Post-traumatic stress disorder (PTSD) affects millions worldwide and remains refractory to existing pharmacological treatments in 30–50% of patients, highlighting a critical need for biologically grounded, precision therapeutic strategies. This preprint presents a comprehensive computational framework for AI-guided mRNA therapeutic design targeting core molecular and circuit-level mechanisms underlying PTSD. We introduce the Rami Framework, a three-stage methodology integrating: (1) protein-specific immunotherapy optimization via molecular weight reduction while preserving biological function; (2) AI-assisted mRNA sequence intelligence, optimizing GC content, codon adaptation index (CAI), secondary structure (MFE), and regulatory elements using brain-specific constraints; and (3) systems-level proteostasis and network modeling to predict circuit restoration and multi-target synergy. The framework is applied in a case-by-case manner to five PTSD-relevant targets: gephyrin (GPHN), dopamine receptor D1 (DRD1), dopamine receptor D2 (DRD2), brain-derived neurotrophic factor (BDNF), and corticotropin-releasing hormone receptor 1 (CRHR1). Each target required a distinct optimization strategy based on protein architecture and functional role. All resulting mRNA constructs exceeded a predefined integrated efficiency threshold (≥0.75), with an average score of 0.854 ± 0.042. Systems modeling reveals nonlinear threshold behavior in gephyrin-mediated inhibitory synapse restoration, providing a mechanistic explanation for stress-induced circuit collapse and highlighting why partial protein loss can lead to disproportionate PTSD symptom severity. Network analysis further predicts synergistic therapeutic effects when combining inhibitory scaffolding restoration with dopaminergic and neurotrophic modulation. This work is purely computational and does not involve human subjects, animals, or biological materials. It does not diagnose, treat, cure, or prevent disease. All findings are theoretical and intended to establish a reproducible, open-source computational foundation for future experimental validation and precision psychiatry research.
Rami Alkhaleeli (Thu,) studied this question.