Journal of Molecular Biology

Scientific agents can produce plausible answers while remaining unable to establish whether the computation behind an answer is executable, recoverable, or reproducible. We present BloClaw, an AI4S workstation built around a simple principle: a scientific agent should know what it can do, show how it did it, and state what remains unvalidated. Each capability declares an execution state, input constraints, dependencies, expected outputs, and scientific limitations. Natural-language requests are translated into structured tasks, validated against this registry, executed through scientific tools, and recorded in a provenance-aware Living Lab Notebook. The system is designed to detect invalid inputs, failed tool calls, missing dependencies, and remote timeouts, and to route them to repair, retry, or escalation. The implemented and tested scope comprises RDKit-based molecular property and rule screening, protein structure analysis, docking-pose inspection, 3D visualization, and structured reporting. We demonstrate the workflow on a PubChem-retrieved osimertinib structure and a supplied 6LU7 docking artifact: the former yields deterministic descriptors (molecular weight 499.619 Da, cLogP 4.5098, TPSA 87.55 A^2), while the latter contains 2,387 protein ATOM records, 309 residues, and nine pose records. These examples are workflow demonstrations, not efficacy or affinity studies. Beyond retrospective prediction, the manuscript specifies a prior-minimized constructive mode in which a desired function is compiled into explicit physical, chemical, and systems constraints, candidate mechanisms are simulated, and observations are reintroduced for calibration and falsification; this is a proposed extension rather than a result of the present case studies. We describe an evaluation protocol that compares BloClaw with a standard single-agent workflow and fixed-script execution using task completion, scientific correctness, recovery success, provenance completeness, reproducibility, human review time, latency, and cost. This manuscript reports the system design, verified capability boundary, deterministic software artifacts, and a reproducible evaluation protocol; it does not claim benchmark improvements before those experiments are run. BloClaw is an execution and accountability layer for AI-assisted research, complementing expert review and experimental validation rather than replacing them.

We present the solution structures of HIV-1 proteins NC(p7)1-55 corresponding to the full-length NC(p7) and mature p6. The studies were carried in water and, to mimic the membrane, in micellar DPC (Dodecylphosphocholine) conditions. Our results unravel for the first time the structure adopted by the N-terminal amino acids of the free NC(p7)1-55, with the formation of a small helix spanning residues F6 to R10. Our NMR and Fluorescence Anisotropy data disclose an interaction between NC(p7)1-55 and p6 both in water and DPC, with respective Kd of 2.5mM and 370 mM at 23{degrees}C. The interaction is thus strengthened in lipidic conditions. Protein p6 stabilizes the N-terminus of NC(p7)1-55 while increasing at the same time the dynamic of the first zinc finger. Although the entire p6 sequence is involved in the interaction, we show that its C-terminal region is particularly sensitive to the presence of NC(p7)1-55, with a propensity of forming a a helix ranging from amino acids S111 to F116. This study brings experimental evidence of a direct protein-protein interaction between p6 and the N-terminal region of NC(p7)1-55. We further show that such interaction is readily accommodated within the NC(p15) framework and hypothesize that it may facilitate the selective assembly of assembly of the viral genomic RNA (gRNA) in the cell.

Initiator-methionine excision is among the earliest protein modifications, yet its relationship to assembly geometry is unknown. Burial of the mature first residue was measured across 7,246 deposited human biological assemblies (22,291 chain-level observations; 1,191 proteins). Among 1,143 analyzable proteins, termini in MetAP-permissive penultimate-residue sequence classes were less often interface-engaged than termini in MetAP-nonpermissive classes (37.4% versus 47.4%; adjusted odds ratio 0.65, p = 7.2e-4). Curated processing annotations did not show a corresponding burial difference, and correlated residue properties preclude attributing the sequence-class association specifically to iMet removal. The analysis identified 264 interface-engaged MetAP-permissive candidates concentrated in cellular machines. In a fully recomputed conformer scan of deeply buried proteasome positions, modeled methionine accommodation was less favorable than at observed-methionine controls (median overlap -0.30 versus -1.12 angstrom, p = 0.0049), although most scoreable sites permitted a nonoverlapping placement. The census therefore reveals a graded structural constraint - not universal steric failure - and prioritizes complexes in which altered packing, assembly kinetics, lipidation or N-terminal methylation can be tested.

BACH1 is a heme-regulated basic-leucine-zipper containing transcriptional repressor that binds its DNA recognition elements as a heterodimer with MAFK. Heme-binding is thought to be mediated by several Cys-Proline (CP) motifs and this results in dissociation of the heterodimer from DNA. The mechanism of heme-binding and heme-mediated DNA dissociation remains unresolved. We have used UV-visible spectroscopy, 2D-NMR and DNA-binding assays to explore both heme-binding and DNA dissociation of a minimal BACH1 construct containing 2 CP motifs (C492(CP5) and C646(CP6)) flanking the DNA-binding domain. We find that heme is able to bind to both CP motifs, but also to other non-CP cysteines and histidines in the construct. Using NMR spectroscopy, we identify a structured binding pocket in which heme interacts with both C646(CP6) and Cys621. However, DNA-binding assays show that C646(CP6) is not required for heme-mediated DNA dissociation of the BACH1:MAFK heterodimer. Using UV-visible spectroscopy we show that C492(CP5) also recruits heme with a second ligand, a conserved histidine, His559, in the BACH1 DNA-recognition helix. Mutation of C492(CP5) reduces but does not abolish heme-mediated dissociation from DNA. Our findings suggest a mechanism for heme-binding to BACH1 and heme-mediated dissociation from DNA.

UCH-L1 is a monomeric deubiquitinating enzyme whose native structure embeds a shallow $5_2$ knot located near the N-terminus, placing the knotted topology in direct proximity to both the substrate-binding pocket and the catalytic site. While our previous work established that N-terminal integrity is critical for catalytic activity, the energetic cost of unknotting and its structural consequences remained unquantified. Here, we combine steered molecular dynamics with an umbrella sampling scheme to generate topologically modified variants of UCH-L1 and, for the first time, reconstruct the free-energy profile of UCH-L1 unknotting. The potential of mean force reveals a steep energetic barrier to knot disruption, consistent with knotting being a late, rate-limiting folding step that is effectively locked in once the native structure is established. Long unbiased MD simulations of fully unknotted variants in both apo and holo states show that knot removal increases local flexibility at the N-terminus without inducing significant global structural destabilization. Binding energy calculations indicate that the unknotted variant binds to ubiquitin less tightly than the wild-type ($\sim$-62~vs~$\sim$-76~kcal/mol), suggesting that topological integrity contributes to substrate affinity. Together, these results show that the $5_2$ knot in UCH-L1 is not a passive structural feature but a functional element that fine-tunes folding kinetics and contributes to substrate binding efficiency.

Protein aggregation is a context-dependent process in which the molecular environment can influence the properties of the resulting assemblies. In biological systems, these interactions can occur sequentially, as aggregates formed in one cellular or tissue context may encounter different molecular partners and act as seeds in subsequent aggregation events. Here, we used sequential seeding as a controlled experimental model of this temporal and contextual complexity to investigate how prion-like sequences from the gut microbiome modulate amyloid-{beta} aggregation across successive aggregation cycles. Combining kinetic, biophysical, conformational, and toxicity analyses, we show that early interactions with exogenous peptides modify the properties of first-generation A{beta}40- and A{beta}42-derived seeds, resulting in propagated A{beta}42 assemblies with distinct molecular and functional properties. These findings support an Interaction History model in which exogenous sequences bias the emergence of aggregate populations whose properties and subsequent propagation depend on the molecular contexts experienced during earlier aggregation events. Overall, our results present A{beta} aggregation as a history-dependent process and suggest that single-step assays may fail to capture aggregate diversity that emerges across successive aggregation cycles.

Recent advances in protein structure prediction, exemplified by AlphaFold, have largely addressed the determination of static structures, one aspect of the protein folding problem. However, predicting folding pathways, by which proteins reach their native states, remains a significant challenge. Here, we present PathFold, a deep learning framework that predicts protein folding pathways directly from sequence information. PathFold leverages an AlphaFold-based module to extract structural information from the sequence and generates a progressive folding trajectory from an extended conformation using a diffusion model. By modeling the full trajectory, it enables prediction of folding intermediates and transition pathways, analogous to those observed in steered molecular dynamics (SMD) simulations. The predicted pathways reveal well-defined intermediates and sequential folding events, and show agreement with experimental folding data, including measured {Phi}-values.

Bioinformatics software and databases are essential components of modern life science research, yet their mentions in the scientific literature are often inconsistent and difficult to systematically identify at scale. The lack of a comprehensive and up-to-date catalog of bioinformatics resources hinders efforts toward automated biomedical knowledge extraction and streamlined data analysis. Here we present SNAIL, a hybrid named entity recognition framework designed to automatically identify bioinformatics software and database (SW/DB) names from biomedical texts. SNAIL integrates complementary lexical and semantic modeling strategies. The lexical component captures orthographic patterns and contextual cues characteristic of SW/DB names, while the semantic component leverages contextual embeddings generated by transformer-based language models such as SciBERT, combined with an explicit token-masking strategy to enhance entity-focused representations. A large training corpus was constructed automatically through a hybrid pipeline that integrates citation-hinted extraction with large language model-assisted distillation. Evaluation on two independent benchmark datasets and real-world research articles demonstrates that SNAIL substantially outperforms existing approaches, including domain-specific methods such as bioNerDS2 and general-purpose large language models such as ChatGPT, Gemini, Grok and Claude. Applying SNAIL to large-scale literature analysis further reveals distinct journal-level preferences across bioinformatics subfields. These results demonstrate that SNAIL provides an accurate and scalable solution for identifying bioinformatics resources in scientific texts and enables systematic meta-analysis of tool usage and research trends.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterised by progressive motor neuron degeneration. Mutations in the SOD1 gene represent the second most common genetic cause of ALS (ALS), and distinct SOD1 missense variants present with markedly different clinical profiles. A4V leads to an aggressive form of the disease (median survival [~]1y), H46R confers a mild, slowly progressive course and I113T exhibits an intermediate phenotype. The molecular basis by which these mutations produce divergent clinical outcomes remains poorly understood. We performed extensive classical molecular dynamics simulations of wild-type SOD1 and the three ALS-associated variants in the apo monomeric state to attempt to investigate the mechanisms behind such phenotypic differences. Structural stability, global compactness, and conformational flexibility, as well as analysis of collective motions between residues and estimation of free energy, were assessed. The H46R, A4V, and I113T variants exhibited distinct dynamic behaviours, highlighting differences in structural stability, local flexibility, and intramolecular interactions. These findings suggest that specific structural regions may contribute differently to protein dysfunction and could represent key elements for understanding the relationship between molecular dynamic properties and the differing clinical severity associated with these variants. Most strikingly, H46R exhibited exceptional structural stability across every analytical level, the lowest global deviation, most attenuated local flexibility, strongest internal dynamic coordination, and the deepest, most confined free energy basins of any system examined. This convergent multi-layered evidence of structural restraint provides a compelling mechanistic basis for the mild and slowly progressive clinical course of H46R ALS, suggesting that enhanced conformational rigidity, rather than bulk destabilisation, is the defining biophysical feature of this variant, and that its pathogenic mechanism operates through a route fundamentally decoupled from the aggregation-driven toxicity that characterises the more aggressive SOD1-ALS mutations.

Amyloid fibrils are highly ordered protein aggregates characterized by a conserved cross-{beta}-sheet architecture despite originating from structurally diverse precursor proteins. Growing evidence suggests that interactions between different amyloidogenic proteins can modulate aggregation pathways through heterologous cross-seeding; however, the influence of seed polymorphism on the structure and biological properties of cross-seeded fibrils remains poorly understood. Here, we investigated the cross-seeding of native human insulin by two structurally distinct polymorphs of hen egg-white lysozyme (HEWL): flexible fibrils (FFs) and rigid fibrils (RFs). Native insulin remained stable under physiological conditions and underwent spontaneous fibrillation only under acidic conditions. In contrast, both HEWL polymorphs efficiently induced insulin aggregation at physiological pH, bypassing the nucleation barrier. Thioflavin T fluorescence, circular dichroism spectroscopy, and transmission electron microscopy revealed that lysozyme FFs templated the formation of insulin flexible fibrils (IFFs), whereas lysozyme RFs produced insulin rigid fibrils (IRFs), demonstrating that the structural characteristics of the parental HEWL polymorphs were propagated during heterologous cross-seeding. The toxicity of the resulting insulin fibrils was evaluated in SH-SY5Y neuronal cells and CCF-STTG1 astrocytes. IFFs exhibited minimal cytotoxicity and only subtle morphological alterations, whereas IRFs caused modest reductions in cell viability accompanied by more pronounced cellular damage. These findings demonstrate that the structural polymorphism of HEWL fibrils governs both the architecture and biological activity of cross-seeded insulin fibrils, highlighting amyloid polymorphism as an important determinant of heterologous amyloid propagation and a potential design principle for engineering functional amyloid-based biomaterials and protein delivery platforms.

tRNAs are characterized by extensive chemical modifications that influence tRNA fate. N1-methyladenosine at position 58 (m1A58) is a widespread core tRNA modification linked to physiological and pathological processes. However, how m1A58 coordinate tRNA folding and processing to ensure translational efficiency in mammalian cells remains largely unknown. Using acute dTAG-mediated degradation and CRISPR-Cas9 knockout, we identified initiator methionine tRNA (tRNAiMet) as selectively vulnerable to m1A58 loss, lacking the isodecoder buffering observed for most other tRNA isoacceptors. NMR analysis of the tRNAiMet showed that m1A58 stabilizes D/T-loop interactions, consistent with a maturation-competent conformation. In vitro processing assays further demonstrated that m1A58 promotes RNase P-mediated 5'-leader removal and RNase Z-mediated 3'-trailer cleavage, while La/SSB protects accumulated precursors. Disrupting this checkpoint impaired the assembly of the eIF2-containing 43S pre-initiation complex and global protein synthesis, which was substantially rescued by adding m1A58-modified tRNAiMet. Acute TRMT6 degradation elicited temporally coordinated gene-expression responses involving proteostasis, transport and signaling. Together, these findings establish m1A58 as a conformational checkpoint coupling human initiator-tRNA maturation to translation initiation and stress responses.

Human cytochrome P450 2C9 (CYP2C9) is a hepatic microsomal enzyme involved in the oxidative metabolism of clinically important drugs, but the structural organization of its oligomeric assemblies outside crystallographic packing environments remains poorly understood. Here, we report the cryo-EM structure of human CYP2C9 determined under aqueous, membrane-free conditions at 3.31 Angstrom resolution. The structure reveals a C2-symmetric hexameric assembly organized as a dimer of trimers. Individual protomers retain the conserved P450 fold and heme-binding architecture observed in previously reported crystal structures, indicating that assembly formation does not substantially perturb the catalytic core. The hexamer is stabilized by defined intra-trimer interfaces involving the N-terminal region and residues around Trp212 and Phe482, together with inter-trimer interfaces involving Leu71 and the 220-227 loop. These interfaces are distinct from the crystal packing contacts observed in CYP2C9 crystal structures, demonstrating that the assembly is not a simple recapitulation of crystallographic packing. Notably, the inter-trimer interface is located near the FG-loop-containing surface previously implicated in membrane association. This suggests that the observed hexamer may represent a membrane-free association of two trimers through membrane-related surfaces, whereas the trimeric arrangement itself may be compatible with membrane-associated organization. The structure therefore provides a framework for investigating how trimer formation, membrane interaction and local conformational changes in the FG-loop region may influence CYP2C9 function.

The folding thermodynamics of proteins are dominated by two opposing forces, the loss in backbone entropy and the packing of hydrophobic groups. The same forces are major contributors to the extension thermodynamics of elastic proteins with the distinction that both processes act in concert, favoring the higher chain and solvent entropy of a relaxed conformation. The relative entropic contributions specify the recoil mechanism; human elastin recoil is primarily driven by hydrophobic forces, whereas fly resilin has a rubber-like mechanism driven by backbone entropy. Despite the importance of elastic proteins to tissue biomechanics, few have been identified, let alone characterized to the same extent as elastin and resilin. We develop a thermodynamic framework that maps proteins by sequence-derived estimates of extension-induced backbone and solvent entropy changes. Putative elastic proteins are proposed and classified by recoil mechanism based on estimated thermodynamic features. Proteins that map to elastic regions are overrepresented by the skin proteome. The set of predicted elastic domains is further extended by incorporating sequence context embedded in protein language models. Protein domains with distinct thermodynamic recoil mechanisms cluster on the latent space manifold. Some of these domains are anticipated to have roles within molecular machines, expanding the scope of elastic protein function beyond mechanical materials like elastin and resilin.

Network-based analyses of molecular interactions are useful for interpreting high-throughput omics data and identifying therapeutic targets. Cytoscape is the standard platform for these tasks, but users face a trade-off between accessible graphical workflows that are difficult to document and reproducible automation in Python or R that requires programming expertise. General-purpose coding assistants can generate Cytoscape Automation scripts, but remain external to Cytoscape. We present CyChat, a Cytoscape Desktop app that integrates a chat interface and a large language model (LLM) agent into the application. CyChat translates natural language into executable Cytoscape Automation workflows, runs generated Python code, and exports chat sessions with executed code as standalone Jupyter notebooks. To reduce setup barriers, CyChat includes an embedded Python runtime and supports both cloud-based and locally hosted LLMs. CyChat was evaluated across ten Cytoscape workflows using seven LLM providers, each represented by one LLM. The strongest configuration achieves a pass rate above 99%. In a qualitative evaluation based on a published network visualization, CyChat completes the task in 1.5-5 minutes, compared with 15-20 minutes for manual GUI workflows by computational biologists. CyChat is available through the Cytoscape App Store at https://apps.cytoscape.org/apps/cychat.

Linker chemistry and conformation are central determinants of PROTAC activity, shaping ternary-complex geometry, cooperativity, target-lysine presentation and cellular permeability. Existing linker generators often lack explicit control over linker flexibility, require predefined attachment sites and linker lengths, or produce structures that demand substantial geometric correction, limiting their utility in practical PROTAC design. Here we introduce FlexiTAC, a Bayesian flow network that jointly generates linker atom types and coordinates from the warhead and E3-ligase-ligand contexts. We also assemble PROTAC-3D, a quality-controlled collection of 63,554 component-resolved PROTAC structures for model training, and PROTAC-Bench, which covers molecular quality, fragment preservation, geometric fidelity, conformational stability, fragment awareness, rediscovery and sampling efficiency. Compared to the best 3D baseline models, FlexiTAC improves validity by 12.0-12.7% and achieves the highest PoseBusters pass rate of 79.5%-80.0%. A differentiable guidance module shifted generated linkers along a conformational ensemble-derived rigidity axis without retraining the generator. In silico case studies further show that the model can accept crystal-derived, redocked or predicted structural inputs. Together, FlexiTAC, PROTAC-3D and PROTAC-Bench establish an integrated and reproducible framework for data-driven PROTAC linker design, combining controllable structure-conditioned generation with standardized training data and evaluation protocols. This framework expands the linker chemical and conformational space accessible to computational exploration, provides a foundation for future method development and enables the systematic generation of structure-conditioned linker designs with tunable conformational flexibility.

Pseudomonas aeruginosa is an opportunistic human pathogen capable of infecting a wide range of tissues and organs. Its persistence during chronic infection is strongly associated with biofilm formation, which depends on extracellular polysaccharides such as alginate. The HtrA-like periplasmic serine protease MucD is a key regulator of bacterial virulence, stress response, and alginate production, yet its molecular mechanism has remained largely unclear. Here, we discovered the alginate acetylation and export proteins AlgX and AlgK as MucD substrates, and characterized their degradation by mass spectrometry and bioinformatic analysis. We further determined the cryo-EM structure of MucD bound to an AlgK-derived substrate peptide, offering atomic insights into MucD oligomerization assembly, substrate recognition, and specificity. Together with structure-guided mutagenesis and biochemical assays, our results revealed that MucD proteolytic activity is governed by an equilibrium between a resting 12-mer and an active trimer. Crucially, we demonstrate that MucD represses alginate biosynthesis post-translationally, in addition to its previously implicated role in transcriptional regulation. These findings define a distinct activation mechanism and regulatory function for MucD and provide new insight into bacterial HtrA-like serine proteases.

High-quality experimental datasets that link protein-ligand structures with binding affinity data are essential for developing and evaluating structure-based machine learning methods. To help address this need, we established OpenBind as an open-science initiative to generate large-scale experimental datasets for structure-based AI and molecular discovery. Here, we describe the first public OpenBind release, which, to the best of our knowledge, is the largest public single-target experimental structure-affinity dataset. The dataset focuses on enteroviral 2A protease, comprising 925 crystallographic binding events from 699 compounds and associated affinity measurements for 601 compounds. It combines structures from an initial fragment screen and follow-on molecules, together with affinity data, linking experimentally determined protein-ligand binding modes to biophysical measurements within a coherent antiviral discovery campaign. We used this dataset to evaluate protein-ligand structure prediction, binding-affinity prediction, and virtual screening using representative structure-based methods, including docking and cofolding. This exposed several challenges that are central to practical structure-based modelling: docking performance depends strongly on binding-pocket conformation, poses are difficult to rank, and structure-based affinity prediction remains challenging. Fine-tuning OpenFold3-p2 on the fragment-screen structures substantially improved pose prediction and virtual screening for related follow-on compounds, demonstrating how early-stage experimental structures can support target-specific model adaptation.

The cluster of differentiation 36 (CD36) is a membrane protein with broad physiological roles in health and disease, and its function is regulated in part by phosphorylation. Experimental evidence shows that phosphorylation of Thr92 reduces CD36 affinity for thrombospondin-1 (TSP-1), binding of which initiates antiangiogenic signaling, whereas phosphorylation of Ser237 decreases CD36-mediated fatty acid uptake, with implications for energy metabolism. However, the only available crystal structure of CD36 lacks phosphorylation, and the molecular mechanisms by which phosphorylation regulates CD36 function remain largely unknown. This study provides an atomically detailed computational characterization of CD36 in unphosphorylated and dual phosphorylated states, using molecular dynamics simulations with a total sampling time of 30 microseconds in combination with Markov state models. We present, to our knowledge, the first evidence of a cryptic pocket on CD36 surface that is formed by phosphorylation. This cryptic surface pocket and a loop spanning residues 121-131 form a high affinity binding site for TSP-1 derived ligands, shifting their binding away from the canonical site. We propose that this altered binding provides a molecular basis for the disruption of antiangiogenic signaling upon CD36 phosphorylation. Additionally, our data indicate that, phosphorylation increases helicity and compaction within the helix-loop region spanning residues 296-331, narrowing one of the entrances to the internal cavity and reducing its overall volume. These conformational changes provide a potential mechanistic explanation for the decrease in fatty acid uptake upon CD36 phosphorylation. Our findings provide structural insights that may inform the future design of CD36 modulators and emphasize the importance of targeting phosphorylation induced CD36 conformations in angiogenic and metabolic diseases.

Carbapenems are the most potent {beta}-lactams, key antibiotics for healthcare-associated infections by Gram-negative bacteria and evade hydrolysis by most {beta}-lactamases, but are increasingly threatened by emergence of enzymes exhibiting hydrolytic activity towards them. Of the four recognised {beta}-lactamase subclasses, class A (active-site serine enzymes that hydrolyse {beta}-lactams via a covalent acylenzyme intermediate) is the most widely disseminated and, while the majority of such enzymes react with carbapenems to form long-lasting acylenzyme complexes, several possess carbapenem-hydrolyzing activity (carbapenemases). Here, we investigate the basis for these differences in a panel of class A {beta}-lactamases using molecular dynamics (MD) simulations of the respective acylenzyme complexes and tetrahedral intermediates (TI). The simulations reveal multiple features associated with catalytic activity across the spectrum of enzymes tested, including more extensive interactions of the carbapenem acylenzyme carbonyl and generally increased lifetimes of active site water molecules positioned for deacylation. Analysis of the dynamic trajectories shows carbapenemases to have reduced root mean-squared fluctuation (RMSF) differences between the acylenzyme and TI, that are not limited to the active site, indicating that the acylenzyme complex is pre-organised for reaction in carbapenemases but not in carbapenem-inhibited enzymes. Similarly, Principal Component Analysis (PCA) of acylenzyme and TI dynamics shows greater overlap between the two states in carbapenemases, providing further evidence for acylenzyme pre-organisation. Such simulations may represent an effective computational assay able to identify enzymes with carbapenemase activity at relatively modest computational cost.

Infectious bronchitis virus (IBV) is a member of the Gammacoronavirus genus responsible for respiratory illness and weakened eggshells in infected chickens, adversely impacting the poultry industry. Escaping innate immune detection during infection is crucial for coronavirus proliferation in the host. The production of double-stranded RNA during coronavirus replication triggers innate immune sensors to create an antiviral state within infected cells. To counter this response, coronaviruses employ nonstructural protein 15 (nsp15) endoribonuclease to degrade double-stranded RNA. Here, we use cryo-electron microscopy and biochemistry to characterize IBV nsp15 interactions with RNA. While the overall structure and active site of IBV nsp15 strongly resemble previous studies of nsp15 from other coronaviral genera, we note that double-stranded RNA contacts several non-conserved residues peripheral to the enzyme active site. Our data show that these residue positions can have strong impacts on RNA cleavage suggesting unique solutions for RNA engagement across coronavirus species. We also demonstrate a preference for IBV nsp15 to cleave double-stranded RNA over single-stranded RNA and observe nsp15 hexamers with two double-stranded RNAs bound simultaneously. This study reinforces the need to study diverse coronavirus species to identify distinct viral enzyme characteristics.