Scalable AI-Driven Computational Modeling of Multi-Epitope Peptides for Diagnostic Innovation in Resource-Constrained Environments

Artificial intelligence and bioinformatics are increasingly critical for scalable health solutions in resource-limited settings. Crimean-Congo hemorrhagic fever (CCHF), a viral illness characterized by severe hemorrhagic manifestations, has provided a model system for computational peptide design. This study shows how AI-driven bioinformatics pipelines can accelerate biomarker discovery and diagnostic innovation, with broader applicability to neglected tropical diseases and rare disorders in Africa.

Protein sequences of CCHF glycoprotein, nucleocapsid protein, and RNA-dependent RNA polymerase were retrieved and screened for antigenicity. Linear and conformational B-cell epitopes, along with IL-10-inducing helper T-cell epitopes, were predicted and linked using peptide linkers. The construct was evaluated for antigenicity (0.5594), allergenicity (nonallergenic), solubility (protein-sol score: 0.623), and stability (instability index 28.33). Structural modeling (AlphaFold2), refinement (Galaxy Refine), and validation (Ramachandran plot: 89.1% residues in favored regions) confirmed construct integrity. Codon optimization achieved a CAI of 1.0, and molecular docking demonstrated strong binding affinity (score -291.82).

The multi-epitope construct exhibited favorable antigenicity, solubility, and stability, with validated structural integrity and strong docking interactions. These findings highlight the potential of computational pipelines to deliver scalable, cost-effective diagnostic solutions. Importantly, the framework is transferable to other diseases, enabling peptide-based biomarker discovery for neglected tropical diseases and rare bleeding disorders.

This work underscores the role of AI and bioinformatics in building scalable health technologies that address diagnostic gaps in resource-constrained environments, supporting equitable access to precision diagnostics across Africa.