Integration of Evolutionary Analysis With RFdiffusion for De Novo Design of Aggregation‐Resistant Frataxin

ABSTRACT Friedreich's ataxia (FRDA) is a neurodegenerative disorder caused by frataxin (FXN) deficiency, where protein replacement therapy is hampered by the inherent instability and aggregation propensity of wild‐type (WT) FXN. The structural flexibility of Loop‐1 (residues 115–123), a critical region within the acidic ridge, represents a key determinant of protein stability. This study introduces a computational pipeline integrating evolutionary conservation analysis (ConSurf) with diffusion‐based de novo design (RFdiffusion) to redesign both the backbone and sequence of Loop‐1. Through systematic filtration of 1000 ProteinMPNN‐generated variants using aggregation propensity screening (AGGRESCAN) and 450 ns of molecular dynamics (MD) simulations, four lead candidates were identified. Design_188 (EERVGGREI) demonstrated optimal performance with 2.3‐fold improvement in aggregation resistance (Na4vSS: −53.8 vs. −23.5 for WT), superior structural stability (RMSD: 0.486 nm), reduced conformational diversity (62.3% dominant cluster occupancy), and 93% retention of ISCU binding capacity (ΔΔG: +6.4 kcal/mol). Experimental validation through 15 N NMR relaxation analysis confirmed computational predictions, with Design_188 exhibiting uniform backbone rigidification (S 2 = 0.81–0.95) and strong MD‐NMR correlation (Pearson r = 0.675, p = 0.003). SEC‐MALS analysis demonstrated near‐complete monomeric behavior (> 98% monomer content) compared to WT's heterogeneous oligomerization (68% monomer, 32% oligomers), directly confirming the predicted anti‐aggregation properties. K‐means clustering analysis revealed an inverse relationship between conformational heterogeneity and stability, while correlation analysis identified a fundamental trade‐off between aggregation resistance and structural stability ( r = −0.82, p < 0.01). This work establishes a generalizable framework for therapeutic protein engineering where backbone redesign enables conformational ensemble modulation beyond the limitations of sequence optimization alone.