MAOMAO: an ontology-guided FAIR resource for harmonized peptide toxicity data

Abstract

Therapeutic peptides have emerged as a rapidly expanding class of biomolecules with applications spanning antimicrobial, antiviral, anticancer, immunomodulatory, metabolic, and diagnostic settings (8). Their high target specificity, favorable safety profiles, and growing clinical success have positioned them as attractive alternatives or complements to traditional small-molecule therapeutics (36). The number of experimentally characterized bioactive peptides has therefore increased substantially during the last decade (38), supporting the development of specialized databases and computational resources for peptide discovery, functional annotation, and rational design (5,15,31,35). Despite these advances, toxicity remains a major limitation to peptide development and clinical translation, constraining candidate selection during early-stage screening and optimization (3).Peptide toxicity encompasses a broad spectrum of adverse biological effects that arise from the interaction between peptides and biological systems. Most toxicity phenotypes are routinely evaluated during peptide development including cytotoxicity, hemolysis, neurotoxicity, embryotoxicity, and ichthyotoxicity (19,27). These toxicity endpoints have direct implications for efficacy, safety, dosage, and regulatory approval. Toxicity annotations have consequently accumulated across public databases (31,18,24), literature-derived resources (26,29,28,34), supplementary datasets (9,4,23), and experimental studies (33,39). However, these resources remain fragmented, employ heterogeneous terminology and annotation criteria, and provide variable levels of metadata and evidence quality (21). Their development for distinct biological objectives further complicates integration, reproducibility, and large-scale analysis (17).The growing availability of toxicity data creates opportunities for computational toxicology, data-driven peptide engineering, and large-scale biological analyses (32). Their utility, however, depends on the quality, consistency, and transparency of the underlying data (14,7). Annotation ambiguity, conflicting evidence, heterogeneous endpoint definitions, incomplete metadata, and limited provenance tracking can reduce reproducibility and complicate downstream reuse (1). The FAIR principles-Findability, Accessibility, Interoperability, and Reusability-provide a framework for organizing and distributing scientific data to support discovery, integration, reproducibility, and long-term reuse (22). Nevertheless, a standardized framework that harmonizes heterogeneous peptidetoxicity annotations while preserving evidence, provenance, and metadata remains largely unavailable (21). FAIR-oriented resources tailored to both biological interpretation and computational reuse are therefore still needed (37,13).To address these limitations, we developed MAOMAO (Metadata-Aware Ontology for Multi-source Annotation Organization), an ontology-guided resource integrating peptide-toxicity information from public databases, literature-derived resources, and curated datasets. MAOMAO standardizes toxicity terminology, preserves source-level provenance, distinguishes uncertainty-related evidence states, and organizes annotations within a unified hierarchical framework. The resource also provides harmonized metadata, descriptor-based representations, protein language model embeddings, benchmark resources, and supporting documentation. These components establish a FAIR-oriented and reusable foundation for biological interpretation, computational toxicology, and predictive modeling of peptide toxicity.The workflow used to construct MAOMAO is summarized in Figure 1A. Toxicity-related information from heterogeneous sources was preprocessed, harmonized, mapped to a unified toxicity ontology, evaluated for annotation conflicts and evidence quality, enriched with standardized metadata, and consolidated into the final resource. To support dissemination, computational reuse, and long-term maintenance, MAOMAO was organized into a modular architecture comprising complementary resource layers (Figure 1B).MAOMAO was constructed by integrating toxicity-related information from publicly available databases, literature-derived resources, supplementary datasets, and manually curated repositories. Candidate resources were identified through a structured exploration of the peptide toxicity landscape using scientific search engines, bibliographic repositories, specialized peptide databases, review articles, and citation tracking (2,16). Searches combined general terms, including peptide toxicity, toxic peptides, toxic peptide databases, and toxic peptide predictive models, with endpoint-specific queries for hemolytic peptides, cytotoxic peptides, cytolytic peptides, neurotoxic peptides, embryotoxic peptides, ichthyotoxic peptides, and anti mammalian cells peptides. Additional resources were identified from toxicity prediction tools, benchmark datasets, supplementary materials, associated repositories, and specialized peptide resources. Searches were conducted using Google Scholar and PubMed (6) between June 2025 and September 2026 and covered resources available through September 2026.Candidate resources were evaluated according to predefined eligibility criteria, including relevance to at least one MAOMAO toxicity endpoint, availability of peptide sequences with corresponding categorical toxicity annotations or quantitative toxicity measurements, sufficient provenance information, and the possibility of programmatic or manual retrieval. Resources were excluded when the underlying dataset could not be retrieved through the reported publication, supplementary material, repository, or web server, or when the available material lacked sequences suitable for sequence-level integration. This included resources containing only numerical representations, peptide names, or functional annotations without the corresponding sequences. Candidate-source screening, eligibility decisions, integrated and excluded resources, and source-specific exclusion reasons are detailed in Supplementary Section S1. (A) Ontology-guided harmonization workflow used to construct the resource. Toxicity-related information collected from public databases, literature-derived datasets, and curated resources undergoes preprocessing, terminology harmonization, ontology mapping, evidence integration, quantitative-property parsing, and metadata harmonization to generate a standardized and provenance-aware toxicity resource. (B) Modular architecture of MAOMAO. The resource is organized into complementary layers comprising harmonized toxicity annotations, numerical toxicity properties, sequence profiles, and metadata (Core Layer), sequence-derived descriptors (Descriptor Layer), protein language model embeddings (Embedding Layer), benchmark resources (Benchmark Layer), practical usage examples, and supporting documentation (Documentation Layer). Together, these components establish a FAIR-oriented and reusable resource for peptide toxicity research.Data acquisition combined direct database downloads, repository and supplementary-file retrieval, and manual extraction when required. Retrieved records were converted into a standardized intermediate representation, preserving peptide sequences, toxicity annotations, quantitative toxicity measurements when available, source identifiers, literature references, and available metadata. Source-level quality control verified the reported access route, the availability of primary sequences and interpretable toxicity information, and the associated publication or repository. Records lacking peptide sequences, containing toxicity information that could not be interpreted, or presenting irrecoverable formatting inconsistencies were excluded. Retained records were then forwarded to preprocessing, terminology harmonization, ontology mapping, metadata harmonization, and evidence integration.Following data collection, records underwent standardized preprocessing to support integration across heterogeneous resources (Figure 1A). This stage included sequence validation, format standardization, annotation parsing, identifier normalization, and quality control. The final resource was restricted to peptides containing the 20 canonical amino acids and lengths between 5 and 70 residues (38,25). Recoverable formatting inconsistencies were corrected, exact duplicates within each source were removed, and identical sequences reported by different sources were retained to preserve provenance and support evidence integration. Each normalized sequence was assigned a deterministic identifier generated by applying SHA-256 to the UTF-8 encoding of the uppercase, whitespace-free sequence. Quantitative HC50, LC50, LD50, and MHC observations were parsed while preserving their reported relation, value, unit, and provenance, and were linked to Core sequences through this identifier. Detailed sequence-processing, identifier-construction, and quantitative-property procedures are provided in Supplementary Section S1.To prevent terminology inconsistencies during cross-source integration, toxicity-related annotations were examined for alternative spellings, synonyms, endpoint-specific descriptions, and sourcedependent nomenclature referring to equivalent biological concepts. These variants were mapped to a standardized vocabulary while preserving their original biological meaning. For example, haemolytic was normalized to hemolytic, and cytolytic to cytolysis.Harmonized annotations were mapped to the MAOMAO toxicity ontology, centered on the root category Toxic. The ontology comprises Cytotoxic, Neurotoxic, Embryotoxic, and Ichthyotoxic as primary classes, with Hemolytic, Cytolysis, and Anti mammalian cells represented as subclasses of Cytotoxic. Hemolytic denotes toxicity involving erythrocyte lysis, whereas Cytolysis captures cell lysis or membrane-disruptive activity without a specified cell type. Anti-mammalian-cell activity denotes damage to mammalian cells or reduced cell viability (20,30,40). The vocabulary was restricted to distinct toxicity concepts supported by retrievable sequence-level evidence. Complete endpoint definitions, mappings, and assignment rules are provided in Supplementary Section S2.Following data collection, harmonization, and ontology mapping, annotations associated with identical peptide sequences were aggregated across sources (Figure 1A). Because the same sequence could be reported with different toxicity annotations, an evidence-management framework was applied to classify each peptide-endpoint pair as positive, negative, ambiguous, unlabeled, or no information. Positive states represented direct or hierarchy-supported toxicity evidence, whereas negative states corresponded to direct endpoint-specific non-toxic evidence. Ambiguous states captured conflicting or unresolved annotations. Unlabeled states denoted sequences retained in endpoint-specific sources without an explicit class assignment, while no information indicated the absence of evidence for that endpoint.Negative evidence was further classified as strong or weak according to the supporting information available in the original source. Strong negatives were explicitly supported by experimental evidence, whereas weak negatives originated from randomly selected negative sets or lacked confirmation of the absence of toxicity annotations. Evidence strength was retained in the metadata and provenance records, enabling downstream analyses to account for confidence, uncertainty, and conflicting evidence. Definitions, assignment criteria, and conflict-resolution rules are provided in Supplementary Section S2.To support transparency, interoperability, and long-term reuse, source-and endpoint-level metadata were normalized into a unified schema. Harmonized fields included source identifiers, literature references, organism information, annotation provenance, retrieval dates, file formats, and version information when available. Missing values were explicitly preserved without inference. The schema also retained original source labels, ontology mappings, evidence categories, publication references, and resource-specific annotations, providing a standardized representation of the integrated biological and technical information.Provenance was maintained by linking harmonized annotations to their source-specific processed datasets, endpoint-integration outputs, and metadata records. The sequence-level pivot provides the harmonized access layer, while provenance records, the ambiguity-support table, and the hierarchyaudit table document evidence aggregation, conflict resolution, and hierarchy-derived changes. These files allow each sequence-endpoint state to be traced to its supporting sources and processing decisions. Metadata fields, controlled vocabularies, and data types are detailed in Supplementary Section S3.To support reproducibility, computational reuse, and long-term maintenance, MAOMAO was organized into complementary functional layers (Figure 1B). This modular architecture separates harmonized biological data, computational representations, benchmark resources, and supporting documentation while preserving interoperability across the resource.The Core Layer contains harmonized peptide sequences, toxicity annotations, ontology mappings, evidence classifications, provenance information, and metadata. It additionally provides numerical toxicity properties and sequence profiles. Descriptor-based representations were generated using Roxy (11), whereas embedding-based representations were obtained with Sylphy (12) using publicly available protein language models. Benchmark resources were generated using partitioning strategies implemented through BioSieve (10). The Documentation Layer contains ontology specifications, metadata schemas, resource descriptions, licensing information, and versioning records. Supported representations, benchmark resources, resource organization, and file structures are detailed in Supplementary Sections S4-S5.FAIR principles guided the design and construction of MAOMAO to support transparency, reproducibility, interoperability, and long-term sustainability. FAIR-oriented practices were incorporated across data collection, terminology harmonization, ontology development, evidence management, metadata integration, and resource organization.Interoperability and reusability were supported through standardized toxicity terminology, ontology-guided endpoint mapping, harmonized metadata schemas, explicit evidence categorization, provenance tracking, and version-aware organization. These components enable heterogeneous toxicity annotations to be integrated while preserving the original information associated with each record.Findability, accessibility, and computational reuse were supported by distributing resource components in machine-readable formats and using standardized identifiers linking sequences, annotations, metadata, ontology terms, and evidence categories. The sequence-derived SHA-256 identifiers provide a stable key for linking corresponding records across the Core, Descriptor, Embedding, Benchmark, and sequence-profile resources. Harmonization procedures, ontology definitions, evidence-management rules, metadata specifications, and generation workflows were also documented to support validation, updates, and downstream use. Resource availability, file organization, versioning, and supporting documentation are detailed in Supplementary Section S5.An overview of MAOMAO is presented in Figure 2A. The resource integrates toxicity-related information from 56 publicly available databases, literature-derived resources, supplementary datasets, and manually curated repositories. Of 1,372,190 non-deduplicated source-level records initially collected, 898,637 were retained after source-specific preprocessing, representing a reduction of approximately 34.5%. Removed records included duplicates, incomplete entries, formatting inconsistencies, and annotations that did not satisfy the inclusion criteria.Following terminology harmonization, ontology mapping, cross-source evidence aggregation, state resolution, and quality control, the final release comprised 71,857 unique peptide sequences across eight toxicity-related endpoints. These were organized into 574,856 sequence-endpoint combinations, together with standardized metadata and provenance records linking annotations, ontology assignments, evidence classifications, and source-specific information.Publication references, repository or server information, download dates, file formats, source types, and resource behavior were documented for all 56 sources. Annotation content and last-update dates were available for 55 sources (98.21%), whereas explicit licensing information was available for 15 sources (26.79%). Missing metadata were preserved without inference. These components provide a harmonized resource for organizing, disseminating, and computationally analyzing peptide toxicity information.The ontology-guided organization of toxicity annotations is presented in Figure 2B. The root category Toxic contains 57,080 available peptide-endpoint states, including 13,824 positives, and comprises four primary classes, namely Cytotoxic (25,454 available states; 7,122 positive), Neurotoxic (3,183; 1,481 positive), Embryotoxic (2 positive), and Ichthyotoxic (5 positive). The Cytotoxic branch is further divided into Hemolytic (35,960 available states; 4,714 positive), Cytolysis (343; 341 positive), and Anti mammalian cells