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

ABSTRACT 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.