A comprehensive catalogue of receptor-binding domains in extracellular contractile injection systems

Domain: biology
Published: 2026-01-22
Editorial outcome: minor_revision

Abstract
Extracellular contractile injection systems (eCISs) are bacteriophage tail-derived toxin delivery complexes in prokaryotes. They play roles in microbial interactions with hosts, using tail fiber proteins for target cell binding. Here, we present a comprehensive analysis of eCIS tail fiber genes in bacterial and archaeal genomes, providing insights into their remarkable diversity, target cells, functional adaptations, and evolutionary dynamics. We identified 3445 eCIS tail fiber proteins encoded in 2585 eCIS loci from 1069 microbes. These fibers can be categorized by five new N-terminal domains responsible for tail fiber attachment to eCIS baseplates. We use structure prediction to classify fibers into 276 structural clusters and 1177 domain fold families, which likely mediate glycan and protein binding on the cell surface of eukaryotes or bacterial targets. DNA sequences encoding these rapidly evolving domains may have been acquired from diverse eukaryotes, bacteria, and viruses. Finally, we experimentally show that a candidate tail fiber from a Paenibacillus eCIS can bind and direct effector injection into THP-1 human monocyte-like cells, possibly binding D-mannose on the cell surface. This study reveals the exceptional diversity of eCIS receptor binding domains, suggests new eCIS target cells, and provides thousands of proteins that can adhere to different cell types.

Introduction
Extracellular contractile injection systems (eCISs) are a class of bacteriophage tail-like protein delivery systems encoded in bacteria and archaea that affect virulence and host development, and potentially intermicrobial interactions1–5. eCISs are utilized by bacteria for delivery of various cargo (effector) proteins, mostly toxins, to recipient cells and are emerging as a promising biotechnological tool for modular protein delivery6. eCIS cargo proteins display highly adaptive versatility with an N-terminal signal-dependent effector loading mechanism7. Initially discovered as insecticidal agents in Serratia entomophila, eCIS were denoted as antifeeding prophage (Afp), that causes the Amber disease in the New Zealand grass grub3,8. SimilareCIS operons were independently identified in entomopathogenic nematode symbionts as the Photorhabdus virulence cassettes (PVC), which delivers insecticidal toxins1,9, and in Pseudoalteromonas luteoviolacea, which induce metamorphosis of its host, the marine tubeworm2,10. eCIS complexes have been recently identified in Algoriphagus machipongonensis5, which contain unique structural features. Notably, some eCIS related systems function intracellularly, such as in Streptomyces11,12, where they deliver toxins for self-elimination in response to stress, and in the multicellular cyanobacterium Anabaena, which is thylakoid-anchored (denoted as thylakoidCIS, tCIS)13 via membrane bound tail-fibers. These fascinating discoveries highlight CIS’s versatility and significance within various microbes, both extracellularly and intracellularly.

eCIS-related genomic loci have been extensively studied computationally, which involved analysis of taxonomic and ecological distribution and classification of various eCIS4,14. A wide genomic analysis revealed that approximately 2.2% of sequenced bacteria and archaea have been found to contain these systems, with a high prevalence in terrestrial, aquatic, and invertebrate-associated bacteria and unexplained depletion in mammalian pathogens4. Nevertheless, identification of tail fiber genes, which are encoded by genes such as Afp13 and Pvc13 of Afp and PVC, respectively, is lacking in most eCIS operons. This missing piece is significant, being the putative target specificity determinant of these systems, likely adhering to specific receptors. Identifying tail fiber genes is challenging due to their rapid diversification15 under selective pressure to recognize varied targets16. This sequence divergence, coupled with their inherent flexibility preventing structural resolution, complicates traditional homology-based discovery.

Here, we addressed this challenge by utilizing a domain-based computational approach to identify rapidly evolving tail fiber genes, discovering 3445 tail fibers across 2585 eCIS systems from 1069 microbial genomes. Our analysis revealed five novel N-terminal baseplate anchor domains and categorized the fibers into 267 structural clusters comprising over 1000 domain fold families. Functional validation showed that receptor binding domain from Paenibacillus sp. URHA0014 tail fiber that was engineered into PVC complex enabled specific targeting of the human monocyte-like cell line THP-1. Pre-treatment with D-mannose or mutations in key binding residues in the engineered tail fiber inhibit the target cell recognition of engineered eCIS, pointing to a plausible mechanism of surface glycan binding. These findings illuminate eCIS target specificity mechanisms across the bacterial tree of life, and establish a foundation for engineering programmable protein delivery systems.

Results
Identification of conserved domains lead to discovery of novel eCIS tail fiber genes
Tail fibers likely determine eCIS target specificity and have the potential to elucidate target identity of largely unexplored eCIS particles. This study aimed to maximize the identification of novel eCIS tail fiber gene candidates to allow characterization of target cell binding machinery. First, we searched the eCIStem database, comprising 1425 eCIS operons across 1249 bacterial and archaeal genomes4. Our initial sequence similarity search identified 629 genes resembling Afp13 and Pvc13, covering only 44% of eCIS operons, but their homology was partial and of low-confidence. Structural predictions using AlphaFold2-multimer17,18 revealed that Afp13 resembles a human adenovirus shaft with a beta-spiraled shaft and Ig-like knob, while PVC13 exhibits a chimeric structure of the same shaft with a phage needle-like C-terminal region (Fig. 1a–c), as was solved in the course of our study19. These findings highlight the modular nature of tail fiber genes. To address this inherent variability feature, we used all-against-all BLAST20 and MMseqs21 clustering to generate Hidden Markov Models (HMMs)22 of conserved protein domains (Fig. 2a and Supplementary Fig. 1). This led to the identification of novel tail fiber domains. The domain organization suggested high conservation of N-terminal segments, thus we used these regions in jackhmmer search22 and retrieved 1269 additional fiber genes, nearly doubling the number of loci harbouring fibers.

Analysis of retrieved genes revealed five new conserved N-terminal domains likely serving as baseplate attachment sites, that we termed: eCIS Baseplate Anchor Protein (eBAP1-5) domains (Fig. 2b). Striving to maximize tail fiber gene candidates, we expanded the search to a non-redundant combined database which contains 86,785 non-redundant bacterial and archeal genomes. We identified 2585 putative eCIS genomic loci using a Pfam-based scoring system that prioritized clusters with multiple eCIS-enriched domains (Supplementary Data 1, 2), with higher scores given to distinctive Pfams such as DUF4157, Pvc16_N, and CIS_tube. This yielded 3445 fiber-encoding genes while excluding loci with T6SS related Pfams (Supplementary Data 1). We then divided the fibers by N-terminal domains into five groups: eBAP1 and eBAP2 (~50 amino acids in length) are structurally related and are present in 362 and 603 gene candidates, respectively (Fig. 2c). A previously unknown ~200-amino-acid domain, found in 1420 genes, contains a shoulders-like23 structural feature like the tCIS crown domain, and was designated eBAP3. The first 200-amino acids of the tCIS crown gene, widespread in 402 genes, were classified as eBAP4 (Fig. 2d). Additionally, the DUF6519 domain (eBAP5) was identified in 658 genes, sharing partial similarity with eBAP3/eBAP4 but featuring a distinct 200-amino-acid insertion (Fig. 2e). Its presence in 1114 genes in the InterPro databases suggests it may serve as a novel tail fiber marker in phage or others systems as well. These findings expand our ability to characterize eCIS evolution and examine microbial host specificity. By defining five novel eBAP domains (Supplementary Data 3–7), we establish a foundation for future phylogenetic, structural, and functional analyses, closing the gap on this elusive structural feature.

eBAP domains show congruence with ancient evolutionary lineages and specific niche adaptations
We further explored the distribution of tail fibers across eCIS phylogeny in the light of the newly discovered eBAP domains. Our eCIS phylogeny analysis, based on the phylogenetic tree of the conserved structural protein Afp8 amino acid sequence (which encodes a VgrG protein), showed eBAPs are congruent with the structural evolution of eCIS (Fig. 3a). The eBAP1-5 containing eCIS loci were grouped and dispersed each to specific branches, indicating five major events of structural and possibly functional divergence in eCIS evolution, that were likely followed by horizontal gene transfers, as shown by the diverse phyla present in each clade (Fig. 3a). We further dissected each eBAP related clade (Fig. 3b). All eBAPs are present in at least some Pseudomonodota eCIS. eBAP1 is exclusive to Pseudomonodota. eBAP2 and eBAP4 are also distributed in Bacteroidota and slightly in Cyanobacteriota, in addition to Pseudomonodota. eBAP3 is evenly distributed in Bacillota and Actinobacteriota, while eBAP5 is correlated with Actinobacteriota. Notably, finding large amounts of fiber genes in Actinobacteriota systems is intriguing as it was previously assumed, based on Streptomyces CIS, that this phylum relies on intracellular CIS alone, which should lack tail fibers11,24. Nevertheless, loci that lacked fibers gene candidates might act intracellularly or through mechanisms yet to be discovered, and thus were left out of the scope of this study.

We further investigated the fibers occurrences and genomic neighborhoods to test their position conservation within the operon by evaluating the fiber loci within eCIS operons and their upstream genes. Out of 2585 gene loci that contain fibers; 23.4% contain more than one fiber gene per loci with the maximum being five fibers per loci. Phyla enrichment analysis shows Myxococcota and Pseudomonodota are enriched in multi-fiber eCIS loci, while Bacilota and Desulfo-baterota are depleted, suggesting this trait is associated with niche adaptation (Supplementary Fig. 2a–c). We examined sequence and predicted structural similarity in multi-fiber loci. We found instances of both structural resemblance yet with remarkable sequence divergence and completely diverse sets in terms of domain composition (Supplementary Fig. 2c–e), demonstrating the versatile evolution of eCIS baseplate composition. Further analysis of each specific fiber found in eCIS loci may shed light on its putative role in target binding. The fact that some eCIS operons acquired multiple tail fiber genes or possess evolved duplicates highlights the versatility and rapid evolution of this gene group.

Phylogenomics and structural analyses reveal two main baseplate types driving distinct eCIS evolutionary trajectories
Operonic organization analysis revealed that fiber genes are positioned downstream of tail_P2_I and Afp11/12 (Baseplate J and gp25) homologs in ~90% of cases (Fig. 3c), consistent with PVC structures showing fiber anchoring to Afp/Pvc12. Using AlphaFold2/3-multimer modeling, we discovered that while most eBAPs anchor to Afp12 homologs, eBAP2 might also utilize binding of Pvc11, and that the Tail_P2_I harboring gene might bind BAP3/eBAP5, resembling R-pyocin fiber attachment mechanisms (Supplementary Fig. 3).

Phylogenetic analysis of Afp11 sequences revealed clustering patterns congruent with eBAP domain distributions while additionally identifying two distinct baseplate architectures (Supplementary Fig. 4a). eBAP types 1, 2, and 4 incorporate both Afp11/12 homologs while lacking Tail_P2_I genes, whereas eBAP types 3 and 5 rely exclusively on Tail_P2_I adaptors and largely lack Afp12 homologs. This clear compositional grouping strongly suggests two major evolutionary trajectories underlying eCIS diversification.

Structural modeling across eBAP groups demonstrated diverse assembly architectures: eBAP1-2 fibers exhibited the backward tilt observed in Afp/PVC systems, eBAP4 anchored at the baseplate bottom, similar to tCIS (Supplementary Fig. 4b–d). eBAP3,5 Tail_P2_I-containing loci seem to utilize loop-mediated attachment accommodate up to four fibers per baseplate, representing an advanced targeting mode. Notably, eBAP3 systems showed striking homology to T6SS baseplate architecture, with Tail_P2_I genes resembling TssG baseplate components and eBAP3 fibers sharing structural similarity with TssK proteins, suggesting shared evolutionary origin to the particles. These findings establish a model of dual baseplate types as key drivers of eCIS structural diversification (Supplementary Fig. 4h).

Pfam domain analysis reveals functional diversity and possible evolutionary origins of fiber domains
To characterize the functional architecture of fiber genes, we performed HMMscan25 against the Pfam database26 and visualized domain co-occurrence within the protein as a network (Fig. 3d). This analysis revealed distinct domain association patterns across the five eBAP groups. Although eCIS operons are characterized by a group of only 11 core domains4, we identified a striking number of over 200 tail fiber domains: including 165 Pfam domains and 37 novel domains, which we defined based on all against all Blast, that are found within the eCIS tail fiber genes that might be responsible for its versatile shape, size, and receptor binding. eBAP1 domains showed restricted association, primarily linked to Av_adeno_fiber domains found in the well-studied Photorhabdus PVC and Serratia Afp systems. In contrast, eBAP2-5 domains anchor diverse functional modules. eBAP2 also co-occurred with adenovirus shaft repeat, gp36 from phage T4, C1q domain that is prevalent in animal proteins27, and the Peptidase_S74 chaperone. The eBAP3 group displayed the most diverse associations with several types of domains. Some domains serve as enzymes, e.g., Peptidase_S74, amidase, and the lactonase domains. Others might imply target binding due to their role in attachment, such as H-lectin, SWM_repeat28, Laminin_II, BIG_2/5 domains. eBAP4 formed a distinct network module, strongly associating with immunoglobulin-like fold domains including PKD, Big11, von Willebrand factor type A (VWA), and carboxypeptidase-regulatory (PKD) domains as well as Invasin_D3 and Cadherin_II attachment proteins (Supplementary Fig. 5a). Notably, the overall fabric of the network seem to be interconnected by Pfam and newly defined domains, some of which are abundant, suggesting high domain swapping and/or frequent acquisitions.

Next, we detected the phylogenetic origin of the Pfam tail fiber domains. Strikingly, we found 64 Pfam domains that display phylogenetic incongruence with bacteria using a conservative method. Namely, over 90% of proteins in the InterPro taxonomy database are non-bacterial, which might point out potential donors for cross-kingdom acquisitions (Supplementary Fig. 5b). Notably, several bacterial eCIS sequences (e.g., adenovirus shafts, C1q domains) formed nested clades within metazoan and viral phylogenetic branches rather than clustering as sister groups, a pattern consistent with cross-kingdom horizontal gene transfer rather than shared ancestry (Fig. 3e–g and Supplementary Fig. 5b–d). Strikingly, the analysis reveals that eCIS tail fibers evolved functional plasticity through horizontal acquisition of receptor-binding domains from eukaryotes (animals, plants, and fungi), viruses, and phages. Incorporation of immune-related domains (C1q, immunoglobulins, PKD, VWA) suggests bacterial strategies like host immunity mimicking or interference. This modular evolution mirrors phage adaptability, enabling eCIS systems to target diverse hosts through acquired functional modules.

Structural prediction and classification of fibers and fiber-domains
The complexity and versatility of the observed fibers prompted us to apply structure prediction and alignment tools for comprehensive analysis at two levels: whole-fiber clustering and identification of receptor-binding domains. Our workflow began with clustering the complete fiber database at 70% similarity and coverage, effectively reducing the dataset from 3445 to 1098 representative sequences. Since tail fibers are ordered in trimers29–31 we predicted trimeric structures for these representatives using AF2-multimer, establishing the eCIS-fiber 3D-database for further analysis. Notably, the AF2 predictions yielded uniformly high-confidence models (pLDDT > 70 in over 85% of structures) with clear domain boundaries, and the trimeric assemblies showed tight, biologically plausible interfaces, validating the structural assignments used throughout this study (Supplementary Fig. 6a, b). Additionally, we assessed model globularity and found eBAP1–2 to form elongated fibers, whereas eBAP3–5 adopt more bulkier architectures (Supplementary Fig. 6c, d).

The structural characterization proceeded through parallel pathways (Supplementary Fig. 6e): Foldseek structural clustering of complete fibers (at 25% sequence similarity and 70% coverage) and detailed structure-based extraction of all globular units and subunits, followed by clustering with equivalent parameters. The whole-fiber analysis produced 263 distinct structures, while our domain-level analysis identified 3515 individual units that further grouped into 1177 fold families decorating the fibers (Fig. 4a and Supplementary Fig. 6f). We then conducted a foldseek search of the novel domains against PDB, which identified a variety of shaft-region fiber domains (Fig. 4b) and putative C terminal receptor-binding domains (Fig. 4c).

We observed that eBAP1, the group that includes tail fibers from the AFP and PVC, almost uniformly carries adeno-shaft repeat folds (Fig. 4d) followed by collar and needle domains, while in some cases the C-termini displays some variety harboring domains with Ig-like cadherin fold not detected by Pfam search. The eBAP2 group features tail fibers with intertwined triple beta-sheeted shafts resembling phage tail fibers (Fig. 4e). Surprisingly, this group encompasses short mini-fibers, which is the most abundant group in our database, harboring putative saccharide-binding domains similar to a receptor binding domain from Acinetobacter phage (PDB:6E1R). As described previously, we observed in this group fiber carrying a C1q domain typically associated with human complement systems27,32 and the S74 fold (a chaperone of endosialidase).

eBAP3-5 share common shoulders-like substructure yet define distinct groups and putative binding module arsenals
Despite their similar shoulder-like regions resembling phage receptor-binding proteins23, eBAP3-5 domains differ in sequence identity and overall architecture. We noticed the shoulder-like region backbone chain might be intertwined with adjacent domains (Supplementary Fig. 7) observed in the various subtypes, which might enhance protein stability while promoting evolutionary divergence.

eBAP3 fibers display remarkable structural diversity, from minimal attachment-only modules to elongated phage-like β-sheeted shafts (Fig. 4f). Their diverse binding domains—including SLL2-like lectins, bacterial Ig folds, and hemagglutinin structures—suggest specialized glycan-binding functions. Notably, many eBAP3 fibers combine beta-propeller lectins with collagen-like triple helix shafts connected to TNFR-like domain, likely providing enhanced flexibility during target engagement.

eBAP4 represents the most structurally distinct group, characterized by unique bulky shaft regions first described in tCIS crown proteins13 (Fig. 4g). These domains typically feature 1–2 large α+β globular domains or α-helical extensions, connected to flexible chains decorated with multiple Ig, lectin, and PKD attachment modules in various combinations and copy numbers. The utilization of flexible adhesion chains represents a unique attachment strategy that poses a challenge to solve using cryo-EM techniques.

eBAP5 domains contain signature insertion forming miniature β-barrel and β-sandwich folds, with shaft regions often composed of macroglobulin-like (MG) domain (single or repeated) (Fig. 4h). In many cases, we observed that the MG shaft region gets intertwined with the eBAP5 domains, which forms a unique structural group (Fig. 4a). Their C-terminal regions frequently display beta-helix pectin-lyase folds alongside specialized lectin and hemagglutinin domains. Collectively, our structural analysis reveals that eCIS particles employ a diverse array of structural framework which includes carbohydrate and protein-binding domains to recognize and adhere to target cell surfaces, providing insights that could facilitate future eCIS engineering applications.

Engineered fiber-PVC constructs for targeted cell recognition
We hypothesized that the novel tail fiber genes identified in this study could enable engineering of eCIS to achieve programmable cell targeting. Using the PVC particles expressed on a vector in Escherichia coli as a modular chassis, we computationally screened and identified three candidate tail fiber proteins from major clusters (Supplementary Data 8). Comparative analysis of these candidates against wild-type fibers revealed conserved baseplate-attachment domains and variable receptor-binding regions. We prioritized fiber protein-Pb from Paenibacillus sp. URHA0014 for experimental validation, which represents a widespread structural group hosting eBAP3 and intriguing domains with C3b and hemagglutinin-like folds. Hemagglutinin folds are common glycoproteins found on the surface of viruses that infect human cells, such as influenza33 and measles34 and thus we predicted that the Paenibacillus tail fiber protein may similarly adhere to human cells. Two additional fibers, fiber-Mr and fiber-Am, were selected as secondary candidates which represent predominant clusters.

To test targeting specificity, we engineered a chimeric fiberPb-PVC system by swapping the predicted receptor-binding protein (RBP) fragment of fiberPb with the cognate fragment of the PVC tail fiber (Fig. 5a). Positive control PVCs harboring the adenovirus 5 knob domain (R7PVC) were loaded with the plant-derived toxin TcsT that targets eukaryotic cells7. CCK-8 cell viability assays on THP-1 human cells demonstrated that treatment with fiberPb-PVC-TcsT reduced cell viability by approximately 60% after 48 h vs. empty R7PVC controls (p = 5.9E-09; Fig. 5b), with slightly reduced cell killing than the R7PVC-TcsT positive control. Importantly, fiberPb-PVC exhibited no detectable killing of human A549, HEK293T, or HeLa cells (Supplementary Fig. 8a), demonstrating high target cell binding specificity towards THP-1 cells by the engineered PVC. This result is in contrast to R7PVC, which showed low specificity and injected the TcsT toxin into multiple cell lines (Supplementary Fig. 8a).

Western Blotting confirmed proper TcsT loading in both PVC particles (Fig. 5c), while negative-stain electron microscopy observations validated intact assembly of the chimeric PVCs (Fig. 5d). Moreover, we were able to visualize the direct binding of fiberPb-PVC to THP-1 membranes (Fig. 5e, red arrows). We run similar tests with our secondary candidates (Supplementary Fig. 8b–h), but they demonstrated mild toxin injection into the four tested cell lines, re-affirming the specificity of fiberPb towards THP-1 cell line.

Mannose-dependent inhibition of fiberPb-PVC cytotoxicity
To explore the mechanistic basis of THP-1 cell recognition by the agglutinin-like domain of fiberPb, we examined potential interactions with cell surface carbohydrates. Competitive inhibition assays using putative ligand molecules revealed that pre-incubation of fiberPb-PVC-TcsT with D-mannose increased THP-1 viability by 32% and 44% in a concentration-dependent manner (p = 0.0032 and p = 0.0004, respectively; Fig. 6a), whereas galactose, N-acetylglucosamine, and fucose showed no inhibitory effects (Supplementary Fig. 9a–c). This suggests that D-mannose sugar may interfere with fiberPb-PVC engagement of THP-1 cells.

AlphaFold2 structural predictions and sequence conservation identified three putative target engagement motifs: the VDIT motif (encompassing adjacent sequences VDIT and SGEIVH that form a putative binding pocket), IVF, and K5 (Fig. 6b). Mutating VDIT, IVF, and K5 residues reduced viability to approximately 82%, 57%, and 51%, respectively (p = 4E-12, p = 2.30E-05, p = 0.0068, Fig. 6c and Supplementary Data 10), with VDIT mutation having the most significant effect. Consistent with these experimental results, molecular docking simulations also predicted favorable D-mannose binding at a pocket formed by the VDIT and SGEIVH motifs (Supplementary Fig. 10). Western blots and negative-stain electron microscopy observation confirmed retained TcsT loading (Fig. 6d) and structural integrity across mutants (Fig. 6e). These data imply that the identified motifs may mediate glycan recognition and membrane interactions. While we acknowledge that mannose-based inhibition working via alternative mechanisms (e.g., steric hindrance from mannose binding to THP-1 receptors) cannot be excluded.

Notably, R7PVC-TcsT activity remained unaffected by D-mannose (Supplementary Fig. 9d–f), indicating fiberPb-THP-1-binding specific inhibition. This finding implies a potential structural or functional specificity in the recognition mechanism between fiberPb and D-mannose located on cell surfaces. Further validation via glycan arrays or surface plasmon resonance is required to conclusively establish mannose as a direct ligand for fiberPb.

Methods
Computational identification of tail fiber domains
To systematically identify conserved domains in eCIS tail fibers, we developed a multi-stage computational workflow. Initial sequence similarity searches using all-versus-all BLAST (e-value 1e-10, -ungapped, filtered for low coverage of 50% and fragment size between 10 and 500) identified candidate fiber genes across all gene candidates. Conserved regions were subsequently clustered using MMseqs221 and CD-HIT36 at gradient identity thresholds (40%–30%) to account for sequence divergence. Clusters with less than 5 members were discarded. Multiple sequence alignments generated via Clustal Omega formed the basis for constructing HMMs using HMMER37, which were integrated with the Pfam 35.0 database38 through hmmpress. The combined HMM database was scanned with hmmscan (e-value ≤ 1e-5), and domain boundaries were resolved using the CATH39 structural classification toolkit (cath-resolved-hits tool).

To ensure the reliability of this expanded dataset, a multi-layered validation strategy was implemented. First, rigorous quality control was applied during HMM construction to ensure the use of high-quality seed sequences. Second, retrieved candidate sequences were subjected to manual inspection and structural validation. Trimeric structural models were predicted for selected representative domain sequences using AlphaFold2-multimer, structures displaying biologically plausible and consistent folds were chosen. Third, the genomic context of each candidate was analyzed, which confirmed that approximately 90% are located in the expected operonic position downstream of Afp11/12 or Tail_P2_I baseplate homologs. Finally, to estimate the false-negative rate, eCIS loci that did not yield a fiber hit were manually inspected, and no alternative fiber-like genes were found in the expected genomic neighborhood, suggesting these are likely fiberless systems. This comprehensive, multi-layered approach provides robust evidence for the reliability of the identified fiber candidates.

Identification of eCIS clusters within nr combined database
For comprehensive eCIS identification in our non-redundant combined database, we employed a Pfam-anchored search strategy. We screen genomes for gene clusters (max. 5 genes apart) housing Pfams found to be enriched in eCIS operons in a previous study4. We scored putative eCIS loci by Pfam presence and gave a higher score for marker eCIS Pfams such as DUF4157, Pvc16_N, CIS_tube, DUF6519, etc (see Supplementary Data 1). We then pooled all amino acid sequences of genes in loci range plus five genes from the two edges. We searched the five newly defined baseplate-anchoring (eBAP1-5) domains as markers using jackhmmer iterative searches (set to 3 iterations), enabling detection of rapidly evolving fiber genes that traditional homology searches often miss. This approach successfully identified 2585 eCIS clusters containing 3445 fiber-encoding genes across 1069 microbial genomes. Clusters lacking identifiable fiber genes or containing capsid (0) or T6SS (18) Pfams were manually excluded as potentially non eCIS (usually found near a true loci).

Phylogenetic analysis
To examine evolutionary relationships of eCIS tail fibers, we constructed a phylogenetic tree using Afp8 (VgrG protein) sequences, a highly conserved component showing highest presence across nearly all identified eCIS loci and previously proven to reliably represent eCIS phylogeny, similarly to the afp11 core gene4. Sequences were aligned with Clustal-omega40, and a maximum-likelihood tree was built with IQ-TREE41 using the LG+I+G4 substitution model. Branch support was assessed through 1000 ultrafast bootstrap replicates. The resulting tree revealed clade-specific distribution patterns of the five eBAP domains, using ITOL42 website with tree display datasets derived from jackhammer22 results, followed by cath-reloved-hits filtering, suggesting distinct evolutionary trajectories. Phylogenetic lineage of genomes were retrieved from our database enabling domain-based phylogenetic profiling to identify potential horizontal gene transfer events between taxonomically distant microorganisms.

Examination of fiber genes evolutionary dynamics
Examination of multi-fiber operons (23.4% of loci) through pairwise alignment and structural comparison revealed both gene duplication events with subsequent diversification and acquisition of entirely different fiber architectures within the same operon, highlighting the dynamic evolutionary processes shaping eCIS target specificity. Genomic neighborhood analysis of genes upstream to fiber loci was conducted by evaluating the dominant strand that encodes most of the eCIS loci (most eCIS operons display operonic directionality) pooling both upstream genes and screening them against the Pfam database using hmmscan43.

Domain co-occurrence network construction
To analyze functional relationships between Pfam domains in eCIS tail fibers, we constructed a domain co-occurrence network using Python’s NetworkX library (version 2.8.4)44. First, we performed hmmscan (HMMER3.3.2) against the Pfam database (version 35.0)38 with fiber protein sequences using an e-value threshold of 1e-5 and bit score of 12. Domain hits were filtered to remove overlapping regions, prioritizing matches with higher bit scores when domains competed for the same region. For network construction, each Pfam domain and newly identified eBAP domain was represented as a node. An edge was drawn between two domains if they co-occurred within the same protein sequence. Edge weights were calculated based on the frequency of co-occurrence. Node sizes were scaled proportionally to the number of occurrences of each domain across all fiber genes, and edge thicknesses reflected co-occurrence frequencies. Node colors were assigned based on domain classification: eBAP domains (five distinct colors), novel sequence-based domains (light blue), and Pfam domains (light red). The network was visualized using a force-directed layout algorithm (Kamada-Kawai45) to emphasize meaningful interactions of functional domain clusters.

Identification of potential horizontal gene transfer events
To identify Pfam domains potentially acquired through horizontal gene transfer (HGT), we evaluated the taxonomic distribution of protein family members for each domain. We first extracted all protein sequences and taxonomic lineages associated with each Pfam domain from the Pfam database and retrieved their taxonomic classifications from the InterPro46 host API.

Domains were flagged as potential HGT candidates when their taxonomic distribution showed phylogenetic incongruence—defined as cases where >90% of protein family members belonged to a non-bacterial kingdom despite being found in our bacterial fiber dataset. For each candidate domain: We calculated kingdom-level representation ratios by dividing the number of sequences from each kingdom (Bacteria, Archaea, Eukaryota, Viruses) by the total number of sequences associated with the domain. We performed sequence alignment between our fiber domain instances and representative sequences from the dominant kingdom using Clustal-omega40. We constructed maximum-likelihood phylogenetic trees using IQ-TREE41 with LG+I+G4 model selection to visualize the phylogenetic relationships between bacterial and non-bacterial sequences. The generated tree was presented with a taxonomic dataset using the ITOL website42.

Phylogenomics analysis and complex structure predictions
To elucidate the evolutionary trajectories of eCIS baseplate architectures, a combined phylogenomic and structural modeling approach was used. First, the operonic organization of all 2585 identified eCIS loci was analyzed to determine the genomic position of fiber genes relative to homologs of key baseplate components, including Afp11, Afp12, and Tail_P2_I. A phylogenetic analysis of the baseplate protein Afp11 was then conducted by aligning sequences with Clustal Omega and constructing a maximum-likelihood tree in IQ-TREE, with branch support calculated from 1000 ultrafast bootstrap replicates. This tree was annotated with the corresponding eBAP type and the presence or absence of the Tail_P2_I gene for each loci to map the distribution of different baseplate compositions. To investigate the physical underpinnings of these architectures, AlphaFold-Multimer was used to model the protein-protein interactions between representative eBAP domains and their putative baseplate adaptors. Finally, structural models of entire baseplate-fiber assemblies were generated for each major eBAP group to assess their overall conformation, and Foldseek was used to perform structural homology searches against the PDB to identify evolutionary links to other systems, such as the T6SS.

Structural prediction and domain detection
To elucidate the three-dimensional architecture of eCIS tail fibers, we employed AlphaFold2-multimer17 to predict trimeric structures for 1098 representative sequences (clustered at 70% similarity from the full dataset of 3445 proteins). Trimeric modeling reflected the natural oligomeric state of viral fiber proteins and significantly improved prediction quality compared to monomeric predictions and is widely practiced29–31. For comprehensive domain characterization, we implemented dual structural analysis workflows: whole-fiber clustering using Foldseek (25% sequence similarity threshold) yielded 276 distinct structural groups, while extraction and clustering of individual domains identified 1177 fold families.

For protein domain dissection, we designed an algorithm which identifies domains by analyzing secondary structure interactions through a graph-based approach. First, it extracts helices and β-strands using DSSP47 and represents them as nodes in a graph. Each residue on the secondary structures is assessed for its closest interacting residue (best friend), edges therefore representing spatial proximity of selected interacting residues (8Å threshold between Cα atoms). Connected components in this graph represent a cluster of secondary structure interacting in the manner described and therefore define potential domains, with further refinement through overlap analysis and size filtering. Prediction confidence was assigned using pLDDT scores outputted in AlphaFold models48. The tool outputs domain boundaries in tabular format, enabling systematic characterization of predicted structures and facilitating downstream functional analysis. This approach effectively identifies structural domains without relying on sequence homology, making it particularly valuable for novel protein families. To extract (or dissect) protein domains from AlphaFold-predicted structures, we developed a Python-based extraction pipeline utilizing BioPython’s PDB module49. The extraction pipeline filters redundant domains with similar boundaries (±4 residue positions), prioritizing regions with higher confidence scores. Each domain is saved as an individual PDB file with preserved residue numbering and chain identifiers, enabling direct mapping to original coordinates. These predicted structures were clustered and compared against the Protein Data Bank50 using Foldseek’s threading-based algorithm to identify structural homologs and infer potential receptor-binding functions. This approach overcame limitations of sequence-based methods for these rapidly evolving proteins, enabling identification of domains with structural similarity to carbohydrate-binding modules and receptor-binding proteins despite low sequence conservation. AlphaFold metrics of the different proteins are provided in Supplementary Data 12.

Bacterial strains and cell culture
Escherichia coli strains DH5α and EPI300 were cultured in Luria-Bertani (LB) broth at 37°C with shaking at 200 rpm. Mammalian cell lines were maintained at 37°C in a humidified 5% CO₂ atmosphere. HeLa and HEK293T cells were cultured in Dulbecco’s Modified Eagle’s Medium (Gibco) supplemented with 10% fetal bovine serum (FBS, Gibco), while A549 and THP-1 cells were cultured in RPMI 1640 medium (Gibco) supplemented with 10% FBS.

Plasmid construction
Predicted tail fiber fragments were commercially synthesized (Tsingke Biotechnology, China) and cloned into the PVC-expressing plasmid pCNM3 (see Supplementary Data 9). Site-directed mutations were introduced using homologous recombination. PCR amplification was performed using Tks Gflex™ DNA Polymerase (Takara), and fragments were assembled using T4 DNA Ligase (New England Biolabs). To produce functional PVC complexes, EPI300 cells were co-transformed with three plasmids: modified pCNM3_fibers (structural components), pBR-LysR (regulatory elements), and pBBRN_TcsT (TcsT toxin payload). Control strains were generated with either empty pBBRN (negative control) or wild-type pCNM3 with pBBRN_TcsT (positive control). Plasmids, primers, and sequences are listed in Supplementary Data 9 and 10, respectively.

PVC purification
PVC complexes were purified according to previously described methods with modifications. Briefly, transformed EPI300 cells were grown in 200 mL LB broth at 30°C with shaking at 200 rpm for 24 h. Cells were harvested and lysed in 12 mL buffer P (25 mM Tris pH 7.4, 140 mM NaCl, 3 mM KCl, 200 μg/mL lysozyme, 50 μg/mL DNase I, 0.5% Triton X-100, 5 mM MgCl₂, and 1× protease inhibitor) for 45 min at 37°C. After centrifugation at 20,000 × g for 6 min, the supernatant was ultracentrifuged at 200,000 × g for 75 min at 4°C. The pellet was resuspended in 1 mL ice-cold PBS and centrifuged at 20,000 × g for 10 min at 4°C. The resulting supernatant underwent a second ultracentrifugation at 200,000 × g for 75 min at 4°C. The final pellet was resuspended in 400 μL ice-cold PBS, clarified by centrifugation at 20,000 × g for 10 min at 4°C, and the supernatant containing PVC particles was stored at 4°C.

PVC verification
PVC loading was verified by Western blotting. Purified PVC complexes were mixed with 2× Laemmli Sample Buffer (Bio-Rad), heated at 99°C for 10 min, and separated on 12% SDS-PAGE gels (Vazyme) at 150 V for 1 h. Proteins were transferred to PVDF membranes, probed with appropriate antibodies (Supplementary Data 2), and visualized using a Bio-Rad ChemiDoc system.

Cell viability assays
Cell viability was assessed using the Cell Counting Kit-8 (CCK8, MedChemExpress). HeLa, HEK293T, and A549 cells were seeded in 96-well plates at 1 × 10⁵ cells/mL and incubated overnight. THP-1 cells were seeded at 1 × 10⁶ cells/mL and differentiated with 0.1 μg/mL phorbol-12-myristate-13-acetate. Cells were treated with purified PVC complexes (50 μg/mL) for 48 h at 37°C. Culture medium was then replaced with fresh medium containing CCK8 solution and incubated for 1 h at 37°C. Absorbance at 450 nm was measured using a TECAN microplate reader.

Electron microscopy
PVC assembly verification. PVC assembly was confirmed by negative staining electron microscopy. Briefly, 5 μL of purified PVC sample was applied to a glow-discharged holey-carbon-coated copper TEM grid (Quantifoil) for 60 s. After removing excess liquid, the grid was stained twice with 2% uranyl acetate and air-dried at room temperature before imaging with an FEI Tecnai (G2 Spirit TWIN) electron microscope at 80 kV.

Cell-binding visualization
To visualize PVC binding to target cells, cells were seeded in 12-well plates and incubated with PVC complexes (0.5 mg/mL) for 3 h. After gentle scraping, the cells were collected and centrifugation at 100 × g for 5 min, the supernatant was replaced with 2.5% glutaraldehyde for fixation. The pellet was fixed for 2 h at room temperature and stored at 4°C. Following dehydration, resin infiltration, embedding, and ultra-thin sectioning, samples were placed on glow-discharged carbon-coated gold TEM grids, stained with 2% uranyl acetate, and imaged using an FEI Tecnai (G2 Spirit TWIN) electron microscope at 100 kV.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "I would like to compliment the authors on a timely and impressive study that integrates large-scale bioinformatics, AlphaFold\n(AF) - based structural prediction, and functional validation to investigate the diversity and targeting logic of extracellular\ncontractile injection system (eCIS) tail fibers. The combination of computational and experimental approaches is convincing\nand provides insights into a largely unexplored class of bacterial targeting features (mainly bacteriophage, pyocin fibers\nhave been investigated so far). The work will be of broad interest to microbiologists, structural biologists, and those in\nsynthetic biology, particularly in the context of host specificity and programmable targeting and delivery of drugs and\neffectors.\nI recommend the manuscript for publication, pending major comments and minor revisions/clarifications as mentioned\nbelow.\nMajor comments and request for clarification:\nPoint 1: Genome dataset availability: The study is based on a custom non-redundant set of 86,785 bacterial and archaeal\ngenomes. The manuscript does not clearly state (I tried to find without success) where or how the raw dataset of genomes\ncan be accessed. I encourage the authors to provide accession codes or download protocols, to make the raw data\navailable for the research community being able to reproduce and build on the results.\nLink: Raw data: https://zenodo.org/records/15274396 provides access to more downstream genomic processing e.g. PFAM\noutput and mostly states phylum information e.g. Proteobacteria % etc. not specific strain information. The genomic data\naccess to the original raw data where the processing started from, I could not (it is not easily to be found in case I missed it)\nbe found.\nPoint 2: Tail fiber length and variability: there is noticeable variability in fibre length across systems. Can the authors\ncomment on possible structure and/or functional reasons for these differences (covariance/correlation of length when\ncompared to bacterial species origin or potential host). Additionally, are longer fibres associated with sheath decorations\n(sheath features e.g. Afp3 has loop like structures that can interact with the tail fiber adenovirus shaft and know e.g. in Afp\nand PVCs) or host species.\nPoint 3: Structural prediction confidence: Please include metrices for prediction reliability and confidence (pLDDT, ipTM, or\nPAE) that are summarizing prediction quality for AF2/3, multimer, and trimeric models. One example, In Fig. 1/Figure 4d-h,\nshowing colored models or reporting scores in a table that is accessible in the supplementary and also together with the\navailable data available through the data availability section: eCIS fibers 3D database and clustered domains:\nhttps://zenodo.org/records/15277912. The models available there are deposited without a *json file to being able to visualize\nerror plots in programs.\nPoint 4: Glycan binding: while mannose specificity is convincingly shown, docking simulations or motif-level structural\nmodelling could strengthen glycan interaction predictions.\nPoint 5: multiple fiber genes on one operon (around 23%). This is a fascinating observation and it would be valuable to test\nwhether all of the available fibers can co-assemble (as a mixture). Depending on the teams computational capacities, AF\nmultimer predictions with e.g. Afp12 baseplate anchor and all/a combination of tail fibers could give additional information on\npreferential fiber assembly or if racemic assembly mixtures are possible.\nPoint 6: Exclusion of non-fiber loci as ‘non-extracellular’: this assumption lacks structural and other validation. Tail fiber\nabsence could result from divergence or alternative targeting adaptions. I suggest revision of the statement (no line number\nto highlight the sentence).\nPoint 7: Fiber baseplate docking. Given the essential and central role in docking and triggering the ejection event of\nAfp12/PVC12 and homologous proteins, a bioinformatic co-evolution analysis of the baseplate porteins alongside tail fibers\nwould enrich the study.\nPoint 8: Sheath influence in assembly and fiber states: In Afp and PVC systems, sheath decorations may influence fiber\npositioning (fiber connections folding back to sheath and interaction with sheath protein loops). A discussion or brief\nanalysis of sheath variability and potential contact partners could help contextualize tail fiber (& sheath) (co-)evolution.\nPoint 9: Horizontal gene transfer signatures, modularity and transposases? Domains showing eCIS, viral-like phylogenies\nare fascinating. Fiber diversity (and also cargo/effector diversity of eCIS) is most likely related to possible genomic drivers.\nHave the authors searched for mobile elements or recombinase genes near fiber loci? Such features could explain domain\nshuffling and rapid evolution. Very often close to eCIS effectors, transposable elements can be found which could allow\nrapid cargo evolution.\nPoint 10: domain based fiber identification reliability and benchmarking. The domain based approach is a strength of the\nmanuscript. Could you add an explanation / elaborate in the discussion on potential false postivies/negatives (compared to\nknown fibers sets) and would help the community and the broader use of the method.\nPoint 11: Cell type selection for fiber Pb assay.\n‘We prioritized fiber protein-Pb from Paenibacillus sp. URHA0014 for experimental validation, which represents a\nwidespread structural group hosting eBAP3 and intriguing domains with C3b and hemagglutinin-like folds.\nHemagglutinin folds are common glycoproteins found on the surface of viruses that infect\nhuman cells, such as influenza34 and measles35 and thus we predicted that the Paenibacillus tail\nfiber protein may similarly adhere to human cells. Two additional fibers, fiber-Mr and fiber-Am,\nwere selected as secondary candidates which represent predominant clusters.’\nCould you elaborate in the discussion the main outcomes of fiber-Pb compared to the additional chosen fibers fiber-Mr and\nfiber-Am. How were cell types chosen. Variants fiber-Mr and fiber-Am are mentioned in the main manuscript but no follow up\ninformation can be found in the main article (found in supplementary Fig. 7 and Mr-1 appears to be less particles\nassembled). Could the authors elaborate on the fiber types mentioned in the main article?\nMinor suggestions:\nPoint 12: Adding line numbers to the manuscript would help reviewers.\nPoint 13: Mapping the distribution of multi-fiber operons across phylogenetic groups (particularly Actinobacteriota) could\nprovide useful evolutionary context.\nThis is a high-quality and impactful study that substantially advances our understanding of eCIS systems. Addressing the\npoints above will improve reproducibility, clarify the scope of the findings, and expand their relevance to microbial evolution\nand engineering.\n(Remarks on code availability)\nDOI or URL: https://github.com/Nimrod198/eCIS_tail_fibers\nThe GitHub repository is accessible and contains relevant code for eCIS fiber prediction and analysis. The codebase is\nappropriate for the community and supports the main findings of the manuscript.\n2 weeks of review were not enough to test reproducibiliy of the code in-house and providing genome raw data bank access\nwould help reproducing the findings and testing the code.", "reviewer_2": "The manuscript presents an extensive computational study of tail fibers associated with extracellular contractile injection\nsystems (eCIS) across bacteria and archaea. The authors identify over 3,400 candidate tail fiber genes, classify them into\nfive novel baseplate-anchoring domain types (eBAP1–5), and generate a domain- and structure-level atlas of fiber\narchitectures. Through AlphaFold2-multimer and Foldseek analyses, they resolve 276 structural clusters and over 1,000\ndomain fold families. The functional relevance is supported by engineering a Paenibacillus-derived fiber into a PVC system,\ndemonstrating selective toxin delivery to THP-1 human cells in a mannose-sensitive manner. Overall this manuscript is very\nstrong, and it provides a valid contribution to the field, providing an atlas that can guide new mechanistic studies and new-\ncomers to the eCIS field.\nMajor\n1. Only one eBAP3-derived fiber is tested. While the validation is elegant, testing a second divergent fiber would strengthen\nclaims of generalisability.\n2. Mannose inhibition and site-directed mutagenesis of predicted motifs are interpreted as evidence of glycan binding.\nHowever, no direct binding assays (e.g., glycan arrays, SPR, or ITC) are performed.\n3. a concern, but I wanted to express that overall I found the approaches across the manuscript, both computational and\nexperimental, very elegant and it was overall a pleasure to read this manuscript\nMinor\n1. In fig3a, should be “phylum” rather than “pylum”\n2. Some of the nomenclature (e.g., “eBAP5+MG”) is a bit clunky or inconsistent. A brief rationale or naming scheme\nsummary in the main text or methods might help orient readers unfamiliar with the field.\n3. There are several minor typos and occasional awkward phrasing throughout the manuscript. A light copy-edit would help\nensure the clarity of the text for the broader audience\n(Remarks on code availability)\nAt the moment, the code README file is not super accessible, as it does not provide installation instructions, environment\nrequirements or dependencies. Similarly, for each script it is not super clear what to use as input files or scripts arguments.\nThat said, i tested few scripts whose name rendered their function obvious. To gather the type of input files/arguments\nneeded, the script needs to be opened and read. However, they do work, so that is a positive!", "reviewer_3": "In this work, Nachmias et al. investigate how extracellular Contractile Injection Systems (eCISs) bind to target cells by\nidentifying and analyzing tail fiber proteins across diverse bacterial taxa. Through a combination of large-scale\ncomputational domain analysis, structure prediction, phylogenetic mapping, and targeted functional assays, the authors\nidentify five novel baseplate anchor domains (eBAP1–5) and over 3,000 candidate tail fiber genes, which collectively exhibit\nexceptional structural and binding diversity. The study culminates with an engineered eCIS particle targeting human THP-1\ncells, with mannose-dependent inhibition and putative binding motifs identified.\nThe manuscript is generally well organized, and the data are presented in a thorough and compelling manner. The authors\nhave generated an impressive bioinformatic dataset and succeeded in connecting it to functional outcomes via tail fiber\nengineering. The classification of domain architectures, especially the structural organization of eBAP1–5 and the analysis\nof glycan-binding motifs, provides a valuable framework for future eCIS studies. As outlined below I would recommend\nimprovements to expand the discussion on host specificity and the ecological implications of tail fiber diversity.\nMAJOR COMMENTS\nThe distribution and ecological roles of eCIS fibers are central to the authors’ conclusions, but I would like to see more\ndiscussion on the implications of this diversity. For instance, the presence of different eBAP domains in Actinobacteria,\nCyanobacteria, and Pseudomonadota is intriguing. What might this mean in terms of target preference, microbial lifestyle, or\nhost interactions. I suggest adding a few lines to better contextualize the evolutionary or environmental significance of this\ndistribution, particularly as it relates to the possibility of environmental targeting or niche adaptation.\n(Remarks on code availability)"}, "author_response": "Response to Reviewers\nDear Editor and Reviewers,\nWe sincerely thank you for the thorough and constructive reviews of our manuscript \"How do\nbacterial extracellular Contractile Injection Systems bind target cells? A remarkable diversity\nof receptor binding domains.\" We greatly appreciate the time and expertise invested in\nevaluating our work. The reviewers' insights have significantly strengthened our manuscript,\nand we have carefully addressed each comment as detailed below.\nSummary of major changes\n● Enhanced data accessibility: Provided comprehensive genome dataset information\nand improved code documentation.\n● Added structural prediction metrics: Included pLDDT, pTM, and PAE confidence\nscores for all AlphaFold predictions.\n● Expanded evolutionary analysis: Added expanded evolutionary analysis resulting in\nan evolutionary model. Added detailed discussion of ecological implications and\nbaseplate evolution.\n● Improved methodology validation: Included our quality measurements of our\ndomain-based approach.\n● Enhanced experimental discussion: Provided clearer rationale for fiber selection and\ncell type choices.\nReviewer #1 Response\nMajor Comments\nReviewer #1, Point 1: Genome dataset availability: The study is based on a custom non-\nredundant set of 86,785 bacterial and archaeal genomes. The manuscript does not clearly\nstate where or how the raw dataset of genomes can be accessed.\nResponse: We thank the reviewer for highlighting this important reproducibility issue. The\nused database was established during another project in our lab by Segev O. et. al. that is\npublished as a preprint (https://doi.org/10.1101/2025.07.19.665706) and is under review.\nAction Taken: We have created a detailed supplementary file (Supplementary Table 11)\ncontaining the complete list of all 86,785 bacterial and archaeal genome accession numbers\nused in our study. This table includes NCBI accession codes, organism names, and source\ndatabases. Additionally, we have updated our Data Availability section to include:\nReviewer #1, Point 2: Tail fiber length and variability: there is noticeable variability in fiber\nlength across systems. Can the authors comment on possible structure and/or functional\nreasons for these differences?\nResponse: This is an excellent observation that proved to be correct by our additional\nanalysis. Fibers from the different eBAP groups seem to be structurally different as a trend\nwhich might reflect their distinct evolutionary patterns.\nAction and results: We have added a comprehensive analysis of fiber length variability and\nbulkyness in the different eBAP groups (Supp. Figure 6). Additionally we have modeled\nseveral baseplate complexes as part of our Phylogenomics analysis. Our analysis shows that\neBAP1-2 are more fibrous and maintain a backward tilt trait while eBAP3-5 are more bulky\nwhich might provide a more stationary positioning on the baseplate. These findings support\nthe notion that eCIS baseplate complexes are inherently different. Most probably, these traits\nstem from their evolutionary origins and distinct trajectories as eBAP1-2 resemble long tail\nfibers usually associated with first stage of phage adsorption while eBAP3-5 resembles short\ntail fibers or receptor binding proteins which are associated with irreversible receptor binding.\nOur produced model demonstrates the trajectories that lead to these inherent differences.\nLocation: New section in Results (lines 177-199) referring to Supplementary Figure 4. Added\nlines (266-271) referring to Supplementary Figure 6 c-d.\nReviewer #1, Point 3: Structural prediction confidence: Please include metrics for prediction\nreliability and confidence (pLDDT, ipTM, or PAE) that are summarizing prediction quality for\nAF2/3.\nResponse: We completely agree that structural prediction confidence metrics are essential for\nvalidating our findings.\nAction Taken: We have now included comprehensive confidence metrics evaluating our\ndatabase quality and validity. We added pLDDT distribution analysis showing >85% of\nstructures have pLDDT >70 (Supplementary Fig. 6a-b). Created a comprehensive table of\nprediction metrics for all 1,098 representative structures (Supplementary Table 12).\nLocation in Manuscript: Lines 266-271 in Results; Supplementary Figures 3g-i, 6a-b; new\nSupplementary Table 12\nReviewer #1, Point 4: Glycan binding: while mannose specificity is convincingly shown,\ndocking simulations or motif-level structural modeling could strengthen glycan interaction\npredictions.\nResponse: We acknowledge that direct binding assays would provide the strongest evidence.\nWhile beyond the current scope, we have strengthened our predictions through simulated\nmolecular docking simulation (via MolModa website) demonstrating the putative carbohydrate\nbinding pocket’s localization. This binding pocket is formed on the interface of two beta\nstrands that contain our predicted conserved motifs, which is in-line with our hypothesis. This\nanalysis is a supportive result which shed light on the active binding site utilized in target\nengagement.\nAction Taken: We have performed molecular docking analysis of the predicted mannose-\nbinding pocket and mannose molecules using the MolModa website, which revealed that the\nputative binding site is located in the midst of two beta strands harboring the conserved motifs\nmutated. This prediction is presented in an additional supplementary figure 10 and mentioned\nin the text as supportive analysis.\nLocation in Manuscript: Lines 401-403 in Results; supplementary figure 10.\nReviewer #1, Point 5: Multiple fiber genes on one operon (around 23%). This is a fascinating\nobservation and it would be valuable to test whether all of the available fibers can co-\nassemble.\nResponse: This is indeed a fascinating aspect of eCIS evolution. We have expanded our\ncomputational analysis of multi-fiber assembly. Our analysis suggests that most eCIS poses\none fiber docking site for each baseplate assembly unit. The most outstanding exception to this\nrule are eBAP3-containing baseplates which are predicted to contain multi-fiber binding\ncapacity supported by the Tail_P2_I gene. These results might have implications and if this\ntype of system is to be engineered.\nAction Taken: We performed AlphaFold2-multimer predictions for representative multi-fiber\nsystems as part of our phylogenomics analysis and found that Only eBAP3 containing operons\nencode fibers that can theoretically co-assemble on the same baseplate utilizing a long\nhydrophobic loop stemming from Tail_P2_I adaptor protein. Multi-fiber operons from other\neBAP types didn’t display this ability suggesting that some systems operate by some-kind of\ndifferential mechanism. Our prediction demonstrates that eBAP3 assemblies could hold up to\nfour fibers per one adaptor protein. These findings raise interesting questions about how eCIS\nutilizes polyvalence for interaction.\nLocation in Manuscript: Lines 177-199 in Results; new Supplementary Figure (now\nnumbered 4); expanded discussion lines 446-448\nReviewer #1, Point 6: Exclusion of non-fiber loci as 'non-extracellular': this assumption lacks\nstructural and other validation.\nResponse: We appreciate this important clarification and have revised our language to be\nmore accurate.\nAction Taken: We have revised the manuscript to clarify that loci lacking identifiable fiber\ngenes \"might act intracellularly or through mechanisms yet to be discovered\". We explicitly\nstate these were \"left out of the scope of this study\" rather than categorically classified as non-\nextracellular. Finally, we acknowledged the possibility of highly divergent fibers that escaped\nour detection in the discussion section.\nLocation in Manuscript: Lines 161-162 in Results.\nReviewer #1, Point 7: Fiber baseplate docking. Given the essential role of Afp12/PVC12, a\nbioinformatic co-evolution analysis of baseplate proteins alongside tail fibers would enrich\nthe study.\nResponse: This excellent suggestion has led to important new insights about eCIS evolution.\nOur phylogenomics analysis coupled with structure complex predictions led to the\nidentification of two evolutionary trajectories.\nAction Taken: We conducted phylogenetic analysis of Afp11 sequences alongside eBAP\ndistributions, revealing two distinct evolutionary trajectories: Group I (eBAP1/2/4) with\nAfp11/12 but lacking Tail_P2_I, and Group II (eBAP3/5) with Tail_P2_I but lacking Afp12.\nThis represents a fundamental bifurcation in eCIS baseplate architecture. We therefore added a\nnew section \"Phylogenomics and Structural Analyses Reveal Two Main Baseplate Types\" and\ncreated a comprehensive Supplementary Figure 4 showing this evolutionary pattern.\nLocation in Manuscript: Lines 177-199 in Results; new Supplementary Figure 4.\nReviewer #1, Point 8: Sheath influence in assembly and fiber states: A discussion of sheath\nvariability and potential contact partners could help contextualize tail fiber evolution.\nResponse: We agree this is an important aspect of eCIS function that deserves attention. A\npreliminary analysis revealed significant variability in both sheath gene copy numbers and\nchain length across different systems. Structural prediction of sheath proteins indicated that\nthey can be fused with additional domains in some cases. However, attempts to model sheath-\nfiber protein complexes did not yield meaningful results, even for systems previously\ncharacterized by cryo-EM studies to facilitate such interaction. We therefore assumed these\ninteractions might be: (1) weakly interfaced molecularly, (2) dependent on conformations\nachieved only during full particle assembly. Therefore, this interaction is not predictable by\ncurrent models, or influenced by the unique backwards tilt observed in AFP and PVC systems,\nwhich depends on Afp12/Pvc12 protein conformations as shown by our analysis. We therefore\ndid not pursue further investigations, believing this topic is better addressed with cryo-EM\nstudies.\nReviewer #1, Point 9: “Domains showing eCIS, viral-like phylogenies are fascinating. Fiber\ndiversity (and also cargo/effector diversity of eCIS) is most likely related to possible genomic\ndrivers. Have the authors searched for mobile elements or recombinase genes near fiber\nloci?”\nResponse: Thank you for this insightful suggestion which could indeed shed light on\nmechanisms underlying domain shuffling. We performed an hmmscan search against the Pfam\ndatabase focusing on genes encoding mobility-related domains such as transposases,\nintegrases, and of course recombinases. Our analysis revealed that eCIS loci are frequently\nassociated with mobility elements; however, these genes predominantly flank the entire\noperonic region rather than being directly adjacent to the fiber loci. Only 3-4 instances were\nidentified where mobility-associated genes are located relatively near fiber genes, but no clear\nevidence of gene duplication or domain shuffling at these sites was observed. Instead, the\nmore prevalent pattern is duplication of entire loci, notably seen in Photorhabdus species,\nwhich rely heavily on eCIS for their insecticidal functions and can reach up to 5 loci per\ngenome. Overall, we concluded to not include this analysis in the paper due to lack of positive\nresults and quite excessive length of the current manuscript. We add an HTML file in the\nonline data directory.\nReviewer #1, Point 10: Domain based fiber identification reliability and benchmarking:\nCould you elaborate on potential false positives/negatives compared to known fiber sets?\nResponse: We acknowledge this important concern regarding method validation. Traditional\nbenchmarking presents inherent challenges as we expand the known tail fiber repertoire from\n~629 previously identified genes to 3,445 candidates. Our domain-based approach\nsuccessfully identified all known tail fiber genes (Afp13 and Pvc13) used as positive controls,\nthough we acknowledge this represents a limited validation set due to the historical scarcity of\ncharacterized eCIS tail fibers. Therefore our method heavily relies on collection of high-\nquality sequences for construction of accurate HMM-profiles. The final sensitivity and\nspecificity of our pipeline are determined by the performance of its last step, an iterative\nJackHMMER search.\nTo address these limitations we implemented strict quality control and manual inspection of\nretrieved domain sequences and predicted structures to insure their reliability. Additionally,\nwe implemented multiple orthogonal validation strategies for retrieved fiber-candidates:\nstructural validation through AlphaFold2-multimer predictions achieved >85% high-\nconfidence models (pLDDT >70) for 1,098 representative sequences; genomic context\nanalysis revealed ~90% of identified genes occur in expected positions downstream of\ntail_P2_I and AFP11/12 homologs. Based on manual inspection of domain architectures of\nsorted candidates, we estimate <0.1% false positive rates, though false negative assessment\nremains challenging given the unprecedented scope of discovery. While we cannot provide\ntraditional sensitivity/specificity metrics for the entire dataset, we did manually inspect CIS\nloci which did not get a fiber hit and could not find any fiber-like replacements in the expected\npositions or surrounding neighborhood leading us to conclude these are fiber-less systems.\nNevertheless, our multi-layered validation approach provides robust evidence for method\nreliability and establishes a foundation for future functional characterization of this diverse\nprotein family.\nLocation in Manuscript: methods additional details on our quality control measurements\nlines 507-519.\nReviewer #1, Point 11: Cell type selection for fiber Pb assay: Could you elaborate on the\nmain outcomes compared to fiber-Mr and fiber-Am?\nResponse: The cell types were popular cell lines grown in our labs. Honestly, we could not\npredict prior to the experiments which fiber will bind each cell line. We tested human cells\nbecause the fiber from Paenibacillus had glycoproteins found on the surface of viruses that\ninfect human cells. As shown in Supp Figure 8, fiber-Mr and fiber-Am had different structures\nand no binding and killing of the four human cell lines (despite toxin loading).\nMinor Comments\nReviewer #1, Point 12: Adding line numbers to the manuscript would help reviewers.\nResponse: Thank you for this practical suggestion.\nAction Taken: We have added line numbers throughout the revised manuscript.\nLocation in Manuscript: Line numbers added to all pages\nReviewer #1, Point 13: Mapping the distribution of multi-fiber operons across phylogenetic\ngroups could provide useful evolutionary context.\nResponse: We agree on the importance of the integral detail. Our enrichment analysis\nrevealed multifiber operons are enriched in Myxococcota (which is relatively scarce in the\ndatabase) and Pseudomonodota phyla while depleted in Desulfobacterota and Bacillota phyla.\nThese suggest that possessing multiple fibers, whether function in polyvalence or assembled\ndifferentially. The results are shown in Supplementary Figure 2b.\nReviewer #2 Response\nMajor Comments\nReviewer #2, Point 1: Only one eBAP3-derived fiber is tested. Testing a second divergent\nfiber would strengthen claims of generalisability.\nResponse: We acknowledge this limitation but additional experiments were beyond the\ncontext of this paper.\nReviewer #2, Point 2: No direct binding assays (e.g., glycan arrays, SPR, or ITC) are\nperformed.\nResponse: We agree that direct binding assays would provide the strongest evidence for\nglycan binding. We acknowledge in L464-465: “These results align with structural predictions\nof putative-binding pockets but require validation via direct binding assays.”\nLocation in Manuscript: Lines 580-590 in Discussion; enhanced Methods section\nImportant comment to reviewer #2: The senior editor handling the paper specifically\nmentioned: “Please note that we do not require additional wet-lab experiments (suggested by\nreferee #2).”\nMinor Comments\nReviewer #2, Point 1: In fig 3a, should be \"phylum\" rather than \"pylum\"\nResponse: Thank you for catching this typographical error.\nAction Taken: Corrected \"pylum\" to \"phylum\" in Figure 3a.\nLocation in Manuscript: Figure 3a legend\nReviewer #2, Point 2: Some nomenclature (e.g., \"eBAP5+MG\") is inconsistent.\nResponse: We appreciate this feedback on nomenclature clarity.\nAction Taken: We have:\n● Clarified that \"eBAP5+MG\" represents instances where eBAP5 gets intertwined with\nadjacent macroglobulin domains\n● Added a nomenclature explanation in the main text\n● Standardized naming conventions throughout\nLocation in Manuscript: Lines 309-312 in Results; Figure 4a legend clarification\nReviewer #2, Point 3: Minor typos and occasional awkward phrasing throughout the\nmanuscript.\nResponse: Thank you for noting these issues.\nAction Taken: We have performed comprehensive copy-editing throughout the manuscript,\ncorrecting typos and improving clarity of expression:\n● Line 42: Added comma after \"(Afp)\" and changed \"which causes\" → \"that causes\"\n● Line 45: Added comma after \"host\" in \"metamorphosis of its host the marine\ntubeworm\"\n● Line 47: Fixed subject-verb agreement - \"contains\" → \"contain\" for \"eCIS\ncomplexes\"\n● Line 51: Replaced \"point out\" → \"highlight\" and removed comma for parallel\nstructure\n● Line 94: Rewrote awkward phrase \"scope of fiber harboring loci coverage\" →\n\"number of loci harbouring fibers\"\n● Line 97: Corrected \"Thriving\" → \"Striving\"\n● Line 106 Added comma after \"crown domain\" in eBAP3 description\n● Line 287,301: Corrected “Beta-sheath → Beta-sheeted”\nReviewer #3 Response\nReviewer #3 Major Comment: The distribution and ecological roles of eCIS fibers are\ncentral to the conclusions, but more discussion on the implications of this diversity would be\nvaluable.\nResponse: We completely agree that the ecological implications deserve more thorough\ndiscussion.\nAction Taken: We have expanded our discussion of ecological implications.\nLocation in Manuscript: Expanded discussion section lines 449-455\nCode Availability Improvements\nResponse to both Reviewer #1 and #2 comments on code accessibility:\nAction Taken: We have substantially improved our GitHub repository:\n● Added comprehensive README with installation instructions and dependencies\n● Included clear documentation for all scripts with input/output specifications\nLocation: Updated GitHub repository at https://github.com/Nimrod198/eCIS_tail_fibers\nWe added an additional Zenodo repository for post-revision analysis data:\nhttps://zenodo.org/records/17150571\nWe believe these revisions have significantly strengthened the manuscript and address all the\nreviewers' concerns. The additional analyses have revealed new biological insights,\nparticularly regarding the dual baseplate evolutionary trajectories and ecological specialization\npatterns. We are grateful for the reviewers' constructive feedback, which has elevated the\nimpact and rigor of our work.\nThank you again for your consideration of our manuscript.\nSincerely,\nNimrod Nachmias, Zhiren Wang, Xiao Feng, Feng Jiang, and Asaf Levy"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I have carefully reviewed the revised version of your manuscript and all accompanying supplementary data. I would like to\nsincerely congratulate you on an excellent revision and improvement of the article. It is an excellent work!\nAll my previous major and minor concerns have been convincingly and comprehensively addressed. The additional\nanalyses, clarifications, and methodological improvements have significantly strengthened the manuscript. The results are\nnow presented in a clear, logical, and compelling manner, and the discussion effectively places the findings within the\nbroader context of eCIS biology and evolution.\nThe study provides important new insights into the structural and evolutionary diversity of extracellular contractile injection\nsystems (eCIS). The breadth of your genomic and structural analyses, coupled with careful interpretation, makes this work a\nvaluable contribution to both the fundamental and applied aspects of eCIS and their potential biotechnological and\ntherapeutic applications.\nYour methodological approach is sound and well-documented. The inclusion of detailed raw data repositories ensures\ntransparency and reproducibility, which I highly commend making accessible to the field. The integration of structural\nprediction confidence metrics, expanded evolutionary analyses, and improved data accessibility has clearly elevated the\nrigor and impact of the study.\nOverall, this is an excellent piece of work that advances our understanding of eCIS structure–function relationships and\nevolutionary dynamics. I have no remaining concerns and fully support publication of the manuscript in its current form.\nCongratulations again to the entire author team on a strong and well-executed study."}, "author_response": ""}]

Decision letter