Mapping Trofinetide Polypharmacology in Rett Syndrome: A Multi-Stage Computational Analysis

Rett syndrome (RTT) is a severe neurodevelopmental disorder caused by mutations in the MECP2 gene. Although trofinetide is the first FDA-approved drug for RTT, its pharmacological mechanism remains unclear. We present a structure-based in silico workflow that integrates target prediction, molecular docking, and 100 ns molecular dynamics (MD) simulations to prioritize potential RTT-relevant targets. Candidate receptors were ranked using a comparative pleiotropic score (P_S), as well as an interaction similarity index (S_SI) relative to endogenous substrates and reference modulators. Five targets emerged as high-priority candidates (GAT1, GABA_A, CHRM1, AMPA, and GSK3β) and were further examined using MD. The simulations supported several binding hypotheses: (i) stable occupation of the orthosteric site in GAT1 and CHRM1, with persistent contacts with ligand-recognition-associated residues (Tyr60 in GAT1 and Asp105 in CHRM1); (ii) sustained binding within the catalytic cleft of GSK3β with recurrent interactions near key catalytic elements (including Lys85); and (iii) dynamic, surface-associated binding modes in GABA_A and AMPA, with peripheral residues. In different targets, the proline fragment frequently contributes to hydrophobic anchoring. Taken together, these results provide testable structural hypotheses for the multi-target interaction of trofinetide in RTT and a computational framework to guide experimental validation and next-generation multi-target design.