Now, a team of researchers from Universidad de Magallanes in Punta Arenas, Chile, the Data Science Institute at Universidad del Desarrollo in Santiago, and PEACCEL in Paris has unveiled MAOMAO (Metadata-Aware Ontology for Multi-source Annotation Organization), an ontology-guided resource designed to bring coherence to the fragmented landscape of peptide toxicity data. Published in Frontiers in Pharmacology as a data report in the Predictive Toxicology section, the resource integrates toxicity information from 56 publicly available databases, literature-derived resources, supplementary datasets, and manually curated repositories into a unified, FAIR-oriented framework that preserves evidence, provenance, and metadata at every step.

The problem MAOMAO addresses is deceptively simple but technically profound. Toxicity annotations for peptides have accumulated across public databases, literature-derived resources, supplementary datasets, and individual experimental studies, but these sources employ heterogeneous terminology, annotation criteria, and metadata standards. A peptide described as haemolytic in one database might appear as hemolytic in another, while cytolytic activity in one source could correspond to cytolysis in a different resource. These inconsistencies complicate integration, reproducibility, and large-scale analysis, and they can introduce annotation ambiguity, conflicting evidence, incomplete metadata, and limited provenance tracking that reduce the reliability of downstream computational reuse.

MAOMAO standardizes toxicity terminology by mapping alternative spellings, synonyms, and source-dependent nomenclature to a unified vocabulary while preserving the original biological meaning. The resource then organizes harmonized annotations within a hierarchical toxicity ontology centered on the root category Toxic, which comprises four primary classes: Cytotoxic, Neurotoxic, Embryotoxic, and Ichthyotoxic. The Cytotoxic branch is further subdivided into Hemolytic, Cytolysis, and Anti-mammalian-cell activity, preserving more specific biological contexts. Hemolytic denotes toxicity involving erythrocyte lysis, whereas Cytolysis captures cell lysis or membrane-disruptive activity without a specified cell type, and anti-mammalian-cell activity denotes damage to mammalian cells or reduced cell viability.

The construction workflow began with structured exploration of the peptide toxicity landscape using scientific search engines, bibliographic repositories, specialized peptide databases, review articles, and citation tracking. Searches combined general terms such as peptide toxicity, toxic peptides, and toxic peptide databases with endpoint-specific queries for hemolytic, cytotoxic, cytolytic, neurotoxic, embryotoxic, ichthyotoxic, and anti-mammalian-cell peptides. Candidate resources were evaluated against 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 lacking retrievable sequences or containing only numerical representations, peptide names, or functional annotations without corresponding sequences were excluded.

Retrieved records underwent standardized preprocessing that 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. Exact duplicates within each source were removed, while 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, providing a stable key for linking records across all resource layers. Quantitative HC50, LC50, LD50, and MHC observations were parsed while preserving their reported relation, value, unit, and provenance.

Because the same peptide sequence could be reported with different toxicity annotations across sources, MAOMAO applied an evidence-management framework 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, and 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, with strong negatives explicitly supported by experimental evidence and weak negatives originating from randomly selected negative sets or lacking confirmation of the absence of toxicity annotations.

The final release comprises 71,857 unique peptide sequences across eight toxicity-related endpoints, organized into 574,856 sequence-endpoint combinations together with standardized metadata and provenance records. 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 percent. Across the resource, 31,153 states were positive, 91,506 negative, 15,717 ambiguous, and 5,044 unlabeled. The Toxic root category contained 57,080 available peptide-endpoint states, including 13,824 positives, while the Cytotoxic class held 25,454 available states with 7,122 positive. Hemolytic had the highest proportion of ambiguous states at 19.05 percent and was the only endpoint with unlabeled states, representing 14.03 percent of its available annotations. Notably, 7,331 of the 9,421 peptides with at least one positive annotation across the seven non-root endpoints were assigned to multiple toxicity categories, corresponding to 77.82 percent of positive peptides across these endpoints.

MAOMAO is organized into complementary functional layers that separate harmonized biological data, computational representations, benchmark resources, and supporting documentation while preserving interoperability. The Core Layer contains harmonized peptide sequences, toxicity annotations, ontology mappings, evidence classifications, provenance information, and metadata, along with 169 quantitative HC50, LC50, LD50, and MHC measurement records. The Descriptor Layer provides 41 features capturing sequence composition and physicochemical properties, generated using the Roxy package. The Embedding Layer includes 10 protein-language-model embedding matrices and a one-hot representation, all covering the complete set of 71,857 sequences, produced with the Sylphy package. The Benchmark Layer contains 300 endpoint-seed-strategy configurations generated through random and stratified five-fold partitioning of five eligible endpoints using 30 random seeds, corresponding to 1,500 data partitions and, across 11 numerical representations, 3,300 representation-specific configurations and 16,500 fold instances. The Documentation Layer includes ontology definitions, metadata schemas, resource descriptions, versioning records, and licensing information, with files distributed in standard formats including CSV, JSON, and YAML.

To facilitate direct reuse, MAOMAO provides five executable notebooks that operate on the released files rather than reconstructing the resource from original sources. These cover resource access, file validation, navigation of the Core Layer, and linkage of annotations, numerical representations, and benchmark partitions through stable sequence identifiers. Two additional notebooks illustrate downstream machine-learning reuse, including a neurotoxicity classification example that joins a released train-validation-test partition with an existing protein language model representation and evaluates logistic regression, a linear support vector machine, and random forest models using fixed settings, and a quantitative regression example showing how numerical toxicity measurements can be combined with a released sequence representation. The complete, versioned MAOMAO resource is archived on Zenodo, and code, workflows, notebooks, and environment specifications are available on GitHub under the MIT License. The researchers acknowledge that endpoint coverage depends on the availability of retrievable sequence-level evidence across curated sources, resulting in class imbalance and limited representation of some endpoints, particularly Cytolysis, Embryotoxic, and Ichthyotoxic, and that the predefined benchmark partitions rely on random and stratified splitting without homology- or similarity-based control. Future releases may address these limitations through the incorporation of newly available toxicity datasets, expanded endpoint and numerical-property coverage, metadata enrichment, updated numerical representations, and similarity-aware benchmark partitions.

Subject of Research: An ontology-guided FAIR resource for harmonized peptide toxicity data

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

Article References: Soto-Garcia, N., García-Vinuesa, J., Uribe-Paredes, R., Murgas, L., Oróstica, K., González-Puelma, J., Navarrete, M. A., Cadet, F., & Medina-Ortiz, D. (2026). MAOMAO: an ontology-guided FAIR resource for harmonized peptide toxicity data. Frontiers in Pharmacology, 17, Article 1952924. https://doi.org/10.3389/fphar.2026.1952924

Image Credits: AI Generated

DOI: 10.3389/fphar.2026.1952924

Keywords: MAOMAO, peptide toxicity, FAIR principles, ontology, predictive toxicology, machine learning, therapeutic peptides, data harmonization, protein language models, bioinformatics, ontology-guided, FAIR