In Silico Design and Engineering of a Multi-Epitope Polyprotein Construct Targeting High-Consequence Global Pathogens and Conserved Cancer-Testis Antigens
TITLE: In Silico Design and Engineering of a Multi-Epitope Polyprotein Construct Targeting High-Consequence Global Pathogens and Conserved Cancer-Testis Antigens ABSTRACT:This study presents the computational architecture of a multi-valent prophylactic and immunotherapeutic mRNA polyprotein designed to optimize global population presentation across highly prevalent human genetic backgrounds. Utilizing an advanced immunoinformatics pipeline, structural sequences from 15 high-consequence global pathogens and 2 widely overexpressed public tumor antigens were systematically screened and filtered. Target 9-mer epitopes were evaluated via deep-learning algorithms (NetMHCpan EL 4.1) against a strict 7-allele human leukocyte antigen (HLA) supertype reference panel, filtering exclusively for high-affinity binders scoring a Percentile Rank < 0.500. Structural safety gates were enforced utilizing machine-learning allergenicity prediction models (AllerTOP v2.0), permanently eliminating all candidate sequences flagged as potential allergens to minimize systemic anaphylactic risks. Autoimmune cross-reactivity was mitigated via comprehensive BLASTp alignments against the Homo sapiens proteome to ensure strict target specificity. The final 337-amino-acid invariant construct integrates a 16-amino acid human signal peptide, processing-optimized tri-peptide (AAY) proteasomal linkers, and 25 tool-verified pathogen-specific anchors. To break historical mutational constraints, volatile oncology point mutations were discarded in favor of full-length public Cancer-Testis Antigens, securing elite-binding universal oncology shields targeting conserved human Telomerase (hTERT, FLDLQVNSL, Rank: 0.020) and NY-ESO-1 (MPFATPMEA, Rank: 0.120) registers. To facilitate cellular transfection and endosomal escape, a theoretical 4-component Lipid Nanoparticle (LNP) delivery vesicle was modeled with precise molar ratios (50.0% Ionizable Lipid, 10.0% DSPC, 38.5% Cholesterol, 1.5% PEG2000-DMG). This complete in silico asset portfolio establishes a technically literate framework for broad-spectrum anti-infective and oncological defense, structured for downstream wet-lab in vitro translation, mass spectrometry cleavage profiling, and cellular immunogenicity validation assays. BIOGRAPHY:The author is an independent researcher and bio-computational designer specializing in immunoinformatics, vaccine platform architecture, and multi-epitope polyprotein design. Over a dedicated three-year independent research cycle, they have engineered "Project Mawingu," a comprehensive pre-clinical mRNA vaccine blueprint that bridges deep-learning epitope prediction with strict structural safety filtering. Their research focuses on optimizing global population coverage by matching highly conserved pathogen and cancer-testis antigens against dominant human leukocyte antigen (HLA) genetic supertypes.