Atlas of predicted protein complex structures across kingdoms

Domain: biology
Published: 2026-03-25
Editorial outcome: major_revision

Abstract
Protein complexes are fundamental to all biological processes. Public repositories have expanded to include millions of potential protein–protein interactions (PPIs) from human and diverse model organisms. Yet, large-scale structural characterization of these complexes—especially across different biological kingdoms—has lagged far behind, leaving most potential and unidentified interactions unresolved. Here, we present a comprehensive atlas of 1.1 million predicted protein–protein interaction structures generated with the AlphaFold2-based ColabFold framework. This dataset spans proteome-wide interactions from bacteria, archaea, humans, mice, plants, and human–virus pairs. Overall, we identify 181,671 high-confidence protein complex structures, especially 37,855 in the human interactome. Structural clustering revealed numerous conserved protein complex architectures shared across kingdoms, providing insights into previously uncharacterized biological functions. Supported by co-immunoprecipitation experiments, we further identify candidate viral receptors for Human mastadenovirus A and Papiine alphaherpesvirus 2. Comparative analyses integrating our complex structures with the AlphaFold monomeric structure database uncovered widespread gene fusion and fission events during evolution. Finally, we demonstrate how our dataset can enhance protein binding–surface prediction using deep learning approaches, illustrating its broad utility beyond structural modeling alone. Altogether, this atlas to our knowledge represents one of the most extensive cross-kingdom resources and opens avenues for future discoveries in various biomedical applications.

Introduction
Deep learning has transformed biological researches by enabling accurate modeling of the three-dimensional structures of proteins and their complexes. Revolutionary tools such as AlphaFold2 (AF2)1, RoseTTAFold2, and ESMFold3 have considerably enhanced our understanding of protein mechanics by predicting protein monomer structures from amino acid sequences. The extension of AlphaFold-Multimer, tailored for multimeric proteins, has significantly improved complex-structure prediction accuracy4. Additionally, combinatorial and hierarchical assembly algorithms or searching approaches with Monte Carlo tree, have shown high-confidence predictions for large protein complexes based on AF25,6. Recently, the development of a unified deep-learning framework that integrates proteins, nucleic acids, small molecules, metals, and chemical modifications , including RoseTTAFold All-Atom7 and AlphaFold38, has achieved unprecedented accuracy across the biomolecular spectrum. These technological breakthroughs are reshaping biological research, accelerating therapeutic discovery, and expanding the reach of structural modeling into nearly every aspect of molecular biology.

Proteome-scale monomer prediction has profoundly expanded our understanding of protein function, evolution, and drug design9-11. The AlphaFold Protein Structure Database (AFDB) now contains over 214 million structures predicted by AlphaFold2, while the ESM Metagenomic Atlas contains more than 617 million structures predicted by ESMFold3. Structural-alignment-based clustering algorithms, such as Foldseek, enable large-scale mining of these protein structure databases12-14. These algorithms have revealed numerous previously unidentified structures and enzymes through the clustering of a broad spectrum of protein structures14,15. Two recent studies have predicted 67,715 and more than 350,000 novel viral protein structures, revealing unprecedented architectural diversity16,17. Furthermore, AI-guided structural mining has enabled a new base-editing tool with enhanced activities and minimal off-target effects18. Collectively, these developments illustrate the growing role of large-scale computational prediction in diverse areas of biological discovery.

The assembly of both stable and transient protein complexes forms the cornerstone of virtually all biological processes. Understanding the architectures of these complexes is essential for elucidating and modifying their functional roles. Repositories of potential protein-protein interactions (PPIs) generated from high-throughput experimental techniques such as yeast two-hybrid systems or computational predictions, have burgeoned, now comprising millions of entries19,20. However, while small-scale predictions and experimental studies have revealed numerous bacterial and eukaryotic heterodimers and homo-oligomers8,21-23, comprehensive and accurate structural characterization of complexes spanning multiple kingdoms remains limited. This gap hampers our ability to analyze interaction mechanisms, evolutionary conservation, and functional regulation across species.

Here, we develop an approach to identify potential protein-protein interacting pairs in prokaryotes using co-localization analysis and operon prediction. We compile potential protein interactions in 36 pathogenic bacteria and adjacent protein pairs in 188 prokaryotic species, identifying a total of 553,814 pairs for structural modeling, of which 108,879 are classified as high-confidence protein-protein interactions. Further, we predict 559,960 protein complexes in human, plant, mouse, and human-virus interaction. Collectively, we predict 1.1 million protein complexes, among which we identify 181,671 high-confidence protein pair structures, especially, more than 37,855 high-confidence predict protein complexes in human interactome. Building upon this extensive protein-protein interaction atlas, we conduct a series of in-depth analyses that include identifying large protein complexes, clustering these complexes across kingdoms for evolutionary analysis, coupling the structural human interactome with the human-virus interactome to assess viral interference in the context of the human protein network, and analyzing gene fusion and fission events throughout evolution. Finally, we further illustrate the applicability of the high-confidence predicted protein complex dataset for identifying protein interaction surfaces. Our results provide a unified structural complex landscape of the proteome across biological domains, revealing evolutionary patterns and mechanistic insights that could not be obtained from sequence data alone. We anticipate that our large-scale atlas of predicted protein complexes across kingdoms and host-pathogen interactions will prove valuable for further research and applications.

Results
Identification of loci-linked protein complexes in bacteria and archaea

In bacteria and archaea, genes encoding proteins that participate in related biological functions are frequently co-localized on the genome, often forming operons. Such organization enables coordinated transcription and efficient regulation through a single promoter, producing polycistronic mRNA24. To identify possible physical (PPIs), we first detected operons using the Operon-mapper server25 for 36 species of widely distributed pathogenic bacteria (Supplementary Table 1) and identified 21,609 operons (gene counts >1), with an average of 3.3 genes per operon (Fig. 1a and Supplementary Fig. 1). To capture protein pairs that are not located in the same operon, we further developed a pipeline to explore co-localization of protein pairs to reflect their interdependence (Fig. 1a). Such an approach has been widely used in the discovery of CRISPR-associated proteins26,27. In this study, we systematically cataloged all pairs of proteins located within 3 kilobases (kb) of each other on the genomes of pathogenic bacteria. A comprehensive protein dataset, including information about their positions in cognate contigs, was collected. This dataset consists of 18 million contigs from bacterial genomes sourced from the National Center for Biotechnology Information (NCBI) and encompasses approximately 0.89 billion proteins identified in coding sequences (CDS) regions. The co-localization score was defined as the proportion of contigs where homologs of two query proteins appeared within 3 kb, normalized by the total number of contigs containing either homolog. To determine the suitable threshold, we calculated the co-localization score of proteins in a batch of biosynthetic gene clusters28. We observed a significant enrichment of protein pairs with co-localization scores exceeding 0.4. Consequently, we established a cutoff of 0.4 for subsequent analyses (Supplementary Fig. 2). Totally, we identified 164,037 potential protein pairs across 36 pathogenic bacteria using both operon analysis and co-localization mining (Fig. 1b).

We used ColabFold v1.5.5 to generate 5 PDBs for each protein pair with up to 20 cycles (Supplementary Fig. 3)29. While preparing this manuscript and in step with the release of AlphaFold38, we also tried to compare the prediction accuracy of AlphaFold3 and other AlphaFold3-inspired models, including Boltz-230, Chai-131 and Protenix32, with ColabFold Multimer. We found that, on heterodimers, AlphaFold3 achieved the highest accuracy, with Boltz-2 performing comparably; for homodimers, multiple sequence alignment (MSA)–based methods showed similar accuracy (Supplementary Fig. 4). To distinguish true interactions from artifacts, we assessed each predicted complex using multiple established confidence metrics, including Predicted DockQ (pDockQ)33, pDock Q234, Local Interaction Score (LIS), Local Interaction Area (LIA)35, AlphaFold Multimer-derived interface predicted Template Modeling (ipTM) score, and linear models specifically designed for homodimer identification36. Previous studies demonstrated that35, when evaluating the top-ranked model among generated PDBs for each predicted PPI complex, the Best LIS metric provides superior Receiver Operating Characteristic (ROAC) performance—indicating better discrimination between true positives and false positives—for heterodimer identification compared to ipTM, pDockQ, and pDockQ2. Furthermore, as this research recommended, we confirmed that the combination of Best LIS and Best LIA metrics yields the highest precision rate among evaluated scoring schemes (Supplementary Fig. 5a). Therefore, we adopted a threshold of Best LIS≥0.203 and Best LIA≥3432 as criteria to identify high-confidence heterodimeric PPIs for subsequent analyses. As a result, we identified a total of 22,216 high-confidence protein pairs across the 36 species of pathogenic bacteria (Fig. 1d). Also, we observed that increasing the Best LIS threshold improved precision but concurrently reduced in the recall rate. For instance, applying the Best LIS threshold of 0.6 resulted in a precision rate exceeding 90%, whereas the recall rate fell below 15% (Supplementary Fig. 5b). Therefore, we recommend selecting different thresholds tailored to the specific objectives and tolerance for false negatives in different studies.

In addition to heterodimer predictions, we also predicted the homo-oligomeric states of 76,429 proteins within the identified potential protein pairs. In addition to heterodimer predictions, we also predicted the homo-oligomeric states of 76,429 proteins within the identified potential protein pairs. To evaluate the metrics for identifying true homodimers in ColabFold modeling, we constructed a benchmark dataset comprising 411 proteins from the PDB, curated in previous studies36 to remove redundancy and eliminate overlap with the AlphaFold2 training set. In the beginning, proteins resolved as monomers by X-ray crystallography were used as the negative dataset. Similarly, Best LIS showed best ROC performance when we analyzed the top-ranked model of each prediction (Supplementary Fig. 6a). Following previous research36, we also reclassified certain assemblies from dimers to monomers to correct for likely crystal-packing artifacts. This adjustment improved the overall classification performance, as evidenced by an increased AUC (Supplementary Fig. 6a and b). When we selected Best LIS with 0.203 and Best LIA with 3432 as the threshold (Supplementary Fig. 6c), consistent with the previous report21, a significant proportion of these proteins formed homodimers (Fig. 1e).

As expected, proteins encoded by adjacent CDSs exhibited the highest likelihood of forming high-confidence physical PPIs (Fig. 1f), and higher co-localization scores were positively associated with a greater prevalence of predicted high-confidence PPIs (Supplementary Fig. 7). To broaden our understanding of protein-protein interactions across the bacterial and archaeal domains, we collected representative genomes from each of the 188 prokaryotic phyla, encompassing all known archaeal or bacterial phyla, after excluding phyla related to our collected pathogens (Fig. 1g, Supplementary Fig. 8 and Supplementary Data 1). After selecting a representative genome for each phylum, we examined the proportion of the entire proteome at the species, phylum, and domain (super-kingdom) levels that is accounted for by our selected proteins and their homologs. To this end, we performed protein clustering using MMseqs237. As expected, at the species level, whether in representative species of archaea or bacteria, the vast majority of protein clusters, apart from a few small ones, contained proteins from the target genome and collectively covered more than 90% of all proteins found in the sampled genomes of that species (Supplementary Fig. 9 and 10). When the analysis was extended to the phylum level, coverage naturally decreased but remained substantial. For example, at a cluster size threshold of ≥200 members, 84.7% of all Chlamydiota proteins were included, and more than 90% of the clusters still contained C. trachomatis proteins. Similarly, across 2,168 Cyanobacteriota genomes, applying a ≥1,000-protein cluster threshold retained 44.4% of all proteins, and more than 90% of these clusters contained N. linckia proteins. These results suggest that numerous high-abundance, conserved proteins are shared well beyond species boundaries. At the domain level, the same trend was observed (Supplementary Fig. 11). Together, these data suggest that selecting only representative species from each phylum is sufficient to capture most evolutionarily conserved proteins, whereas additional predictions are required to resolve proteins such as strain-specific gene products that lie in the long tail of small clusters.

In total, we extracted 313,348 neighboring CDS pairs and used ColabFold to predict their corresponding protein assemblies. Notably, 47,668 predicted heterodimers were identified as high-confidence interacting pairs (Supplementary Fig. 12), underscoring the remarkable diversity of protein complexes across bacterial and archaeal kingdoms and highlighting their potential as a valuable resource for further investigation.

Beyond colocalized gene pairs, we aimed to identify interactions between proteins encoded at distant loci within prokaryotic genomes. As a proof of concept, we analyzed the Campylobacter jejuni genome and surveyed 21,676 reference genomes. Additionally, as shown in Supplementary Fig. 13, by reducing the original 200-step diffusion process to a single step, the time-efficient MSA-free Chai-1 model (MSA-free Light Chai-1 model), although lacking 3D structure generation capability, produced ipTM scores that correlated well with ColabFold Multimer predictions (r ≈ 0.68) and exhibited strong discriminatory power for PPIs (AUC = 0.783) (Supplementary Fig. 14). Therefore, all Campylobacter jejuni protein pairs that passed stringent genomic co-occurrence (score ≥ 0.5) and length filters were first evaluated using the MSA-free Light Chai-1 model, and the top-ranked candidates were subsequently modeled in 3D with ColabFold-Multimer. For example, it revealed a heterodimer between an RDD family protein and an SH3 domain–containing C40 family peptidase. Such an integrated approach could serve as a general strategy to expand the putative prokaryotic interactome (Supplementary Fig. 15).

In total, we predicted 553,814 protein pairs, including both heterodimers and homodimers, and identified 108,879 high-confidence physical protein complexes for subsequent analyses.

Reconstruction of multi-component protein complexes in prokaryotes

To de novo chart multi-component protein assemblies from our predicted binary PPIs, we built comprehensive interaction maps across 36 pathogenic bacterial species. In these networks, proteins form discrete communities corresponding to putative complexes; for example, Klebsiella pneumoniae exhibits highly cohesive clusters (Fig. 2a). A prominent and recurring feature across species is the ribosome, which forms a robust community in most organisms (Supplementary Fig. 16). Additionally, we recovered multiple large assemblies, including the ethanolamine-utilization carboxysome, type I fimbriae, the urease complex, and the recently described propanediol-utilization metabolosome38. Across the 36 pathogens, we identified 3,803 communities comprising >2 proteins, with the per-species counts ranging from 16 to 242. The largest community contained 39 subunits (Fig. 2b). Collectively, these results indicate that community detection on predicted PPI maps can recover diverse multi-component protein assemblies at proteome scale.

Having delineated communities largely from heterodimeric edges, we next asked whether their constituent subunits also self-associate, thereby assembling into higher-order complexes. As a case study in a virulence-linked module, we examined copper homeostasis. Bacteria can deploy copper intoxication to restrict invading pathogens, and bacteria counter with copper-sensing and efflux systems39. In Salmonella enterica, we identified a CopRS two-component system in which the sensor kinase CopS was predicted to form a homodimer, while CopS and the response regulator CopR were predicted to associate as a heterodimer. By combining these interfaces, we reconstructed a quaternary CopRS assembly using CombFold6 (Fig. 2c), providing a structural hypothesis that could inform the design of targeted inhibitors.

Building on complexes inferred from heterodimeric interactions, we next examined whether homodimerization could nucleate higher-order assemblies, especially in virulence modules. The virulence factor database encompasses 27,982 potential virulence factors across various bacterial species, including a spectrum of mechanisms such as exotoxin production, adherence, and immune modulation40. We generated a comprehensive set of 26,490 homodimeric structures and identified structural symmetries using QSproteome21. For instance, we reconstructed the hexametric assembly of the Hcp family type VI secretion system effector, which displays a hollow, ring-shaped configuration (Fig. 2d). We hypothesized that in 36 pathogenic bacteria, part of the predicted homodimers could further assemble into homo-oligomers, whose subunits might also engage in interactions with other proteins, to form advanced assemblies. To test this hypothesis, we developed a computational pipeline for the comprehensive screening (Supplementary Fig. 17 and Fig. 2e-j). For example, we discovered that in Listeria monocytogenes, proteins EutL and TIGR02536 family ethanolamine utilization protein interact with each other and can each form a trimer separately. We thereby assembled the two-layered 6-subunit complex using our pipeline. Furthermore, we reconstructed several advanced complexes such as phage baseplate, phage tail, and ethanolamine utilization complex, revealing the potential for the assembly of higher-order complexes through the integration of homologous and heterologous dimers (Fig. 2e-j).

Collectively, these analyses show that predicted structural interactomes recover a wide range of multi-component assemblies, underscoring the architectural and mechanistic breadth of prokaryotic protein complexes.

The Atlas of human and model organism protein complex structures

To extend the atlas to eukaryotic systems, we compiled candidate PPIs from multiple large-scale resources, including the HI-Union Human Reference Interactome41, which have been predicted by FoldDock23,33 and experimentally supported human protein–protein interaction candidates from the STRING database19. We filtered the candidates from the STRING database using the pLDDT score of each monomer obtained from the AFDB database, retaining only those with pLDDT scores above 70 and protein lengths between 150 and 800 amino acids. The overlap between two datasets is 3,231 and totally we collected 278,167 potential candidates. Subsequently, we predicted the structures by ColabFold with up to 20 cycles to improve the accuracy. We identified 37,855 high-confidence structures (Best LIS≥0.203 and Best LIA≥3432), representing a 12-fold increase relative to previously published AlphaFold-based predictions of human PPIs23 (Fig. 3a-c).

Since entries from the STRING database were included without applying internal confidence score (combined score) filtering, a total of 1.47 million redundant protein–protein interaction pairs were initially considered. We further compared the STRING combined scores with our predicted LIS scores. Overall, no clear linear relationship was observed between the two measures (Pearson correlation coefficient, r = 0.155), suggesting that even interactions with low STRING scores should be considered in subsequent predictions (Supplementary Fig. 18).

Compared with proteins from prokaryotic species, human proteins are generally longer, which complicates structural prediction and reduces accuracy (Supplementary Fig. 19a). Accordingly, we observed a higher proportion of low-confidence predictions among human proteins (Supplementary Fig. 19b). To further assess whether AlphaFold-based modeling can discriminate true human protein interactions, we selected the PRKCZ–NPM1 pair for detailed analysis. As shown in Fig. 3d, although both monomers contained extensive low-confidence regions and the ipTM score was low (0.366), the Best LIS and Best LIA metrics exceeded the defined thresholds, indicating a high-confidence interaction. In endogenous assays using H1299 cells, we performed co-immunoprecipitation (Co-IP) and cellular co-localization analyses, which confirmed a significant protein–protein interaction (Fig. 3d-g).

In addition, we collected 200,558 potential PPIs across multiple model organisms, including Mus musculus and Arabidopsis thaliana, leading to the identification of a total of 19,812 high-confidence protein complex structures (Supplementary Fig. 20 and 21). Comparative structural analyses across divergent kingdoms may also reveal the evolutionary trajectories that have driven the diversification of these complexes. Remarkably, several protein complexes were found to be highly conserved across the tree of life, underscoring their fundamental biological importance (Fig. 3h). For example, Human glutaredoxin 3 (Glrx3) is an essential [2Fe-2S]-binding protein that forms [2Fe-2S]-bridged complexes with human BolA2. It plays key roles in immune cell responses, embryogenesis, cancer cell proliferation, and the regulation of cardiac hypertrophy42. We found that this complex closely resembles the BolA/IbaG family iron-sulfur metabolism protein and the Grx4 family monothiol glutaredoxin in Brucella melitensis. Notably, human Glrx3 has evolved to contain three tandem-repeat domains (Fig. 3i and 3j). Altogether, we totally identified 57,667 high-confidence protein-protein interaction complexes. This expansion suggests a significant potential step forward in our understanding of the structural landscape of protein complexes in humans and other model organisms.

Atlas of structure-predicted human-virus protein interactions

Viruses exploit a sophisticated network of host-virus PPIs to hijack cellular processes, including endocytosis, nuclear transport, protein translation, and secretion. In response, host cells activate a complex transcriptional program mediated by PPIs, thereby triggering innate antiviral defenses, regulating viral replication, and stimulating the adaptive immune response43. High-throughput experimental and computational approaches have collectively identified a large number of PPI candidates, greatly advancing our understanding of the human-virus interactome44. However, the number of host-virus PPI complexes with experimentally resolved three-dimensional structures remains extremely limited. The comprehensive structural characterization of these PPIs, including the precise three-dimensional arrangements of the interacting proteins, will provide critical insights into the molecular mechanisms underlying viral pathogenesis and host defense strategies. Two curated databases of predicted human–virus protein–protein interactions, HVIDB45 and P-HIPSTer44, provide precise resources for predictions. Due to computational constraints (P-HIPSTer >280k interactions; HVIDB 48,643 PPIs), we retained HVIDB entries and subsampled P-HIPSTer. After removing duplicates, we totally collected 81,235 unique PPI candidates covering 3,531 virus proteins and 8,532 host proteins (Fig. 4a). Novelty was evaluated against the AF-Multimer training set, and 62 virus–human PPIs (at a 70% sequence identity threshold) were found to match training-set structures for both chains. Among the analyzed virus–human PPIs, 5,119 (5.72%) were predicted to have Best LIS scores above 0.203 and Best LIA scores above 3432, indicating high-confidence physical interactions.

In addition, we found that some human proteins could interact with distinct viral proteins, whereas certain viral proteins were capable of binding to multiple host target proteins, suggesting the potential involvement of key viral mediators and critical human target proteins (Fig. 4a). Among these interactions, the top 15 viral or human proteins are illustrated in Fig. 4b and 4c, where several 14-3-3 family members display broad interactions with diverse viral proteins. We specifically focused on host membrane proteins, given their interactions may mediate viral entry into cells and thus represent important targets for therapeutic intervention. CD4, a crucial receptor for Human Immunodeficiency Virus (HIV) and an important cellular biomarker, was predicted to interact with both the glycoprotein of Zaire ebolavirus and the membrane glycoprotein E3 CR1-beta of Human mastadenovirus A (Fig. 4d). These interactions exhibited similar binding sites on CD4 and we validated the interaction between the extracellular domains of CD4 and E3 from Human mastadenovirus A using co-immunoprecipitation (Co-IP) assays (Fig. 4e). Additionally, we also confirmed the interaction of human HLA-DRA and UL44 in Papiine alphaherpesvirus 2 using the same experimental approach (Fig. 4e), suggesting that our AlphaFold-based protein complex structure modeling approach could potentially identify key viral entry receptors on host cells (Supplementary Fig. 22a).

We hypothesized that viral proteins interfere with normal host protein–protein interactions by competing for binding sites or forming alternative complex assemblies. We integrated our predicted human protein complex atlas with the human–virus interactome and identified 78,191 ternary relationships with high confidence. In these relationships, viral proteins interact with human proteins that, in turn, engage additional host partners. To distinguish whether a viral protein forms a ternary complex with two host proteins or instead competes with host proteins for binding, we calculated the binding interface violations for each pairwise combination. Our analysis revealed that 57.4% human protein-protein interactions could potentially be disrupted by viral proteins, as indicated by interface violations exceeding 50% (Supplementary Fig. 22b). For example, we found that the ORF128 ankyrin repeat protein from Orf Virus can potentially interact with human SKP1 (Fig. 4f), which also binds to human FBXL20. SKP1 and FBXL20 constitute core components of the SCF (Skp1–Cul1–F-box) ubiquitin ligase complex, which regulates cell-cycle progression, DNA-damage responses, autophagy and apoptosis46. Interestingly, the ORF128 ankyrin repeat protein targets an overlapping binding surface on FBXL20. Such competitive binding may displace FBXL20 from SKP1, thereby potentially facilitating Orf virus pathogenesis (Fig. 4f).

Collectively, we constructed a structural atlas of virus–human interactions, identifying key host factors and potential viral receptor proteins. Integration of predicted human interactomes revealed potential competitive interactions in which viral proteins may disrupt native human protein–protein interactions, underscoring the importance of the AlphaFold-derived structural atlas in elucidating viral pathogenic mechanisms. In total, we predicted 1.1 million protein complexes, covering proteome-wide interactions across bacteria, archaea, humans, mice, plants, and human–virus pairs, thereby substantially expanding structural coverage and providing a comprehensive reference dataset for diverse analyses (Supplementary Fig. 23).

Identifying protein fusion and fission events through structural alignments of protein monomers and complexes

The AlphaFold Protein Structure Database (AFDB), which contains structural predictions for over 200 million protein monomers, has garnered widespread enthusiasm for its transformative potential, not only in structural biology but across the entire field of life sciences. During evolution, proteins undergo fusion and fission events, in which open reading frames (ORFs) merge or split, thereby enabling the acquisition of new functions47. However, the discovery of protein fusion or fission events remains challenging because of the scarcity of protein complex structures and the difficulty in detecting remote sequence homology12.

We hypothesized that this evolutionary transition can be detected through comprehensive structural alignment analyses of two large datasets: the AlphaFold monomer structure database and our predicted cross-kingdom protein complex dataset, both encompassing eukaryotic and prokaryotic species. Because two subunits within a complex may share structural similarity and correspond to the same monomer position, we aligned each subunit separately to monomers in the pre-clustered AFDB50 database, which contains about 50 million protein structures. The alignment was performed using Foldseek with an overlap threshold of 30% (Fig. 5a). By comparing the predicted protein complexes from the bacterial, archaeal, human, Arabidopsis, mouse, and human–virus datasets with those in AFDB50, we identified 668,992 matches, the majority of which were found in prokaryotic species (Fig. 5b). Surprisingly, we found that thousands of AFDB50 entries also overlapped with human–viral complexes. One representative example, shown in Fig. 5c, involves thymidylate kinase from the vaccinia virus, which was predicted to bind to the human thymidylate kinase (gene: DTYMK). This complex exhibited high structural similarity to an uncharacterized protein from Bremia lactucae, a plant-pathogenic fungus, suggesting that the virus may have evolved to preserve its dimerization capability for survival. Additionally, we identified an essential protein complex in human mitochondria that resembles a hypothetical protein from Perkinsus marinus, suggesting that our comparative analysis could aid in uncovering the unknown functions of proteins (Fig. 5d).

Further comparative analyses revealed that such rearrangements are widespread across kingdoms (Supplementary Fig. 24a). Conversely, in a distinct case, we observed both protein monomers and protein complexes with high sequence similarity within the same bacterial species, indicating the potential occurrence of ORF read-through interruptions caused by occasional stop codons (Supplementary Fig. 24b-c). We identified multiple instances in which protein complexes and their corresponding full-length monomers coexist within the same bacterial species, highlighting the prevalence of gene fusion and fission phenomena across prokaryotic lineages (Supplementary Fig. 24d-e). In addition to clearly identified direct protein fusions, we also discovered cases in which small protein complexes resembled partial regions of long monomers, possibly resulting from complex structural rearrangements of the proteins. Notably, a predicted protein complex from Promethearchaeota archaeon (archaea) exhibited structural similarity to a much larger protein from Macrostomum lignano (Fig. 5j).

Overall, through comprehensive comparative analyses of two large protein structure datasets, we identified numerous instances of protein fusion and fission events, underscoring their pivotal roles in protein evolution.

Improving protein-binding site prediction using our predicted protein complexes dataset

The AlphaFold Database (AFDB) of protein monomer structures not only provides an extensive repository of structural information but also serves as a critical source of training data for developing advanced predictive models. For example, high-quality subsets of AFDB data have been curated to train the AlphaFold3 model8. Over 40 million high-quality monomer structures were used to train the unified structural and protein sequence embedding model SaProt48. AFDB is also applied during fine-tuning stages for protein domain segmentation annotation49. Identifying functional sites on protein surfaces is crucial for drug discovery, protein engineering, and vaccine design. Among our dataset, a substantial array of protein interaction surfaces is available. MPBind, a recently developed model for protein-binding site prediction, is built upon protein language models and equivariant graph neural networks50. We continued training MPBind, referred to as MPBind-PHC, using 9,609 predicted non-redundant protein complexes that were selected according to Best LIS ≥ 0.4 and pLDDT ≥ 80.

To evaluate binding-site prediction performance, we first curated a validation dataset from predicted protein complex structures by removing redundant entries based on interface and monomer structural similarity using FoldSeek. The fine-tuned model was then tested on an independent test set, Pro_Test_315, derived from experimentally resolved structures51, as well as on a filtered version of this dataset (Filtered Pro_Test_315) constructed with the same deduplication procedure. Across all three evaluation datasets, the fine-tuned model exhibited improved AUC values, highlighting the potential of predicted protein complexes as a valuable resource for applications in biomedicine and beyond (Supplementary Fig. 25).

In sum, we demonstrated deep-learning applications built on a large-scale protein complex dataset, showing that the massive predictions generated by AlphaFold not only facilitate the discovery of individual PPIs but also provide substantial value to protein science through high-quality predicated data-driven modeling.

Methods
Pathogenic bacteria operon prediction

We obtained the translated CDS FASTA files of the reference genomes for each of the 36 pathogenic bacterial species from the NCBI genome dataset (https://www.ncbi.nlm.nih.gov/datasets/genome/). The operons were predicted by Operon Mapper (https://biocomputo.ibt.unam.mx/operon_mapper/)25. Proteins encoded within in the same operon were pairwise combined as operonic protein pairs.

Representative genome analysis of archaeal and bacterial phyla

For each phylum of Archaea and Bacteria, a representative sequence was selected from the GTDB database (https://gtdb.ecogenomic.org/tree). The selection criteria prioritized the largest order encompassing the greatest number of classes, followed by the largest family within that order, and continuing this hierarchical approach to identify the GTDB species representative. Corresponding translated CDS FASTA files were retrieved from NCBI based on the GenBank Assembly accession numbers. When a CDS FASTA file was unavailable, alternative representative sequences were chosen. The final selection included 21 representative sequences from Archaea (spanning 21 phyla) and 167 representative sequences from Bacteria (covering 181 phyla). From the acquired FASTA files, all adjacent protein sequence pairs located on the same genome were extracted.

Phylogenetic tree analysis

Phylogenetic tree analysis was conducted using OrthoFinder 2.5.5 (https://github.com/davidemms/OrthoFinder) on the genomic proteins of 36 pathogenic bacteria, 21 representative species of Archaea, and 167 representative species of Bacteria. The resulting phylogenetic trees were visualized with iTOL v6 (https://itol.embl.de/).

Protein complex prediction using ColabFold

We employed ColabFold v1.5.5 (https://github.com/YoshitakaMo/localcolabfold) to predict protein complexes, employing databases including UniRef30 (uniref30_2302), ColabFoldDB (colabfold_envdb_202108), and the template database (pdb100_230517) for the structure inference. ColabFold operates in two main stages: Multiple Sequence Alignment (MSA) generation and protein structure prediction. For the MSA generation, we used the colabfold_search command line tool with --use-templates 1 option enabled to perform template-based searches, while keeping all other parameters at their default settings. Subsequently, we utilized the colabfold_batch command line tool to predict protein structures, and performed the structure refinement on GPUs (-S-amber --use-gpu-relax) with the Alphafold2_Multimer_v3 model. When template matches were identified in the MSA, we incorporated template information into the prediction using --template option. For cases without template matching, predictions were carried out without using the --template parameter. ColabFold uses the num-recycle parameter to control the number of prediction recycles. Increasing the number of recycles can improve prediction quality but increases computation time. Without any special instructions, we used 20 recycles (--num-recycle 20) to ensure high prediction quality. For mice, arabidopsis and 188 representative bacterial and archaeal species, we used 3 recycles (--num-recycle 3) to significantly reduce inference time while maintaining sufficient prediction quality. With default parameter --num-models 5, Colabfold outputs five PDB-formatted files ranked from 1 to 5, for each protein complex prediction. We selected the ipTM highest-ranked PDB file as the final prediction. The corresponding confidence scores, including pLDDT, pTM, and ipTM, were recorded in the inference log files and scoring JSON files. The LIS and LIA metric scores were calculated as the guidance (https://github.com/flyark/AFM-LIS).

Comparison of AlphaFold3 with Protenix, Chai-1, Boltz-2 and ColabFold-Multimer

Heterodimeric and homodimeric complexes deposited in PDB, since 17 May 2023, were retrieved by Dockground server55 filtered to a maximum resolution of 3.5 Å, an interface comprising of at least 12 contacting residues, and a buried surface area greater than 800 Å². Redundancy was removed with Foldseek-multimer with default parameters, yielding 54 heterodimers and 75 homodimers that served as the definitive test set. The AlphaFold3 predictions were performed on the online server (https://alphafoldserver.com/). ColabFold-Multimer was predicted as described above. Boltz-2 was predicted as the following command line: boltz predict input_path --recycling_steps 3 --sampling_steps 200 --diffusion_samples 5 --use_msa_server. Protenix predictions with ESM embeddings was executed as protenix predict --input input.json --out_dir ./output_no_msa --seeds 101 --use_esm. Protenix predictions using MSA, and Chai-1 prediction were performed with scripts available on GitHub (https://github.com/wensm77/Protein-Complex-Atlas). Prediction quality was assessed using lDDT module in OpenStructure56 to evaluate residue-level accuracy and DockQ57 to measure overall chain–chain docking performance.

Co-localization score calculation

Initially, we used each of the 36 pathogenic bacterial genomes as a query in the search against the NCBI database, which served as the target and comprised 890 million proteins (across 18 million contigs). Searches were performed using the MMseqs2 version 15.6f452, filtering for sequences with the similarity ranging from 0.3 to 0.9. The search parameter —max-seqs was set to 10,000 to ensure sufficient confidence in the subsequent co-localization score calculations. Prior to calculating this matrix, we defined co-localization as occurring between two protein sequences when both of the following conditions are met: (1) the proteins are located on the same contig, and (2) the shortest distance between the two proteins does not exceed 3 kbp.

Considering a pathogenic bacterium with n query protein sequences, we denote 𝑞 as the i-th query protein and 𝑇 = [𝑡𝑖,𝑡𝑖,…,𝑡𝑖 ] as the searched target protein list after identity filtering, where 𝐿𝑖 ≥ 0 denotes the number of the corresponding i-th found sequences. For query protein pair 𝑞 ,𝑞 , we iterated through every possible pair of protein sequences in 𝑇 and 𝑇 and counted whether they were co-localized. In other words, 𝑓 (𝑖,𝑗) is initialized to 0 for all 𝑖,𝑗 ≤ 𝑛. Moreover, ∀𝑡𝑖 ∈ 𝑇,𝑡 𝑗 ∈ 𝑇 , 𝑓 (𝑖,𝑗)= 𝑓 (𝑖,𝑗)+1 if 𝑡𝑖,𝑡 𝑗 are co-localized. Accordingly, calculate 𝑓 (𝑖,𝑗) for all combinations of 𝑖,𝑗 ≤ 𝑛 except 𝑖 = 𝑗, since we ignore self-localization. Actually, it is unnecessary to calculate 𝑓 (𝑖,𝑗) for the 𝑖 < 𝑗 situation owing to 𝑓 (𝑖,𝑗)= 𝑓 (𝑗,𝑖).

Based on the analysis above, the co-localization score matrix 𝑀 = [𝑚 ] ∈ ℝ𝑛×𝑛 is calculated as follows:

0 if 𝑖 = 𝑗 or 𝑞 ,𝑞 is not co−localized or 𝑐𝑜𝑛𝑡𝑖𝑔_𝑐𝑛𝑡(𝑇) = 0
𝑚 𝑖𝑗 = { 𝑓𝑐𝑜𝑢𝑛𝑡 (𝑖,𝑗) 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 (1)
𝑐𝑜𝑛𝑡𝑖𝑔_𝑐𝑛𝑡(𝑇𝑖)

where 𝑐𝑜𝑛𝑡𝑖𝑔_𝑐𝑛𝑡(𝑇 𝑖 ) indicates the number of contigs associated with T_i. It should be noted that if at least one of {𝐿𝑖,𝐿𝑗} is small, then one of {𝑚 ,𝑚 } will be may become disproportionately high while the other is close to 0. To avoid such situation and ensure the accuracy of co-localization scores, we applied the principle of mutual interaction between co-localized protein pairs as follows: (1) 𝑚 𝑖𝑗 ≥ 0.4 or 𝑚 𝑗𝑖 ≥ 0.4; (2) 𝑞 𝑖 ,𝑞 𝑗 are co-localized; (3) 𝐿𝑖 ≥ 2000 and 𝐿𝑗 ≥ 2000 . Protein pairs satisfying these conditions were considered associated co-localized protein pairs.

Cell culture and plasmids transfection

H1299 (CRL-5803) and 293T (CRL-3216) were obtained from ATCC (Manassas, VA, USA) and cultured in DMEM medium (Gibco, Rockville, MD, USA) supplemented with 10% fetal bovine serum (FBS; Hyclone, Logan, UT, USA), 100 units/mL penicillin (GIBCO), and 100 μg/mL streptomycin (GIBCO). Cells were maintained in a humidified 37°C incubator under a 5% CO₂ atmosphere. All cell lines used in this study were routinely tested negative for mycoplasma contamination and were maintained at low passage numbers to maintain their identity and were authenticated by morphology check and growth curve analysis.

For plasmids transfection, 293T cells at 60% confluence were transfected using Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA). The plasmids expressing Flag- or HA-tagged proteins (Flag-E3A, Flag-UL44, HA-CD4, and HA-HLA) were synthesized and constructed by GenScript Biotech (Sequences showed in Supplementary Table 2).

Immunofluorescence staining

Cells grown on coverslips were fixed with 4% polyformaldehyde in PBS, permeabilized with 0.1% Triton X-100 in PBS, blocked with 4% bovine serum albumin in PBS, hybridized to an appropriate primary antibody (PKCζ: 1:100, 340815, Zenbio; NPM1:1:200, MA5-12508, ThermoFisher), followed by incubation with a second antibody (Goat anti-Mouse Alexa Fluor 488, A-11029 or Goat anti- Rabbit Alexa Fluor 514, A-31558, ThermoFisher). The cells were counterstained with ProLong® Gold Antifade Reagent with DAPI (82961, CST) prior to visualization and photographed using a Leica TCS SP5II confocal laser scanning microscope. LAS X (version3.3.0) was used to analyze fluorescent images. To evaluate the PKCζ or NPM1 co-localization with DAPI, the free software Image J. Fiji coupled with the Coloc 2 plugin and Pearson's correlation coefficient were used to calculate double fluorescence correlation coefficients, and co-localized fluorescence quantifications were presented by scatter plots.

Co-immunoprecipitation (Co-IP) and western blot

Cells were lysed in IP buffer (1 mM Tris pH 7.5, 5 mM NaCl, 0.25% Nonidet P-40, 0.1% deoxycholate, and protease inhibitors), and equal amounts of total protein were incubated with primary antibodies or normal indicated IgG overnight at 4°C, and then 30 µl of protein A/G beads were added for an additional 2 h of incubation. For exogenous Co-IP, anti-HA beads were added to equal amounts of total protein and incubated overnight. Beads were centrifuged (500 g for 30 s) and washed three times using wash buffer (20 mM Tris-HCl, 250 mM NaCl, 0.2 mM EGTA, and 0.1% Nonidet P-40). The beads were heated at 100 °C for 10 min before western blot analyses. Anti-HA magnetic beads (88836) were obtained from Thermo Fisher Scientific (Waltham, MA, USA).

For western blot, cell lysates were loaded, separated by SDS-PAGE, transferred to PVDF membranes (Millipore), and hybridized to an appropriate primary antibody and horseradish peroxidase (HRP)-conjugated secondary antibody for subsequent detection by enhanced chemiluminescence (Bio-Rad). Western blot images were analyzed using Image Lab Software 5.0. Antibody for GAPDH (AB0037,1:5000) was purchased from from Abways (Shanghai, China). Antibodies for SP1 (SC-59,1:1000) were purchased from Santa Cruz (USA). Antibody for PKCζ (PRKCZ) (340815, 1:1000) was purchased from Zenbio (Chengdu, China). Antibody for KPNA2 (ET1705-61, 1:1000) was purchased from Huabio (Hangzhou, China). Antibody for NPM1 (MA5-125081:1000) was purchased from ThermoFisher Scientific (Wilmington, DE, USA). Antibodies for HA (3724, 1:1000), rabbit lgG (2729, 1:1000), and Flag (117935, 1:1000) were purchased from Cell Signaling Technology (Danvers, MA, USA).

Homo oligomer-contained large protein complex assembly

To construct a large homo-oligomeric protein complex, we adopted a strategy that combines QSproteome with AlphaFold2-bigbang21. Specifically, we first used QSproteome to truncate the flexible regions of homodimers using the default parameters, and then calculated the symmetry-related structure based on this truncation. After determining the optimal symmetry, we used AnAnaS to generate the untruncated symmetric complex structure. This untruncated symmetric structure, along with the MSA and sequence information of the homologous dimers, was provided as input to AlphaFold2-bigbang to predict the full-length symmetric complex structure. This approach allowed us to overcome the negative impact of flexible region truncation on AlphaFold2’s prediction accuracy.

In the high-quality protein structure prediction database, for a heterodimer composed of two distinct monomers, a and b, we first identified homodimers composed solely of a and b, respectively. Then, using the aforementioned method, we calculated the full-length optimal symmetric structures for these two homodimers and determined their optimal symmetries. If the two homodimers share the same optimal symmetry, we used CombFold (https://github.com/dina-lab3D/CombFold) as instructed to assemble them into a complete homo-oligomer containing a large protein complex.

Finetuing MPBind

We fine-tuned MPBind to accommodate our specific task requirements. We curated a subset comprising 71,667 high-quality complexes by filtering based on Best LIS ≥ 0.4 and pLDDT ≥ 80. To ensure diversity and non-redundancy in the training data, we implemented a deduplication strategy based on structural similarity. Utilizing FoldSeek, we eliminated complexes where the TM-score between any two protein monomers was ≥ 0.5. Following this process, the final non-redundant training set consisted of 9,609 dimeric complexes. Based on this dataset, we conducted continued training of the MPBind model (https://github.com/jianlin-cheng/MPBind) for 45 epochs on an NVIDIA RTX3090 GPU. Subsequently, we conducted deduplication on partial of remaining samples based on interface similarity and monomer similarity. In the interface deduplication phase, we employed a strategy that integrates structural alignment with interface site information to identify and eliminate proteins that, despite differing overall structures, exhibit highly similar key functional interfaces. We first utilized FoldSeek to compute structural alignment information between all protein single chains in the candidate samples (command: foldseek easy-search query.pdb targetdb out.m8 tmp --format-output "query,target,evalue,bits,qstart,qend,tstart,tend" -s 9.5). For any pair of proteins (denoted as A and B), we examined the extent of overlap between their structural alignment regions and the pre-defined interface residues. Interface residues in these complexes were defined as surface residue with a relative solvent accessibility greater than 5% that lost more than 1 Å² of absolute solvent accessibility upon protein-protein complex formation. If the overlap between the alignment region of protein A and the entire interface region of protein B exceeds 50%, or if the overlap between the alignment region of protein B and the entire interface region of protein A exceeds 50%, then the pair of proteins is deemed to exhibit interface similarity. Any complex containing protein chains determined to have interface similarity will be removed from the validation set candidate pool. Subsequently, in the monomer deduplication phase, we aimed to thoroughly exclude any single chains in the validation set complexes that display structural similarity with those in the training set. We employed FoldSeek to perform structural clustering of all monomers in the dataset, using the command: foldseek easy-cluster pdb res tmp -c 0.9. Each monomer was mapped to a unique structural cluster representative. Any validation-candidate dimer sharing a representative (from either chain) with the training set was discarded, yielding a validation set whose dimers are structurally independent of all training chains. We ultimately obtained a validation set comprising 205 monomers. Similarly, we performed deduplication on the independent dataset Pro_Test_315, resulting in the construction of a test set containing 28 monomers derived from PDB structures. The predicted residues were selected as the threshold with 0.5 as before.

Genomic co-occurrence rate calculation

Reference genomes were downloaded from NCBI (https://www.ncbi.nlm.nih.gov/datasets/genome/). The homologs were searched by MMseqs2 with similarity between 0.3 and 0.9. Then the genomic co-occurrence was calculated as the number of genomes in which both genes are present, divided by the total number of genomes in which either gene is present.

Protein complex clustering and Gene fusion hit discovery

Protein complexes were predicted by Foldseek Multimer as the command: foldseek easy-multimercluster INPUT_PDB_DIR OUTPUT_CLUSTER_DIR TMP_WORK_DIR --multimer-tm-threshold 0.65 --chain-tm-threshold 0.5 --cov-mode 2 -e 0.01 --exhaustive-search. AFDB50 Foldseek database file was downloaded from https://foldseek.steineggerlab.workers.dev/. Each monomer extracted from the protein complexes was searched against AFDB50 with Foldseek (command: foldseek easy-search query.pdb targetdb out.m8 tmp --format-output "query,target,evalue,bits,qstart,qend,tstart,tend" -s 9.5). For each hit, we considered the aligned query and target segments as intervals, measured the length of their intersection (overlap) and union (combined coverage), and calculated an overlap/union ratio; only hits with a ratio < 0.30 were accepted to avoid mapping to the same region.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The use of PDockQ with AlphaFold2.3 (or ColabFoldv3) is unfortunately not that good (see doi: 10.1093/bioinformatics/btad424). THerefore the entire conclusions on \"reliable\" predictions is flawed and needs to be redone.\nFurther it has also been observed that high ipTM scores are not sufficient to identify high-confident homomeric interactions, this needs to be benchmarked carefully first.", "reviewer_2": "In this article, the authors utilized AlphaFold2-based ColabFold to construct an atlas of around 1 million protein complexes, accompanied by their evaluated predictive confidence scores. They then applied this protocol to identify protein-protein interaction networks in bacteria, archaea, and human proteome. They also investigated human-virus protein interactions and proteins involved in fusion and fission events. Finally, the authors utilized this structural dataset to develop a deep-leaning model for predicting the protein surface binding sites and fine-tuned the proteinMPNN model to enhance protein design accuracy. Several computational studies were validated by their experiments.\nThis study demonstrates an effective way to utilize AlphaFold-predicted structural databases for expanding the existing protein-complex database, as well as facilitating the development of new models for evaluating protein-protein interactions. The authors also released their predicted protein complexes and codes, ensuring the transparency of the research. I have a few suggestions listed below:\n1. The identification of PPIs in bacteria and archaea based on sequence co-localization is interesting, but can the authors illustrate how they address those protein pairs that are located far apart in the genomic sequence?\n2. Since the code of AlphaFold3 has been released, it would be interesting to compare its predictions with the model’s performance in this paper, as well as with other deep-learning structural complex prediction tools.\n3. In the evaluation of Multimer-Surface and the finetuned ProteinMPNN, the authors utilized the predicted complexes. It is better to evaluate the model performance using an independent dataset composed of experimentally determined structures.\n4. Extended Figure 3: the workflow in panel a is a little confusing, especially the distinction between the shaded and unshaded parts.\n5. Figure 6e: The definition of accuracy and perplexity in this context is somewhat confusing. For example, what does the “percentage of correct amino acids recovered” refer to?", "reviewer_3": "Qi et al. present a large-scale resource of protein-protein interaction (PPI) predictions across multiple kingdoms. Using ColabFold, they predicted 1.15 million PPIs, identifying 170,001 high-confidence interactions. While most large-scale PPI studies focus on human proteins, this work expands the scope to other kingdoms, addressing an essential gap in the field.\nThe study explores multiple fronts, including evolutionary analysis of domain fusion, detection of viral-human PPIs, and the use of predicted interactions for training machine learning classifiers—demonstrating broad applicability. However, the breadth of the study appears to come at the expense of depth in each specific area. My main concerns with this work are around comparison to previously published work and homology leakage of the classifiers.\nMajor:\n- The paper does not include a comprehensive comparison to other core resources, despite many studies having already produced large-scale PPI prediction datasets. While some of this work is cited, it is not compared against it. For example, the Predictomes study by Schmid et al. (2024) and Zhang et al. (2024), Computing the Human Interactome, as well as the virulence factor study by Humphreys et al., Protein interactions in human pathogens revealed through deep learning, are highly relevant. Please include a comparison to quantify the novelty of predictions.\n- Figure 1g: The tree visualization is not very informative, as the labels are difficult to read. Additionally, it is unclear how much novelty has been generated per species. A quantitative assessment of novelty at different taxonomic levels (species, phylum, and kingdom) would be critical to understanding the contribution of this resource.\n- Figure 3a and 3j: These figures are difficult to interpret. I recommend reconsidering the analysis and exploring alternative visualization. A more intuitive approach, e.g. for j maybe some Hierarchical clustering, could help convey the key insights more easily.\n- The application of fine-tuning ProteinMPNN is inconclusive. Structural similarity can be detected beyond 30% sequence identity, meaning sequence-based separation may not be sufficient. I recommend using Foldseek-Multimer or a PINDER-like approach to separate training and testing.\n- The Multimer-Surface predictor is presented as an application of the database, but its evaluation has multiple issues. Currently, a random split is used, which may lead to homology leakage, making it unclear whether the model genuinely improves surface extraction. To prevent data leakage, I recommend splitting the test set based on interface similarity, ensuring that the system does not train and test on the same interface type. PINDER provides a well-structured benchmark dataset, and adopting their de-replication strategy could improve the potential leakage issue.\nAdditionally, a comparison against a PDB-trained method is necessary to determine whether the predicted database provides meaningful improvements over existing resources.\n- The manuscript states: \"All other data supporting the findings of this study are available from the corresponding author on reasonable request.\" To ensure full transparency and reproducibility, all necessary data should be made openly available. Currently, only the 170,000 high-quality predictions have been released—please consider also providing the remaining low-quality predictions to allow for further analysis.\nAdditionally, the code repository is missing essential scripts for the analysis and lacks documentation necessary for reproducing the study.\nMinor:\n- For others in the community trying to do large-scale PPI runs it would be great to have some technical information regarding runtime for MSA generation and inference of the models.\n- Kim et al. (2024) predicted over 350,000 viral protein structures through BFVD. This could be as well referenced in the following sentence: \"Recent research has predicted 67,715 newly identified viral protein structures, uncovering a multitude of structurally distinct viral proteins.\".\n- The code for in GitHub and has no license: https://github.com/Protein-Complex-Lab/Protein-Complex-Atlas"}, "author_response": "Response to Referees Letter\nReviewer #1 (Remarks to the Author):\nThe use of PDockQ with AlphaFold2.3 (or ColabFoldv3) is unfortunately not that good (see\ndoi: 10.1093/bioinformatics/btad424). THerefore the entire conclusions on \"reliable\"\npredictions is flawed and needs to be redone.\nFurther it has also been observed that high ipTM scores are not sufficient to identify high-\nconfident homomeric interactions, this needs to be benchmarked carefully first.\nRe: We thank the reviewer for highlighting recent evidence that (i) pDockQ performance\ndegrades for the AF2.3 (ColabFold v3) model, and (ii) high ipTM alone does not reliably\ndistinguish true from false multimeric (especially homomeric) interactions. We have\ntherefore re-evaluated and redone all downstream analyses using metrics whose\ndiscriminative performance we explicitly benchmarked in the revised manuscript, and we\nno longer rely on pDockQ/ipTM as a sufficiency criterion for “high-confidence” complexes.\nRecently, multiple metrics have been developed to distinguish between true and false\ninteraction in predicted protein complexes, including Predicted DockQ (pDockQ)1,\npDockQ22, Local Interaction Score (LIS)3, Local Interaction Area (LIA)3, AlphaFold\nMultimer-derived interface predicted Template Modeling (ipTM) score4, and linear models\nspecifically designed for homodimer identification5.\n[... Response figures and detailed rebuttal text ...]"}, {"round": 2, "reviewer_comments": {"reviewer_1": "In this paper the authors provide an impressive set of predictions of PPIs. Unfortunately, this is also the major problem with the paper. It reads as a list of rather unrelated problems, all of potential interest, but none of them is explained in sufficient detail to provide interesting insights.\nThe most obvious part that is out of sight is the fine-tuning of ProteinMPNN. What does that add for our understanding of PPIs?\nAnother unclear part is what negative set is used for the homomer benchmark (Fig. S5) or how the virus-human dataset is constructed (Homology reduction to training set, etc).\nI am convinced that this can become an interesting resource, but unfortunately, by trying to add too much into a single paper, the authors make it almost unreadable.\nAvailability;\nIt is unclear what is available where and under what license, see remarks below. Ideally the website www.biopredictnavigator.cn should provide an easy access to the data. However it is far from ready (https does not work), it contains still images of the proteins, the coloring scheme in the 3D viewer is not consistent with the labels (it is colored from N-C and not by pLDDT). etc.\nLanguage:\nThe paper contains non-optimal expressions making it hard to read. It really needs a good rewrite of someone who can tell a story and not just staple sentences on top of each others.\nOne of these is \"showed outperformed efficacy\", I guess it should be \"showed an efficacy\" or something like that.\nAnother one is \"Then we fine-tuned the released sourced model parameters using the distilled high-quality data\". WHat do the authors mean here?\n/Arne Elofsson", "reviewer_2": "The authors have addressed my questions, and I appreciate their efforts.", "reviewer_3": "I would like to thank the authors for considering all my comments."}, "author_response": "Response to Referees Letter\nWe thank the Reviewers for their careful assessment of our manuscript. In response\nto the key points raised—particularly by Reviewer #1 regarding focus,\nmethodological clarity, and availability—we have substantially revised the paper to\nstreamline the narrative and deepen the explanations. Specifically, we (i) removed the out\n-of-scope ProteinMPNN fine-tuning section, bacteria l defense system identification\nsectionand reorganize the main text to ensure a more coherent and logically structured\nnarrative;(ii) explicitly define the negative dataset used in the homomer benchmark (Fig.\nS6) and detailed construction of the virus–human dataset; (iii) improve\naccessibility by consolidating code/data links, enabling HTTPS on the website,\ncorrecting the 3D viewer’s coloring scheme to match pLDDT l abels, a nd providing\nthe citation and a license description that clarifies compatibility of all components;\nand (iv) thoroughly edit the language of whole manuscript to improve readability,\nincluding rewriting ambiguous phrases. A point-by-point response follows, with reviewe r\n r emarks reproduced verbatim."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The choice of using the \"Commons CC BY-NC 4.0 license\" makes this paper unsuitable for publication in a open access journal. The authors should change it to Apache 2.0 to allow commercial and non-commercial use of this method.\nGiven this serious shortcoming, I have not bothered about reading the rest of the paper."}, "author_response": ""}]

Decision letter