverage and a multiplicity of infection (MOI) of 0.5 to ensure predominantly single integrations per cell. Following infection, cells were subjected to two rounds of puromycin selection (each round for 48h). Throughout the screen, cells were maintained at a minimum coverage of 50-fold per sgRNA to preserve representation of the library. After ~10 cell doublings (≈ 14 days), cells were harvested and genomic DNA was extracted following previously established protocols76.

NGS preparation was performed in two steps. First, 900 μg (50X coverage) of genomic DNA was used in a 50 μL PCR-1 reaction with primers amplifying both sgRNA library cassettes. PCR-1 had the following composition: 25 μL of NEBNext® UltraTM II Q5® Master Mix (NEB, M0544L), 2 μL of primer mix (0.4 μM each, IDT), 2.25 μg genomic DNA, and H 2O to 50 μL. PCR cycling conditions were as follows: 1 min at 98 °C, followed by 15 s at 98 °C, 20 s at 65 °C, and 60 s at 72 °C for 20 cycles, with a final extension for 60 s at 72 °C using an Eppendorf Mastercycler® X50. PCR-1 products from the same sample were then pooled. For each sample, a second PCR reaction was performed to add P5 and P7 Illumina adaptors, including demultiplexing barcodes. PCR-2 primers also include eight different stagger lengths, all of which were pooled in equimolar amounts. PCR-2 reaction mix was as follows: 25 μL of NEBNext® UltraTM II Q5® Master Mix (NEB, M0544L), 1 μL of P5 primer and 1 μL P7 primer, including barcodes (0.2 μM each, SIGMA), 10% input from PCR1 and H 2O to 50 μL. PCR cycling conditions were as follows: 1 min at 98 °C, followed by 15 s at 98 °C, 20 s at 68 °C, and 30 s at 72 °C for 11 cycles, with a final extension for 60 s at 72 °C using an Eppendorf Mastercycler® X50. Barcoded PCR-2 products were then pooled and gel-purified according to the Zymoclean Gel DNA Recovery Kit (Zymo, D4001). NGS Sequencing was performed at Genewiz. Read Counting of the library and both biological replicates of end time points were done with ReCo78. For each replicate, we performed log 2 (CPM+1) transformation for each gRNA to obtain normalized read counts.

NRCi = log 2 ( gRNA i * n * 100 / P n i=1 gRNA i + 1 ) (13)

Where gRNA i = RC of gRNA targeting gene i; n = total number of guides in the library. We computed log-fold changes (LFCs) as the differences between the NRCi of both gRNAs. Gene-level LFCs were obtained by averaging the LFC values of guides targeting the same gene. Using Limma79, we fit a linear model for each gene, considering the different gRNAs as technical replicates and the biological replicates as blocks. We used the Correlation function to estimate the intra-block correlation. Then, we calculated p values using empirical Bayes (eBayes) followed by correction for multiple testing using a Benjamini-Hochberg correction. We defined genes with significant contribution to cell fitness if their corresponding |LFCi | ≥ 1 and FDR ≤ 0.05.

Identifying generic and specific functions of the E3 ligome
GO enrichment analysis for E3 ligases corresponding to individual 13 families was performed using Metascape80, which implements a hierarchical clustering approach based on κ-similarity ≥ 0.380. The resulting networks of GO terms at the biological process, cellular component, and molecular function ontologies were rendered using Cytoscape. Nodes were colored and drawn as pie charts to reflect E3 family contribution (number of proteins) and enrichment. Individual GO terms were considered significantly enriched within a ligase family if the enrichment factor, C obs: / C exp: ≥ 2, a minimum of 3 proteins corresponding to the family are annotated explicitly with the corresponding GO terms, and a p value ≤ 0.01. Within each resulting GO cluster, the GO term with the lowest p value was selected as the cluster label for visualization. Heatmaps showing the enriched GO clusters for each family were drawn to highlight the functional specialization of individual E3 families. We ensured high-confidence evidence codes (Experimental, Phylogenetic, and Computational evidence) for enriched GO terms. GO enrichment analysis for essential E3 ligases (background all E3 ligases present in UPS CRISPR dataset) was performed using ShinyGO v0.8281, employing a Hypergeometric test with Benjamini–Hochberg correction, FDR ≤ 0.05.

Cell culture, sample preparation, mass spectrometry data collection, and analysis
HeLa cells were either challenged with (i) global starvation (EBSS treatment triggering nutrient starvation and activating autophagy) or (ii) DNA damage (CPT treatment inhibiting topoisomerase I to induce single-ended DNA double-strand breaks). Cells (ATCC) were cultured in DMEM supplemented with 10% FBS and 100 I.U./mL penicillin-streptomycin in a 5% CO2 atmosphere at 37 °C. Starvation response was induced by incubating cells in EBSS medium (Gibco) for 6 h. For triggering DNA damage response, cells were treated with 100 nM Camptothecin for 4 h before sample preparation. Cells were regularly tested for Mycoplasma contamination using the Mycoplasma PCR Detection Kit (Sigma). Cells were lysed in Lysis Buffer (2% SDS, 50 mM Tris pH 8.5, 10 mM TCEP, 40 mM CAA, supplemented with protease inhibitor cocktail and phosphatase inhibitors), sonicated at 4 °C in a Bioruptor Pico 2 (30/30, 10 cycles), and boiled at 95 °C. Proteins were precipitated using methanol-chloroform and digested with 1:50 w/w LysC (Wako Chemicals) and 1:100 w/w trypsin (Promega) overnight at 37 °C. De-salted peptides were dried and resuspended in TMT-labeling buffer (200 mM EPPS pH 8.2, 20% acetonitrile) before being subjected to TMT labeling with a 1:2.5 peptide TMT ratio (w/w) for 1 h at room temperature. The labeling reaction was quenched by the addition of 0.5% hydroxylamine final concentration. Successful TMT labeling was verified by mixing equimolar ratios of peptides and subjecting the mix to single-shot LC-MS/MS analysis. Peptides were fractionated using high-pH liquid chromatography on a micro-flow HPLC (Dionex U3000 RSLC, Thermo Scientific). Pooled fractions were dried in a vacuum concentrator and resuspended in 2% ACN, 0.1% TFA for LC-MS analysis.

Tryptic peptides were analyzed on an Orbitrap Fusion Lumos coupled to an easy nLC 1200 (Thermo Fisher Scientific) using a 35 cm long, 75 μm ID fused-silica column packed in-house with 1.9 μm C18 particles (Reprosil pur, Dr. Maisch), and kept at 50 °C using an integrated column oven (Sonation). Peptides were eluted from 8% to 28% Buffer B (80% ACN, 0.1% FA) over 75 minutes, followed by a step-wise increase to 90% Buffer B in 21 minutes, which was held for another 9 minutes. Asynchronous precursor selection (SPS) multi-notch MS3 method was used82. Full scan MS spectra (350-1400 m/z): resolution of 120,000 at m/z 200, maximum injection time of 100 ms, and AGC target value of 4×10 5. MS2 scans (of precursors with charge state between 2-6): maximum injection time of 50 ms, AGC target value of 15000, CID fragmentation with a normalized collision energy (NCE) of 35%. MS3: 10 most intense MS2 fragment ions with an isolation window of 0.7 Th (MS) and 2 m/z (MS2), HCD fragmentation, NCE 50%, resolution of 50000 at m/z 200, scan range of 100-500 m/z, AGC target value of 150000, and a maximum injection time of 86 ms. Repeated sequencing of already acquired precursors was limited by setting a dynamic exclusion of 60 seconds and 7 ppm, and advanced peak determination was deactivated. All spectra were acquired in centroid mode.

MS raw data were analyzed using FragPipe v21.1, with MSFragger v.4.083 and Philosopher v.5.1.084. Acquired spectra were searched against the human reference proteome (Taxonomy ID 9606) downloaded from UniProt (07/25/2024; 20,418 entries) with a precursor mass tolerance of 20 ppm and fragment mass tolerance of 20 ppm. Identifications were filtered to obtain false discovery rates (FDR) below 1% for both peptide spectrum matches (minimum peptide length of 7) and proteins using a target-decoy strategy. For all searches, carbamidomethylated cysteine was set as a fixed modification and oxidation of methionine and N-terminal protein acetylation as variable modifications with allowing up to 3 modifications per peptide. Strict trypsin cleavage was set as the protein digestion rule. Label-free quantification was performed using IonQuant v.1.10.2785. The differential expression analysis was performed within FragPipe analyst86 using Limma79, and derived p values were corrected for multiple comparisons using Benjamini-Hochberg. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD067015.

Integrating PPI and ESI datasets
To identify E3 ligases likely functioning in complex mode, we combined data from PDB (https://www.rcsb.org/) and IntAct87. Using the refined lists of proteins corresponding to the E3 ligome, E1, E2, adaptors, receptors, and scaffold proteins (Ubihub and manually curated lists), we retrieved all the PDB structures (as of Jul. 2025) involving E3-adaptors, E3-receptors, and E3-scaffold protein complexes. Additionally, PPIs obtained between E3-adaptor, E3-receptor, and E3-scaffold proteins filtered ("experimentally validated” PPIs, MI:0045 ≥ 0.5) for high confidence entries. E3s interacting, or in a resolved structure, with at least one receptor, adaptor, or scaffold protein, were re-annotated as complex E3s. To assemble the E3–substrate interaction map, we integrated multiple data sources, including experimentally validated enzyme-substrate interactions (known ESIs) from UbiNet 2.057 and UbiBrowser88, a set of predicted ESIs from UbiBrowser (top 1% of predictions), physically interacting protein pairs (PPIs) from the IntAct database (mapped PPIs), and indirect PPIs involving ligases and potential substrates mediated by adaptor, receptor, or scaffold proteins from IntAct (indirect PPIs).

To obtain a cut-off for filtering PPIs, we first detected a subset of the well-known ESIs (experimentally curated from UbiNet; blue) overlapping with the direct PPIs obtained from IntAct. The PSI-MI confidence scores for these overlapping ESIs (n = 239) display a median value of 0.55. This encouraged us to use a threshold value ≥ 0.5 for filtering PPIs. This cutoff strikes a balance between including enough interactions for meaningful analysis while excluding lower-confidence edges that disproportionately contribute noise. Further, major PPI resources (IntAct/IMEx, Reactome, Open Targets) typically use 0.4–0.45 as a medium-confidence baseline. Rounding that to 0.5 provides us with a threshold that sits squarely in the “medium-to-high” confidence range, increasing the proportion of PPIs detected using diverse approaches89.

Known ESIs and the PPIs dataset were enriched using substrates detected mainly by pull-down experiments, followed by two-hybrid techniques. A map of the ubiquitinated human proteome was obtained by cross-checking the ubiquitination status and mapping ubiquitination sites for each identified substrate from dbPTM90 and PhosphoSitePlus91. All substrates were categorized based on their interactions with E3 ligases: those paired with a single, unique E3 ligase were classified as E3-specific; those associated with multiple E3 ligases from the same family were designated as family-specific; and those linked to two or more E3 ligases from different families were labeled promiscuous.

Mapping small molecule interaction data
A unified dataset of E3 handles (corresponding to all publicly documented PROTACs) and E3 binders targeting specific E3 ligases, adaptors, receptors, and scaffold proteins was obtained by combining data from PROTACpedia (https://protacpedia.weizmann.ac.il), and PROTAC-DB 3.092 and ChEMBL v3493. All small molecules were uniquely identified by their chemical structure, represented using the canonical SMILES format, and mapped to their target proteins and E3 families. Information from ChEMBL v34 was gathered using an SQL query combining compound data, experimental data, and target protein information, and filtered using data from binding assays using p-ChEMBL values.

p-ChEMBL = log 10 (Activity) (14)

where activity is given by IC 50, EC 50, K i, K d, or some measure of potency in molar units. It enables comparison across different bioactivity types. p-ChEMBL ≥ 6.0 is a commonly used threshold in early-stage screening as a baseline for biologically relevant activity (≤ 1×10 -6 M)94. 2048-bit Morgan fingerprint95 for each small molecule was obtained using RDKit (2048-bit array, radius = 3, http://www.rdkit.org). Dimensionality reduction was performed using PCA, t-SNE, and UMAP using the Python Scikit-learn package (default parameters) and visualized along a 2D subspace, projections showing the highest variance. Further, clustering of small molecules was performed by fast search and identification of local density peaks44 using a scaled Euclidean distance measure defined on the corresponding normalized 2D subspaces. Using threshold values for local density (ρ = 20) and nearest neighboring peak (δ = 0.1) demonstrated the superiority of UMAP embedding and resulted in 20 small molecule clusters (indexed by i, targeting proteins, j, with population n i = ∑ j n ij). For each cluster, i, representative small molecules were automatically determined from local density peaks, and their distinct set of targeted proteins j from the E3 ligome were also mapped. We then quantified the binding likelihood of individual clusters to target distinct proteins by computing log-transformed propensities, LP ij.

LP ij = log 2 [ ( n ij / P n i ) / ( P n j / P n ) ] (15)

Compound-E3 interaction structures were retrieved from the PDB by converting compound SMILEs to InChI keys with RDKit, querying the EBI UniChem API for all small-molecule PDB entries, and merging them with target-protein PDB entries from our dataset, retaining only entries containing both. E3 pocketome data from ELIOT45 was then mapped to these compound–E3 pairs.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The present work is highly valuable and has a significant impact, as PROTACs and E3 ligases are currently of great interest\nin both scientific and pharmaceutical fields. The classification efforts presented in this study are particularly appreciated, as\nE3 ligases are often challenging to study, poorly characterized, and sometimes misinterpreted. This systematic approach\nprovides much-needed clarity in an area that remains complex, poorly explored but with a great potential.\nSuggested minor reviews:\n1. In the section \"Interaction landscape of the human E3 ligome\", better clarify the difference between scaffold proteins,\nsubstrate receptors, and adaptors.\n2. In the section \"Interaction landscape of the human E3 ligome\", line 335, explain what is meant by \"holo complex\nstructure\". The term may be confusing, as it typically refers to a structure bound to a ligand, inhibitor, etc.\n3. The description of the filtering criteria applied to PPI and ESI data is detailed, but it would be helpful to specify the\nreference databases for each criterion, for example:\n• When referring to experimentally validated interactors (MI:0045), clarify whether the PSI-MI score threshold was selected\nbased on standard criteria or if it was an arbitrary choice.\n• For the p-ChEMBL ≥ 6 filter, explain whether this threshold was chosen based on a biological criterion or if it follows\nspecific conventions in pharmaceutical chemistry.\n4. In the part on dimensionality reduction with t-SNE, it might be useful to justify the choice of t-SNE over other methods (e.g.,\nPCA, UMAP). Also, specify whether clustering was performed automatically or if categories were assigned manually.\n5. The study mentions the use of PDB structures to assess interactions, but the specific PDB structures considered are not\nexplicitly provided (only the Uniprot associated to the E3 ligase). Including a supplementary file listing all PDB structures\nused in the analyses would improve transparency and reproducibility, allowing others to verify and build upon the findings.\n6. The classification of E3 ligases proposed by the authors is innovative and significantly clearer than existing\nclassifications, making it a valuable addition to the field. The analyses performed are also interesting and well-executed.\nHowever, the study is not entirely novel, as a 2022 publication introduced a web-based platform called ELIOT, which\npresents a conceptually similar approach. Given these similarities, it would be appropriate to cite ELIOT and discuss how\nthe current study compares to or builds upon its methodology. Doing so would enhance the contextualization of the work\nwithin the existing literature and highlight its unique contributions. https://doi.org/10.1111/cbdd.14123\nSome comments:\n1. The druggability analysis is particularly interesting, as it considers a diverse range of ligands from multiple databases.\nThis comprehensive approach enhances the study’s relevance and applicability. However, the comparison relies on a 2D\nsimilarity assessment, which may not always be sufficient. While 2D molecular similarity is a valuable starting point, it does\nnot fully capture the potential for structurally dissimilar molecules to exhibit similar 3D interactions. In some cases,\ncompounds with low 2D similarity may still share key binding characteristics when analyzed in three dimensions.\n2. The same limitation applies to the clustering approach. The distance matrix used to classify E3 ligases is based on their\n2D structures, but it does not account for the fact that different residues can contribute to similar interaction profiles.\nAdditionally, a single protein may adopt multiple conformations, leading to variations in its interaction potential that are not\nreflected in a purely 2D-based classification. Given the significant role of 3D structural considerations in molecular\ninteractions, this aspect should either be integrated into the analysis or at least acknowledged as a limitation of the current\nmethodology.\nFinal evaluation:\nThe work is well written and has a highly valuable impact in the scientific field. The results are well described and\naccompanied by attractive and well-made images. The methodology is explained quite well, though adding a bit more detail\ncould be beneficial. Only minor revisions are necessary, specifically adding more details and explanation as described in\nthe previously highlighted points.\nOverall, the work is very interesting, presenting partially original data regarding the classification of E3 ligases and\nproposing an innovative and significantly clearer classification compared to existing ones, making it a valuable addition to\nthe field. However, beyond the minor revisions mentioned earlier, two major comments need to be addressed.\nFirst, the presented study is not entirely novel, as a 2022 publication introduced a web-based platform called ELIOT, which\nshares a conceptually similar aim. While the methodological approach used in this study differs from that of ELIOT, the final\nproduct is quite similar. The key distinction is that ELIOT focuses less on classification and instead provides an easily\nnavigable platform where users can directly access and explore information on E3 ligases, binding sites, known interactors,\ndruggability analysis, and clustering analysis. It would be beneficial to discuss this in the manuscript.\nSecondly, while the supplementary files related to the dataset used are well-provided, a file regarding the PDBs used is\nmissing. Results are included as supplementary files, but having a more practical way to access and utilize them would be\nadvantageous. For instance, a final, consolidated file in the form of a structured database could allow the scientific\ncommunity to directly consult and leverage the findings. Without a clear, comprehensive output, the study leans more\ntowards a review that collects and refines data from literature and various databases rather than a research article\npresenting new results. Providing a tangible and accessible resource would significantly enhance the usability of this\nimportant work, ensuring that the broader community can fully benefit from the significant efforts undertaken in this study.", "reviewer_2": "The study constructs a multi-scale classification system for human E3 ubiquitin ligases by integrating multi-dimensional\ndata, holding important theoretical value in deciphering the complexity of E3s and their functional specialization\nmechanisms. The proposed metric learning framework offers new insights for enzyme family classification, presenting\ncertain application value to the field. The reviewer suggests the following revisions:\n1. The significant value of this paper lies in that the new classification system can predict novel functions for E3 subfamilies.\nHowever, the current analyses rely on computational methods such as GO enrichment and structural predictions, lacking wet\nlab validation. The absence of validation experiments at present results in the mapping between the classification system\nand actual biological functions remaining at the level of data correlation, failing to form a logically closed loop of\nexperimental confirmation. Regarding the author’s claim of \"identified over 60 biological processes enriched with E3s\ncorresponding to distinct families,\" could 1 - 2 key wet lab experiments be added? For example, the statement that \"RING5\nE3s are enriched in regulating antiviral response, type-I interferon production, regulation of viral entry, and NF-κB signaling\",\nand the conclusion that \"RBR family E3s specialize in K6-linked ubiquitination\" are without ubiquitination experiments.\n2. The set of E3s studied here relies on the understanding of catalytic domains and is limited by existing data (only covering\nthree types of proteins: RING/HECT/RBR), potentially missing enzymes with non-canonical catalytic features or those\ndependent on cofactors. This study more closely resembles a refinement of the three major RING/HECT/RBR families rather\nthan an analysis of the entire human E3 ligome as claimed by the authors. By screening only based on known catalytic\ndomains, \"986 proteins with low consensus scores and remain unclassified and unannotated, excluding them from the\ncurated E3 ligome,\" the study may overlook E3s with non-canonical or unknown catalytic mechanisms, making it difficult to\ncover all E3 types and limiting the comprehensive understanding of the E3 ligome. Authors need to evaluate the\ncompleteness of ubiquitin ligase data.\n3. Among the 12 distance metrics established by the authors, only four of them were\nultimately selected for protein subfamilies classification. What is the rationale behind\nthese selected four distance metrics? Only the related biological features are important for E3 family. When calculating the distance for expression levels, both protein and mRNA expression levels were used—what is the consistency between them?\n4. The paper, especially in the abstract, overemphasizes the degradative function of the ubiquitin-proteasome system\n(\"target specific proteins for degradation\"). In reality, the non-degradative functions of ubiquitination are also very important,\nand a revision is suggested.\n5. What about the confidence of protein-protein interaction data on the paper’s analysis for \"Interaction landscape of the\nhuman E3 ligome\". Is there any bias from the interaction datasets which can affect the analysis results？"}, "author_response": "We thank the reviewer for their positive and encouraging feedback. We are\ndelighted that our systematic classification of E3 ligases and our focus on PROTAC-related\nmechanisms are recognized as valuable contributions. We appreciate the reviewer’s acknowl-\nedgment of the relevance and potential impact of this work.\nSuggested minor revisions\n1. In the section “Interaction landscape of the human E3 ligome”, better clarify the\ndifference between scaffold proteins, substrate receptors, and adaptors.\nResponse: We have clarified the definitions of scaffold, adaptor, and receptor\nsubunits (see Methods, Fig. 1b, and Supplementary Fig. 1e-g). We have added\nthe following paragraph to the Methods section:\nInmultisubunitE3ubiquitin-ligases–mostprominentlytheCRLs–threefunc-\ntionally distinct classes of subunits cooperate to bring an E2∼Ub in prox-\nimity to a specific substrate. Large, rigid, central scaffold proteins (e.g.,\nthe Cullin family, Cul1–Cul5) organize the ligase complex by simultaneously\nbinding the catalytic RING-finger subunit and the docking sites for adap-\ntors or receptors. Adaptor proteins bridge modules that link a scaffold’s\nN-terminal docking surface to a separate substrate-receptor. They often rec-\nognize a conserved motif on the receptor subunit (e.g., F-box, SOCS-box,\nWD40 proteins). The receptor proteins are specificity determinants that di-\nrectly recognize and bind degron motifs on substrates. They define which\nsubstrates get ubiquitinated (e.g., Skp2, Keap1, VHL). We independently\nannotate and classify each of the three classes: 151 adaptors, 106 receptors,\nand 8 scaffold proteins, and use their PPIs to map distinct substrates for\nmulti-subunit E3s.\n2. In the section “Interaction landscape of the human E3 ligome”, line 335, explain what\nis meant by “holo complex structure”. The term may be confusing, as it typically\nrefers to a structure bound to a ligand, inhibitor, etc.\nResponse: We have now rephrased “holo complex structure” with “multi-subunit\ncomplex structure ” for clarity throughout the manuscript.\n3. The description of the filtering criteria applied to PPI and ESI data is detailed, but\nit would be helpful to specify the reference databases for each criterion, for example:\nPSI-MI score threshold was selected based on standard criteria or if it was an arbitrary\nchoice. For the p-ChEMBL ≥ 6 filter, explain whether this threshold was chosen based\nonabiologicalcriterionorifitfollowsspecificconventionsinpharmaceuticalchemistry.\nResponse: We thank the reviewer for pointing this out. We have now also mentioned\nthe reference databases along with appropriate filtering steps with threshold values\n(see Methods; Supplementary Fig. 20).\nTo obtain a cut-off for filtering PPIs, we first detected a subset of the well-known\nESIs (experimentally curated from UbiNet; blue) overlapping with the PPIs obtained\nfrom IntAct (purple, Supplementary Fig. 20a). The PSI-MI confidence scores for\nthese overlapping ESIs (n = 239) range between 0.25 and 1.0 with a median value of\n0.55 (red line in Supplementary Fig. 20b). This encouraged us to use a threshold\nvalue ≥0.5 for filtering PPIs. This cutoff strikes a balance between including enough\ninteractionsformeaningfulanalysiswhileexcludinglower-confidenceedgesthatdispro-\nportionately contribute noise. Further, major PPI resources (IntAct/IMEx, Reactome,\nOpen Targets) typically use 0.4–0.45 as a medium-confidence baseline. Rounding that\nto 0.5 provides us with a threshold that sits squarely in the “medium-to-high” con-\nfidence range, increasing the proportion of PPIs detected using diverse approaches\n(Supplementary Fig. S20c) (Porras et al., 2020).\nFor the p-ChEMBL value, we first define it and provide the following clarification.\np-ChEMBL = −log (Activity), (1)\n10\nwhere activity is given by IC , EC , K , K , or some measure of potency in molar\n50 50 i d\nunits. It enables comparison across different bioactivity types. pChEMBL ≥ 6.0 is a\ncommonly used threshold in early-stage screening as a baseline for biologically relevant\nactivity (≤ 1 × 10−6 M) (Smaji´c et al., 2023), and also a common default value in\nchemo-informatics tools like RDkit, and other repositories such as PubChem BioAssay.\n4. In the part on dimensionality reduction with t-SNE, it might be useful to justify\nthe choice of t-SNE over other methods (e.g., PCA, UMAP). Also, specify whether\nclustering was performed automatically or if categories were assigned manually.\nResponse: We thank the reviewer for this comment. We have now redone the di-\nmensionality reduction comparing three different methods: PCA, t-SNE, and UMAP.\nFor our small molecule dataset (n = 13620, including E3 handles and E3 binders), we\nfind that UMAP preserves both local neighborhoods and global structure, offers high\ncluster separability and exhibits both stable and scalable performance (see Fig. 6b\nand Supplementary Fig. 21c-d).\nWe then used the scaled Euclidean distance in the reduced dimensions (2D subspaces:\nPCA, t-SNE, or UMAP) as a measure to cluster all the compounds by searching and\nfinding local density peaks Rodriguez and Laio (2014). This approach provided more\nrefined cluster definitions (Fig. 6b), allowing identification of cluster representatives,\nand patterns inherent in the dataset, enabling quantification of association between\ncompound structure and their target proteins (Supplementary Fig. 22).\n3\n5. The study mentions the use of PDB structures to assess interactions, but the specific\nPDB structures considered are not explicitly provided (only the Uniprot associated\nwith the E3 ligase). Including a supplementary file listing all PDB structures used in\nthe analyses would improve transparency and reproducibility, allowing others to verify\nand build upon the findings.\nResponse: We thank the reviewer for pointing this out. We have now updated our\nmapping to E3 systems and added additional Data files.\ne3ligome structures.xlsx: PDB structures associated with the human E3 ligome\nand allied proteins.\ne3ligome modes of action.xlsx: The PDB structures of complex E3s.\ne3ligome sm clustering.xlsx: PDBs corresponding to Compound-E3 interactions.\n6. The classification of E3 ligases proposed by the authors is innovative and significantly\nclearer than existing classifications, making it a valuable addition to the field. The\nanalyses performed are also interesting and well-executed. However, the study is not\nentirely novel, as a 2022 publication introduced a web-based platform called ELIOT,\nwhich presents a conceptually similar approach. Given these similarities, it would be\nappropriate to cite ELIOT and discuss how the current study compares to or builds\nupon its methodology. Doing so would enhance the contextualization of the work\nwithin the existing literature and highlight its unique contributions (Palomba et al.,\n2022).\nResponse: We thank the reviewer for this insightful comment. We are pleased that\nthese aspects of our work are recognized as valuable additions to the field.\nWe now explicitly cite ELIOT: a valuable resource of E3 pocketome Palomba et al.\n(2022) and discuss the overlap and complementary aspects of our study, within the\npurview of analyzing E3 ligase structures and their druggable pockets for E3 handle\ndesign (see Results, section 6, paragraph 3, Fig. 6).\nWe clarify that our primary goal in the current manuscript is the classification of\nthe human E3 ligome and how this offers a unique perspective on functional segre-\ngation, enables identification of substrates via mapping protein-interaction landscape,\nand provides a druggability map. Only our last section on “Druggability map of the\nhuman E3 ligome” overlaps partially with EILIOT (Fig. 6). Here, we prioritize map-\nping of small-molecule E3 handles and E3 binders onto the new refined classification,\nhighlighting (1) similarity patterns of current E3 binders using clustering in UMAP\nspace, (2) potential avenues for PROTAC re-purposing, and (3) potential identification\nof new lead compounds for handle design.\nA search against the PDB identified structures for 231 compound-E3 pairs. Extending\nthis to ELIOT pocketome data resulted in 74 overlapping pairs (out of the 209 pairs\npresent in ELIOT; Fig. 6e). This information proved useful to learn pocket charac-\nteristics of potential new E3 binders, which could be developed into lead compounds\n(Fig. 6g). We found that pocket similarity data and PROTAC scores of individual\npockets complemented our hit identification process for lead development from initial\nresolved E3 pockets.\nSome comments (Reviewer 1)\n1. The druggability analysis is particularly interesting, as it considers a diverse range of\nligands from multiple databases. This comprehensive approach enhances the study’s\nrelevance and applicability. However, the comparison relies on a 2D similarity as-\nsessment, which may not always be sufficient. While 2D molecular similarity is a\nvaluable starting point, it does not fully capture the potential for structurally dis-\nsimilar molecules to exhibit similar 3D interactions. In some cases, compounds with\nlow 2D similarity may still share key binding characteristics when analyzed in three\ndimensions.\nResponse: We thank the reviewer for highlighting the limitations of relying solely\non 2D similarity assessments in the context of our E3 druggability map (Fig. 6b).\n2D fingerprints may overlook critical aspects of molecular behavior that become ap-\nparent only in 3D, such as shape complementarity, distinguishing stereoisomers, and\nconformational flexibility.\nMorgan (circular) fingerprints paired with the clustering remain among the most pop-\nular tools for rapid similarity searching in chemo-informatics. 2D Morgan fingerprints\nare topological descriptors that encode the presence of atom-centered substructures.\nThey can be computed with relative ease, speed (milliseconds for 1000 compounds),\nand flexibility (alter radius and bit-length). They are easy to interpret (each bit corre-\nspondstoaspecificlocalsubstructure)andconsistentlyrankamongthetop-performing\nfingerprints in QSAR/QSPR modeling, scaffold hopping, and target prediction bench-\nmarks (Capecchi et al., 2020). Furthermore, using retrospective benchmarks, e.g., the\nDirectory of useful Decoys (DUD dataset; http://dud.docking.org/), have shown\nthat 2D fingerprints often outperform 3D-shape methods in early enrichment metrics,\nbecause many active compounds share core substructures that drive binding (Hu et al.,\n2012).\nWe now include a more explicit discussion on the trade-offs of using 2D descriptors.\nSpecifically, we now write (see Discussion):\nAlthough 2D-based UMAP visualizations offer a computationally efficient\noverview of chemical diversity and aid in hypothesis generation, they may\nfail to capture activity cliffs or bio-isosteric relationships. Complementing\nthe druggability map generated here through physics-based approaches, in-\ncluding 3D conformational sampling and HTP docking screens of ligands\nagainst the druggable E3 pocketome (Palomba et al., 2022) will propel the\nrational design of new E3 handles.\nHowever, extensive 3D conformational sampling and molecular docking of potential\nnew E3 binders are beyond the current scope of the manuscript, which focuses on\nthe classification problem of human E3 ligases. Finally, the mapping of small\n5\nmoleculesintowell-definedclustersobtainedhere,aidsinidentifyingspecificityfeatures\nofpotentialE3binderstoanexpandedsetofE3ligases,andmustbeviewedasacritical\nhypotheses generation step in (1) establishing the current coverage of the E3 ligome,\nand (2) bridge gaps in druggability space for the design of new E3 handles.\n2. The same limitation applies to the clustering approach. The distance matrix used\nto classify E3 ligases is based on their 2D structures, but it does not account for the\nfactthatdifferentresiduescancontributetosimilarinteractionprofiles. Additionally, a\nsingleproteinmayadoptmultipleconformations, leadingtovariationsinitsinteraction\npotentialthatarenotreflectedinapurely2D-basedclassification. Giventhesignificant\nrole of 3D structural considerations in molecular interactions, this aspect should either\nbe integrated into the analysis or at least acknowledged as a limitation of the current\nmethodology.\nResponse: In classifying E3 ligases, we initially used a total of 12 different distance\nmeasures from different granular layers (see Methods, Eq. 2–12). In the final metric\nlearning, we used 4 individual distance measures, explicitly including 3D structures\nof E3 ligases, dStr (Fig. 2), spanning all features from the molecular scale. We\nPQ\nused TM-scores (Eq. 8) to quantify the structural similarity of E3 ligases. We limit\nour computations to the catalytically active structured domains and exclude terminal\ndisordered regions (regions with low pLDDT).\nTM-scores accurately capture the trends in similarities, given the fluctuations around\nthe X-ray models. Moreover, recent deep learning methods predict TM-scores from\nprotein sequences to establish remote homologues Hamamsy et al. (2023). In fact\nour final optimization and metric learning exercise, resulted in the highest weight\n(wˆ = 0.70) for the 3D structural distance (Fig 2f and Eq. 1), reaffirming the\nStr\nimportance of 3D structure, and their preserved patterns in contributing to family and\nsub-family organization. Therefore, our distance measurements accurately consider\nand compare 3D structures of E3 ligases (TM-score based) and are robust to the small\ninternal dynamics of the catalytic domains, if any, during the functional cycle. We\nemphasize this point more explicitly in the revised manuscript (Results section on\nMetric learning, and Discussion).\nFunctional dynamics is not always captured by static structures (from either PDB or\nAlphaFold models) and requires detailed characterization of functional conformational\nchanges using explicit molecular dynamics simulations (Wang et al., 2024; Dixon et al.,\n2022; Zheng et al., 2022; Liwocha et al., 2020; Gonz´alez et al., 2023), beyond the scope\nof the current manuscript.\nFinal evaluation (Reviewer 1)\nThe work is well written and has a highly valuable impact in the scientific field. The results\nare well described and accompanied by attractive and well-made images. The methodology\nis explained quite well, though adding a bit more detail could be beneficial. Only minor\nrevisions are necessary, specifically adding more details and explanation as described in the\npreviously highlighted points. Overall, the work is very interesting, presenting partially\n6\noriginal data regarding the classification of E3 ligases and proposing an innovative and sig-\nnificantly clearer classification compared to existing ones, making it a valuable addition to\nthe field. However, beyond the minor revisions mentioned earlier, two major comments need\nto be addressed.\nResponse: We thank the reviewer for overall positive feedback on the significance, clarity,\nand presentation of our work. We have incorporated additional methodological details as\nsuggested and addressed both the major and minor comments to further strengthen the\nmanuscript.\n• First, the presented study is not entirely novel, as a 2022 publication introduced a\nweb-based platform called ELIOT, which shares a conceptually similar aim. While the\nmethodological approach used in this study differs from that of ELIOT, the final prod-\nuct is quite similar. The key distinction is that ELIOT focuses less on classification and\ninstead provides an easily navigable platform where users can directly access and ex-\nplore information on E3 ligases, binding sites, known interactors, druggability analysis,\nand clustering analysis. It would be beneficial to discuss this in the manuscript.\nResponse: In the revised version, we now explicitly map ELIOT data onto our\ncompound-E3 interaction pairs, highlighting how ELIOT data complements our drug\nre-purposing and lead development strategy by providing a clear structural context\n(Fig. 6). We further note that extending ELIOT analysis for a larger dataset of\ncompound-E3 pairs using predicted/docked compound-E3 interaction models from\nhigh-throughput in silico screens will reveal a much richer landscape of uncharted\npocketome.\n• Secondly, while the supplementary files related to the dataset used are well-provided,\na file regarding the PDBs used is missing. Results are included as supplementary files,\nbut having a more practical way to access and utilize them would be advantageous.\nFor instance, a final, consolidated file in the form of a structured database could allow\nthe scientific community to directly consult and leverage the findings. Without a clear,\ncomprehensive output, the study leans more towards a review that collects and refines\ndata from literature and various databases rather than a research article presenting\nnew results. Providing a tangible and accessible resource would significantly enhance\nthe usability of this important work, ensuring that the broader community can fully\nbenefit from the significant efforts undertaken in this study.\nResponse: We now provide a dedicated repository (downloadable https://gitlab.\ncom/ccblab/e3_ligome) and a web-platform (https://e3-ligome-91adc4.gitlab.\nio/index.html) to access “The human E3 ligome”. This website provides informa-\ntion on E3 ligome, data collection, filtering process, curation, and final curated data,\nalong with the classification of the E3 ligome into distinct families and sub-families in\na downloadable format. Further, for each family, we provide summaries of our find-\nings, along with enriched functions and substrates, and small-molecule interactions.\nFor each E3 catalytic and non-catalytic component protein, we provide database confi-\ndence scores, basic information on classification, and hyperlinks (UniProt, GeneCards,\nraw data used in the Human E3 ligome project.\nThe study constructs a multi-scale classification system for human E3 ubiquitin ligases by\nintegrating multi-dimensional data, holding important theoretical value in deciphering the\ncomplexity of E3s and their functional specialization mechanisms. The proposed metric\nlearning framework offers new insights for enzyme family classification, presenting certain\napplication value to the field. The reviewer suggests the following revisions:\nResponse: We thank the reviewer for recognizing the value of our multi-scale classification\nand the potential application of our metric learning framework. We reinforce the focus on\nhow this classification reflects the complexity and functional specialization of E3 ligases by\naddressing all questions raised.\n1. The significant value of this paper lies in that the new classification system can predict\nnovel functions for E3 subfamilies. However, the current analyses rely on computa-\ntional methods such as GO enrichment and structural predictions, lacking wet lab\nvalidation. The absence of validation experiments at present results in the mapping\nbetween the classification system and actual biological functions remaining at the level\nof data correlation, failing to form a logically closed loop of experimental confirmation.\nRegarding the author’s claim of “identified over 60 biological processes enriched with\nE3s corresponding to distinct families,” could 1–2 key wet lab experiments be added?\nFor example, the statement that “RING5 E3s are enriched in regulating antiviral re-\nsponse, type-I interferon production, regulation of viral entry, and NF-κB signaling”,\nand the conclusion that “RBR family E3s specialize in K6-linked ubiquitination” are\nwithout ubiquitination experiments.\nResponse:\nWe thank the reviewer for this valuable comment. We have now revised the entire sec-\ntion on “Functional segregation of the human E3 ligome”, and added new experimental\ndata.\n(1) We performed CRISPR-cas9 dropout screens for UPS genes, identifying essential\ncatalytic and non-catalytic E3 components for cell viability (see Fig. 4a-c). We\nalso show that these findings correlated with the depMap data (Supplementary Fig.\n18a).\n(2) We revisited our functional cluster enrichment analysis (family-specific functions\nof the E3 ligome) and limited our predictions using only high-confidence GO annota-\ntions (see Methods; Fig. 4d-e, Supplementary Figs. 18-19). Further, we map\nexplicit previous research (PMIDs) consistent with each functional segregation pattern\nobserved. Predictions using low-confidence annotations are also provided and serve as\nnew hypotheses for future testing (Supplementary Figs. 19).\n(3) We discuss 2 examples of family-specific functions corresponding to the RBR and\nRING5 family in detail, providing mechanistic insights (Fig. 4f).\n(4) We measured starvation and DNA damage response of HeLa cells and identified\noverlapping E3 ligases with significant changes in proteomics experiments consistent\n9\nwith our predictions. We outline the mechanistic basis for these E3s in activating\nautophagic response and DNA damage repair processes (non-degradative functions of\nthe E3 ligome; Fig. 4g-h).\n2. The set of E3s studied here relies on the understanding of catalytic domains and is\nlimited by existing data (only covering three types of proteins: RING/HECT/RBR),\npotentially missing enzymes with non-canonical catalytic features or those dependent\non cofactors. This study more closely resembles a refinement of the three major\nRING/HECT/RBR families rather than an analysis of the entire human E3 ligome\nas claimed by the authors. By screening only based on known catalytic domains, “986\nproteins with low consensus scores remain unclassified and unannotated, excluding\nthem from the curated E3 ligome,” the study may overlook E3s with non-canonical\nor unknown catalytic mechanisms, making it difficult to cover all E3 types and limit-\ning the comprehensive understanding of the E3 ligome. Authors need to evaluate the\ncompleteness of ubiquitin ligase data.\nResponse: We thank the reviewer for highlighting these important points.\n(1) We now provide detailed definitions of catalytic and non-catalytic components of\nthe E3 ligome (adaptors, receptors, scaffold proteins, and E2 proteins n = 151+106+\n8+33 = 298) and explain the exclusion process (see edited Fig. 1b-c, Supplemen-\ntary Fig. 1, and Methods). We now exclude only the unclassified proteins and\nthose lacking clear domain annotations, n = 514+174 = 688, thus accounting for all\nproteins in the unified datasets. Further, the confidence scores provide an estimate of\nour current knowledge on each protein.\n(2) We now add a dedicated section on the “Completeness of our E3 ligome” (see\nSupplementary Text 1) covering detailed analysis on the errors of omission (missed\ndiscuss the scope and limitations of our curation approach, covering pseudo-E3 ligases,\nE3s with untested and minimal activity, and other non-catalytic components (E2s,\nreceptors, adaptors, and scaffold proteins).\n(3) Capturing atypical or non-canonical ligase mechanisms computationally remains a\nchallenge. Gathering experimental proof for atypical E3 mechanisms is already quite\nhard and an active area of research. However, in addition to canonical RING,\nHECT, and RBR mechanisms, our E3 ligome captures several known atypical E3 ligases (see\nSupplementary Text 2 for a detailed account). Their inclusion in our E3 ligome is\nexplained by the presence of evolutionarily related RING-, HECT-, or RBR-like do-\nmains with atypical mechanisms. The heterogeneous HECT2 family contains an out-\nlier RING-domain containing sub-family, which is populated with atypical E3 catalytic\nmechanisms (e.g., MYCBP2, RNF213, etc.). We have now highlighted interesting fea-\ntures of our classification in the revised version.\nWe acknowledge that ongoing research may further expand or refine the human E3\nligome, just as in the case of the human Kinome project. Our current version of the\nE3 ligome provides a robust and objective foundation for functional analysis, while\nrecognizing existing knowledge gaps. We host this data with version control on our\nGitLab repository to be updated as more information emerges.\n10\n3. Among the 12 distance metrics established by the authors, only four of them were\nultimately selected for protein subfamilies classification. What is the rationale behind\nthese selected four distance metrics? Only the related biological features are important for the E3 family. When calculating the distance for expression levels, both protein and mRNA expression levels were used—what is the consistency between them?\nResponse: We selected only 4/12 distance measures as they span fundamental granu-\nlarity layers corresponding to the molecular scale (Fig. 2b). These four distances\ncapture intrinsic molecular attributes and their relationships. Sequence-, domain\ncomposition-, 3D structure-, and GO molecular function-based distances provide ac-\ncurate information on conserved residues/motifs, preserved catalytic and non-catalytic\ndomains, alignedspatialorganization, andcomparableenzymaticactivity, respectively.\nThese attributes maximize clustering of mechanistic and functionally similar E3s. Fur-\nthermore,theyreflectE3evolution,capturinggene/domainduplication,diversification,\nandrearrangementevents, forinstance, inco-clusteringofparalogswithconservedpat-\nterns within the E3 ligome.\nBycontrast, theotherdistancesbasedonGObiologicalprocesses, cellularcomponents,\nand sub-cellular localization, cell and tissue expression profiles describe more systems-\nlevel organization. These distances have broad and noisy distributions, and are non-\nspecific to known E3 classes, limiting their relevance to classify related E3 mechanisms\nand functions. Including such noisy features would dilute the classification.\nWe find that for E3 ligases, the protein abundance (pTPM (protein) from PaxDb) and\nexpression (pTPM (mRNA) from Human Protein Atlas) of HeLa cells are positively\ncorrelated (Pearson r = 0.56, Supplementary Fig. 23b). Further, this data is only\navailable for a few cell and tissue types, limiting its applicability for metric learning.\nFurther, the computing time and the optimization complexity limit the total number of\ndistance measures for the linear combination metric learning approach here. Current\nbrute-force sampling of all of 105 metrics would increase to 1013 metric evaluations,\nmaking it costly. Although more conservative sampling procedures could reduce the\nnumber of metric evaluations and computational cost, their efficiency in exploring\ninformative parameter space remains untested.\nFor these reasons, we focused on the four most informative distances. However, we\ninclude all twelve in the paper for completeness. The other distance measures provide\nsystems-level information on E3 systems more relevant for understanding E3 biology\nand developing an efficient drugging strategy (see Supplementary Fig. 23). More\nspecifically, they provide complementary information in the form of unique projection\nmapsforE3sontosub-cellularcompartmentstotargetdistinctsubstrateproteins, Cell-\nline expression for new assay development, and tissue-specificity relevant to human\nphysiology.\n4. The paper, especially in the abstract, overemphasizes the degradative function of the\nubiquitin-proteasome system (“target specific proteins for degradation”). In reality,\nthe non-degradative functions of ubiquitination are also very important, and a revision\nis suggested.\nsection discussing the non-degradative functions of ubiquitination (see Supplemen-\ntary Text 16). In addition, we have revised the abstract, main text, and discussion\nto highlight the importance of these non-degradative roles.\n5. What about the confidence of protein-protein interaction data on the paper’s analysis\nfor “Interaction landscape of the human E3 ligome”? Is there any bias from the\ninteraction datasets that can affect the analysis results?\nResponse: The confidence of the protein-protein interaction (PPI) data in the anal-\nysis of the “Interaction landscape of the human E3 ligome” largely depends on the\nstringency of data curation and the filtering process applied to minimize false posi-\ntives.\nTobeginwith,onlyexperimentalinteractions(MI:0045)areconsidered,asannotatedin\nIntAct. This first step filters interactions based on inference, predictions, and unspeci-\nfied methods, which could otherwise introduce systematic bias. To obtain a cut-off for\nfurther filtering of these interactions, we first detected a subset of the well-known\nESIs (experimentally curated from UbiNet; blue) overlapping with the direct PPIs obtained\nfrom IntAct (purple, Supplementary Fig. 20a). The PSI-MI confidence scores for\nthese overlapping ESIs (n = 239) range between 0.25 and 1.0 (Supplementary Fig.\n20b) with a median value of 0.55 (red line in Supplementary Fig. 20b). This\nencouraged us to use a threshold value ≥0.5 for filtering PPIs. This cutoff strikes a\nbalance between including enough interactions for meaningful analysis while exclud-\ning lower-confidence edges that disproportionately contribute noise. Further, major\nPPI resources (IntAct/IMEx, Reactome, Open Targets) typically use 0.4–0.45 as a\nmedium-confidence baseline. Rounding that to 0.5 provides us with a threshold that\nsits squarely in the “medium-to-high” confidence range, increasing the proportion of\nPPIs detected using diverse approaches (Supplementary Fig. 20c) (Porras et al.,\n2020).\nFurther, for known ESIs (UbiNet), the majority of interactions were detected via anti-\ntag coIP, anti-bait coIP, and pulldown experiments, with only a small number derived\nfrom other methods (see Supplementary Fig. 20c, top). In contrast, the PPIDirect\ndataset is dominated by Y2H-based approaches, including standard two-hybrid, vali-\ndated two-hybrid, and prey-pooling assays, which are designed to identify direct binary\ninteractions (see Supplementary Fig. 20c, bottom). Y2H-derived data is partic-\nularly valuable because it is more sensitive to transient interactions, such as those\nbetween E3 ligases and their potential substrates. Alternatively, CoIP methods might\nbe useful to detect stable multi-subunit complexes containing E3s. This emphasizes\nthe complementary nature of the datasets and the importance of integrating high-\nconfidence Y2H-derived PPIs with CoIP-based PPIs to achieve a more comprehensive\nand accurate E3-substrate interaction map.\nDespite these precautions, PPI datasets could exhibit methodological and literature\nbiases, which are important to consider for E3-substrate studies. For example, inter-\nactions from high-throughput experiments (e.g., AP-MS) often over-represent stable\ncomplexes, whiletransientorweakinteractionsaremorerelevanttoE3-substratepairs.\ncharacterized ligases or substrates are more likely to be reported in the literature and\ncurated into databases."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The work has been updated according to reviewers' requests, and any doubts have been thoroughly addressed. I have\nnothing further to add and consider it a very valid work, ready for publication.", "reviewer_3": "I would like to express my sincere gratitude to the authors for their response to the comments. The authors have addressed\nthe questions raised by Reviewer 2 and provided a relatively detailed discussion.\nBased on the authors' response, particularly regarding Question 2, I wish to further explore this topic. The refined\nclassification of E3 ligases (E3s) has made research on them more standardized and logical. In this context, what practical\nguiding significance does this paper hold for mass spectrometry-based high-throughput ubiquitination studies? For instance,\nthe multi-omics study on lung squamous cell carcinoma (PMID: 34358469) involves ubiquitination-based omics analysis.\nWill the more precise classification of E3s, as reported in this study, help uncover aspects that are difficult to explore under\nthe existing analytical framework—such as more specific drug targets that are hard to identify with current analytical\napproaches?"}, "author_response": "Response: We thank the Reviewer #1 for their suggestions in improving the manuscript.\nResponse: We greatly appreciate Reviewer #3’s kind remark and are pleased that our re-\nvisions and detailed responses have satisfactorily addressed all previous questions. We thank\nthe reviewer for recognizing our efforts to improve the manuscript.\nResponse: To address this additional query, we added Supplementary Text 17 (cited\nin Results and Discussion sections), outlining practical and actionable guidelines on how\nthe proposed human E3 ligome can be harnessed.\nIn Supplementary Text 17, we describe how our classification framework enables\nsystematic annotation of large-scale omics datasets by integrating diverse HTP screening\ndata—from proteomics to genetic and small-molecule screening studies to uncover previously\nuncharacterizedE3s, substrates, andchemicalmodulators. TheE3ligomestructureprovides\na framework for validating family- and subfamily-specific functions, mapping degrons, and\nidentifying CRISPR-based genetic dependencies. We also highlight how mechanistic screens\nusing catalytic mutants or activity-based probes can assess functional convergence within E3\nfamilies. Finally, integrating the E3 ligome data obtained here with spatial transcriptomics,\npheotypic profiling and chemical proteomics can reveal context-specific E3s and inform ra-\ntional PROTAC design, underscoring the broad analytical and experimental utility of our\nframework. In summary, we envision that the human E3 ligome classification presented\nhere will catalyze E3 biology much like the Human Kinome project (Manning et al., 2002)\ntransformed kinase research."}, {"round": 3, "reviewer_comments": {"reviewer_3": "My comments have been addressed and I therefore recommend the revised manuscript for publication."}, "author_response": ""}]

Decision letter